Low-light image enhancement is highly desirable for outdoor image processing and computer vision applications. Research conducted in recent years has shown that images taken in low-light conditions often pose two main...
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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...
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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.
Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity ...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliabi...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliability. Metamorphic testing (MT) enhances reliability by generating follow-up tests from mutated DNN source inputs, identifying inconsistencies as defects. Various MT techniques for ADSs include generative/transfer models, neuron-based coverage maximization, and adaptive test selection. Despite these efforts, significant challenges remain, including the ambiguity of neuron coverage’s correlation with misbehaviour detection, a lack of focus on DNN critical pathways, inadequate use of search-based methods, and the absence of an integrated method that effectively selects sources and generates follow-ups. This paper addresses such challenges by introducing DeepDomain, a grey-box multi-objective test generation approach for DNN models. It involves adaptively selecting diverse source inputs and generating domain-oriented follow-up tests. Such follow-ups explore critical pathways, extracted by neuron contribution, with broader coverage compared to their source tests (inter-behavioural domain) and attaining high neural boundary coverage of the misbehaviour regions detected in previous follow-ups (intra-behavioural domain). An empirical evaluation of the proposed approach on three DNN models used in the Udacity self-driving car challenge, and 18 different MRs demonstrates that relying on behavioural domain adequacy is a more reliable indicator than coverage criteria for effectively guiding the testing of DNNs. Additionally, DeepDomain significantly outperforms selected baselines in misbehaviour detection by up to 94 times, fault-revealing capability by up to 79%, output diversity by 71%, corner-case detection by up to 187 times, identification of robustness subdomains of MRs by up to 33 percentage points, and naturalness by two times. The results confirm that stat
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 combination of structural health monitoring and vibration control is of great importance to provide components of smart *** synthetic algorithms have been proposed,adaptive control that is compatible with changing...
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The combination of structural health monitoring and vibration control is of great importance to provide components of smart *** synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive *** this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are ***,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)***,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be *** is also performed based on the probability density function of the energy under the seismic excitation at any ***,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed *** simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.
The new generation of communication systems is moving towards using a millimeter-wave *** the shadowing effects are undeniable in this type of propagation,the proposed Generalized Fisher(GF)distribution can be useful ...
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The new generation of communication systems is moving towards using a millimeter-wave *** the shadowing effects are undeniable in this type of propagation,the proposed Generalized Fisher(GF)distribution can be useful in modeling shadowed fading channels,considering the non-linearity and the multi-cluster nature of the diffusion *** introducing the model,its main statistics,including Probability Density Function(PDF),Cumulative Distribution Function(CDF),Moment Generating Function(MGF),and the distribution of the sum of an arbitrary number of independent and non-identically distributed(i.n.i.d.)random variables with GF distribution are ***,some wireless communication application criteria such as ergodic and outage capacities,are ***,considering the classic Wyner's wiretap model and passive eavesdropping scenario,specific security criteria,such as the probability of non-zero secrecy capacity and secrecy outage probability,are also *** expressions are measured in terms of either univariate or multivariate Fox's H-function.
This paper presents a novel, energy-efficient routing approach for underwater sensor networks in tsunami early warning. Our system utilizes sensor nodes equipped with piezoelectric energy harvesting to extend network ...
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Resolvers are widely employed as position sensors to detect both rotational and linear displacement. Among the various types, wound rotor (WR) resolvers offer superior accuracy in absolute measurements, especially und...
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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 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.
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