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Path planning and obstacle avoidance are fundamental problems in unmanned ground vehicle path planning. Aiming at the limitations of Deep Reinforcement Learning (DRL) algorithms in unmanned ground vehicle path planning, such as low sampling rate, insufficient exploration, and unstable training, this paper proposes an improved algorithm called Dual Priority Experience and Ornstein–Uhlenbeck Soft Actor-Critic (DPEOU-SAC) based on Ornstein–Uhlenbeck (OU noise) and double-factor prioritized sampling experience replay (DPE) with the introduction of expert experience, which is used to help the agent achieve faster and better path planning and obstacle avoidance. Firstly, OU noise enhances the agent's action selection quality through temporal correlation, thereby improving the agent's detection performance in complex unknown environments. Meanwhile, the experience replay is based on double-factor preferential sampling, which has better sample continuity and sample utilization. Then, the introduced expert experience can help the agent to find the optimal path with faster training speed and avoid falling into a local optimum, thus achieving stable training. Finally, the proposed DPEOU-SAC algorithm is tested against other deep reinforcement learning algorithms in four different simulation environments. The experimental results show that the convergence speed of DPEOU-SAC is 88.99% higher than the traditional SAC algorithm, and the shortest path length of DPEOU-SAC is 27.24, which is shorter than that of SAC. [ABSTRACT FROM AUTHOR]

This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of biological data and wildlife videos regarding animal behavioral strategies during confrontations for hunting or food competition, two strategies are been utilized to design a novel bio-inspired intelligent swarm confrontation algorithm. The first one is the "fire concentration" strategy, which assigns a target for each tank in a way that the isolated opponent will be preferentially attacked with concentrated firepower. The second one is the "back and forth maneuver" strategy, which makes the tank tactically retreat after firing in order to avoid being hit when the shell is reloading. Two state-of-the-art swarm confrontation algorithms, namely the reinforcement learning algorithm and the assign nearest algorithm, are chosen as the opponents for the bio-inspired swarm confrontation algorithm proposed in this paper. Data of comprehensive confrontation tests show that the bio-inspired swarm confrontation algorithm has significant advantages over its opponents from the aspects of both win rate and efficiency. Moreover, we discuss how vital algorithm parameters would influence the performance indices. [ABSTRACT FROM AUTHOR]

To reduce safety accidents caused by distracted driving and address issues such as low recognition accuracy and deployment difficulties in current algorithms for distracted behavior detection, this paper proposes an algorithm that utilizes an improved KNN for classifying driver posture features to predict distracted driving behavior. Firstly, the number of channels in the Lightweight OpenPose network is pruned to predict and output the coordinates of key points in the upper body of the driver. Secondly, based on the principles of ergonomics, driving behavior features are modeled, and a set of five-dimensional feature values are obtained through geometric calculations. Finally, considering the relationship between the distance between samples and the number of samples, this paper proposes an adjustable distance-weighted KNN algorithm (ADW-KNN), which is used for classification and prediction. The experimental results show that the proposed algorithm achieved a recognition rate of 94.04% for distracted driving behavior on the public dataset SFD3, with a speed of up to 50FPS, superior to mainstream deep learning algorithms in terms of accuracy and speed. The superiority of ADW-KNN was further verified through experiments on other public datasets. [ABSTRACT FROM AUTHOR]

Clustering algorithms play a crucial role in early warning cybersecurity systems. They allow for the detection of new attack patterns and anomalies and enhance system performance. This paper discusses the problem of clustering data collected by a distributed system of network honeypots. In the proposed approach, when a network flow matches an attack signature, an appropriate label is assigned to it. This enables the use of semi-supervised learning algorithms and improves the quality of clustering results. The article compares the results of learning algorithms conducted with and without partial supervision, particularly non-negative matrix factorization and semi-supervised non-negative matrix factorization. Our results confirm the positive impact of labeling a portion of flows on the quality of clustering. [ABSTRACT FROM AUTHOR]

To improve the accuracy of signal recognition in delay-based optical reservoir computing (RC) systems, this paper proposes the use of nonlinear algorithms at the output layer to replace traditional linear algorithms for training and testing datasets and apply them to the identification of frequency-modulated continuous wave (FMCW) LiDAR signals. This marks the inaugural use of the system for the identification of FMCW LiDAR signals. We elaborate on the fundamental principles of a delay-based optical RC system using an optical-injected distributed feedback laser (DFB) laser and discriminate four FMCW LiDAR signals through this setup. In the output layer, three distinct training algorithms—namely linear regression, support vector machine (SVM), and random forest—were employed to train the optical reservoir. Upon analyzing the experimental results, it was found that regardless of the size of the dataset, the recognition accuracy of the two nonlinear training algorithms was superior to that of the linear regression algorithm. Among the two nonlinear algorithms, the Random Forest algorithm had a higher recognition accuracy than SVM when the sample size was relatively small. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a machine-learning framework that effectively addresses privacy concerns. It harnesses fragmented data from devices across the globe for model training and optimization while strictly adhering to user privacy protection and regulatory compliance. This framework holds immense potential for widespread applications in the smart-grid domain. Through FL, power companies can collaborate to train smart-grid models without revealing users' electricity consumption data, thus safeguarding their privacy. However, the data collected by clients often exhibits heterogeneity, which can lead to biases towards certain data features during the model-training process, therefore affecting the fairness and performance of the model. To tackle the fairness challenges that emerge during the federated-learning process in smart grids, this paper introduces FedCSGP, a novel federated-learning approach that incorporates client sampling and gradient projection. The main idea of FedCSGP is to categorize the causes of unfairness in federated learning into two parts: internal conflicts and external conflicts. Among them, the client-sampling strategy is used to resolve external conflicts, while the gradient-projection strategy is employed to address internal conflicts. By tackling both aspects, FederCSGP aims to enhance the fairness of the federated-learning model while ensuring the accuracy of the global model. The experimental results demonstrate that the proposed method significantly improves the accuracy of poorly performing clients in smart-grid scenarios with lower communication costs, therefore enhancing the fairness of the federated-learning algorithm. [ABSTRACT FROM AUTHOR]

Match-3 puzzle games have garnered significant popularity across all age groups due to their simplicity, non-violent nature, and concise gameplay. However, the development of captivating and well-balanced stages in match-3 puzzle games remains a challenging task for game developers. This study aims to identify the optimal algorithm for reinforcement learning to streamline the level balancing verification process in match-3 games by comparison with Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms. By training the agent with these two algorithms, the paper investigated which approach yields more efficient and effective difficulty level balancing test results. After the comparative analysis of cumulative rewards and entropy, the findings illustrate that the SAC algorithm is the optimal choice for creating an efficient agent capable of handling difficulty level balancing for stages in a match-3 puzzle game. This is because the superior learning performance and higher stability demonstrated by the SAC algorithm are more important in terms of stage difficulty balancing in match-3 gameplay. This study expects to contribute to the development of improved level balancing techniques in match-3 puzzle games besides enhancing the overall gaming experience for players. [ABSTRACT FROM AUTHOR]

Visual tracking is a key research area in computer vision, as tracking technology is increasingly being applied in daily life, it has high-research significance. Visual tracking technology usually faces various challenging interference factors, among which, a similar background is one of the factors that has a greater impact on the tracking process. Kernelized Correlation Filter (KCF) tracking algorithm can track targets quickly by using circulant matrix, and has good tracking effect, so it is widely used in the tracking field. However, when the target is interfered by similar objects, the filter template in KCF cannot effectively distinguish between the target and the interfering object. This is because the filter only uses the texture gradient feature as the description object of the target, which will make the KCF algorithm extremely sensitive to the change of the target; therefore, the filter has difficultly making a judgment in the unstable scene, cannot accurately describe the target state, and finally leads to tracking failure. Therefore, this paper fuses Color Names (CN) on the basis of the original Histogram of Oriented Gradients (HOG) feature of KCF, which can obtain a more comprehensive feature representation, and realize the application of combined features to improve the anti-interference ability of KCF in complex scenes. In addition, this paper also uses the peak response of correlation filtering as the judgment condition to determine whether the current tracking result is stable. When the filter is in an unstable tracking state, the proposed algorithm will select the value with high confidence from its multiple responses as the candidate target of the Siamese network, and the deep learning network is used as the incremental learning method of the filter. The Channel Attention is introduced into the network layer, so that the network can adaptively reason and adjust the extracted universal features, and the enhanced feature information is used as the final discriminant basis. Finally, according to the response, the target with the smallest error compared with the target template is selected from multiple candidate targets as the final tracking result. The experimental results show that the average accuracy and average success rate of the proposed algorithm are significantly improved compared with the classical tracking algorithm, especially in dealing with similar target interference. [ABSTRACT FROM AUTHOR]

A Non-Terrestrial Network (NTN) is a network system that enables service for areas where terrestrial networks cannot cover. An NTN provides communication services using flying objects such as UAVs, HAPs, and satellites. In the case of satellites, they move in Earth's orbit at a constant speed. Ground services from continuously moving satellites cause frequent handovers. In addition, frequent handovers may come as a load between User Equipment (UE) and the communication system, which leads to degradation of service quality. Unlike Terrestrial Networks (TN), communication services are provided to UEs at altitudes ranging from 20 km to 35,584 km, rather than from base stations close to the ground. Service at high altitudes is unreliable due to the measurement values that were previously used as quality indicators to operate terrestrial networks. Moreover, service at high altitudes demands long-distance communication, and propagation delay occurs from the long-distance communication. In the 3GPP Rel. 17 document, it is suggested that the above problems should be solved. This paper tries to solve the problem by proposing the two-step XGBOOST, a CART-based Gradient Boosting Model. Handover in TN uses measurement-based conditional handover (CHO), but the measured values in the NTN environment are not valid. Using this, the distance between the UE and the center of the cell and the elevation angle are used to construct a model that predicts the HO triggering time point. In order to overcome the propagation delay caused by communication at a high altitude, a model that predicts the distance and elevation angle between the UE and the center of the cell considering the propagation delay is proposed. The model is composed of two-step XGBOOST. The one-step model is a model in which the UE predicts the distance and elevation angle between cell centers after propagation delay at the time when satellite position information is transmitted to the UE. The two-step model predicts handover triggering occurrence based on the data predicted by the one-step result. As a result of the experiment, the model considering the propagation delay showed about 8% better performance on average than the model not considering the propagation delay, and the XGBOOST model achieved an average F1-score of 0.9891 in the propagation delay experiments. [ABSTRACT FROM AUTHOR]

In the traditional text detection process, the text area of the small receptive field in the video image is easily ignored, the features that can be extracted are few, and the calculation is large. These problems are not conducive to the recognition of text information. In this paper, a lightweight network structure on the basis of the EAST algorithm, the Convolution Block Attention Module (CBAM), is proposed. It is suitable for the spatial and channel hybrid attention module of text feature extraction of the natural scene video images. The improved structure proposed in this paper can obtain deep network features of text and reduce the computation of text feature extraction. Additionally, a hybrid feature pyramid + BLSTM network is designed to improve the attention to the small acceptance domain text regions and the text sequence features of the region. The test results on the ICDAR2015 demonstrate that the improved construction can effectively boost the attention of small acceptance domain text regions and improve the sequence feature detection accuracy of small acceptance domain of long text regions without significantly increasing computation. At the same time, the proposed network constructions are superior to the traditional EAST algorithm and other improved algorithms in accuracy rate P, recall rate R, and F-value. [ABSTRACT FROM AUTHOR]

Helmet recognition algorithms based on deep learning aim to enable unmanned full-time detection and record violations such as failure to wear a helmet. However, in actual scenarios, weather and human factors can be complicated, which poses challenges for safety helmet detection. Camera shaking and head occlusion are common issues that can lead to inaccurate results and low availability. To address these practical problems, this paper proposes a novel helmet detection algorithm called DAAM-YOLOv5. The DAAM-YOLOv5 algorithm enriches the diversity of datasets under different weather conditions to improve the mAP of the model in corresponding scenarios by using Mosaic-9 data enhancement. Additionally, this paper introduces a novel dynamic anchor box mechanism, K-DAFS, into this algorithm and enhances the generation speed of the blocked target anchor boxes by using bidirectional feature fusion (BFF). Furthermore, by using an attention mechanism, this paper redistributes the weight of objects in a picture and appropriately reduces the model's sensitivity to the edge information of occluded objects through pooling. This approach improves the model's generalization ability, which aligns with practical application requirements. To evaluate the proposed algorithm, this paper adopts the region of interest (ROI) detection strategy and carries out experiments on specific, real datasets. Compared with traditional deep learning algorithms on the same datasets, our method effectively distinguishes helmet-wearing conditions even when head information is occluded and improves the detection speed of the model. Moreover, compared with the YOLOv5s algorithm, the proposed algorithm increases the mAP and FPS by 4.32% and 9 frames/s, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy management approach (imitation-Q-learning) based on imitation learning (IL) and reinforcement learning (RL). The proposed approach reinforces a decision-making agent based on a modified Q-learning algorithm to mimic an expert demonstration to solve a microgrid (MG) energy management problem. Those demonstrations are derived from solving a set of linear programming (LP) problems. Consequently, the imitation-Q-learning algorithm learns by interacting with the MG simulator and imitating the LP demonstrations to make decisions in real time that minimize the MG energy costs without prior knowledge of uncertainties related to photovoltaic (PV) production, load consumption, and electricity prices. A real-scale MG at the Lille Catholic University in France was used as a case study to conduct experiments. The proposed approach was compared to the expert performances, which are the LP algorithm and the conventional Q-learning algorithm in different test scenarios. It was approximately 80 times faster than conventional Q-learning and achieved the same performance as LP. In order to test the robustness of the proposed approach, a PV inverter crush and load shedding were also simulated. Preliminary results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended to process multiview datasets in the literature, called the multiview FCM (MV-FCM). However, most of the MV-FCM clustering algorithms and their extensions in the literature need prior information about the number of clusters and are also highly influenced by initializations. In this paper, we propose a novel MV-FCM clustering algorithm with an unsupervised learning framework, called the unsupervised MV-FCM (U-MV-FCM), such that it can search an optimal number of clusters during the iteration process of the algorithm without giving the number of clusters a priori. It is also free of initializations and parameter selection. We then use three synthetic and six benchmark datasets to make comparisons between the proposed U-MV-FCM and other existing algorithms and to highlight its practical implications. The experimental results show that our proposed U-MV-FCM algorithm is superior and more useful for clustering multiview datasets. [ABSTRACT FROM AUTHOR]

The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Deterministic Policy Gradient (VN-MADDPG) algorithm to address these issues. The variable-noise mechanism reduces noise dynamically, enabling the agent to utilize the learned policy more effectively to complete tasks. This significantly improves the stability of the decision-making model in making optimal decisions. The importance sampling module addresses the inconsistency between outdated experience in the replay buffer and current environmental features. This enhances the model's learning efficiency and improves the robustness of the decision-making model. Experimental results on the CARLA simulation platform show that the success rate of decision making at unsignalized intersections by autonomous vehicles has significantly increased, and the pass time has been reduced. The decision-making model based on the VN-MADDPG algorithm demonstrates stable and excellent decision-making performance. [ABSTRACT FROM AUTHOR]

An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) positioning systems often operate in a non-line-of-sight (NLOS) environment. NLOS propagation has become the main source of ultra-wideband indoor positioning errors. As such, how to identify and correct NLOS errors has become a key problem that must be solved in high-accuracy indoor positioning technology. This paper firstly describes the influence of the NLOS propagation path on localization accuracy and the generation method of ultra-wideband signals, and secondly classifies and analyzes the currently available algorithms for ultra-wideband non-line-of-sight (NLOS) identification and error suppression. For the identification of NLOS, the residual analysis judgement method, statistical feature class identification method, machine learning method and geometric feature judgement method are discussed. For the suppression of NLOS propagation errors, weighting-based methods, filtering-based methods, line-of-sight reconstruction algorithms, neural network algorithms, optimization methods with constraints, and path tracing methods are discussed. Finally, we conclude the paper and point out the problems that need to be solved in NLOS indoor positioning. [ABSTRACT FROM AUTHOR]

In the 5G era, the amount of network data has grown explosively. A large number of new computation-intensive applications have created demand for edge computing in mobile networks. Traditional optimization methods are difficult to adapt to the dynamic wireless network environment because they solve the problem online, which is not suitable in edge computing scenarios. Therefore, in order to obtain a mobile network with better performance, we propose a network frame with a resource allocation algorithm based on power consumption, delay and user cooperation. This algorithm can quickly realize the optimization of a network to improve performance. Specifically, compared with heuristic algorithms, such as particle swarm optimization, ant colony algorithm, etc., commonly used to solve such problems, the algorithm proposed in this paper can reduce some aspects of network performance (including delay and user energy consumption) by about 10% in a network dominated by downlink tasks. The performance of the algorithm under certain network conditions was demonstrated through simulations. [ABSTRACT FROM AUTHOR]

Eye fatigue has a fatiguing effect on the eye muscles, and eye movement performance is a macroscopic response to the eye fatigue state. To detect and prevent the risk of eye fatigue in advance, this study designed an eye fatigue detection experiment, collected experimental data samples, and constructed experimental data sets. In this study, eye-tracking feature extraction was completed, and the significance difference of eye-tracking features under different fatigue states was discussed by two-way repeated-measures ANOVA (Analysis of Variance). The experimental results demonstrate the feasibility of eye fatigue detection from eye-tracking signals. In addition, this study considers the effects of different feature extraction methods on eye fatigue detection accuracy. This study examines the performance of machine learning algorithms based on manual feature calculation (SVM, DT, RM, ET) and deep learning algorithms based on automatic feature extraction (CNN, auto-encoder, transformer) in eye fatigue detection. Based on the combination of the methods, this study proposes the feature union auto-encoder algorithm, and the accuracy of the algorithm for eye fatigue detection on the experimental dataset is improved from 82.4% to 87.9%. [ABSTRACT FROM AUTHOR]

During unexpected driving situations in autonomous vehicles, such as a system failure, the driver should take over control from the vehicles in SAE Level 3 to cope with unexpected situations. Therefore, it is necessary to develop reasonable takeover technologies to ensure safe driving. In this study, an electroencephalogram (EEG)-based driver status classification model and a safety index-based integrated longitudinal control algorithm considering the takeover time and driving characteristics are proposed. The driver status is classified into two states: road monitoring and non-driving-related tasks. EEG data are acquired while the driver performs certain tasks. The driver status classification model is presented using the EEG data based on a machine learning method. It is designed such that the desired takeover time is determined based on the classified driver state. To design the integrated longitudinal control algorithm, a safety index is designed and calculated based on the vehicle state and driver's driving characteristics. The desired clearances based on the desired takeover time and driver characteristics are calculated and selected based on the safety index. A sliding-mode control algorithm is adopted to allow the vehicle to track the desired clearance reasonably. The performance of the proposed control algorithm is evaluated using the MATLAB/Simulink R2019a (Mathworks, Natick, Massachusetts, U.S.A) and CarMaker software 8.1.1 (IPG Automotive, Karlsruhe, Germany). [ABSTRACT FROM AUTHOR]

This research suggests a multi-level federated edge learning algorithm by leveraging the advantages of Edge Computing Paradigm. Model aggregation is partially moved from a cloud center server to edge servers in this framework, and edge servers are connected hierarchically depending on where they are located and how much computational power they have. At the same time, we considered an important issue: the heterogeneity of different client computing resources (such as device processor computing power) and server communication channels (which may be limited by geography or device). For this situation, a client and edge server selection algorithm ( C E S A ) based on a greedy algorithm is proposed in this paper. Given resource constraints, C E S A aims to select as many clients and edge servers as possible to participate in the model computation in order to improve the accuracy of the model. The simulation results show that, when the number of clients is high, the multi-level federated edge learning algorithm can shorten the model training time and improve efficiency compared to the traditional federated learning algorithm. Meanwhile, the C E S A is able to aggregate more clients for training in the same amount of time compared to the baseline algorithm, improving model training accuracy. [ABSTRACT FROM AUTHOR]

The success of machine learning (ML) techniques in the formerly difficult areas of data analysis and pattern extraction has led to their widespread incorporation into various aspects of human life. This success is due in part to the increasing computational power of computers and in part to the improved ability of ML algorithms to process large amounts of data in various forms. Despite these improvements, certain issues, such as privacy, continue to hinder the development of this field. In this context, a privacy-preserving, distributed, and collaborative machine learning technique called federated learning (FL) has emerged. The core idea of this technique is that, unlike traditional machine learning, user data is not collected on a central server. Nevertheless, models are sent to clients to be trained locally, and then only the models themselves, without associated data, are sent back to the server to combine the different locally trained models into a single global model. In this respect, the aggregation algorithms play a crucial role in the federated learning process, as they are responsible for integrating the knowledge of the participating clients, by integrating the locally trained models to train a global one. To this end, this paper explores and investigates several federated learning aggregation strategies and algorithms. At the beginning, a brief summary of federated learning is given so that the context of an aggregation algorithm within a FL system can be understood. This is followed by an explanation of aggregation strategies and a discussion of current aggregation algorithms implementations, highlighting the unique value that each brings to the knowledge. Finally, limitations and possible future directions are described to help future researchers determine the best place to begin their own investigations. [ABSTRACT FROM AUTHOR]

The latest international video coding standard, H.266/Versatile Video Coding (VVC), supports high-definition videos, with resolutions from 4 K to 8 K or even larger. It offers a higher compression ratio than its predecessor, H.265/High Efficiency Video Coding (HEVC). In addition to the quadtree partition structure of H.265/HEVC, the nested multi-type tree (MTT) structure of H.266/VVC provides more diverse splits through binary and ternary trees. It also includes many new coding tools, which tremendously increases the encoding complexity. This paper proposes a fast intra coding algorithm for H.266/VVC based on visual perception analysis. The algorithm applies the factor of average background luminance for just-noticeable-distortion to identify the visually distinguishable (VD) pixels within a coding unit (CU). We propose calculating the variances of the numbers of VD pixels in various MTT splits of a CU. Intra sub-partitions and matrix weighted intra prediction are turned off conditionally based on the variance of the four variances for MTT splits and a thresholding criterion. The fast horizontal/vertical splitting decisions for binary and ternary trees are proposed by utilizing random forest classifiers of machine learning techniques, which use the information of VD pixels and the quantization parameter. Experimental results show that the proposed algorithm achieves around 47.26% encoding time reduction with a Bjøntegaard Delta Bitrate (BDBR) of 1.535% on average under the All Intra configuration. Overall, this algorithm can significantly speed up H.266/VVC intra coding and outperform previous studies. [ABSTRACT FROM AUTHOR]

Considering the high noise and chromatic aberration in the Retinex-Net image enhancement results, this paper put forward a modified Retinex-Net algorithm for weak illumination image enhancement based on the Decom-Net and Enhance-Net structures of Retinex-Net. The improved structure proposed in this paper adds the attention mechanism ECA-Net into the Decom-Net and Enhance-Net convolution layer of the original Retinex-Net structure, which can effectively reduce the problem of irrelevant background and local brightness imbalance, activate sensitive features, and improve the image's details and brightness processing ability. Additionally, deep connected attention networks are embedded between the introduced attention modules, so that all of the attention modules can be trained jointly to improve the learning ability. Furthermore, the improved method also introduces a noise reduction loss function and a color loss function to suppress noise and to reduce image color distortion. The test results of the proposed method indicate that the image's overall brightness can be balanced, the local areas cannot be overexposed, and more image details and color information can be retained than with other enhancement algorithms. [ABSTRACT FROM AUTHOR]

High-precision systems such as civil aviation have put forward higher requirements for navigation systems, including indicators such as accuracy and integrity. Signal distortions and evil waveforms (EWF) generated by the signal-generating hardware on the satellite can severely affect the cross-correlation function of the signal, thereby affecting the integrity of the navigation system. With the further development of the BeiDou Navigation System (BDS), the types of signal distortion are subdivided into three types: analog distortion, subcarrier distortion, and PN code distortion. Traditional multi-correlator methods are no longer applicable under the requirements of modern navigation systems. In this paper, a machine learning-based BeiDou B1C signal anomaly monitoring algorithm is proposed. We detected and classified the signals using a quadratic discriminant analysis (QDA) method. The results show that our method can accurately classify the distortion types under the condition that the accuracy of distortion detection can be greatly improved. Meanwhile, our method is also highly effective and robust. [ABSTRACT FROM AUTHOR]

To improve the accuracy of the You Only Look Once v5s (YOLOv5s) algorithm for object detection, this paper proposes an improved YOLOv5s algorithm, CBAM-YOLOv5s, which introduces an attention mechanism. A convolutional block attention module (CBAM) is incorporated into the YOLOv5s backbone network to improve its feature extraction ability. Furthermore, the complete intersection-over-union (CIoU) loss is used as the object bounding-box regression loss function to accelerate the speed of the regression process. Experiments are carried out on the Pascal Visual Object Classes 2007 (VOC2007) dataset and the Microsoft Common Objects in Context (COCO2014) dataset, which are widely used for object detection evaluations. On the VOC2007 dataset, the experimental results show that compared with those of the original YOLOv5s algorithm, the precision, recall and mean average precision (mAP) of the CBAM-YOLOv5s algorithm are improved by 4.52%, 1.18% and 3.09%, respectively. On the COCO2014 dataset, compared with the original YOLOv5s algorithm, the precision, recall and mAP of the CBAM-YOLOv5s algorithm are increased by 2.21%, 0.88% and 1.39%, respectively. [ABSTRACT FROM AUTHOR]

Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions. [ABSTRACT FROM AUTHOR]

The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic routing information communication and an unprotected public media make them easy targets for a wide variety of security threats. IDSs are helpful tools that can detect and prevent system vulnerabilities in a network. Unfortunately, there is no possibility to construct advanced protective measures within the basic infrastructure of the WSN. There seem to be a variety of machine learning (ML) approaches that are used to combat the infiltration issues plaguing WSNs. The Slime Mould Algorithm (SMA) is a recently suggested ML approach for optimization problems. Therefore, in this paper, SMA will be integrated into an IDS for WSN for anomaly detection. The SMA's role is to reduce the number of features in the dataset from 41 to five features. The classification was accomplished by two methods, Support Vector Machine with polynomial core and decision tree. The SMA showed comparable results based on the NSL-KDD dataset, where 99.39%, 0.61%, 99.36%, 99.42%, 99.33%, 0.58%, and 99.34%, corresponding to accuracy, error rate, sensitivity, specificity, precision, false positive rate, and F-measure, respectively, are obtained, which are significantly improved values when compared to other works. [ABSTRACT FROM AUTHOR]

The use of theory-based knowledge in machine learning models has a major impact on many engineering and physics problems. The growth of deep learning algorithms is closely related to an increasing demand for data that is not acceptable or available in many use cases. In this context, the incorporation of physical knowledge or a priori constraints has proven beneficial in many tasks. On the other hand, this collection of approaches is context-specific, and it is difficult to generalize them to new problems. In this paper, we experimentally compare some of the most commonly used theory-injection strategies to perform a systematic analysis of their advantages. Selected state-of-the-art algorithms were reproduced for different use cases to evaluate their effectiveness with smaller training data and to discuss how the underlined strategies can fit into new application contexts. [ABSTRACT FROM AUTHOR]

In the last two decades, Radio Frequency Identification (RFID) technology has attained prominent performance improvement and has been recognized as one of the key enablers of the Internet of Things (IoT) concepts. In parallel, extensive employment of Machine Learning (ML) algorithms in diverse IoT areas has led to numerous advantages that increase successful utilization in different scenarios. The work presented in this paper provides a use-case feasibility analysis of the implementation of ML algorithms for the estimation of ALOHA-based frame size in the RIFD Gen2 system. Findings presented in this research indicate that the examined ML algorithms can be deployed on modern state-of-the-art resource-constrained microcontrollers enhancing system throughput. In addition, such utilization can cope with latency since the execution time is sufficient to meet protocol needs. [ABSTRACT FROM AUTHOR]

In compressed sensing (CS), one seeks to down-sample some high-dimensional signals and recover them accurately by exploiting the sparsity of the signals. However, the traditional sparsity assumption cannot be directly satisfied in most practical applications. Fortunately, many signals-of-interest do at least exhibit a low-complexity representation with respect to a certain transformation. Particularly, total variation (TV) minimization is a notable example when the transformation operator is a difference matrix. Presently, many theoretical properties of total variation have not been completely explored, e.g., how to estimate the precise location of phase transitions and their rigorous understanding is still in its infancy. So far, the performance and robustness of the existing algorithm for phase transition prediction of TV model are not satisfactory. In this paper, we design a new approximate message passing algorithm to solve the above problems, called total variation vector approximate message passing (TV-VAMP) algorithm. To be specific, we first consider the problem from the Bayesian perspective, and formulate it as an optimization problem. Then, the vector factor graph for the TV model is constructed based on the formulized problem. Finally, the TV-VAMP algorithm is derived according to the idea of probabilistic inference in machine learning. Compared with the existing algorithm, our algorithm can be applied to a wider range of measurements distributions, including the non-zero-mean Gaussian distribution measurements matrix and ill-conditioned measurements matrix. Furthermore, in experiments with various settings, including different measurement distribution matrices, signal gradient sparsity, and measurement times, the proposed algorithm can almost reach the target mean squared error (−60 dB) with fewer iterations and better fit the empirical phase transition curve. [ABSTRACT FROM AUTHOR]

The rapid technological development of computing power and system operations today allows for increasingly advanced algorithm implementation, as well as path planning in real time. The objective of this article is to provide a structured review of simulations and practical implementations of collision-avoidance and path-planning algorithms in autonomous underwater vehicles (AUVs). The novelty of the review paper is to consider not only the results of numerical research but also the newest results of verifying collision-avoidance and path-planning algorithms in real applications together with a comparison of the difficulties encountered during simulations and their practical implementation. Analysing the last 20 years of AUV development, it can be seen that experiments in a real environment are dominated by classical methods. In the case of simulation studies, artificial intelligence (AI) methods are used as often as classical methods. In simulation studies, the APF approach is most often used among classical methods, whereas among AI algorithms reinforcement learning and fuzzy logic methods are used. For real applications, the most used approach is reactive behaviors, and AI algorithms are rarely used in real implementations. Finally, this article provides a general summary, future works, and a discussion of the limitations that inhibit the further development in this field. [ABSTRACT FROM AUTHOR]

The globalization of the integrated circuit (IC) industry has raised concerns about hardware Trojans (HT), and there is an urgent need for efficient HT-detection methods of gate-level netlists. Machine learning (ML) is a powerful tool for this purpose. A Trojan-detection framework is proposed in this paper to solve the data imbalance and low accuracy problems of existing ML-based HT-detection algorithms. To solve the problem of data imbalance, we propose the node-filtering algorithm, which extracts structure templates from HT circuits and removes most normal nodes based on them. To enhance the identification of unknown HT payload, we propose the load-expansion algorithm, which expands the identified HT nodes based on their fanout features. We evaluate the framework using different ML algorithms. The results show that the framework significantly improves the Trojan-detection rate of the original algorithms, and achieves a 10% improvement in true positive rate compared to the original algorithms. [ABSTRACT FROM AUTHOR]

E-bikes, i.e., bikes equipped with a small electrical engine, are becoming increasingly widespread, thanks to their positive contribution to mobility and sustainability. A key component of an e-bike is the battery that feeds the drive unit: clearly, the higher the capacity of the battery, the longer the distances that the biker will cover under engine support. On the negative side, the additional weight incurred by the electric components is likely to ruin the riding experience in case the battery runs out of power. For this reason, an integrated hardware-software system that provides accurate information about the remaining range is essential, especially for older or "not-in-shape" bikers. Many e-bikes systems are already equipped with a small control unit that displays useful information, such as speed, instantaneous power consumption, and estimated range as well. Existing approaches rely on machine learning techniques applied to collected data, or even on the remaining battery capacity and the assistance level required by the drive unit. They do not consider crucial aspects of the planned route, in particular the difference in altitude, the combined weight of bike and biker, and road conditions. In this paper, we propose a mathematical model implemented in an application to compute battery consumption, and hence the presumed remaining range, in a more accurate way. Our application relies on external sources to compute the route and the elevation data of a number of intermediate points. We present the mathematical model on which our application is based, we show the implemented application in shape of an app, and we report the results of the experiments. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) acts as a vital part of enhancing network security using intrusion detection and anomaly detection. These AI-driven approaches have become essential components of modern cybersecurity strategies. Conventional IDS is based on predefined signatures of known attacks. AI improves signature-based detection by automating the signature generation and reducing false positives through pattern recognition. It can automate threat detection and response, allowing for faster reaction times and reducing the burden on human analysts. With this motivation, this study introduces an Improved Sine Cosine Algorithm with a Deep Learning-Enabled Security Solution (ISCA-DLESS) technique. The presented ISCA-DLESS technique relies on metaheuristic-based feature selection (FS) and a hyperparameter tuning process. In the presented ISCA-DLESS technique, the FS technique using ISCA is applied. For the detection of anomalous activities or intrusions, the multiplicative long short-term memory (MLSTM) approach is used. For improving the anomaly detection rate of the MLSTM approach, the fruitfly optimization (FFO) algorithm can be utilized for the hyperparameter tuning process. The simulation value of the ISCA-DLESS approach was tested on a benchmark NSL-KDD database. The extensive comparative outcomes demonstrate the enhanced solution of the ISCA-DLESS system with other recent systems with a maximum accuracy of 99.69%. [ABSTRACT FROM AUTHOR]

The exponential function a x is widespread in many fields of science. Its calculation is a complicated issue for Central Processing Units (CPUs) and Graphics Processing Units (GPUs), as well as for specialised Digital Signal Processing (DSP) processors, such as Intelligent Processor Units (IPUs), for the needs of neural networks. This article presents some simple and accurate exponential function calculation algorithms in half, single, and double precision that can be prototyped in Field-Programmable Gate Arrays (FPGAs). It should be noted that, for the approximation, the use of effective polynomials of the first degree was proposed in most cases. The characteristic feature of such algorithms is that they only contain fast 'bithack' operations ('bit manipulation technique') and Floating-Point (FP) addition, multiplication, and (if necessary) Fused Multiply-Add (FMA) operations. We published an article on algorithms for this class of function recently, but the focus was on the use of approximations of second-degree polynomials and higher, requiring two multiplications and two additions or more, which poses some complications in FPGA implementation. This article considers algorithms based on piecewise linear approximation, with one multiplication and one addition. Such algorithms of low complexity provide decent accuracy and speed, sufficient for practical applications such as accelerators for neural networks, power electronics, machine learning, computer vision, and intelligent robotic systems. These are FP-oriented algorithms; therefore, we briefly describe the characteristic parameters of such numbers. [ABSTRACT FROM AUTHOR]

Object tracking has gained much interest in the last few years, especially in the context of multiple object tracking. Many datasets used for tracking provide video sequences of people and objects in very different contexts. Although it has been attracting much attention, no dataset or tracking algorithm has been applied to coastal surveillance and ship tracking. Besides image/video-based tracking technologies, other technologies, such as radar and automatic identification systems (AISs), are also used for this task, especially in maritime applications. In the AIS case, commonly known issues, such as information omission, remain to be dealt with. As for radars, the most important issue is the impossibility of identifying the ship type/class and correlating it with AIS information. However, image/video-based solutions can be combined with these technologies to mitigate or even solve these issues. This work aims to review the most recent datasets and state-of-the-art tracking algorithms (also known as trackers) for single or multiple objects tracking for objects in general and its possibilities for maritime scenarios. The goal is to gain insights for developing novel datasets; benchmarking metrics; and mainly, novel ship tracking algorithms. [ABSTRACT FROM AUTHOR]

In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social media platforms, healthcare systems, etc., there is a lot of data online today. Machine learning (ML) is something we need to understand to do smart analyses of these data and make smart, automated applications that use them. There are many different kinds of machine learning algorithms. The most well-known ones are supervised, unsupervised, semi-supervised, and reinforcement learning. This article goes over all the different kinds of machine-learning problems and the machine-learning algorithms that are used to solve them. The main thing this study adds is a better understanding of the theory behind many machine learning methods and how they can be used in the real world, such as in energy, healthcare, finance, autonomous driving, e-commerce, and many more fields. This article is meant to be a go-to resource for academic researchers, data scientists, and machine learning engineers when it comes to making decisions about a wide range of data and methods to start extracting information from the data and figuring out what kind of machine learning algorithm will work best for their problem and what results they can expect. Additionally, this article presents the major challenges in building machine learning models and explores the research gaps in this area. In this article, we also provided a brief overview of data protection laws and their provisions in different countries. [ABSTRACT FROM AUTHOR]

As a mainstream approach in the quantum machine learning field, variational quantum algorithms (VQAs) are frequently mentioned among the most promising applications for quantum computing. However, VQAs suffer from inefficient training methods. Here, we propose a pretraining strategy named near Clifford circuits warm start (NCC-WS) to find the initialization for parameterized quantum circuits (PQCs) in VQAs. We explored the expressibility of NCCs and the correlation between the expressibility and acceleration. The achieved results suggest that NCC-WS can find the correct initialization for the training of VQAs to achieve acceleration. [ABSTRACT FROM AUTHOR]

This research aims to analyze the effect of feature selection on the accuracy of music popularity classification using machine learning algorithms. The data of Spotify, the most used music listening platform today, was used in the research. In the feature selection stage, features with low correlation were removed from the dataset using the filter feature selection method. Machine learning algorithms using all features produced 95.15% accuracy, while machine learning algorithms using features selected by feature selection produced 95.14% accuracy. The features selected by feature selection were sufficient for classification of popularity in established algorithms. In addition, this dataset contains fewer features, so the computation time is shorter. The reason why Big O time complexity is lower than models constructed without feature selection is that the number of features, which is the most important parameter in time complexity, is low. The statistical analysis was performed on the pre-processed data and meaningful information was produced from the data using machine learning algorithms. [ABSTRACT FROM AUTHOR]

The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selection of the best-performing classifiers as well as for the analysis of higher-dimensionality data. The last aspect is especially important when analyzing large datasets. The approach of cross-validation between different datasets for supervised learning is applied in the meta-algorithm. Three machine-learning methods allowing a user to classify e-mails as desirable (ham) or potentially harmful (spam) messages were compared in the paper to illustrate the operation of the meta-algorithm. The used methods are simple, but as the results showed, they are powerful enough. We use the following classifiers: k-nearest neighbours (k-NNs), support vector machines (SVM), and the naïve Bayes classifier (NB). The conducted research gave us the conclusion that multinomial naïve Bayes classifier can be an excellent weapon in the fight against the constantly increasing amount of spam messages. It was also confirmed that the proposed solution gives very accurate results. [ABSTRACT FROM AUTHOR]

Malicious botnet applications have become a serious threat and are increasingly incorporating sophisticated detection avoidance techniques. Hence, there is a need for more effective mitigation approaches to combat the rise of Android botnets. Although the use of Machine Learning to detect botnets has been a focus of recent research efforts, several challenges remain. To overcome the limitations of using hand-crafted features for Machine-Learning-based detection, in this paper, we propose a novel mobile botnet detection system based on features extracted from images and a manifest file. The scheme employs a Histogram of Oriented Gradients and byte histograms obtained from images representing the app executable and combines these with features derived from the manifest files. Feature selection is then applied to utilize the best features for classification with Machine-Learning algorithms. The proposed system was evaluated using the ISCX botnet dataset, and the experimental results demonstrate its effectiveness with F1 scores ranging from 0.923 to 0.96 using popular Machine-Learning algorithms. Furthermore, with the Extra Trees model, up to 97.5% overall accuracy was obtained using an 80:20 train–test split, and 96% overall accuracy was obtained using 10-fold cross validation. [ABSTRACT FROM AUTHOR]

Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and precision. Therefore, we have researched and analysed, among others, impact of the way the training data are provided to the system, their pre-processing, the choice of model and its structure, and also the ways of regularisation. Furthermore, we also carefully analysed the network predictions and proposed an algorithm for its post-processing. The proposed approach enabled for obtaining high precision (89.74%), sensitivity (92.62%), and F1 score (90.84%). The paper presents the main challenges connected with automatic cerebral microbleeds detection, its deep analysis and developed system. The conducted research may significantly contribute to automatic medical diagnosis. [ABSTRACT FROM AUTHOR]

Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%. [ABSTRACT FROM AUTHOR]

Botnets often apply domain name generation algorithms (DGAs) to evade detection by generating large numbers of pseudo-random domain names of which only few are registered by cybercriminals. In this paper, we address how DGA-generated domain names can be detected by means of machine learning and deep learning. We first present an extensive literature review on recent prior work in which machine learning and deep learning have been applied for detecting DGA-generated domain names. We observe that a common methodology is still missing, and the use of different datasets causes that experimental results can hardly be compared. We next propose the use of TF-IDF to measure frequencies of the most relevant n-grams in domain names, and use these as features in learning algorithms. We perform experiments with various machine-learning and deep-learning models using TF-IDF features, of which a deep MLP model yields the best results. For comparison, we also apply an LSTM model with embedding layer to convert domain names from a sequence of characters into a vector representation. The performance of our LSTM and MLP models is rather similar, achieving 0.994 and 0.995 AUC, and average F1-scores of 0.907 and 0.891 respectively. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) is one of the diseases with the highest morbidity and mortality in the world. In 2019, the number of deaths caused by CAD reached 9.14 million. The detection and treatment of CAD in the early stage is crucial to save lives and improve prognosis. Therefore, the purpose of this research is to develop a machine-learning system that can be used to help diagnose CAD accurately in the early stage. In this paper, two classical ensemble learning algorithms, namely, XGBoost algorithm and Random Forest algorithm, were used as the classification model. In order to improve the classification accuracy and performance of the model, we applied four feature processing techniques to process features respectively. In addition, synthetic minority oversampling technology (SMOTE) and adaptive synthetic (ADASYN) were used to balance the dataset, which included 71.29% CAD samples and 28.71% normal samples. The four feature processing technologies improved the performance of the classification models in terms of classification accuracy, precision, recall, F1 score and specificity. In particular, the XGBboost algorithm achieved the best prediction performance results on the dataset processed by feature construction and the SMOTE method. The best classification accuracy, recall, specificity, precision, F1 score and AUC were 94.7%, 96.1%, 93.2%, 93.4%, 94.6% and 98.0%, respectively. The experimental results prove that the proposed method can accurately and reliably identify CAD patients from suspicious patients in the early stage and can be used by medical staff for auxiliary diagnosis. [ABSTRACT FROM AUTHOR]

This paper is an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of unknown terrains. Within this scope, MarsExplorer, an openai-gym compatible environment tailored to exploration/coverage of unknown areas, is presented. MarsExplorer translates the original robotics problem into a Reinforcement Learning setup that various off-the-shelf algorithms can tackle. Any learned policy can be straightforwardly applied to a robotic platform without an elaborate simulation model of the robot's dynamics to apply a different learning/adaptation phase. One of its core features is the controllable multi-dimensional procedural generation of terrains, which is the key for producing policies with strong generalization capabilities. Four different state-of-the-art RL algorithms (A3C, PPO, Rainbow, and SAC) are trained on the MarsExplorer environment, and a proper evaluation of their results compared to the average human-level performance is reported. In the follow-up experimental analysis, the effect of the multi-dimensional difficulty setting on the learning capabilities of the best-performing algorithm (PPO) is analyzed. A milestone result is the generation of an exploration policy that follows the Hilbert curve without providing this information to the environment or rewarding directly or indirectly Hilbert-curve-like trajectories. The experimental analysis is concluded by evaluating PPO learned policy algorithm side-by-side with frontier-based exploration strategies. A study on the performance curves revealed that PPO-based policy was capable of performing adaptive-to-the-unknown-terrain sweeping without leaving expensive-to-revisit areas uncovered, underlying the capability of RL-based methodologies to tackle exploration tasks efficiently. [ABSTRACT FROM AUTHOR]

Today, research trends clearly confirm the fact that machine learning technologies open up new opportunities in various computing environments, such as Internet of Things, mobile, and enterprise. Unfortunately, the prior efforts rarely focused on designing system-level input/output stacks (e.g., page cache, file system, block input/output, and storage devices). In this paper, we propose a new page replacement algorithm, called ML-CLOCK, that embeds single-layer perceptron neural network algorithms to enable an intelligent eviction policy. In addition, ML-CLOCK employs preference rules that consider the features of the underlying storage media (e.g., asymmetric read and write costs and efficient write patterns). For evaluation, we implemented a prototype of ML-CLOCK based on trace-driven simulation and compared it with the traditional four replacement algorithms and one flash-friendly algorithm. Our experimental results on the trace-driven environments clearly confirm that ML-CLOCK can improve the hit ratio by up to 72% and reduces the elapsed time by up to 2.16x compared with least frequently used replacement algorithms. [ABSTRACT FROM AUTHOR]

In Network Function Virtualization, the resource demand of the network service evolves with the change of network traffic. VNF dynamic migration has become an effective method to improve network performance. However, for the time-varying resource demand, how to minimize the long-term energy consumption of the network while guaranteeing the Service Level Agreement (SLA) is the key issue that lacks previous research. To tackle this dilemma, this paper proposes an energy-efficient reconfiguration algorithm for VNF based on short-term resource requirement prediction (RP-EDM). Our algorithm uses LSTM to predict VNF resource requirements in advance to eliminate the lag of dynamic migration and determines the timing of migration. RP-EDM eliminates SLA violations by performing VNF separation on potentially overloaded servers and consolidates low-load servers timely to save energy. Meanwhile, we consider the power consumption of servers when booting up, which is existing objectively, to avoid switching on/off the server frequently. The simulation results suggest that RP-EDM has a good performance and stability under machine learning models with different accuracy. Moreover, our algorithm increases the total service traffic by about 15% while ensuring a low SLA interruption rate. The total energy cost is reduced by more than 20% compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition and monitoring system. After analyzing the data and establishing a model, the system can automatically learn the features from the input data to predict the failure of the maintenance and diagnosis equipment, which is important for motor maintenance. This research proposes a medium Gaussian support vector machine (SVM) method for the application of machine learning and constructs a feature space by extracting the characteristics of the vibration signal collected on the spot based on experience. Different methods were used to cluster and classify features to classify motor health. The influence of different Gaussian kernel functions, such as fine, medium, and coarse, on the performance of the SVM algorithm was analyzed. The experimental data verify the performance of various models through the data set released by the Case Western Reserve University Motor Bearing Data Center. As the motor often has noise interference in the actual application environment, a simulated Gaussian white noise was added to the original vibration data in order to verify the performance of the research method in a noisy environment. The results summarize the classification results of related motor data sets derived recently from the use of motor fault detection and diagnosis using different machine learning algorithms. The results show that the medium Gaussian SVM method improves the reliability and accuracy of motor bearing fault estimation, detection, and identification under variable crack-size and load conditions. This paper also provides a detailed discussion of the predictive analytical capabilities of machine learning algorithms, which can be used as a reference for the future motor predictive maintenance analysis of electric vehicles. [ABSTRACT FROM AUTHOR]

Vibration signal analysis is an efficient online transformer fault diagnosis method for improving the stability and safety of power systems. Operation in harsh interference environments and the lack of fault samples are the most challenging aspects of transformer fault diagnosis. High-precision performance is difficult to achieve when using conventional fault diagnosis methods. Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized S-transform) are proposed to convert vibration signals into different images, effectively expanding the samples and extracting effective features of signals. Second, a Two-stream Densely Connected Residual Shrinkage (TSDen2NetRS) network is presented to achieve a high accuracy fault diagnosis under different working conditions. Furthermore, the Residual Shrinkage layer (RS layer) is applied as a nonlinear transformation layer to the deep learning framework to remove unimportant features and enhance anti-interference performance. Lastly, an adaptive transfer learning algorithm that can automatically select the source data set by using the domain measurement method is proposed. This algorithm accelerates the training of the deep learning network and improves accuracy when the number of samples is small. Vibration experiments of transformers are conducted under different operating conditions, and their results show the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]