1,151 results
Search Results
2. An Established Theory of Digital Twin Model for Tunnel Construction Safety Assessment.
- Author
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Zhao, Yuhong, Wang, Naiqiang, and Liu, Zhansheng
- Subjects
DIGITAL twins ,TUNNEL design & construction ,ELECTRONIC paper ,MODEL theory ,PROBLEM solving - Abstract
In traditional construction safety assessment, it is difficult to describe the safety status of different construction stages. To solve this problem, this paper proposes a digital twin modeling theory for construction safety assessment. Firstly, this paper analyzes the requirements of a digital twin model. Secondly, the required information is collected by IoT. Finally, the DT model is established based on the collected information. This DT model analyzes the collected information by ML, which aims to conducting the assessments of construction safety. To verify this method, this paper analyzes the vault settlement during tunnel construction. The analysis results show that the DT model can predict the settlement value with high accuracy. Moreover, the safety state is assessed dynamically based on the settlement value by DT. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. Efficient crowdsourcing of crowd-generated microtasks.
- Author
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Hotaling A and Bagrow JP
- Subjects
- Computer Simulation, Humans, Algorithms, Crowdsourcing methods, Machine Learning, Problem Solving, Task Performance and Analysis
- Abstract
Allowing members of the crowd to propose novel microtasks for one another is an effective way to combine the efficiencies of traditional microtask work with the inventiveness and hypothesis generation potential of human workers. However, microtask proposal leads to a growing set of tasks that may overwhelm limited crowdsourcer resources. Crowdsourcers can employ methods to utilize their resources efficiently, but algorithmic approaches to efficient crowdsourcing generally require a fixed task set of known size. In this paper, we introduce cost forecasting as a means for a crowdsourcer to use efficient crowdsourcing algorithms with a growing set of microtasks. Cost forecasting allows the crowdsourcer to decide between eliciting new tasks from the crowd or receiving responses to existing tasks based on whether or not new tasks will cost less to complete than existing tasks, efficiently balancing resources as crowdsourcing occurs. Experiments with real and synthetic crowdsourcing data show that cost forecasting leads to improved accuracy. Accuracy and efficiency gains for crowd-generated microtasks hold the promise to further leverage the creativity and wisdom of the crowd, with applications such as generating more informative and diverse training data for machine learning applications and improving the performance of user-generated content and question-answering platforms., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2020
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4. Automatic Detection of Surface Defects on Underwater Pile-Pier of Bridges Based on Image Fusion and Deep Learning.
- Author
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Jiang, Shaofei, Wang, Wei, Su, Zhenheng, and Wang, Shengxian
- Subjects
SURFACE defects ,MACHINE learning ,DEEP learning ,SUBMERGED structures ,IMAGE fusion ,IMAGE enhancement (Imaging systems) ,PROBLEM solving - Abstract
As an important part of the bridge structure system, the underwater pile-pier structure usually occurs various defects on its surfaces due to its complex hydrological environment. The existing conventional defect detection approaches exist two aspects of problems: (1) insufficient definition and color distortion of the underwater images, and (2) low efficiency and error-prone. To solve these problems, this paper proposed the target defect detection model by integrating the image-fusion enhancement algorithm and the deep learning algorithm. Firstly, by analyzing the reasons for the degradation of the underwater images, the ACE (automatic color equalization) and CLAHE (contrast limited adaptive histogram equalization) algorithms are selected to enhance the image, respectively. Secondly, the two enhanced images are fused based on the point sharpness weight, and then the fusion results are further sharpened by the USM (unsharp mask) algorithm, thus obtaining the final fused images. Thirdly, 3,200 fused images are taken as the training set, by adopting the YOLOv3 algorithm to train the detection model, and then the training model is validated and tested by the other each 400 fused images, thus building up the target automatic detection model of underwater pile-pier surface defects. Finally, a series of comparison and discussion were conducted to validate the effectiveness of image-fusion and the robustness and effectiveness of the target detection model. The results found that the target detection model has excellent robustness against noise and effectiveness in the surface defect detection. This indicates that the image-fusion approach proposed in this paper can effectively enhance the image features, and the target detection model is feasible, robust, and effective in the automatic detection of surface defects on underwater pile-pier structures. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
5. Weather Radar High-Resolution Spectral Moment Estimation Using Bidirectional Extreme Learning Machine.
- Author
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Zhongyuan Wang, Ling Qiao, Yu Jiang, Mingwei Shen, and Guodong Han
- Subjects
MACHINE learning ,POWER spectra ,RADAR meteorology ,PROBLEM solving ,ALGORITHMS - Abstract
Since the performance of the spectral moment estimation algorithm commonly used in engineering degrades under the conditions of low SNR, this paper introduces the Extreme Learning Machine (ELM) to the spectral moment estimation of weather signals based on the correlation of the signals of adjacent range cells. To solve the problem that the hidden layer nodes of ELM algorithm are difficult to be determined, the Bidirectional Extreme Learning Machine (B-ELM) algorithm is applied to achieve the high resolution of spectral moments. Firstly, to improve the SNR of the training samples, time-domain pulse signals are converted into weather power spectrum by Welch method. Then, the parameters of the B-ELM hidden layer nodes are directly calculated by backpropagation of network residuals. The model parameters are optimized according to the least-squares solution, where the optimal number of hidden layer nodes is determined adaptively. Finally, the optimized B-ELM model is employed for the spectral moment estimation of weather signals. The algorithm is validated to be fast and accurate for spectral moment estimation using the measured IDRA weather radar data and is easy to implement in engineering. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. Recommendation Algorithm of Industry Stock Trading Model with TODIM.
- Author
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Lv, Dongdong, Gong, Yingli, Chen, Jianting, and Xiang, Yang
- Subjects
STOCKS (Finance) ,MACHINE learning ,FINANCIAL markets ,ALGORITHMS ,PROBLEM solving ,CRYPTOCURRENCIES - Abstract
In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China's A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. Evolving blocks by segmentation for neural architecture search.
- Author
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Zhao, Xiaoping, Jiang, Liwen, Slowik, Adam, Zhang, Zhenman, and Xue, Yu
- Subjects
CONVOLUTIONAL neural networks ,PROBLEM solving ,ARTIFICIAL intelligence ,MACHINE learning ,DIGITAL technology - Abstract
Convolutional neural networks (CNNs) play a prominent role in solving problems in various domains such as pattern recognition, image tasks, and natural language processing. In recent years, neural architecture search (NAS), which is the automatic design of neural network architectures as an optimization algorithm, has become a popular method to design CNN architectures against some requirements associated with the network function. However, many NAS algorithms are characterised by a complex search space which can negatively affect the efficiency of the search process. In other words, the representation of the neural network architecture and thus the encoding of the resulting search space plays a fundamental role in the designed CNN performance. In this paper, to make the search process more effective, we propose a novel compact representation of the search space by identifying network blocks as elementary units. The study in this paper focuses on a popular CNN called DenseNet. To perform the NAS, we use an ad-hoc implementation of the particle swarm optimization indicated as PSO-CNN. In addition, to reduce size of the final model, we propose a segmentation method to cut the blocks. We also transfer the final model to different datasets, thus demonstrating that our proposed algorithm has good transferable performance. The proposed PSO-CNN is compared with 11 state-of-the-art algorithms on CIFAR10 and CIFAR100. Numerical results show the competitiveness of our proposed algorithm in the aspect of accuracy and the number of parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
8. A Stabilisation System Synthesis for Motion along a Preset Trajectory and Its Solution by Symbolic Regression.
- Author
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Diveev, Askhat, Sofronova, Elena, and Konyrbaev, Nurbek
- Subjects
MACHINE learning ,PROBLEM solving ,MOTION ,DIFFERENTIABLE dynamical systems ,TRAJECTORY optimization - Abstract
The problem of a stabilisation system synthesis for the motion of a control object along a given spatial trajectory is considered. The complexity of the problem is that the preset trajectory is defined in the state subspace and not in time. This paper describes a stabilisation system synthesis for motion along a trajectory specified in time and along a trajectory specified in the form of a manifold in a state space. In order to construct a stabilisation system, it is necessary to determine a distance between an object and the given trajectory at each moment in time. For trajectories that are not given in time, the determination of this distance can be ambiguous. An object may be exactly on a trajectory but at a different time. This paper proposes some approaches to solve the problem. One of the approaches is to transform a given trajectory in a state subspace into a trajectory given in time. A description of a universal method to perform this transformation is presented. In order to solve the synthesis problem automatically, without having to analyse the mathematical model of the control object, it is suggested that machine learning control by symbolic regression is used. In computational experiments, examples of stabilisation system syntheses for quadcopter motion along a given spatial trajectory are presented. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
9. Realization of English Instructional Resources Clusters Reconstruction System Using the Machine Learning Model.
- Author
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Li, Xiaohui
- Subjects
SEMANTICS ,PROBLEM solving ,MACHINE learning ,EDUCATIONAL resources - Abstract
Based on ML algorithm, this paper puts forward a method that can search instructional resources through keyword indexing technology, and then cluster and recombine the related results and present them centrally. In this paper, the semantic processing of user query based on the subject index of educational resources is adopted to make up for the deficiency of query semantics, solve the problem of mismatch between query words and document words, and improve the recall and precision of resource retrieval. It is proposed to select the category feature items manually and establish the category feature model. In the environment of small sample set, the weight of category feature items is trained by ML method. The research shows that the user rating of this system is ideal, reaching 93.21% at the highest. In addition, the stability of the system can still reach 89.31% under the condition of relatively large usage, and its performance is excellent. This system can effectively solve the problem of scattered distribution of English instructional resources and make the presentation of knowledge more in line with the needs of users, thereby further improving the utilization rate of English instructional resources and users' satisfaction. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
10. DenseNet weed recognition model combining local variance preprocessing and attention mechanism.
- Author
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Ye Mu, Ruiwen Ni, Lili Fu, Tianye Luo, Ruilong Feng, Ji Li, Haohong Pan, Yingkai Wang, Yu Sun, He Gong, Ying Guo, Tianli Hu, Yu Bao, and Shijun Li
- Subjects
WEEDS ,FIELD crops ,PROBLEM solving ,MACHINE learning - Abstract
Introduction: The purpose of this paper is to effectively and accurately identify weed species in crop fields in complex environments. There are many kinds of weeds in the detection area, which are densely distributed. Methods: The paper proposes the use of local variance pre-processing method for background segmentation and data enhancement, which effectively removes the complex background and redundant information from the data, and prevents the experiment from overfitting, which can improve the accuracy rate significantly. Then, based on the optimization improvement of DenseNet network, Efficient Channel Attention (ECA) mechanism is introduced after the convolutional layer to increase the weight of important features, strengthen the weed features and suppress the background features. Results: Using the processed images to train the model, the accuracy rate reaches 97.98%, which is a great improvement, and the comprehensive performance is higher than that of DenseNet, VGGNet-16, VGGNet-19, ResNet-50, DANet, DNANet, and U-Net models. Discussion: The experimental data show that the model and method we designed are well suited to solve the problem of accurate identification of crop and weed species in complex environments, laying a solid technical foundation for the development of intelligent weeding robots. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
11. Supervised actor-critic reinforcement learning with action feedback for algorithmic trading.
- Author
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Sun, Qizhou and Si, Yain-Whar
- Subjects
REINFORCEMENT learning ,ACTIVE learning ,MACHINE learning ,SUPERVISED learning ,FINANCIAL markets ,PROBLEM solving - Abstract
Reinforcement learning is one of the promising approaches for algorithmic trading in financial markets. However, in certain situations, buy or sell orders issued by an algorithmic trading program may not be fulfilled entirely. By considering the actual scenarios from the financial markets, in this paper, we propose a novel framework named Supervised Actor-Critic Reinforcement Learning with Action Feedback (SACRL-AF) for solving this problem. The action feedback mechanism of SACRL-AF notifies the actor about the dealt positions and corrects the transitions of the replay buffer. Meanwhile, the dealt positions are used as the labels for the supervised learning. Recent studies have shown that Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) are more stable and superior to other actor-critic algorithms. Against this background, based on the proposed SACRL-AF framework, two reinforcement learning algorithms henceforth referred to as Supervised Deep Deterministic Policy Gradient with Action Feedback (SDDPG-AF) and Supervised Twin Delayed Deep Deterministic Policy Gradient with Action Feedback (STD3-AF) are proposed in this paper. Experimental results show that SDDPG-AF and STD3-AF achieve the state-of-art performance in profitability. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
12. Conti Inc.: understanding the internal discussions of a large ransomware-as-a-service operator with machine learning.
- Author
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Ruellan, Estelle, Paquet-Clouston, Masarah, and Garcia, Sebastián
- Subjects
MACHINE learning ,NATURAL language processing ,MACHINISTS ,DATA modeling ,CUSTOMER service management ,PROBLEM solving - Abstract
Ransomware-as-a-service (RaaS) is increasing the scale and complexity of ransomware attacks. Understanding the internal operations behind RaaS has been a challenge due to the illegality of such activities. The recent chat leak of the Conti RaaS operator, one of the most infamous ransomware operators on the international scene, offers a key opportunity to better understand the inner workings of such organizations. This paper analyzes the main discussion topics in the Conti chat leak using machine learning techniques such as Natural Language Processing (NLP) and Latent Dirichlet Allocation (LDA), as well as visualization strategies. Five discussion topics are found: (1) Business, (2) Technical, (3) Internal tasking/Management, (4) Malware, and (5) Customer Service/Problem Solving. Moreover, the distribution of topics among Conti members shows that only 4% of individuals have specialized discussions while almost all individuals (96%) are all-rounders, meaning that their discussions revolve around the five topics. The results also indicate that a significant proportion of Conti discussions are non-tech related. This study thus highlights that running such large RaaS operations requires a workforce skilled beyond technical abilities, with individuals involved in various tasks, from management to customer service or problem solving. The discussion topics also show that the organization behind the Conti RaaS operator shares similarities with a large firm. We conclude that, although RaaS represents an example of specialization in the cybercrime industry, only a few members are specialized in one topic, while the rest runs and coordinates the RaaS operation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
13. AI for tribology: Present and future.
- Author
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Yin, Nian, Yang, Pufan, Liu, Songkai, Pan, Shuaihang, and Zhang, Zhinan
- Subjects
ARTIFICIAL intelligence ,MEDICAL informatics ,NURSING informatics ,MATHEMATICAL optimization ,TRIBOLOGY ,RESEARCH personnel ,MAGNETICS ,PROBLEM solving - Abstract
With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of "tribo-informatics" as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of "AI for tribology". In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (T
s ), and derived state information (Ds ). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
14. An Inexact Semismooth Newton-Based Augmented Lagrangian Algorithm for Multi-Task Lasso Problems.
- Author
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Lin, Lanyu and Liu, Yong-Jin
- Subjects
COGNITIVE neuroscience ,ALGORITHMS ,MACHINE learning ,SIGNAL processing ,PROBLEM solving - Abstract
This paper is concerned with the ℓ 1 , ∞ -norm ball constrained multi-task learning problem, which has received extensive attention in many research areas such as machine learning, cognitive neuroscience, and signal processing. To address the challenges of solving large-scale multi-task Lasso problems, this paper develops an inexact semismooth Newton-based augmented Lagrangian (Ssnal) algorithm. When solving the inner problems in the Ssnal algorithm, the semismooth Newton (Ssn) algorithm with superlinear or even quadratic convergence is applied. Theoretically, this paper presents the global and asymptotically superlinear local convergence of the Ssnal algorithm under standard conditions. Computationally, we derive an efficient procedure to construct the generalized Jacobian of the projector onto ℓ 1 , ∞ -norm ball, which is an important component of the Ssnal algorithm, making the computational cost in the Ssn algorithm very cheap. Comprehensive numerical experiments on the multi-task Lasso problems demonstrate that the Ssnal algorithm is more efficient and robust than several existing state-of-the-art first-order algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
15. Multiscale Feature Fusion Attention Lightweight Facial Expression Recognition.
- Author
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Ni, Jinyuan, Zhang, Xinyue, and Zhang, Jianxun
- Subjects
FACIAL expression ,FEATURE extraction ,HUMAN-robot interaction ,MACHINE learning ,PROBLEM solving - Abstract
Facial expression recognition based on residual networks is important for technologies related to space human-robot interaction and collaboration but suffers from low accuracy and slow computation in complex network structures. To solve these problems, this paper proposes a multiscale feature fusion attention lightweight wide residual network. The network first uses an improved random erasing method to preprocess facial expression images, which improves the generalizability of the model. The use of a modified depthwise separable convolution in the feature extraction network reduces the computational effort associated with the network parameters and enhances the characterization of the extracted features through a channel shuffle operation. Then, an improved bottleneck block is used to reduce the dimensionality of the upper layer network feature map to further reduce the number of network parameters while enhancing the network feature extraction capability. Finally, an optimized multiscale feature lightweight attention mechanism module is embedded to further improve the feature extractability of the network for human facial expressions. The experimental results show that the accuracy of the model is 73.21%, 98.72%, and 95.21% on FER2013, CK+ and JAFFE, respectively, with a covariance of 10.14 M. Compared with other networks, the model proposed in this paper has faster computing speed and better accuracy at the same time. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
16. Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends.
- Author
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Shahbazian, Reza, Macrina, Giusy, Scalzo, Edoardo, and Guerriero, Francesca
- Subjects
MACHINE learning ,INTERNET of things ,PROBLEM solving - Abstract
The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper's main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
17. Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents.
- Author
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Aleksić, Aleksandar, Ranđelović, Milan, and Ranđelović, Dragan
- Subjects
MACHINE learning ,HUMAN activity recognition ,WEB-based user interfaces ,MULTIAGENT systems ,PROBLEM solving ,INFORMATION society - Abstract
The opportunity for large amounts of open-for-public and available data is one of the main drivers of the development of an information society at the beginning of the 21st century. In this sense, acquiring knowledge from these data using different methods of machine learning is a prerequisite for solving complex problems in many spheres of human activity, starting from medicine to education and the economy, including traffic as today's important economic branch. Having this in mind, this paper deals with the prediction of the risk of traffic incidents using both historical and real-time data for different atmospheric factors. The main goal is to construct an ensemble model based on the use of several machine learning algorithms which has better characteristics of prediction than any of those installed when individually applied. In global, a case-proposed model could be a multi-agent system, but in a considered case study, a two-agent system is used so that one agent solves the prediction task by learning from the historical data, and the other agent uses the real time data. The authors evaluated the obtained model based on a case study and data for the city of Niš from the Republic of Serbia and also described its implementation as a practical web citizen application. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
18. Detection of Miss-Seeding of Sweet Corn in a Plug Tray Using a Residual Attention Network.
- Author
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Gao, Lulu, Bai, Jinqiang, Xu, Jingyao, Du, Baoshuai, Zhao, Jingbo, Ma, Dexin, and Hao, Fengqi
- Subjects
CORN seeds ,SWEET corn ,TRAYS ,ARTIFICIAL intelligence ,MACHINE learning ,PROBLEM solving - Abstract
With the promotion of artificial intelligence in agriculture and the popularization of plug tray seedling-raising technology, seedling raising and transplanting have become the most popular planting modes. Miss-seeding is one of the most serious problems affecting seedling raising and transplanting. It not only affects the germination rate of seeds but also reduces the utilization rate of the plug tray. The experimental analysis of traditional machine vision-based miss-seeding showed that because of uneven lighting, the plug tray was wrongly identified as a seed under bright light, but the seeds in the dark were not easy to identify. When using the seeding area to identify seeds and noise, sweet corn seeds in a small area can be easily screened out. This paper proposes a method using the ResNet network with an attention mechanism to solve the above-mentioned problems. In this paper, the captured image was segmented into the images of a single plug tray, and a residual attention network was built; the detection scheme of miss-seeding was also converted into a dichotomous picture recognition task. This paper demonstrates that the residual attention network can effectively recognize and detect the seed images of sweet corn with very high accuracy. The results of the experiment showed that the average accuracy of this recognition model was 98%. The feature visualization method was used to analyze the features, further proving the effectiveness of the classification method of plug tray seedlings. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
19. Burst Diagnosis Multi-Stage Model for Water Distribution Networks Based on Deep Learning Algorithms.
- Author
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Peng, Sen, Wang, Yuxin, Fang, Xu, and Wu, Qing
- Subjects
MACHINE learning ,DEEP learning ,WATER distribution ,SEARCH algorithms ,DIAGNOSIS ,PROBLEM solving - Abstract
Pipe bursts in water distribution networks (WDNs) pose significant threats to the safety of distribution networks, driving attention to deep learning-based burst detection and localization. However, the applicability of different pressure features still needs to be compared and verified. A large number of nodes challenges deep learning with the excessive number of classification categories and low recognition accuracy. To address these problems, this paper extracts different burst pressure features, including pressure value, pressure difference, and pressure fluctuation ratio, and inputs one of these features into a Burst Diagnosis Multi-Stage Model (BDMM) based on three CS-LSTMs (a combination of the Cuckoo Search algorithm and a long short-term memory network). The first model addresses a binary classification problem, outputting labels indicating whether a pipe burst has occurred. The second one solves a multi-classification problem, outputting the label of the burst partition, and the third model also solves a multi-classification problem, outputting the ID of the bursting junction. The model is tested on a real network and outperforms ELM. For basic burst identification tasks using CS-LSTM, differences among the three features are minimal, while pressure difference and pressure fluctuation ratio exhibit superior performance to pressure value when resolving more complex problems like burst junction localization. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
20. FLCP: federated learning framework with communication-efficient and privacy-preserving.
- Author
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Yang, Wei, Yang, Yuan, Xi, Yingjie, Zhang, Hailong, and Xiang, Wei
- Subjects
FEDERATED learning ,MACHINE learning ,DATA distribution ,ARTIFICIAL intelligence ,DATA privacy ,PROBLEM solving - Abstract
Within the federated learning (FL) framework, the client collaboratively trains the model in coordination with a central server, while the training data can be kept locally on the client. Thus, the FL framework mitigates the privacy disclosure and costs related to conventional centralized machine learning. Nevertheless, current surveys indicate that FL still has problems in terms of communication efficiency and privacy risks. In this paper, to solve these problems, we develop an FL framework with communication-efficient and privacy-preserving (FLCP). To realize the FLCP, we design a novel compression algorithm with efficient communication, namely, adaptive weight compression FedAvg (AWC-FedAvg). On the basis of the non-independent and identically distributed (non-IID) and unbalanced data distribution in FL, a specific compression rate is provided for each client, and homomorphic encryption (HE) and differential privacy (DP) are integrated to provide demonstrable privacy protection and maintain the desirability of the model. Therefore, our proposed FLCP smoothly balances communication efficiency and privacy risks, and we prove its security against "honest-but-curious" servers and extreme collusion under the defined threat model. We evaluate the scheme by comparing it with state-of-the-art results on the MNIST and CIFAR-10 datasets. The results show that the FLCP performs better in terms of training efficiency and model accuracy than the baseline method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. Assessing the Efficiency of Foreign Investment in a Certification Procedure Using an Ensemble Machine Learning Model.
- Author
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Kemiveš, Aleksandar, Barjaktarović, Lidija, Ranđelović, Milan, Čabarkapa, Milan, and Ranđelović, Dragan
- Subjects
MACHINE learning ,FOREIGN investments ,FEATURE selection ,CITIES & towns ,PROBLEM solving - Abstract
Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. High-Dimensional Ensemble Learning Classification: An Ensemble Learning Classification Algorithm Based on High-Dimensional Feature Space Reconstruction.
- Author
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Zhao, Miao and Ye, Ning
- Subjects
MACHINE learning ,CLASSIFICATION algorithms ,FEATURE selection ,NAIVE Bayes classification ,HIGH-dimensional model representation ,CLASSIFICATION ,ALGORITHMS ,PROBLEM solving - Abstract
When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper explores the high-dimensional data from both the data feature dimension and the model ensemble dimension. We propose a high-dimensional ensemble learning classification algorithm focusing on feature space reconstruction and classifier ensemble, called the HDELC algorithm. First, the algorithm considers feature space reconstruction and then generates a feature space reconstruction matrix. It effectively achieves feature selection and reconstruction for high-dimensional data. An optimal feature space is generated for the subsequent ensemble of the classifier, which enhances the representativeness of the feature space. Second, we recursively determine the number of classifiers and the number of feature subspaces in the ensemble model. Different classifiers in the ensemble system are assigned mutually exclusive non-intersecting feature subspaces for model training. The experimental results show that the HDELC algorithm has advantages compared with most high-dimensional datasets due to its more efficient feature space ensemble capability and relatively reliable ensemble operation performance. The HDELC algorithm makes it possible to solve the classification problem for high-dimensional data effectively and has vital research and application value. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
23. Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems.
- Author
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Priyadarshini, Ishaani
- Subjects
OPTIMIZATION algorithms ,BIOLOGICALLY inspired computing ,DEEP learning ,MACHINE learning ,METAHEURISTIC algorithms ,PROBLEM solving ,ALGORITHMS - Abstract
In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO's wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. Low-Rank Riemannian Optimization for Graph-Based Clustering Applications.
- Author
-
Douik, Ahmed and Hassibi, Babak
- Subjects
RIEMANNIAN manifolds ,RIEMANNIAN geometry ,STATISTICS ,STOCHASTIC matrices ,MACHINE learning ,PROBLEM solving - Abstract
With the abundance of data, machine learning applications engaged increased attention in the last decade. An attractive approach to robustify the statistical analysis is to preprocess the data through clustering. This paper develops a low-complexity Riemannian optimization framework for solving optimization problems on the set of positive semidefinite stochastic matrices. The low-complexity feature of the proposed algorithms stems from the factorization of the optimization variable $\mathbf {X}=\mathbf {Y}\mathbf {Y}^{\mathrm{T}}$ X = Y Y T and deriving conditions on the number of columns of $\mathbf {Y}$ Y under which the factorization yields a satisfactory solution. The paper further investigates the embedded and quotient geometries of the resulting Riemannian manifolds. In particular, the paper explicitly derives the tangent space, Riemannian gradients and Hessians, and a retraction operator allowing the design of efficient first and second-order optimization methods for the graph-based clustering applications of interest. The numerical results reveal that the resulting algorithms present a clear complexity advantage as compared with state-of-the-art euclidean and Riemannian approaches for graph clustering applications. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
25. IEEE Transactions on Neural Networks information for authors.
- Subjects
ARTIFICIAL neural networks ,MACHINE learning ,PROBLEM solving - Abstract
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
26. An Oversampling Method of Unbalanced Data for Mechanical Fault Diagnosis Based on MeanRadius-SMOTE.
- Author
-
Duan, Feng, Zhang, Shuai, Yan, Yinze, and Cai, Zhiqiang
- Subjects
MACHINE learning ,DIAGNOSIS methods ,PROBLEM solving ,FAULT diagnosis - Abstract
With the development of machine learning, data-driven mechanical fault diagnosis methods have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it is a difficult problem for fault diagnosis to solve the problem of unbalanced data sets. Under unbalanced data sets, faults with little historical data are always difficult to diagnose and lead to economic losses. In order to improve the prediction accuracy under unbalanced data sets, this paper proposes MeanRadius-SMOTE based on the traditional SMOTE oversampling algorithm, which effectively avoids the generation of useless samples and noise samples. This paper validates the effectiveness of the algorithm on three linear unbalanced data sets and four step unbalanced data sets. Experimental results show that MeanRadius-SMOTE outperforms SMOTE and LR-SMOTE in various evaluation indicators, as well as has better robustness against different imbalance rates. In addition, MeanRadius-SMOTE can take into account the prediction accuracy of the overall and minority class, which is of great significance for engineering applications. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
27. Improved FunkSVD Algorithm Based on RMSProp.
- Author
-
Yue, Xiaochen and Liu, Qicheng
- Subjects
ALGORITHMS ,DEEP learning ,MACHINE learning ,MATHEMATICAL optimization ,PROBLEM solving - Abstract
To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
28. Design of Interactive Vocal Guidance and Artistic Psychological Intervention System Based on Emotion Recognition.
- Author
-
Mo, Wenwen and Yuan, Yuan
- Subjects
COMPUTER software ,STATISTICS ,PROBLEM solving ,MACHINE learning ,FACE perception ,BIOFEEDBACK training ,MUSIC therapy ,DATABASE management ,PEARSON correlation (Statistics) ,SIGNAL processing ,ART therapy ,COMMUNICATION ,QUESTIONNAIRES ,EMOTIONS ,MUSIC ,RECEIVER operating characteristic curves ,DATA analysis software ,PSYCHOTHERAPY ,ALGORITHMS ,VIDEO recording - Abstract
The research on artistic psychological intervention to judge emotional fluctuations by extracting emotional features from interactive vocal signals has become a research topic with great potential for development. Based on the interactive vocal music instruction theory of emotion recognition, this paper studies the design of artistic psychological intervention system. This paper uses the vocal music emotion recognition algorithm to first train the interactive recognition network, in which the input is a row vector composed of different vocal music characteristics, and finally recognizes the vocal music of different emotional categories, which solves the problem of low data coupling in the artistic psychological intervention system. Among them, the vocal music emotion recognition experiment based on the interactive recognition network is mainly carried out from six aspects: the number of iterative training, the vocal music instruction rate, the number of emotion recognition signal nodes in the artistic psychological intervention layer, the number of sample sets, different feature combinations, and the number of emotion types. The input data of the system is a training class learning video, and actions and expressions need to be recognized before scoring. In the simulation process, before the completion of the sample indicators is unbalanced, the R language statistical analysis tool is used to balance the existing unbalanced data based on the artificial data synthesis method, and 279 uniformly classified samples are obtained. The 279 ∗ 7 dataset was used for statistical identification of the participants. The experimental results show that under the guidance of four different interactive vocal music, the vocal emotion recognition rate is between 65.85%-91.00%, which promotes the intervention of music therapy on artistic psychological intervention. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
29. Level-Based Learning Algorithm Based on the Difficulty Level of the Test Problem.
- Author
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Hong, You-Sik, Han, Chang-Pyoung, Cho, Seong-Soo, and Colomo-Palacios, Ricardo
- Subjects
MACHINE learning ,MULTIPLE Signal Classification ,VIRTUAL universities & colleges ,PROBLEM solving ,BILEVEL programming ,MUSIC software ,TEST methods - Abstract
These days, because of the coronavirus, all countries are introducing online university systems. Online universities have the advantage of allowing students to take classes anytime, anywhere, 24 h a day, but lectures are given in a non-face-to-face manner between instructors and students. Thus, while students are taking classes on a web-based basis, the problem arises that concentration on the lectures is significantly reduced. Therefore, in order to solve these problems, in this paper, we propose a level-wise learning algorithm based on the difficulty level of the test problem, and we present the simulation results. In order to improve this problem, in this paper, we propose an automatic music recommendation algorithm based on fuzzy reasoning that can improve the level of learning and lecture concentration, and we show our results on developing a web-based, smart e-learning software. As a result of computer simulation, it was proved that the learning test method, considering by level the difficulty of the test and the incorrect answer rate, was more effective than the existing test method, judged the student's grades fairly, and improved the risk of unfairly failing the test by 30%. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
30. Machine Learning Methods with Noisy, Incomplete or Small Datasets.
- Author
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Caiafa, Cesar F., Sun, Zhe, Tanaka, Toshihisa, Marti-Puig, Pere, and Solé-Casals, Jordi
- Subjects
MACHINE learning ,MEDICAL sciences ,PROBLEM solving ,ARTIFICIAL intelligence ,APPLIED sciences - Abstract
In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue "Machine Learning Methods with Noisy, Incomplete or Small Datasets", Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
31. Artificial intelligence-based hybrid forecasting models for manufacturing systems.
- Author
-
Rosienkiewicz, Maria
- Subjects
ARTIFICIAL neural networks ,PRODUCTION planning ,PROBLEM solving ,FORECASTING ,MACHINE learning ,INDUSTRY 4.0 ,SUPPORT vector machines ,QUALITY control - Abstract
The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
32. The Adaptive Optimal Output Feedback Tracking Control of Unknown Discrete-Time Linear Systems Using a Multistep Q-Learning Approach.
- Author
-
Dong, Xunde, Lin, Yuxin, Suo, Xudong, Wang, Xihao, and Sun, Weijie
- Subjects
LINEAR systems ,DISCRETE-time systems ,MACHINE learning ,ARTIFICIAL satellite tracking ,SYSTEM dynamics ,ADAPTIVE control systems ,PROBLEM solving - Abstract
This paper investigates the output feedback (OPFB) tracking control problem for discrete-time linear (DTL) systems with unknown dynamics. To solve this problem, we use an augmented system approach, which first transforms the tracking control problem into a regulation problem with a discounted performance function. The solution to this problem is derived using a Bellman equation, based on the Q-function. In order to overcome the challenges of unmeasurable system state variables, we employ a multistep Q-learning algorithm that surpasses the advantages of the policy iteration (PI) and value iteration (VI) techniques and state reconstruction methods for output feedback control. As such, the requirement for an initial stabilizing control policy for the PI method is removed and the convergence speed of the learning algorithm is improved. Finally, we demonstrate the effectiveness of the proposed scheme using a simulation example. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. Graph neural network comparison for 2D nesting efficiency estimation.
- Author
-
Lallier, Corentin, Blin, Guillaume, Pinaud, Bruno, and Vézard, Laurent
- Subjects
CONVOLUTIONAL neural networks ,PROBLEM solving - Abstract
Minimizing the level of material consumption in textile production is a major concern. The cornerstone of this optimization task is the nesting problem, whose goal is to lay a set of irregular 2D parts out onto a rectangular surface, called the nesting zone, while respecting a set of constraints. Knowing the efficiency—ratio of usable to used up material enables the optimization of several textile production problems. Unfortunately, knowing the efficiency requires the nesting problem to be solved, which is computationally intensive and has been proven to be NP-hard. This paper introduces a regression approach to estimate efficiency without solving the nesting problem. Our approach models the 2D nesting problem as a graph where the nodes are images derived from parts and the edges hold the constraints. The method then consists of combining convolutional neural networks for addressing the image-based aspects and graph neural networks (GNNs) for the constraint aspects. We evaluate several neural message passing approaches on our dataset and obtain results that are sufficiently accurate for enabling several business use cases, where our model best solves this task with a mean absolute error of 1.65. We provide open access to our dataset, whose properties differ from those of other graph datasets found in the literature. This dataset is constructed on 100,000 real customers' nesting data. Along the way, we compare the performance and generalization capabilities of four GNN architectures obtained from the literature on this dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies.
- Author
-
Wang, Ziming, Liu, Xiaotong, Chen, Haotian, Yang, Tao, and He, Yurong
- Subjects
LEARNING strategies ,MATERIALS science ,DATA science ,DEEP learning ,PROBLEM solving ,RESOURCE allocation ,MACHINE learning - Abstract
Machine learning techniques offer tremendous potential for optimizing resource allocation in solving real-world problems. However, the emergence of multi-fidelity data introduces new challenges. This paper offers an overview of the definition, applications, data preprocessing methodologies, and learning approaches associated with multi-fidelity data. To validate the algorithms, we examine three widely-used learning methods relevant to multi-fidelity data through the design of multi-fidelity datasets that encompass various types of noise. As we expected, employing multi-fidelity data learning methods yields better results compared to solely using high-fidelity data learning methods. Additionally, considering the inherent various types of noise within datasets, the comprehensive correction strategy proves to be the most effective. Moreover, multi-fidelity learning methods facilitate effective decision-making processes by enabling the combination of datasets from various sources. They extract knowledge from lower fidelity data, improving model accuracy compared to models solely relying on high-fidelity data. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. Detection Method of Hardware Trojan Based on Attention Mechanism and Residual-Dense-Block under the Markov Transition Field.
- Author
-
Chen, Shouhong, Wang, Tao, Huang, Zhentao, and Hou, Xingna
- Subjects
- *
DEEP learning , *INTEGRATED circuits , *MACHINE learning , *HARDWARE , *PROBLEM solving - Abstract
Since 2007, methods that utilize side-channel data to detect hardware Trojan (HT) problems have been widely studied. Machine learning methods are widely used for hardware Trojan detection, but with the development of integrated circuits (ICs), better results are usually obtained using deep learning methods. In this paper, we propose an architecture inspired by Residual-Block and Dense-Block and combine it with SE Attention Mechanism, which we named the Res-Dense-SE-Net network. By combining residual connectivity, dense connectivity, and attention mechanism, the Res-Dense-SE-Net network can enjoy the advantages of these three network architectures at the same time, which can improve the expressiveness and performance of the model. The Res-Dense-SE-Net network can capture the key features in the image better, and it can solve the problems of gradient vanishing and feature transfer efficiently, which can in turn improve the classification accuracy and the generalization ability of the model. Based on the publicly available AES series of hardware Trojans from TrustHub and the publicly available hardware Trojan-side channel data by Faezi et al., we evaluate the effectiveness of the method proposed in this paper. The experimental results show that when a single Trojan exists, the method proposed in this paper has a high accuracy rate; and when multiple types of hardware Trojans exist at the same time and need to be categorized, the categories of hardware Trojans can also be effectively identified, and the categorization accuracy is high compared with the existing deep learning methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. Research on Product Design Strategy Based on User Preference and Machine Learning Intelligent Recommendation.
- Author
-
Wu, Jie
- Subjects
ARTIFICIAL intelligence ,PRODUCT design ,RECOMMENDER systems ,MACHINE learning ,EXPERIMENTAL design ,PROBLEM solving - Abstract
In the machine learning model, intelligent recommendation system can select valuable information from a lot of data to help users find the products or services they need, which has been more and more widely used in recent years. However, there are still many problems in machine learning recommender systems, such as data sparsity, natural noise, and cold start, which leads to the fact that machine learning recommender systems cannot obtain accurate user preferences. When a project is rated, the quality of the recommendation is greatly affected. In order to solve the problem that the existing recommendation algorithms have poor recommendation results in sparse data sets, this paper proposes a machine learning method for recommendation rating prediction based on user interest concept lattice. Firstly, the nearest neighbors are divided into direct nearest neighbors and indirect nearest neighbors by user interest concept lattice. Then, different methods are used to calculate the similarity between the direct "nearest neighbor" and the target user, and the similarity between the indirect "nearest neighbor" and the target user. Finally, the invisible item score of the target user is calculated by the similarity value. Experiments are carried out on real data sets, and the experimental results show that the CFCNN-CL algorithm and RRP-UI CL algorithm proposed in this paper have high recommendation accuracy and still have good performance in the case of sparse data. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
37. A Smoke Detection Model Based on Improved YOLOv5.
- Author
-
Wang, Zhong, Wu, Lei, Li, Tong, and Shi, Peibei
- Subjects
DEEP learning ,SMOKE ,MACHINE learning ,NETWORK performance ,INTEREST rates ,REQUIREMENTS engineering ,PROBLEM solving - Abstract
Fast and accurate smoke detection is very important for reducing fire damage. Due to the complexity and changeable nature of smoke scenes, existing smoke detection technology has the problems of a low detection rate and a high false negative rate, and the robustness and generalization ability of the algorithms are not high. Therefore, this paper proposes a smoke detection model based on the improved YOLOv5. First, a large number of real smoke and synthetic smoke images were collected to form a dataset. Different loss functions (GIoU, DIoU, CIoU) were used on three different models of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l), and YOLOv5m was used as the baseline model. Then, because of the problem of small numbers of smoke training samples, the mosaic enhancement method was used to randomly crop, scale and arrange nine images to form new images. To solve the problem of inaccurate anchor box prior information in YOLOv5, a dynamic anchor box mechanism is proposed. An anchor box was generated for the training dataset through the k-means++ clustering algorithm. The dynamic anchor box module was added to the model, and the size and position of the anchor box were dynamically updated in the network training process. Aiming at the problem of unbalanced feature maps in different scales of YOLOv5, an attention mechanism is proposed to improve the network detection performance by adding channel attention and spatial attention to the original network structure. Compared with the traditional deep learning algorithm, the detection performance of the improved algorithm in this paper was is 4.4% higher than the mAP of the baseline model, and the detection speed reached 85 FPS, which is obviously better and can meet engineering application requirements. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
38. Data Generation with Variational Autoencoders and Generative Adversarial Networks †.
- Author
-
Devyatkin, Daniil and Trenev, Ivan
- Subjects
GENERATIVE adversarial networks ,PYTHON programming language ,DATA distribution ,PROBLEM solving ,DATA modeling - Abstract
The paper considers the problem of modelling the distribution of data with noise in the input data. In this paper, we consider encoders and decoders, which solve the problem of modelling data distribution. The improvement of variational autoencoders (VAEs) is discussed. Practical implementation is performed using the Python programming language and the Keras framework. Generative adversarial networks (GANs) and VAEs with noisy data are demonstrated. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Additional Requirement in the Formulation of the Optimal Control Problem for Applied Technical Systems †.
- Author
-
Shmalko, Elizaveta and Diveev, Askhat
- Subjects
OPTIMAL control theory ,PROBLEM solving ,REGRESSION analysis ,MACHINE learning ,DEVIATION (Statistics) - Abstract
This paper considers the difficulties that arise in the implementation of solutions to the optimal control problem. When implemented in real systems, as a rule, the object is subject to some perturbations, and the control obtained as a function of time as a result of solving the optimal control problem does not take into account these factors, which leads to a significant change in the trajectory and deviation of the object from the terminal goal. This paper proposes to supplement the formulation of the optimal control problem. Additional requirements are introduced for the optimal trajectory. The fulfillment of these requirements ensures that the trajectory remains close to the optimal one under perturbations and reaches the vicinity of the terminal state. To solve the problem, it is proposed to use numerical methods of machine learning based on symbolic regression. A computational experiment is presented in which the solutions of the optimal control problem in the classical formulation and with the introduced additional requirement are compared. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. НЕЙРОННІ МЕРЕЖІ: ДОСЛІДЖЕННЯ ПРАВИЛ ПРИЙНЯТТЯ НИМИ РІШЕНЬ.
- Author
-
ПЕТРЕНКО, А. І. and ВОХРАНОВ, І. А.
- Subjects
ARTIFICIAL neural networks ,IMAGE recognition (Computer vision) ,IMAGE databases ,PROBLEM solving ,DECISION trees - Abstract
The question of a better understanding of the behavior of neural networks is quite relevant, especially in industries with a high level of risks. To solve this problem, the possibilities of the new DeepRED decomposition algorithm, capable of extracting decision-making rules by deep neural networks with several hidden layers, are explored in the paper. The study of the DeepRED algorithm was carried out on the example of extracting the rules of an experimental neural network during the classification of images of the MNIST database of handwritten digits, which made it possible to reveal a number of limitations of the DeepRED algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
41. Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes.
- Author
-
Zhou, Feng, Hu, Shijing, Du, Xin, Wan, Xiaoli, Lu, Zhihui, and Wu, Jie
- Subjects
NURSING care facilities ,PREDICTION models ,ARTIFICIAL intelligence ,PATIENT monitoring ,HEALTH of older people ,PROBLEM solving - Abstract
With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision based on a particular physiological indicator. Therefore, such decisions cannot effectively predict health risks. To achieve this goal, we propose a model Lidom (A LightGBM-based Disease Prediction Model) based on the combination of the LightGBM algorithm, InterpretML framework, and sequence confrontation network (SeqGAN). The Lidom model first solves the problem of uneven samples based on the sequence confrontation network (SeqGAN), then trains the model based on the LightGBM algorithm, uses the InterpretML framework for analysis, and finally obtains the best model. This paper uses the public dataset MIMIC-III, subject data, and the early diabetes risk prediction dataset in UCI as sample data. The experimental results show that the Lidom model has an accuracy rate of 93.46% for disease risk prediction and an accuracy rate of 99.8% for early diabetes risk prediction. The results show that the Lidom model can provide adequate support for the prediction of the health risks of the elderly. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
42. EfferDeepNet: An Efficient Semantic Segmentation Method for Outdoor Terrain.
- Author
-
Wei, Yuhai, Wei, Wu, and Zhang, Yangbiao
- Subjects
DEEP learning ,MACHINE learning ,IMAGE segmentation ,IMAGE sensors ,FEATURE extraction ,PROBLEM solving ,ROBOTICS - Abstract
The recognition of terrain and outdoor complex environments based on vision sensors is a key technology in practical robotics applications, and forms the basis of autonomous navigation and motion planning. While traditional machine learning methods can be applied to outdoor terrain recognition, their recognition accuracy is low. In order to improve the accuracy of outdoor terrain recognition, methods based on deep learning are widely used. However, the network structure of deep learning methods is very complex, and the number of parameters is large, which cannot meet the actual operating requirements of of unmanned systems. Therefore, in order to solve the problems of poor real-time performance and low accuracy of deep learning algorithms for terrain recognition, this paper proposes the efficient EfferDeepNet network for pixel level terrain recognition in order to realize global perception of outdoor environment. First, this method uses convolution kernels with different sizes in the depthwise separable convolution (DSC) stage to extract more semantic feature information. Then, an attention mechanism is introduced to weight the acquired features, focusing on the key local feature areas. Finally, in order to avoid redundancy due to a large number of features and parameters in the model, this method uses a ghost module to make the network more lightweight. In addition, to solve the problem of pixel level terrain recognition having a negative effect on image boundary segmentation, the proposed method integrates an enhanced feature extraction network. Experimental results show that the proposed EfferDeepNet network can quickly and accurately perform global recognition and semantic segmentation of terrain in complex environments. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
43. Abstractive Summary of Public Opinion News Based on Element Graph Attention.
- Author
-
Huang, Yuxin, Hou, Shukai, Li, Gang, and Yu, Zhengtao
- Subjects
PUBLIC opinion ,MACHINE learning ,PROBLEM solving ,HOMICIDE - Abstract
The summary of case–public opinion refers to the generation of case-related sentences from public opinion information related to judicial cases. Case–public opinion news refers to the judicial cases (intentional homicide, rape, etc.) that cause large public opinion. The public opinion news in these cases usually contains case element information such as the suspect, victim, time, place, process, and sentencing of the case. In the multi-document summary of case–public opinion, due to the problem of information cross and information redundancy between different documents under the same case, in order to generate a concise and smooth summary, this paper proposes an abstractive summary model of case–public opinion based on the attention of a case element diagram. Firstly, multiple public opinion documents in the same case are split into paragraphs, and then the paragraphs and case elements are coded based on the transformer method to construct a heterogeneous graph containing paragraph nodes and case element nodes. Finally, in the decoding process, the two-layer attention mechanism is applied to the case element node and paragraph node, so that the model can effectively solve the redundancy problem in summary generation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Pre-Layout Parasitic-Aware Design Optimizing for RF Circuits Using Graph Neural Network.
- Author
-
Li, Chenfeng, Hu, Dezhong, and Zhang, Xiaoyan
- Subjects
ANALOG circuits ,PROBLEM solving - Abstract
The performance of analog and RF circuits is widely affected by the interconnection parasitic in the circuit. With the progress of technology, interconnection parasitics plays a larger role in performance deterioration. To solve this problem, designers must repeat layout design and validation process. In order to achieve an upgrade in the design efficiency, in this paper, a Graph Neural Network (GNN)-based pre-layout parasitic parameter prediction method is proposed and applied to the design optimization of a 28 nm PLL. With the new method adopted, the frequency band overlap rate of the VCO is improved by 2.3 percents for an equal design effort. Similarly, the optimized CP is superior to the traditional method with a 15 ps mismatch time. These improvements are achieved under the premise of greatly saving the optimization iteration and verification costs. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
45. Extended Statement of the Optimal Control Problem and Machine Learning Approach to Its Solution.
- Author
-
Shmalko, Elizaveta and Diveev, Askhat
- Subjects
AUTOMATIC control systems ,PROBLEM solving ,MACHINE learning ,ENGINEERS - Abstract
Engineers strive to realize the goal of control in the best possible way based on a given quality criterion when developing control system. The well-known optimal control problem requires finding a solution as a function of time. Such a function cannot be directly used to control a real object because its application corresponds to an open-loop control system and engineers usually complete it with a feedback stabilization system. Hence, there is an obvious need to reformulate the optimal control problem so that its solution can be directly applied to real objects. The paper presents a new extended statement of the optimal control problem. An additional requirement for the control function is introduced to give the system describing the control object properties that will ensure the stability of solutions. The desired control function must provide for the optimal trajectory given the properties of the attractor in the neighbourhood. The solution to the extended optimal control problem can be directly used to control a real object. The paper presents a computational machine learning approach to solving the extended problem of optimal control based on the application of a synthesized optimal control technique. Examples of the practical solution to the stated problem are given to illustrate the efficiency of the approach, where the solution to the conventional optimal control problem is compared with the proposed extended one in the presence of perturbations in models and initial conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
46. A fixed structure learning automata‐based optimization algorithm for structure learning of Bayesian networks.
- Author
-
Asghari, Kayvan, Masdari, Mohammad, Soleimanian Gharehchopogh, Farhad, and Saneifard, Rahim
- Subjects
ANT algorithms ,BEES algorithm ,MATHEMATICAL optimization ,MACHINE learning ,ALGORITHMS ,PROBLEM solving ,KNOWLEDGE representation (Information theory) ,METAHEURISTIC algorithms - Abstract
One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
47. Doubly elastic net regularized online portfolio optimization with transaction costs.
- Author
-
Yao, Xiaoting and Zhang, Na
- Subjects
TRANSACTION costs ,EXPECTED returns ,COST control ,MACHINE learning ,PETRI nets ,PROBLEM solving - Abstract
Online portfolio optimization with transaction costs is a big challenge in large-scale intelligent computing community, since its undersample from rapidly-changing market and complexity from varying transaction costs. In this paper, we focus on this problem and solve it by machine learning system. Specifically, we reformulate the optimization problem with the minimization over simplex containing three items, which are negative expected return, the elastic net regularization of transaction costs controlled term and portfolio variable, respectively. We propose to apply linearized augmented Lagrangian method (LALM) and the alternating direction method of multipliers (ADMM) to solve the optimization model in a higher efficiency, meanwhile theoretically guarantee their convergence and deduce closed-form solutions of their subproblems in each iteration. Furthermore, we conduct extensive experiments on five benchmark datasets from real market to demonstrate that the proposed algorithms outperform compared state-of-the-art strategies in most cases in six dimensions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. A Federated Learning Method Based on Blockchain and Cluster Training.
- Author
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Li, Yue, Yan, Yiting, Liu, Zengjin, Yin, Chang, Zhang, Jiale, and Zhang, Zhaohui
- Subjects
ARCHITECTURAL design ,BLOCKCHAINS ,DATA warehousing ,MACHINE learning ,PROBLEM solving - Abstract
Federated learning (FL) is an emerging machine learning method in which all participants can collaboratively train a model without sharing their raw data, thereby breaking down data silos and avoiding privacy issues caused by centralized data storage. In practical applications, client data are non-independent and identically distributed, resulting in FL requiring multiple rounds of communication to converge, which entails high communication costs. Moreover, the centralized architecture of traditional FL remains susceptible to privacy breaches, network congestion, and single-point failures. In order to solve these problems, this paper proposes an FL framework based on blockchain technology and a cluster training algorithm, called BCFL. We first improved an FL algorithm based on odd–even round cluster training, which accelerates model convergence by dividing clients into clusters and adopting serialized training within each cluster. Meanwhile, compression operations were applied to model parameters before transmission to reduce communication costs and improve communication efficiency. Then, a decentralized FL architecture was designed and developed based on blockchain and Inter-Planetary File System (IPFS), where the blockchain records the FL process and IPFS optimizes the high storage costs associated with the blockchain. The experimental results demonstrate the superiority of the framework in terms of accuracy and communication efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
49. Learning-Based Multi-Domain Anti-Jamming Communication with Unknown Information.
- Author
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Li, Yongcheng, Wang, Jinchi, and Gao, Zhenzhen
- Subjects
MACHINE learning ,WIRELESS channels ,RADAR interference ,NASH equilibrium ,TRANSMITTERS (Communication) ,PROBLEM solving - Abstract
Due to the open nature of the wireless channel, wireless networks are vulnerable to jamming attacks. In this paper, we try to solve the anti-jamming problem caused by smart jammers, which can adaptively adjust the jamming channel and the jamming power. The interaction between the legitimate transmitter and the jammers is modeled as a non-zero-sum game. Considering that it is challenging for the transmitter and the jammers to acquire each other's information, we propose two anti-jamming communication schemes based on the Deep Q-Network (DQN) algorithm and hierarchical learning (HL) algorithm to solve the non-zero-sum game. Specifically, the DQN-based scheme aims to solve the anti-jamming strategies in the frequency domain and the power domain directly, while the HL-based scheme tries to find the optimal mixed strategies for the Nash equilibrium. Simulation results are presented to validate the effectiveness of the proposed schemes. It is shown that the HL-based scheme has a better convergence performance and the DQN-based scheme has a higher converged utility of the transmitter. In the case of a single jammer, the DQN-based scheme achieves 80% of the transmitter's utility of the no-jamming case, while the HL-based scheme achieves 63%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
50. OHO: A Multi-Modal, Multi-Purpose Dataset for Human-Robot Object Hand-Over.
- Author
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Stephan, Benedict, Köhler, Mona, Müller, Steffen, Zhang, Yan, Gross, Horst-Michael, and Notni, Gunther
- Subjects
ROBOT hands ,THERMOGRAPHY ,MACHINE learning ,PROBLEM solving ,POINT cloud ,ROBOTICS - Abstract
In the context of collaborative robotics, handing over hand-held objects to a robot is a safety-critical task. Therefore, a robust distinction between human hands and presented objects in image data is essential to avoid contact with robotic grippers. To be able to develop machine learning methods for solving this problem, we created the OHO (Object Hand-Over) dataset of tools and other everyday objects being held by human hands. Our dataset consists of color, depth, and thermal images with the addition of pose and shape information about the objects in a real-world scenario. Although the focus of this paper is on instance segmentation, our dataset also enables training for different tasks such as 3D pose estimation or shape estimation of objects. For the instance segmentation task, we present a pipeline for automated label generation in point clouds, as well as image data. Through baseline experiments, we show that these labels are suitable for training an instance segmentation to distinguish hands from objects on a per-pixel basis. Moreover, we present qualitative results for applying our trained model in a real-world application. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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