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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sample balance is crucial for machine learning. The importance of certain classes may be higher than its number on the imbalanced datasets. This paper studies the imbalanced datasets for speech emotion recognition. Firstly, the imbalanced baseline datasets EMODB and IEMOCAP are augmented with different signal-to-noise ratios, and the datasets EMODBM and IEMOCAPM are constructed. Secondly, six techniques namely SMOTE, RandomOverSampler, SMOTEENN, ADASYN, TomekLinks and SMOTETomek are adopted to resample the baseline datasets, and the augmented datasets are constructed to achieve the category balance. Thirdly, 21-dimensional low-level descriptor features are extracted from the baseline datasets and the augmented datasets. Finally, a novel model MA-CapsNet is proposed to validate the effectiveness of the resampling techniques. The results show that all types of emotion samples are basically balanced after resampling, which makes the learning of the model MA-CapsNet fairer. In addition, the model MA-CapsNet has better robustness on the resampling datasets. [ABSTRACT FROM AUTHOR]

Hepatic encephalopathy is a clinical syndrome of central nervous system dysfunction caused by liver insufficiency. It severely affects the quality of life of patients and may lead to death. Accurate prediction of the risk of developing hepatic encephalopathy is crucial for early intervention and treatment. In order to identify the risk of hepatic encephalopathy in patients in advance, many studies have been devoted to efforts to develop tools and methods to identify the risk of hepatic encephalopathy as early as possible, so as to develop preventive and early management strategies. Most conventional hepatic encephalopathy risk prediction models currently assess the probability of a patient developing hepatic encephalopathy by analysing factors such as clinical data and biochemical indicators, however, their accuracy, sensitivity and positive predictive value are not high. The application of artificial intelligence to clinical predictive modelling is a very hot and promising area, which can use large amounts of data and complex algorithms to improve the accuracy and efficiency of diagnosis and prognosis. To date, there have been few studies using AI techniques to predict hepatic encephalopathy. Therefore, this paper reviews the research progress of hepatic encephalopathy risk prediction models, and also discusses the prospect of AI application in hepatic encephalopathy risk prediction models. It also points out the challenges and future research directions of AI in HE risk prediction model research in order to promote the development and clinical application of hepatic encephalopathy risk prediction models. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional monitoring model for concrete dam displacement often ignores analyses of pertinent information within the residual sequence, leading to suboptimal predictive performance for dam displacement. The purpose of this paper is to improve the accuracy of prediction models. To address this issue, a dual-layer displacement prediction model for concrete dam, which considers signal residual amendment, is proposed. Firstly, influential factors affecting dam displacement are selected based on the statistical model of traditional concrete dam displacement. Secondly, the sparrow search algorithm (SSA) is employed to determine the hyperparameters of extreme learning machines (ELM) in order to establish the single-layer prediction model SSA-ELM, which yields single-layer model displacement prediction values for dams. Next, the Variational Mode Decomposition (VMD) technique along with the Minimum Sample Entropy (SE) and correlation analysis is utilized, the residual sequence is decomposed and reconstructed. Finally, by using the SSA-ELM model to correct the reconstructed sequence and combining the corrected values with the single-layer model displacement prediction values, the dual-layer prediction model SSA-ELM-VMDr+ is constructed, allowing for the final displacement prediction values. The engineering validation of the model demonstrates that the dual-layer prediction model exhibits higher prediction accuracy and stronger generalization ability compared with other models. Moreover, the model effectively extracts valuable information from the residual and overcomes noise interference. This paper provides a new reference for dam safety monitoring, health service diagnosis and management operations. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the most dynamic component of terrestrial water cycling, soil moisture plays a pivotal role in the formation, transformation, and consumption of surface water. Simultaneously, it serves as a crucial parameter influencing hydrological processes, vegetation status, and climatic conditions. Therefore, highprecision, high spatiotemporal resolution soil moisture data holds significant importance across various fields, including agriculture, forestry, and meteorology. Presently, traditional methods such as the timeconsuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring demands, highlighting the advantages of remote sensing methods. However, optical sensors are susceptible to cloud cover and vegetation obscuration, while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions. The space-borne global navigation satellite system-reflectometry (GNSS R) technology, characterized by short revisit cycles and pseudo-random sampling, presents a new opportunity for soil moisture retrieval. This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process. This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected signals, as well as the impact of water bodies on reflectivity. Existing error correction methods and their limitations are summarized, and potential avenues for improvement are discussed. Subsequently, the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three perspectives: (1) The development status of space-borne GNSS-R constellation. (2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval, including empirical models, semi-empirical models, and machine learning methods. (3) Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval. In conclusion, based on the scattering mechanism of reflected signals, the selection of auxiliary data, and the utilization of incident angle information, this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into future research directions. [ABSTRACT FROM AUTHOR]

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional geological stratification methods usually rely on manual interpretation and empirical judgment, which have shortcomings such as cumbersome processes, heavy workload, and large impact from human factors. This paper aims to study the ideas and methods of regional engineering geological stratification based on machine learning, and convert the stratigraphic stratification problem into a sequence-to-sequence prediction task of geological body spatial units and a geological attribute feature classification task, so as to improve the accuracy of geological stratification. and efficiency. Based on in-depth research on the original data of engineering geology, this paper proposes to use deep learning methods to train and predict geological stratification of static cone test data, and use classification algorithms to stratify borrow hole soil samples and quantify them. The applicability of the relevant algorithms was evaluated. By comparing the results of traditional methods and machine learning methods, the advantages of machine learning in geological stratification are verified. This study provides a new geological layering idea for regional engineering geology research, which has important practical significance. [ABSTRACT FROM AUTHOR]

Carbon emissions prediction in the textile and garment industry makes the industry' s carbon reduction policies more practical. In order to predict the industry's carbon emissions more accurately, this paper proposed a prediction model based on Long-Short Term Memory neural network (LSTM), which was optimized by an improved Whale Optimization Algorithm (WOA). The machine learning method was introduced to provide a basis for exploring the industry's carbon reduction path. Firstly, the LSTM model used WOA to optimize the key parameters, and chaotic mapping initialization population and adaptive weights were taken to improve the algorithm. At the same time, an improved WOA-LSTM model was constructed. Secondly, the carbon emissions of the textile and garment industry were calculated from 1990 to 2020, and the STIRPAT model was used to screen the influencing factors of carbon emissions in the industry. Comparative analysis was used to confirm the model's performance, and several scenarios were used to predict the industry's carbon emissions trend. Experiments show that the prediction accuracy is significantly improved, and the MAE, RMSE, and MAPE values for the model test set are 4.868, 4.984, and 0.024, respectively. Meanwhile, the industry will primarily rely on technological innovation to achieve carbon neutrality. This study offers new perspectives to research on carbon emissions prediction in industrial sectors. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the last couple of years, there has been an increasing need for Human-Computer Interaction (HCI) systems that do not require touching the devices to control them, such as ATMs, self service kiosks in airports, terminals in public offices, among others. The use of hand gestures offers a natural alternative to achieve control without touching the devices. This paper presents a solution that allows the recognition of hand gestures by analyzing three-dimensional landmarks using deep learning. These landmarks are extracted by using a model created with machine learning techniques from a single standard RGB camera in order to define the skeleton of the hand with 21 landmarks distributed as follows: one on the wrist and four on each finger. This study proposes a deep neural network that was trained with 9 gestures receiving as input the 21 points of the hand. One of the main contributions, that considerably improves the performance, is a first layer of normalization and transformation of the landmarks. In our experimental analysis, we reach an accuracy of 99.87% recognizing of 9 hand gestures. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With its strong adversary learning ability, generative adversarial networks (GAN) is favored by more and more researchers in many fields. This paper expounds the development background, framework and objective function of GAN, analyzes the causes of pattern collapse and gradient disappearance in the training process, and introduces in detail the GAN derived model proposed through the change of architecture and the modification of objective function. Then, it summarizes some standards used to evaluate the quality and diversity of generated images, and summarizes the wide application of GAN in different fields, Finally, this paper summarizes and puts forward some prospects for the future research direction in this field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Autonomous path planning represents a cornerstone technology for enabling planetary rovers to carry out exploration missions on extraterrestrial planets. This technology facilitates planetary rovers in navigating safely and efficiently toward their mission goal. However, the special conditions of extraterrestrial planetary environments pose significant challenges for autonomous path planning compared with those faced by ground-based mobile robots. These challenges include communication delays and bandwidth limitations between the planetary rover and the ground control station, limited light, unknown and unpredictable terrain, distinct environmental features, and the difficulty of accurately modeling interactions between the rover wheels and the ground. Therefore, addressing how to generate a safe, efficient, and reasonable traversable path for a planetary rover under conditions of incomplete environmental data and uncertain positioning is a focal point of current research. This paper reviews advancements in perception map construction and autonomous path planning for planetary rovers, examining both research progress and engineering applications. First, this paper summarizes advances in perception map construction methods that rely on binocular vision. This method uses stereo vision to generate parallax maps, which in turn help construct a digital elevation model of the planet’s surface. Stereo matching algorithms are typically used to acquire parallax maps. The discussion includes an analysis of two main types of methods: region-based matching algorithms and feature-based matching algorithms. Second, the paper classifies existing autonomous path-planning methods for planetary rovers into two categories: those based on cost evaluation and those leveraging machine learning. As a typical representative of cost-evaluation–based path planning methods, the optional arc-based autonomous path planning method is spotlighted for its algorithmic principles and iterative applications in ongoing planetary rover missions. Furthermore, the potential of A* heuristic search algorithm, rapidly-exploring random tree star (RRT*) algorithm, and fast marching method(FMM) algorithms for enhancing planetary rover path planning is explored in a categorical manner. The application of machine learning in autonomous path planning for planetary rovers is also reviewed. Such methods are classified into two groups: end-to-end path planning methods and auxiliary approaches leveraging machine learning. Ultimately, based on the above analysis of the key technologies of planetary rover autonomous path planning, the paper identifies and discusses future research directions for improving autonomous path planning methods for planetary rovers. These include enhancing perception capabilities, improving optional arc paths, reducing slippage, integrating multiple path planning methods, and strengthening the use of machine learning.

In materials genetic engineering, data-driven machine learning techniques have garnered significant attention as a powerful new tool in the field of magnesium alloys. Traditional empirical trial-and-error methods and those based on density functional theory have struggled to keep pace with the continuous advancements in material science needs owing to high time costs and low efficiency. By relying on statistical methods instead of solving physical equations, machine learning can quickly predict material properties at a low cost, provided the connection between descriptors and target properties is identified. This capability can streamline the experimental process. Magnesium and its alloys show tremendous potential in aerospace, automotive, and other fields owing to their low density and high specific strength. However, their industrialization has been limited by several challenges, including the varied effects of different alloying elements, preparation and processing defects, deformation difficulties, and the common trade-off between strength and ductility. Machine learning can accelerate the discovery of novel magnesium alloys or processing parameters, and explore the relationships between their physicochemical characteristics and target properties. This paper comprehensively and systematically reviews the research progress of machine learning applications in magnesium alloys. It introduces the basic processes and various methods of machine learning, including data set collection, data preprocessing, model building, and performance evaluation. The classification of machine learning algorithms is summarized briefly. The paper then focuses on the research achievements of machine learning applied in many aspects, such as machining processes, microstructure, mechanical properties, corrosion resistance, hydrogen storage properties, intrinsic properties (reinforcement mechanism, anisotropy, etc.) and inverse design. Factors such as alloy compositions, test temperature and time, second phase, and Schmid factor can be considered as features and input into machine learning models for training. These models not only accelerate the design of novel high-performance magnesium alloys but also enhance the understanding of magnesium alloy mechanisms. Additionally, the paper analyzes some urgent issues in the research and application of machine learning in magnesium alloys. These include insufficient prediction of the chemical and physical properties of magnesium alloys, the nascent stage of predicting the design and service performance of magnesium alloy components, and the lack of high-quality data sets. Finally, the paper proposes future research directions and development trends in the application of machine learning in magnesium alloys.

This paper explores the application and development trends of artificial intelligence (AI) technology, particularly machine learning and natural language processing in the field of failure analysis. Failure analysis is a crucial method for ensuring the reliability and safety of equipment, and is widely used in aerospace, automotive manufacturing, electronic devices, and other fields. Traditional failure analysis methods often rely on expert experience, which is time-consuming and laborious. By integrating AI’s powerful data processing capabilities with traditional methods, the accuracy and efficiency of analysis have been significantly enhanced. In terms of failure mode diagnosis, AI can rapidly and accurately identify various fault modes and provide precise diagnostic results. In failure cause diagnosis, AI integrates data from multiple sources to uncover complex failure factors and potential causal relationships, improving diagnostic reliability. In failure prediction, machine learning can accurately forecast material lifespan and strength, reducing experimental time and costs. In failure prevention, AI offers new approaches to effectively reduce the risk of failure and lower product maintenance costs. The paper also looks forward the future development prospects of AI in failure analysis and highlights challenges and recommendations in the areas, such as data quality improvement, model optimization, interdisciplinary collaboration, and ethical and safety issues.

Copyright of Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to further improve the accuracy of text generated by image description, this paper proposes an image description method that combines global-local features and attention mechanism. This method is an improvement on the traditional encoder-decoder model. From an overall perspective, the encoder stage uses the residual network ResNet101 to extract the global and local features of the image to avoid object loss or object prediction errors. In the processor stage, a two-way GRU embedded with an improved attention mechanism is used to generate text sequences. From a local point of view, the attention mechanism proposed by this model is an independent loop structure. The attention weight is obtained by calculating the similarity between the image local feature vector and the semantic vector, and the mapping relationship between image features and semantic information is enhanced. The experimental results on the MSCOCO dataset show that the algorithm in this paper has achieved varying degrees of improvement in evaluation indicators such as BLEU, CIDEr, and METEOR, indicating that the description text generated by this model is highly accurate and rich in details. [ABSTRACT FROM AUTHOR]

High precision segmentation of organs or lesions in medical image is a vital challenge issue for intelligent analysis of medical image, it has important clinical application value for auxiliary diagnosis and treatment of diseases. Recently, in solving challenging problems such as medical image information representation and accurate modeling of non-Euclidean spatial physiological tissue structures, the graph deep learning based medical image segmentation technology has made important breakthroughs, and it has shown significant information feature extraction and characterization advantages. The merged technology also can obtain more accurate segmentation results, which has become an emerging research hotspot in this field. In order to better promote the research and development of the deep learning segmentation algorithm for medical image graphs, this paper makes a systematic summary of the technological progress and application status in this field. The paper introduces the definition of graphs and the basic structure of graph convolutional networks, and elaborates on spectral graph convolution and spatial graph convolution operations. Then, according to the three technical structure modes of GCN combined with residual module, attention mechanism module and learning module, the research progress in medical image segmentation has been encapsulated. The application and development of graph deep learning algorithms based medical image segmentation are summarized and prospected to provide references and guiding principles for the technical development of related researches. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Precious Metals / Guijinshu is the property of Precious Metals Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Current researches on NDP flooding behavior detection mainly focus on detecting RA flooding and NS flooding behaviors, and there is insufficient flooding detection for other messages of the NDP protocol. Moreover, traditional threshold rule detection methods suffer from poor dynamics and low accuracy, while most of the Al-based detection methods can only perform binary classification detection, and there are still challenges in performing multi-classification detection. In short, there is a lack of corresponding research in multi-classification flooding detection of all messages of NDP protocol. Therefore, this paper proposed a multi-classification detection framework for NDP protocol flooding behaviors, and proposed a flooding behavior detection method for NDP protocol based on time interval characteristics. The framework constructed the first multiclassification dataset for NDP flooding detection through the processes of traffic collection and data processing, it compared and used 5 machine learning and 5 deep learning algorithms to train the detection model. The experimental results show that the detection accuracy of the XGBOOST algorithm in machine learning can reach 99.18%, and the detection accuracy of the Transformer algorithm in deep learning can reach 98.45%. Compared with the existing detection methods, the accuracy is higher. Meanwhile, the detection framework can detect 9 types of flooding behaviors for all 5 types of messages of NDP protocol and classify the flooding behaviors into multiple types. [ABSTRACT FROM AUTHOR]

The estimation of multiple matching models between wide baseline or large angle images is a quite challenging task in image processing. The existing algorithms can be used to estimate multiple matching models and their inliers between images well, but their results are prone to matching pairs mis-classification issues. In order to accurately estimate the multiple matching models and allocate matching pairs, this paper proposed an image multi-matching model estimation algorithm based on the aggregation of matching points of the same model (AMPSM). Firstly, for improve the proportion of correct matching pairs, it filtered out incorrect matching pairs based on the distribution characteristics of correct matching points in the neighboring region. Furthermore, based on the different matching model degrees to which the matching pairs belong, searched for the suspected intersection matching pairs of multiple models, that was interference matching pairs. Meantime, for reducing the impact of interference matching pairs on the accuracy of matching classification, they were removed. Afterwards, for improve the clustering degree of matching points with the co-model, the position was dynamically moved based on the distance between the points within the same model and the center of gravity of the point set during the sampling process. Finally, classifying clustered matching points by Mean Shift to obtain a multi matching model. And the proposed method was compared with classical framework based algorithms RANSAC, PROSAC, MAGSAC++, GMS, AdaLAM, PEARL, MTC, Sequential RANSAC, and deep learning based algorithms SuperGlue, OANet, CLCNet, CONSAC, etc. Results indicate over 30% increase in the inlier rate, 8.39% reduction in the mis-classification rate of multi model estimation. It is concluded that the new algorithm has significant advantages in incorrect matches filtering and multi-model estimation. [ABSTRACT FROM AUTHOR]

For charging problem in AGV scheduling in automated container terminals, this paper constructed a mixed integer optimization model considering the shallow charging and shallow discharging charging strategy. The model aimed to minimize the final completion time of the AGV. Under the constraints of considering the change of AGV battery power and the difference in power consumption in different states of the AGV, the model used the AGV idle time and the end time of a work cycle to make up power, reducing the number of AGV charging times, and thus reducing the total completion time. The model was solved by Wolf-PHC reinforcement learning, and the results were compared with GAMS solver, Q-learning algorithm and genetic algorithm (GA) respectively to verify the effectiveness of the model and superiority of the algorithm. The example analysis shows that AGV utilization efficiency is higher under the shallow charging and shallow discharging charging strategy, and the combination of Wolf-PHC and GA is better for the model solution. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nonlinear optical crystals are the core devices of all-solid-state lasers, and have extensive and important applications in information technology and national security. With the development of high-performance computing, the "top-down" computer-aided design methods have gradually become an important part of nonlinear optical materials design. In addition, the large-scale structural and properties information obtained based on high-throughput computing provides a solid data foundation for data mining and machine learning algorithm training, accelerating the development of the fourth paradigm of material design. This paper starts with the computational design of nonlinear optical materials, and then, discusses the new paradigm of data-driven nonlinear optical materials theoretical design. Finally, the recent research progress of our team in high-throughput screening, crystal structure prediction, and machine learning accelerated nonlinear optical materials are reviewed. [ABSTRACT FROM AUTHOR]

Federated learning, a novel distributed machine learning paradigm, collaboratively trains a global model while preserving data privacy. It faces challenges of slow convergence and low accuracy in the global model under data heterogeneity. Aiming at the problem, this paper proposed a personalized federated multi-task learning optimization (FedMTO) algorithm tailored for heterogeneous data. In a multi-task learning framework that included global and local tasks, it considered the personalized federated optimization problem. Initially, FedMTO adopted the idea of parameter decomposition, coordinating global and local classifiers through the learning of adaptive classifier combination weights. This process extracted knowledge from global classifiers to achieve personalized modeling for local tasks. Furthermore, due to the varying data distributions of local tasks, FedMTO incorporated a regularization multi-task learning strategy during local updates. This approach focused on the relevance between tasks to reduce the differences among various local tasks, thus ensuring fairness in the federated learning process. Finally, experiments were conducted on the MNIST and CIFAR-10 datasets under different data heterogeneity scenarios. The results demonstrate that compared with existing algorithms, FedMTO achieves higher accuracy and better fairness, verifying the effectiveness of this method in addressing heterogeneous data problems in federated learning. [ABSTRACT FROM AUTHOR]

Threshold private set intersection (TPSI) is a special case of secure multi-party computation, which is widely used in many fields such as machine learning, ride-sharing, and fingerprint recognition. However, currently existing solutions are all based on algorithms with high computational complexity and are only implemented under a semi-honest model, resulting in high computational overhead and inability to resist attacks by malicious adversaries. In order to solve the above problems, this paper first proposed a vector oblivious match test(VOMT) protocol and designed an efficient semi-honest TPSI protocol based on VOMT and cuckoo hashing. In addition, it combined VOMT and symmetric key encryption schemes to construct the vector oblivious decryption matching test(VODMT) protocol and designed a TPSI protocol based on VODMT and oblivious pseudorandom functions that could resist malicious adversaries. Subsequently, it proved the security of the protocol under the semihonest model and the malicious model respectively, and analyzed that the computational complexity and communication complexity of the two protocols were linear. When the set size is 4 096, the online running time of the two proposed protocols is 0.81 s and 1.81 s respectively, while the previous work required 5 627 s, so both protocols are efficient. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Transit-oriented development (TOD) is a crucial way to achieve compact, livable, and sustainable urban development. The integration of shared bikes and rail transit significantly expands the service scope of public transportation and promotes the implementation of TOD planning strategies. Based on TOD planning concepts, this paper identifies the transfer flow patterns of shared bikes. Considering the semantic functions of metro stations, an interpretable machine learning model is used to explore the tidal flow patterns of shared bikes and their nonlinear correlations with the built environment in the main urban area of Wuhan. The conclusions are as follows. (1) Based on the tidal balance index of shared bikes during morning and evening peak periods, two types of tidal flow patterns are identified in the main urban area of Wuhan, namely "convergence in the morning and divergence in the evening" and "divergence in the morning and convergence in the evening". The "convergence in the morning and divergence in the evening" tidal pattern mainly occurs in residential areas at a moderate distance from the urban center and employment areas on the edge of the Hankou central activity zone, while the "divergence in the morning and convergence in the evening" tidal pattern mainly occurs in employment-dense areas near the Second Ring Road. (2) Four types of semantic functional themes and two mixed functional clusters of metro stations are identified through POI semantic information modeling and hierarchical clustering. The semantic functional types of metro stations are strongly correlated with the flow patterns of shared bikes. (3) The nonlinear effects of built environment factors are significant. The moderate supporting living facilities and road density, and the large residential scale can effectively promote the occurrence of stable connection pattern of shared bikes with high riding flow. This study can provide a basis for alleviating the tidal flow problem of shared bikes from the aspects of optimizing shared bikes scheduling and adjusting the urban built environment. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)