Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. Howeve...
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Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.
Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection b...
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Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.
The network security analyzers use intrusion detection systems(IDSes)to distinguish malicious traffic from benign *** deep learning-based(DL-based)IDSes are proposed to auto-extract high-level features and eliminate t...
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The network security analyzers use intrusion detection systems(IDSes)to distinguish malicious traffic from benign *** deep learning-based(DL-based)IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and costly signature extraction ***,this new generation of IDSes still needs to overcome a number of challenges to be employed in practical *** of the main issues of an applicable IDS is facing traffic concept drift,which manifests itself as new(i.e.,zero-day)attacks,in addition to the changing behavior of benign users/***,a practical DL-based IDS needs to be conformed to a distributed(i.e.,multi-sensor)architecture in order to yield more accurate detections,create a collective attack knowledge based on the observations of different sensors,and also handle big data challenges for supporting high throughput *** paper proposes a novel multi-agent network intrusion detection framework to address the above shortcomings,considering a more practical scenario(i.e.,online adaptable IDSes).This framework employs continual deep anomaly detectors for adapting each agent to the changing attack/benign patterns in its local *** addition,a federated learning approach is proposed for sharing and exchanging local knowledge between different ***,the proposed framework implements sequential packet labeling for each flow,which provides an attack probability score for the flow by gradually observing each flow packet and updating its *** evaluate the proposed framework by employing different deep models(including CNN-based and LSTM-based)over the CICIDS2017 and CSE-CIC-IDS2018 *** extensive evaluations and experiments,we show that the proposed distributed framework is well adapted to the traffic concept *** precisely,our results indicate that the CNNbased models are well suited for continually adapting to the traffic concept drift(i.e.,achieving
The apple picking robot makes use of a number of technologies, one of which is the apple target identification algorithm. When it comes to automated apple picking, the robots' optical systems are crucial. Generall...
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The apple picking robot makes use of a number of technologies, one of which is the apple target identification algorithm. When it comes to automated apple picking, the robots' optical systems are crucial. Generally speaking, it finds ripe apples by taking photographs of its environment, processing them, and then analyzing the findings. The inability of traditional vision algorithms to process complex backdrops hinders the efficiency of harvesting robots. The continuous development and refining of the CNN have led to a substantial improvement in its efficacy in target identification during the last several years. The current crop of apple recognition algorithms struggles to tell the difference between partially obscured apples and ones entirely concealed by tree branches. Direct use of the algorithm endangers the harvesting robot's mechanical arm, apples, as well as gripping end-effector. In response to this real-world issue, we provide a lightweight apple targets identification approach for picking robots based on enhanced YOLOv5s. This method can automatically identify which apples in an apple tree picture are graspable and which ones are not. This method is able to circumvent the impact of light transformation, in contrast to the conventional segmentation approach. When there is a lot of resemblance between the fruit and the backdrop, though, it becomes more challenging to get strong recognition results. With a recall rate of 98%, a detection speed of 47 f/s, and a mAP (mean Average Precision) of apple detection of 98.13%, the findings demonstrate that the YOLO v5 network has perfect properties. The YOLO v5 is able to simultaneously fulfill the accuracy and speed criteria of apple identification, in contrast to more conventional network models like Faster R-CNN and YOLO v4. The experiment culminates with the employment of the apple-harvesting robot that the researcher developed themselves. Results demonstrate that the robot has a harvesting success rate of 99.2% i
Content-based image retrieval (CBIR) using visual saliency in the pixel domain has shown promising retrieval results at lesser computational cost as features are extracted only from salient regions. CBIR in the JPEG c...
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This paper presents a novel approach of establishing a multichannel optical communication link, combining optical fiber cable (OFC) and free space optics (FSO) technology. By leveraging multiple lengths of optical fib...
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By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and fun...
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By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE
Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the pre...
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Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident *** learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and *** prior success,there is a need for an efficient system recognizing ASL in diverse road *** address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient *** framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved *** and factor contribution analyses identify influential *** on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB *** proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.
This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this paper model introduces few...
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