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The traditional method for transformer moisture diagnosis is to establish empirical equations between feature parameters extracted from frequency domain spectroscopy (FDS) and the transformer’s moisture content. However, the established empirical equation may not be applicable to a novel testing environment, resulting in an unreliable evaluation result. In this regard, it is acknowledged that FDS combined with machine learning is more suitable for estimating moisture content in a variety of test environments. Nonetheless, the accuracy of the estimation results obtained using the existing method is limited by the algorithm’s inability to generalize. To address this issue, we propose an AdaBoost algorithm-enhanced back-propagation neural network (BP_AdaBoost). This study creates a database by extracting feature parameters from the FDS that characterize the insulation states of the prepared samples. Then, using the BP_AdaBoost algorithm and the newly constructed database, the moisture estimation models are trained. Finally, the results of the estimation are discussed in terms of laboratory and field transformers. By comparing the proposed BP_AdaBoost algorithm to other intelligence algorithms, it is demonstrated that it not only performs better in generalization, but also maintains a high level of accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Surface roughness is one of the most important attributes of paper and its measurement is normally used in quality control applications. Currently, measurement of surface roughness is done by using air leakage, optical, or subjective methods. In this article, an acoustical method for the determination of surface roughness of papers is described. The method is applied off-line and uses the friction noise recorded when a small brush slide over the surface of the paper. The analysis is done consider the friction noise ranging from 300Hz to 10kHz. Considering that friction noise is associated to surface roughness, a signal analysis algorithm was developed to take advantage of this fact. The algorithm consists of a frequency-domain feature extraction routine and an artificial neural network that represents the variation of the spectral features in a roughness scale. The results show that an affordable and efficient friction noise based system can be used for measuring surface roughness of tissue papers and other soft surfaces as an alternative to other methods. [Copyright &y& Elsevier]

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

Train wheels are crucial components for ensuring the safety of trains. The accurate and fast identification of wheel tread defects is necessary for the timely maintenance of wheels, which is essential for achieving the premise of conditional repair. Image-based detection methods are commonly used for detecting tread defects, but they still have issues with the misdetection of water stains and the leaking of small defects. In this paper, we address the challenges posed by the detection of wheel tread defects by proposing improvements to the YOLOv8 model. Firstly, the impact of water stains on tread defect detection is avoided by optimising the structure of the detection layer. Secondly, an improved SPPCSPC module is introduced to enhance the detection of small targets. Finally, the SIoU loss function is used to accelerate the convergence speed of the network, which ensures defect recognition accuracy with high operational efficiency. Validation was performed on the constructed tread defect dataset. The results demonstrate that the enhanced YOLOv8 model in this paper outperforms the original network and significantly improves the tread defect detection indexes. The average precision, accuracy, and recall reached 96.95%, 96.30%, and 95.31%. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The paper constructs a massive data mining model of distributed spatiotemporal databases for the Internet of Things. Then a homologous data fusion method based on information entropy is proposed. The storage space required by the tree structure is reduced by constructing the data schema tree of the merged data set. Secondly, the optimal dynamic support degree is obtained by using a neural network and genetic algorithm. Frequent items in the Internet of Things data are mined to achieve the normalization of the clustered feature data based on the threshold value. Experiments show that the F-measure of the data mining algorithm improves the efficiency by 15.64% and 18.25% compared with the kinds of other literatures respectively. RI increased by 21.17% and 26.07%, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of pure electric vehicles, how to improve the range has become the focus of research. Due to the large shell mass of the battery pack shell (BPE), it is necessary to optimize its structure in turn. In this paper, the BPE model was reconstructed by improving the upper shell material and adding reinforcements following the direction of the leaf vein fibers of the Magnolia Grandiflora. A quasi-Monte Carlo method based on Sobol sequences and a Latin hypercube design with variance sensitivity analysis was used to determine the seven design variables. The 122 data sets were trained and predicted using the basic gradient descent algorithm combined with the conjugate direction method, and the predictions were compared with static mechanical simulations for sharp cornering conditions on bumpy roads. The results showed that the BPE weight reduction ratio was 19.5%, and the maximum stress reduction ratio was 27.49%, and the displacement reduction ratio was 29.29% respectively, which satisfied the material requirements. It had a 20.88% increase in first-order mode frequency, which effectively prevented resonance. [ABSTRACT FROM AUTHOR]

In this paper, the radial basis function neural network based on the orthogonal least squares (OLS-RBFNN) method is proposed for the first time for the calibration of the six-port receiver, which realises six-port accurate reception in the true sense. This algorithm overcomes the problems of an ageing circuit, narrow working frequency band and nonlinearity of the six-port receiver hardware. The RBFNN is a three-layer forward network, including an input layer, a hidden layer and an output layer. The transformation function of the hidden layer is the radial basis function. A six-port receiver test platform with an operating frequency band of 2.0–8.0 GHz is built to verify the performance of the six-port receiver based on OLS-RBFNN. The test signal is a 16-QAM signal with different bandwidths and different frequency bands. The test indicator uses error vector magnitude (EVM)， which represents the error between the received signal and the ideal signal sent. At last, the EVM of all tested data is less than 1.07%, and the best EVM is 0.57%. Therefore, the calibration accuracy of the OLS-RBFNN proposed in this paper is very high. [ABSTRACT FROM AUTHOR]

To improve the effect of commercial insurance risk decision, this paper applies neural network algorithms to commercial insurance risk decision under the guidance of machine learning ideas, and selects the neural network algorithm based on the actual situation. Moreover, this paper analyzes the nature of risks of commercial insurance, analyzes the types of risks and risk relevance, constructs a commercial insurance risk decision model based on neural network algorithms, and determines the system process. In addition, this paper uses a combination method of qualitative and quantitative to identify the influencing factors of risk estimation to obtain relevant influencing factors, and verify the model proposed in this paper in combination with experimental research. From the experimental research results, it can be seen that the commercial insurance risk decision system based on neural network algorithm is very good in terms of decision effect. [ABSTRACT FROM AUTHOR]

In order to improve the management efficiency of software vulnerability classification, reduce the risk of system being attacked and destroyed, and save the cost for vulnerability repair, this paper proposes an automatic algorithm for Software Vulnerability Classification based on convolutional neural network (CNN) and gate recurrent unit neural network (GRU), called SVC-CG. It has conducted a fusion between the models of CNN and GRU according to their advantages (CNN is good at extracting local vector features of vulnerability text and GRU is good at extracting global features related to the context of vulnerability text). The merger of the features extracted by the complementary models can represent the semantic and grammatical information more accurately. Firstly, the Skip-gram language model based on Word2Vec is used to train and generate the word vector, and the words in each vulnerability text are mapped into the space with limited dimensions to represent the semantic information. Then the CNN is used to extract the local features of the text vector, and the GRU is used to extract the global features related to the text context. We combine two complementary models to construct a SVC-CG neural network algorithm, which can represent semantic and grammatical information more accurately to realize automatic classification of vulnerabilities. The experiment uses the vulnerability data from the national vulnerability database (NVD) to train and evaluate the SVC-CG algorithm. Through experimental comparison and analysis, the SVC-CG algorithm proposed in this paper has a good performance on Macro recall rate, Macro precision rate and Macro F1-score. [ABSTRACT FROM AUTHOR]

With the increasing complexity of building structures and interior materials, the danger of indoor fires has become more severe. It is effective to improve the accuracy and timeliness of fire-sensing devices in order to reduce the harm caused by fires. This paper focuses on the temporal characteristics of sensor information, creatively introducing second-order exponential smoothing into the information fusion algorithm. The RNN structure is used to fit the formula and adaptively trained with various types of fire data. Experimental results show that the proposed algorithm achieves an accuracy of 98% in fire recognition, significantly improving the accuracy of fire recognition. To avoid the issue of imbalanced positive and negative samples, this paper comprehensively evaluates parameters such as F1-score and Matthews correlation coefficient (MCC). The achieved scores are 0.97 and 0.95, respectively, indicating the algorithm's good performance in detecting the presence or absence of fire. Furthermore, the proposed algorithm is tested for its alarm time. The experimental results show that the proposed algorithm can timely identify various types of fires and can give an alarm earlier than traditional fire alarms. [ABSTRACT FROM AUTHOR]

This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

We draw on a substantial body of theoretical and empirical research on movement and behavior tracking tools, remote sensing and geospatial artificial intelligence technologies, and metaverse assets and services. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2023, with search terms including "Web3-powered metaverse worlds" + "neural network-based recognition and virtual simulation algorithms," "interactive 3D geo-visualization and neuromorphic computing systems," and "tactile sensing and cognitive modeling technologies." As we analyzed research published in 2022 and 2023, only 187 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 41, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage stochastic mathematical model is developed for an asset protection routing problem under a wildfire. The main aim of this study is to reduce the negative impact of a wildfire. Some parameters, such as travel and service times, obtaining profit by protecting an asset, and upper bounds of time windows, are considered as stochastic parameters. Generating proper scenarios for uncertain parameters has a large impact on the accuracy of the obtained solutions. Therefore, artificial neural networks are employed to extract possible scenarios according to previous actual wildfire events. The problem cannot be solved by exact solvers for large instances, so two matheuristic algorithms are proposed in this study to solve the problem in a reasonable time. In the first algorithm, a set of feasible routes is generated based on a heuristic approach, then a route-based mathematical model is used to obtain the final solution. Also, another matheuristic algorithm based on adaptive large neighbourhood search (ALNS) is proposed. In this algorithm, routing decisions are determined using the ALNS algorithm while other decisions are achieved by solving an intermediate mathematical model. The numerical analysis confirms the efficiency of both proposed algorithms; however, the first algorithm performs more efficiently. [ABSTRACT FROM AUTHOR]

Photocatalysts have made great contributions to the degradation of pollutants to achieve environmental purification. The traditional method of developing new photocatalysts is to design and perform a large number of experiments to continuously try to obtain efficient photocatalysts that can degrade pollutants, which is time-consuming, costly, and does not necessarily achieve the best performance of the photocatalyst. The rapid development of photocatalysis has been accelerated by the rapid development of artificial intelligence. Intelligent algorithms can be utilized to design photocatalysts and predict photocatalytic performance, resulting in a reduction in development time and the cost of new catalysts. In this paper, the intelligent algorithms for photocatalyst design and photocatalytic performance prediction are reviewed, especially the artificial neural network model and the model optimized by an intelligent algorithm. A detailed discussion is given on the advantages and disadvantages of the neural network model, as well as its application in photocatalysis optimized by intelligent algorithms. The use of intelligent algorithms in photocatalysis is challenging and long term due to the lack of suitable neural network models for predicting the photocatalytic performance of photocatalysts. The prediction of photocatalytic performance of photocatalysts can be aided by the combination of various intelligent optimization algorithms and neural network models, but it is only useful in the early stages. Intelligent algorithms can be used to design photocatalysts and predict their photocatalytic performance, which is a promising technology. [ABSTRACT FROM AUTHOR]

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training. [ABSTRACT FROM AUTHOR]

Falls are one of the significant causes of accidental injuries to the elderly. With the rapid growth of the elderly population, fall detection has become a critical issue in the medical and healthcare fields. In this paper, we propose a model based on an improved attention mechanism, CBAM-IAM-CNN-BiLSTM, to detect falls of the elderly accurately and in time. The model includes a convolution layer, bidirectional LSTM layer, sampling layer and dense layer, and incorporates the improved convolutional attention block module (CBAM) into the network structure so that the one-dimensional convolution layer replaces the dense layer to aggregate the information from channels, which allows the model to accurately extract different behavior characteristics. The acceleration and angular velocity data of the human body, collected by wearable sensors, are respectively input into the convolution layer and bidirectional LSTM layer of the model and then classified and identified by softmax after feature fusion. Based on comparison with models such as CNN and CNN-BiLSTM, as well as with different attention mechanisms such as squeeze-and-excitation (SE), efficient channel attention (ECA) and the convolutional block attention module (CBAM), this model improves the accuracy, sensitivity and specificity to varying degrees. The experimental results showed that the accuracy, sensitivity and specificity of the CBAM-IAM-CNN-BiLSTM model proposed in this paper were 97.37%, 97.29% and 99.56%, respectively, which proves that the model has good practicability and strong generalization ability. [ABSTRACT FROM AUTHOR]

In the network economy domain, urban competitiveness refers to the comparison between cities in terms of competition and development. It is the ability to gain competitive advantage under different factors. The evaluation of urban competitiveness will help cities to learn from each other, and provides reference for the government to enhance urban competitiveness. Unlike various studies in the literature exploiting only the non-linear characteristics of urban competitiveness, this paper selects BP (Back Propagation) network as the main framework for evaluation. A Genetic Algorithm BP (GABP) network based on genetic optimization is utilized. The weights are optimized besides the crossover mutation of GA algorithm. To compensate the slow prediction in the stand-alone mode, this work proposes a MapReduce (MP) based method; MR-GABP via cloud computing. The model ensures effective urban competitiveness evaluation with improved convergence speed and threshold generation speed. The systematic experiments conducted verify effectiveness of the method while the results obtained reveal that performance of the method is better than the other methods in terms of accuracy and recall yielded as 95.1% and 92.6% respectively. [ABSTRACT FROM AUTHOR]

This paper examines the problem of predictive gear scheduling for fuel consumption minimization in connected/automated heavy trucks. The literature highlights the fuel economy benefits of such predictive scheduling, but there is a need to optimize such scheduling online, in real time. To address this need, we begin by using dynamic programming (DP) to schedule gear shifting offline, in a manner that achieves a globally optimal Pareto tradeoff between the conflicting objectives of minimizing fuel consumption and shift frequency. The computational cost of DP is unfavorable for online implementation, but we present two algorithms addressing this challenge. Both algorithms rely on the fact that in the Pareto limit where fuel consumption minimization is the sole objective, DP furnishes a simple static shift map. Our first algorithm trains a recurrent neural network to prune the shift schedule generated by this map. The second algorithm performs this pruning in a direct manner tailored to reduce the schedule's rain flow count. We simulate these algorithms for different drive cycles. Both algorithms achieve a reasonable tradeoff between fuel consumption and gear shift frequency. However, the rain flow count algorithm is both more effective in approaching the DP-based Pareto front and more computationally efficient. [ABSTRACT FROM AUTHOR]

With the advancement of computer performance, deep learning is playing a vital role on hardware platforms. Indoor scene segmentation is a challenging deep learning task because indoor objects tend to obscure each other, and the dense layout increases the difficulty of segmentation. Still, current networks pursue accuracy improvement, sacrifice speed, and augment memory resource usage. To solve this problem, achieve a compromise between accuracy, speed, and model size. This paper proposes Multichannel Fusion Network (MFNet) for indoor scene segmentation, which mainly consists of Dense Residual Module(DRM) and Multi-scale Feature Extraction Module(MFEM). MFEM uses depthwise separable convolution to cut the number of parameters, matches different sizes of convolution kernels and dilation rates to achieve optimal receptive field; DRM fuses feature maps at several levels of resolution to optimize segmentation details. Experimental results on the NYU V2 dataset show that the proposed method achieves very competitive results compared with other advanced algorithms, with a segmentation speed of 38.47 fps, nearly twice that of Deeplab v3+, but only 1/5 of the number of parameters of Deeplab v3 +. Its segmentation results were close to those of advanced segmentation networks, making it beneficial for the real-time processing of images. [ABSTRACT FROM AUTHOR]

Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms. [ABSTRACT FROM AUTHOR]

Finding all the roots of a nonlinear equation is an important and difficult task that arises naturally in numerous scientific and engineering applications. Sequential iterative algorithms frequently use a deflating strategy to compute all the roots of the nonlinear equation, as rounding errors have the potential to produce inaccurate results. On the other hand, simultaneous iterative parallel techniques require an accurate initial estimation of the roots to converge effectively. In this paper, we propose a new class of global neural network-based root-finding algorithms for locating real and complex polynomial roots, which exploits the ability of machine learning techniques to learn from data and make accurate predictions. The approximations computed by the neural network are used to initialize two efficient fractional Caputo-inverse simultaneous algorithms of convergence orders ς + 2 and 2 ς + 4 , respectively. The results of our numerical experiments on selected engineering applications show that the new inverse parallel fractional schemes have the potential to outperform other state-of-the-art nonlinear root-finding methods in terms of both accuracy and elapsed solution time. [ABSTRACT FROM AUTHOR]

To further reduce the size of the neural network model and enable the network to be deployed on mobile devices, a novel fusion pruning algorithm based on information entropy stratification is proposed in this paper. Firstly, the method finds similar filters and removes redundant parts by Affinity Propagation Clustering, then secondly further prunes the channels by using information entropy stratification and batch normalization (BN) layer scaling factor, and finally restores the accuracy training by fine-tuning to achieve a reduced network model size without losing network accuracy. Experiments are conducted on the vgg16 and Resnet56 network using the cifar10 dataset. On vgg16, the results show that, compared with the original model, the parametric amount of the algorithm proposed in this paper is reduced by 90.69% and the computation is reduced to 24.46% of the original one. In ResNet56, we achieve a 63.82%-FLOPs reduction by removing 63.53% parameters. The memory occupation and computation speed of the new model are better than the baseline model while maintaining a high network accuracy. Compared with similar algorithms, the algorithm has obvious advantages in the dimensions of computational speed and model size. The pruned model is also deployed to the Internet of Things (IoT) as a target detection system. In addition, experiments show that the proposed model is able to detect targets accurately with low reasoning time and memory. It takes only 252.84 ms on embedded devices, thus matching the limited resources of IoT. [ABSTRACT FROM AUTHOR]

Infrared image target detection technology has been one of the essential research topics in computer vision, which has promoted the development of automatic driving, infrared guidance, infrared surveillance, and other fields. However, traditional target detection algorithms for infrared images have difficulty adapting to the target's multiscale characteristics. In addition, the accuracy of the detection algorithm is significantly reduced when the target is occluded. The corresponding solutions are proposed in this paper to solve these two problems. The final experiments show that this paper's infrared image target detection model improves significantly. [ABSTRACT FROM AUTHOR]

Building an effective algorithm model for large key power equipment has very important research significance and application value. Aiming at the typical operating state characteristics of large generators and taking the temperature changes as the main research indicators, the improved fireworks algorithm was used to optimize the process neural network, and the key data characteristics were studied based on the machine experiment and actual operation data of a 300 MW generator so as to find the variation and development trends of the maximum temperature rise caused by negative-sequence current. Furthermore, the effectiveness of the neural network model suitable for large generators established in this paper was verified by test functions and experiments. On this basis, the calculation method was applied to different working conditions, component materials, and heating positions of the generator. Moreover, the temperature-rise prediction results of the structural components for the generator rotor were obtained, and the optimization scheme of the slot wedge material given, which provide a reference for temperature-rise research and the selection of component materials for large generators. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent control system technique is proposed to model and control of a nonlinear coupled three tank system. Two pumps fed the tank 1 and tank 2 and a fractional flow of these two pumps fed tank 3. The main aim of this paper is to make a set point tracking experiments of the tanks level using a nonlinear autoregressive moving average L-2 (NARMA L-2) and neural network predictive controllers. The proposed controllers are designed with the same neural network architecture and algorithm. Comparison of the system with the proposed controllers for tracking a step and random level set points for a fixed and variable flow parameter and some good results have been obtained. [ABSTRACT FROM AUTHOR]

Over the past 10 years, lightning disaster has caused a large number of casualties and considerable economic loss worldwide. Lightning poses a huge threat to various industries. In an attempt to reduce the risk of lightning-caused disaster, many scholars have carried out in-depth research on lightning. However, these studies focus primarily on the lightning itself and other meteorological elements are ignored. In addition, the methods for assessing the risk of lightning disaster fail to give detailed attention to regional features (lightning disaster risk). This paper proposes a grid-based risk assessment method based on data from multiple sources. First, this paper considers the impact of lightning, the population density, the economy, and geographical environment data on the occurrence of lightning disasters; Second, this paper solves the problem of imbalanced lightning disaster data in geographic grid samples based on the K-means clustering algorithm; Third, the method calculates the feature of lightning disaster in each small field with the help of neural network structure, and the calculation results are then visually reflected in a zoning map by the Jenks natural breaks algorithm. The experimental results show that our method can solve the problem of imbalanced lightning disaster data, and offer 81% accuracy in lightning disaster risk assessment. [ABSTRACT FROM AUTHOR]

Due to the complexity of indoor environments, there are still some problems in indoor positioning, such as low position calculation efficiency and positioning accuracy. To solve the above two problems, an improved indoor positioning algorithm based on backpropagation neural network (BPNN) is proposed in this paper. Computation efficiency is increased by area partition and area screening; at the same time, positioning accuracy is improved by using BPNN algorithm considering the environment factors. For comparison, an improved K-nearest neighbor (KNN) algorithm based on area partition and area screening is also proposed. Experiment results show that the improved BPNN-based algorithm can obtain better positioning performance compared with both the traditional KNN method and the improved KNN method. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic bandwidth allocation (DBA) algorithm based on differentiated service cycle (DSC) in multiple service (multi-service) hybrid Virtual Passive Optical Network (VPON) is proposed. The DSC algorithm introduces neural network prediction and improves the polling mechanism between Optical Network Unit (ONU) and Optical Line Terminal (OLT). DSC can be applied to multi-service VPON scenarios. By introducing neural network, the problem that traditional prediction methods can only predict one polling cycle service traffic will be solved. By establishing the separation mechanism of report cycle and prediction cycle, the idle time in multi-wavelength VPON is eliminated, and the bandwidth consumption caused by a large number of REPORT messages is reduced. By proposing differentiated service cycle polling mechanism, the transmission delay of high priority service traffic caused by the simultaneous interpreting of multiple services in offline DBA algorithm can be reduced. Finally, by the simulation and analysis, the effectiveness of the proposed model is demonstrated. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to deal with application of artificial intelligence for solving real time control complication adhered with the controlled operation of a buck power converter. This type of converter finds application for power conversion at various levels for the direct current-direct current power industry to step down the input voltage. Design/methodology/approach: Use of ANN-RL (Artificial Neural Networks- Reinforcement Learning)-based control algorithm to control buck power converter shows robustness against parameter and load variation. Because of non-linearity instigated by element used for switching, control of this converter becomes an arduous control predicament. All the classical control techniques are based on an approximate linear model of the step down converter and these techniques fail to handle actual non-linearity. Findings: In this paper, a reinforcement learning-based algorithm has been used to handle and control buck power converter output voltage, without approximating the model of converter. The non-linearity instigated in converter is subjected to state of switch. Model of buck power converter is defined as a multi-step decision problem so that it can be solved using mathematical model of Markov decision process (MDP) and, in turn, reinforcement learning can be implemented. As MDP model is available for a discrete state system so model of converter has to be discretized and then value iteration is applied and output is analyzed. Load regulation and integral time absolute error analysis is done to show efficacy of this technique. Originality/value: To mitigate the effect of discretization function approximation using neural network is applied. MATrix LABoratory has been used for implementation and result indicates an improvement in the overall response. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is the most dangerous type of skin cancer worldwide. Early detection of melanoma is crucial for better outcomes, but this often can be challenging. This research explores the use of artificial intelligence (AI) techniques combined with non-invasive imaging methods to improve melanoma detection. The authors aim to evaluate the current state of AI-based techniques using tools including dermoscopy, optical coherence tomography (OCT), and reflectance confocal microscopy (RCM). The findings demonstrate that several AI algorithms perform as well as or better than dermatologists in detecting melanoma, particularly in the analysis of dermoscopy images. This research highlights the potential of AI to enhance diagnostic accuracy, leading to improved patient outcomes. Further studies are needed to address limitations and ensure the reliability and effectiveness of AI-based techniques. Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability. [ABSTRACT FROM AUTHOR]

Recently, higher structure complicacy and performance requirements of the aero-engine have brought higher demands on its control system. With the rapid development of artificial intelligence technology, the intelligent controller with self-learning ability will be able to make a great difference. In the paper, we propose an aero-engine intelligent controller design method based on twin delayed deep deterministic policy gradient (TD3) algorithm. The design method allows the intelligent controller to interact autonomously with the aero-engine system to acquire the optimal control sequence. The JT9D turbofan engine is used to introduce the controller design workflow proposed in the paper. First, the problem of aero-engine control is described as a Markov decision process for deep reinforcement learning (DRL) algorithms. Second, a complete intelligent controller design process is constructed by reasonably designing the network structures and reward function. Finally, the comparison simulations are carried out to verify the superior performance of the controller design methods. The simulation results indicate that low-pressure turbine speed has no overshoot, and the settling time does not exceed 0.88s during the engine acceleration process. In the deceleration process, the overshoot of the low-pressure turbine speed is limited to 0.74% and the settling time does not exceed about 0.6s. The results prove that the TD3 controller outperforms deep deterministic policy gradient (DDPG) and the proportional-integral-derivative (PID) in the speed tracking control. [ABSTRACT FROM AUTHOR]

As a novel neural network learning framework, Twin Extreme Learning Machine (TELM) has received extensive attention and research in the field of machine learning. However, TELM is affected by noise or outliers in practical applications so that its generalization performance is reduced compared to robust learning algorithms. In this paper, we propose two novel distance metric optimization-driven robust twin extreme learning machine learning frameworks for pattern classification, namely, CWTELM and FCWTELM. By introducing the robust Welsch loss function and capped L 2 , p -distance metric, our methods reduce the effect of outliers and improve the generalization performance of the model compared to TELM. In addition, two efficient iterative algorithms are designed to solve the challenges brought by the non-convex optimization problems CWTELM and FCWTELM, and we theoretically guarantee their convergence, local optimality, and computational complexity. Then, the proposed algorithms are compared with five other classical algorithms under different noise and different datasets, and the statistical detection analysis is implemented. Finally, we conclude that our algorithm has excellent robustness and classification performance. [ABSTRACT FROM AUTHOR]

In recent years, in Space-Ground-Sea Wireless Networks, the rapid development of image recognition also promotes the development of images fusion. For example, the content of a single-mode medical image is very single, and the fused image contains more image information, which provides a more reliable basis for diagnosis. However, in wireless communication and medical image processing, the image fusion effect is poor and the efficiency is low. To solve this problem, an image fusion algorithm based on fast finite shear wave transform and convolutional neural network is proposed for wireless communication in this paper. This algorithm adopts the methods such as fast finite shear wave transform (FFST), reducing the dimension of the convolution layer, and the inverse process of fast finite shear wave transform. The experimental results show that the algorithm has a very good effect in both objective indicators and subjective vision, and it is also very feasible in wireless communication. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic optimization problem focusing on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network models, along with a novel parallel computing strategy, coined the weighted aggregating stochastic gradient descent (WASGD). Following a theoretical analysis on the characteristics of the new objective function, WASGD introduces a decentralized weighted aggregating scheme based on the performance of local workers. Without any center variable, the new method automatically gauges the importance of local workers and accepts them by their contributions. Furthermore, we have developed an enhanced version of the method, WASGD+, by (1) implementing a designed sample order and (2) upgrading the weight evaluation function. To validate the new method, we benchmark our pipeline against several popular algorithms including the state-of-the-art deep neural network classifier training techniques (e.g., elastic averaging SGD). Comprehensive validation studies have been conducted on four classic datasets: CIFAR-100, CIFAR-10, Fashion-MNIST, and MNIST. Subsequent results have firmly validated the superiority of the WASGD scheme in accelerating the training of deep architecture. Better still, the enhanced version, WASGD+, is shown to be a significant improvement over its prototype. [ABSTRACT FROM AUTHOR]

Landslides, widespread and highly dangerous geological disasters, pose significant risks to humankind and the ecological environment. Consequently, predicting landslides is vital for disaster prevention and mitigation strategies. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. Prior studies have shown that in the context of landslide susceptibility, these models frequently underperform relative to tree-based machine learning algorithms. This shortcoming has restricted the application of deep learning in this domain. To overcome this challenge, this study presents the TabNet algorithm, which combines the interpretability and selective feature extraction of tree models with the representation learning and comprehensive training capabilities of neural network models. This paper explores the potential of employing the TabNet algorithm for landslide susceptibility analysis in China's WanZhou region and evaluates its performance against traditional machine learning techniques. The experimental data indicate that the TabNet algorithm achieves a recall score of 0.898 and an AUC of 0.915, demonstrating a generalization capability that is comparable to that of classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

Intelligent video analysis has broad application prospects. How to automatically analyze and identify human behavior in video has attracted extensive attention from researchers at home and abroad. Moreover, researching effective video behavior recognition algorithms and designing efficient behavior recognition systems has important theoretical and practical value. This paper studies the nonlinear classification technique and applies the video behavior recognition algorithm to basketball recognition. Moreover, this paper studies the classical convolutional neural network model and several improvements. In addition, this paper explains the advantages of convolutional neural networks in feature extraction compared with traditional neural networks and analyzes the performance of the algorithm by designing actual experiments. The research results show that the algorithm can quickly identify multiple players on the field, and the method can effectively deal with occlusion and other issues with high accuracy and real-time. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of visual SLAM algorithms in a dynamic scene, instance segmentation is widely used to eliminate dynamic feature points. However, the existing segmentation technology has low accuracy, especially for the contour of the object, and the amount of calculation of instance segmentation is large, limiting the speed of visual SLAM based on instance segmentation. Therefore, this paper proposes a contour optimization hybrid dilated convolutional neural network (CO-HDC) algorithm, which can perform a lightweight calculation on the basis of improving the accuracy of contour segmentation. Firstly, a hybrid dilated convolutional neural network (HDC) is used to increase the receptive field, which is defined as the size of the region in the input that produces the feature. Secondly, the contour quality evaluation (CQE) algorithm is proposed to enhance the contour, retaining the highest quality contour and solving the problem of distinguishing dynamic feature points from static feature points at the contour. Finally, in order to match the mapping speed of visual SLAM, the Beetle Antennae Search Douglas–Peucker (BAS-DP) algorithm is proposed to lighten the contour extraction. The experimental results have demonstrated that the proposed visual SLAM based on the CO-HDC algorithm performs well in the field of pose estimation and map construction on the TUM dataset. Compared with ORB-SLAM2, the Root Mean Squared Error (Rmse) of the proposed method in absolute trajectory error is about 30 times smaller and is only 0.02 m. [ABSTRACT FROM AUTHOR]

The parameter optimization of a multistage synchronous induction coilgun (SICG) is a time-consuming task. Traditional machine learning methods can accelerate the process by building predictive models, but they require separate modeling for an SICG with different stages, which requires numerous datasets and is a cumbersome process. This paper proposes a method for building a predictive model for an SICG with different stages based on a recurrent neural network (RNN). In this method, the feed time of a 2- to 10-stage SICG is selected from the standard orthogonal design table as the training and test datasets, and the current filament method (CFM) is used to calculate the dataset label. The gate recurrent unit (GRU) neural network is used to study the training dataset, and the predictive model has good accuracy with respect to the test dataset, with an average error of 0.022. The predictive model and a particle swarm optimization (PSO) algorithm are applied to optimize the feed time of the SICG with different stages. The results show that the three-stage SICG can achieve a muzzle velocity of 50 m/s for a projectile, while the maximum muzzle velocity of the three-stage SICG in all datasets is 46.87 m/s. [ABSTRACT FROM AUTHOR]

Threshold-based methods are prevalent across numerous domains, with specific relevance to image binarization, which traditionally employs global and local threshold algorithms. This paper presents a novel approach to image binarization, where the capacity of neural networks is utilized not just for determining optimal thresholds, but also for combining multiple global thresholds sourced from existing binarization techniques. The primary objective of our method is to develop a robust binarization strategy capable of managing a wide array of image conditions. By integrating the strengths of various thresholding techniques, our approach aims to establish a significant connection between traditional thresholding methods and those underpinned by deep learning. [ABSTRACT FROM AUTHOR]

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets. [ABSTRACT FROM AUTHOR]

A two-stage lightweight online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear filter with a single-channel single-frame post-filter. Both components rely on power spectral density (PSD) estimates provided by deep neural networks (DNNs). By deriving new metrics analyzing the dereverberation performance in various time ranges, we confirm that directly optimizing for a criterion at the output of the multi-channel linear filtering stage results in a more efficient dereverberation as compared to placing the criterion at the output of the DNN to optimize the PSD estimation. More concretely, we show that training this stage end-to-end helps further remove the reverberation in the range accessible to the filter, thus increasing the early-to-moderate reverberation ratio. We argue and demonstrate that it can then be well combined with a post-filtering stage to efficiently suppress the residual late reverberation, thereby increasing the early-to-final reverberation ratio. This proposed two-stage procedure is shown to be both very effective in terms of dereverberation performance and computational demands, as compared to, e.g., recent state-of-the-art DNN approaches. Furthermore, the proposed two-stage system can be adapted to the needs of different types of hearing-device users by controlling the amount of reduction of early reflections. [ABSTRACT FROM AUTHOR]

Clustering is a fundamental task in the field of data analysis. With the development of deep learning, deep clustering focuses on learning meaningful representation with neural networks. Ensemble clustering algorithms combine multiple base partitions into a robust and better consensus clustering. Current deep ensemble clustering algorithms usually neglect shallow and original features. Besides, rarel algorithms use graph attention networks to explore clustering structures. This paper proposes a novel Clustering algorithm based on Multi-layer Features and Graph attention Networks (CMFGN). CMFGN obtains multi-layer features through the hierarchical convolutional layers. Moreover, CMFGN combines the co-association matrix with original features as the Graph Attention Networks (GAT) input to obtain consensus clustering, which reuses original information and leverages GAT to inherit a good clustering structure. Extensive experimental results show that CMFGN remarkably outputs competitive methods on four challenging image datasets. In particular, CMFGN achieves the ACC of 82.14% on the Digits dataset, which is almost up to 6% performance improvement compared with the best baseline. [ABSTRACT FROM AUTHOR]

With the rapid development of global automotive industry intelligence and networking, the Internet of Vehicles (IoV) service, as a key communication technology, has been faced with an increasing spectrum of resources shortage. In this paper, we consider a spectrum utilization problem, in which a number of co-existing cellular users (CUs) and prioritized device-to-device (D2D) users are equipped in a single antenna vehicle-mounted communication network. To ensure a business-aware spectrum access mechanism with delay granted in a complex dynamic environment, we consider optimizing a metric that maintains a trade off between maximizing the total capacity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) links and minimizing the interference of high priority links. A low complexity priority-based spectrum allocation scheme based on the deep reinforcement learning method is developed to solve the proposed formulation. We trained our algorithm using the deep Q-learning network (DQN) over a set of public bandwidths. Simulation results show that the proposed scheme can allocate spectrum resources quickly and effectively in a high dynamic vehicle network environment. Concerning improved channel transmission rate, the V2V link rate in this scheme is 2.54 times that of the traditional random spectrum allocation scheme, and the V2I link rate is 13.5% higher than that of the traditional random spectrum allocation scheme. The average total interference received by priority links decreased by 14.2 dB compared to common links, realized service priority distinction and has good robustness to communication noise. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and Back-Propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search Algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA. [ABSTRACT FROM AUTHOR]

Fuzzy English recognition is affected by many factors, which leads to certain accuracy problems in intelligent recognition results. In order to improve the automatic recognition efficiency of fuzzy English, based on machine learning technology, this study constructs a neural network model. At the same time, this paper analyzes the research status and existing problems of handwritten character recognition, analyzes the model, and adopts multiple modules for automatic English recognition. In addition, the system is built on the basis of algorithms and model support, which makes fuzzy English recognition intelligent. Finally, in order to study the algorithm and model performance, the fuzzy English recognition is carried out through experiments. The research shows that the model constructed in this paper has certain recognition effect, which can be applied to practice, and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to determine which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose a novel algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region. The paper also reports extensive analyses of the RL-Cache sensitivity to its features and hyperparameter values. The analyses validate the made design choices and reveal interesting insights into the RL-Cache behavior. [ABSTRACT FROM AUTHOR]

A large amount of sample data is very important for implementing the dispatching rule mining in the job shop scheduling problem (JSP). This paper attempts to study the dispatching rule mining method for JSP based on Multi-Pass simulation. To this end, it introduces the Multi-Pass simulation mechanism and builds an optimal simulation platform, to provide sample data for the dispatching rule mining in JSP. Then, combining neural network (NN) theory and concept lattice theory, a dispatching rule mining algorithm of job shop based on attribute selection was proposed to avoid the redundant scheduling attributes from affecting the performance of this algorithm. On this basis, the authors further studied the proposed dispatching rule mining algorithm suitable for dynamic production environment. Finally, the simulation results showed that the proposed algorithm based on attribute selection has a better mining accuracy than the rule mining method without considering attribute selection; it is superior to others in terms of performance, and can adaptively obtain the optimal dispatching rules. The research findings have important guiding significance and reference value for solving uncertain and dynamic scheduling problems. [ABSTRACT FROM AUTHOR]