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Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations

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CAAI Transactions on Intelligence technology 2022年第2期7卷 292-300页

作者： Tianyan Jiang Xiao Yang Yuan Yang Xi Chen Maoqiang Bi Jianfei Chen School of Electrical and Electronic Engineering Chongqing University of TechnologyChongqingChina DepartmentState Key Laboratory of Power Transmission Equipment&System and New Technology Chongqing UniversityChongqingChina Department of Electrical and Computer Engineering University of MarylandCollege ParkMarylandUSA

Partial discharge(PD)signals are an important index to evaluate the operation state of intelligent *** correct distinction of PD pulse and interference pulse has become a challenging *** of the noise and the low signal-to-noise ratio,the stable signals become *** selection of a wavelet basis,the selection rule of thresholdλand the design of the threshold function are the key factors affecting the final denoising ***,an enhanced ant colony optimisition wavelet(ACOW)algorithm was applied to find the global optimal threshold through the continuous derivative threshold function and the ant colony optimisation(ACO)*** the same time the efficiency of adaptive search calculation,was also significantly *** method of the ACOW algorithm was compared with the soft wavelet method,gradient-based wavelet method and the genetic optimisation wavelet(GOW)*** these four methods to denoise four typical signals,different mean square errors(MSE),magnitude errors(ME)and time costs were ***,the results show that the ACOW method can achieve the minimum MSE and has less time *** generates significantly smaller waveform distortion than the other three threshold estimation *** addition,the high efficiency and good quality of the output signals are beneficial to the diagnosis of local discharge signals in intelligent substations.

关键词： algorithm noise method

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SAT-based Formal Verification of Fault Injection Countermeasures for Cryptographic Circuits∗

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded systems 2024年第4期2024卷 1-39页

作者： Tan, Huiyu Gao, Pengfei Song, Fu Chen, Taolue Wu, Zhilin ShanghaiTech University Shanghai201210 China Wingsemi Technology Co. Ltd. Shanghai201203 China Bytedance Beijing100098 China Key Laboratory of System Software Chinese Academy of Sciences and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Nanjing Institute of Software Technology Nanjing211135 China Birkbeck University of London WC1E 7HX United Kingdom

Fault injection attacks represent a type of active, physical attack against cryptographic circuits. Various countermeasures have been proposed to thwart such attacks, however, the design and implementation of which are intricate, error-prone, and laborious. The current formal fault-resistance verification approaches are limited in efficiency and scalability. In this paper, we formalize the fault-resistance verification problem and show that it is coNP-complete. We then devise a novel approach for encoding the fault-resistance verification problem as the Boolean satisfiability (SAT) problem so that modern off-the-shelf SAT solvers can be utilized. The approach is implemented in an open-source tool FIRMER which is evaluated extensively on realistic cryptographic circuit benchmarks. The experimental results show that FIRMER is able to verify fault-resistance of almost all (72/76) benchmarks in 3 minutes (the other three are verified in 35 minutes and the hardest one is verified in 4 hours). In contrast, the prior approach fails on 31 fault-resistance verification tasks even after 24 hours (per task). © 2024, Ruhr-University of Bochum. All rights reserved.

关键词： Formal verification

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High-resolution waveguide array-based astronomical spectrograph with cascaded phase modulation

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Chinese Optics Letters 2025年第3期23卷 62-66页

作者：仲韵贤林栋徐彪朱壮壮童雪钟晴冯吉军何晋平 Laboratory of Solar and Space Instruments Nanjing Institute of Astronomical Optics&TechnologyChinese Academy of Sciences CAS Key Laboratory of Astronomical Optics&Technology Nanjing Institute of Astronomical Optics&TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and SystemMinistry of EducationSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology

Integrated photonic spectrographs could provide a new generation of low-cost,highly integrated,high-performance optical terminal instruments for astronomical ***,these spectrographs still face the challenge of high spectral *** this Letter,we demonstrate a cascaded phase-modulated waveguide array (CPMWA) spectrograph,with designed and measured spectral resolutions of 100,000 and 68,000,respectively.A spectral reconstruction method is proposed to minimize the influence of the phase error induced during the chip fabrication process and increase the spectral contrast to 20 d *** type of spectrograph demonstrates promising potential for high-resolution spectrum observations in astronomy.

关键词： astronomical spectrograph waveguide array cascaded phase modulation high spectral resolution spectrum reconstruction

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Nonlinear Deep Subspace Network for Micro-expression Recognition 3

Nonlinear Deep Subspace Network for Micro-expression Recogni...

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3rd Workshop on Facial Micro-Expression: Advanced Techniques for Multi-Modal Facial Expression Analysis, FME 2023

作者： Feng, Weijia Xu, Manlu Chen, Yuanxu Wang, Xiaofeng Guo, Jia Dai, Lei Wang, Nan Zuo, Xinyu Li, Xiaoxue College of Computer and Information Engineering Tianjin Normal University Tianjin China Ping An Technology Shenzhen China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400702853

Deep learning (DL) models have been widely studied in the field of micro-expression recognition (MER). However, micro-expressions (MEs) suffer from small number of samples and difficulty in extracting subtle and transient features, resulting in limited improvement in the recognition performance. In addition, DL models are prone to overfitting problems and are difficult to extract discriminative features of facial actions from ME images or sequences. To address these issues, we propose a MER method that combines a nonlinear deep subspace network and optical flow features. Firstly, facial motion features are captured using optical flow computation, and then the optical flow features are input into a kernel principal component analysis network (KPCANet) to further learn deeper spatio-temporal features. Finally, a linear support vector machine (SVM) is used for ME classification. Experiments conducted on four public spontaneous ME datasets including SMIC, CASME, CASME II and SAMM validate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method achieves even better recognition performance compared to existing state-of-the-art MER methods. © 2023 ACM.

关键词： Principal component analysis

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Learning to see speckle in the weak laser field through multimode fiber

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Optoelectronics Letters 2025年

作者： JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the Key Laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer Science and Engineering， Tianjin University of Technology

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model

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Space-Filling Input Design for Nonlinear State-Space Identification ⁎

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IFAC-PapersOnLine 2024年第15期58卷 562-567页

作者： Máté Kiss Roland Tóth Maarten Schoukens Control System group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modeling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this paper focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals. The presented experiment design approach can straightforwardly be extended to a wider range of system classes (e.g., NARMAX). The effectiveness of the proposed approach is illustrated on the experiment design for a nonlinear mass-spring-damper system, using a multisine input signal.

关键词： Experiment Design Nonlinear system Identification State-Space

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Exploring Global and Local Information for Anomaly Detection with Normal Samples

Exploring Global and Local Information for Anomaly Detection...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Fan Wang, Nan Zhao, Xibin School of Computer Science and Technology University of Science and Technology of China Hefei230026 China School of Software Beijing Jiaotong University Beijing100044 China Key Laboratory for Information System Security Beijing100084 China

ISBN: (纸本)9798350337020

Anomaly detection aims to detect data that do not conform to regular patterns, and such data is also called outliers. The anomalies to be detected are often tiny in proportion, containing crucial information, and are suitable for application scenes like intrusion detection, fraud detection, fault diagnosis, e-commerce platforms, et al. However, in many realistic scenarios, only the samples following normal behavior are observed, while we can hardly obtain any anomaly information. To address such problem, we propose an anomaly detection method GALDetector which is combined of global and local information based on observed normal samples. The proposed method can be divided into a three-stage method. Firstly, the global similar normal scores and the local sparsity scores of unlabeled samples are computed separately. Secondly, potential anomaly samples are separated from the unlabeled samples corresponding to these two scores and corresponding weights are assigned to the selected samples. Finally, a weighted anomaly detector is trained by loads of samples, then the detector is utilized to identify else anomalies. To evaluate the effectiveness of the proposed method, we conducted experiments on three categories of real-world datasets from diverse domains, and experimental results show that our method achieves better performance when compared with other state-of-the-art methods. © 2023 IEEE.

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Noninvasive Data-Driven Prediction of Reactor Power Field: An MLP-Based Approach 9

Noninvasive Data-Driven Prediction of Reactor Power Field: A...

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9th IEEE Smart World Congress, SWC 2023

作者： Shi, Binhui Qiu, Qi Gong, Helin Li, Qing Luo, Yang University of South China School of Computer Hengyang China Shanghai Jiao Tong University ParisTech Elite Institute of Technology Shanghai China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (纸本)9798350319804

The Reactor Operation Digital Twin (RODT) is a numerical representation method designed for the operational phase of nuclear reactors, aiming to solve forward problems (such as state prediction) based on input parameters and inverse problems (such as parameter identification) utilizing additional measurement data, in order to enhance the economic benefits and safety of nuclear power. In this paper, we propose a solution for the forward problem in RODT based on a Multilayer Perceptron (MLP) approach, where the MLP is used to directly learn the mapping relationship from low-dimensional inputs to high-dimensional outputs. Compared to previous methods, the proposed approach shows improved prediction accuracy, especially in situations with limited training data, demonstrating superior generalization capabilities and accuracy. To validate our contributions, we conducted a comparative experiment based on a engineering case, namely the reactor core simulation in the life cycle of a real-world nuclear ractor, which confirmed the effectiveness of the proposed method. © 2023 IEEE.

关键词： limited training data Multilayer Perceptron (MLP) reactor core simulation Reactor Operation Digital Twin (RODT) reactor power field

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DD-UNet: Densely Dilated U-Net for Curvilinear Structure Segmentation in Fundus Image

DD-UNet: Densely Dilated U-Net for Curvilinear Structure Seg...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yindong Chen, Jie Wang, Li Chen, Miaohong Zhang, Guoming Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Eye Hospital China

ISBN: (纸本)9798350337488

Retinopathy of Prematurity (ROP) is a retina disorder that affects premature infants with lower weights. If the patient cannot get the treatment in time when the illness reaches the last stage, irreversible vision loss will be caused. Nevertheless, there has been relatively little consideration given to the segmentation of the ridge, the key clinical characteristic of the illness. Additionally, existing research has not adequately addressed several segmentation issues, such as fragmentary topology, class imbalance, and false positives. This paper proposes a Densely Dilated U-Net (DD-UNet) improved from U-Net to tackle these challenges. Furthermore, the post-processing techniques based on the spatial relationship between vessels and ridges, along with the relative pixel counts of ridges and false positive results is integrated to mitigate false positive results in the predicted ridge. To enhance the precision of thin vessel, a sliding window sampling method is introduced for refined training. Compared with the state-of-the-art models in medical image segmentation, DD-UNet performs well in curvilinear structure segmentation of fundus image. For instance, our DD-UNet outperforms the Attention U-Net by 6.26% in terms of sensitivity and exhibits a 1.85% higher dice score in ridge segmentation. © 2023 IEEE.

关键词： curvilinear structure segmentation fundus image Retinopathy of Prematurity (ROP) ridge segmentation vessel segmentation

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Probability distribution based resource management for multitenant cloud clusters

Probability distribution based resource management for multi...

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作者： Zhou, Jun Feng, Dan Wang, Fang Future Network Research Center Purple Mountain Laboratories Nanjing China School of Computer Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Wuhan National Laboratory for Optoelectronics Wuhan China

Accurate resource allocation and colocating jobs are effective ways to increase resource utilization and reduce costs in modern datacenters. The main challenges are fluctuations in resource consumption and interference among colocated jobs. Therefore, we propose a resource management schema based on the probability distributions of resource consumption and completion time for multitenant cloud clusters. First, we found that the characteristics of the task can be well described by the probability distribution of resource consumption and completion time, and the probability distribution function can be obtained by Gaussian fitting. Second, we propose a probability distribution based resource allocation (PDRA) strategy for batch jobs. Third, we design a tail latency aware allocation (TLAA) strategy to use transient resources efficiently while ensuring tail latency requirements. Finally, we design a cost-effective resource revocation (CERR) strategy to revoke transient resources with minimal eviction costs. Experimental results demonstrate the efficiency of our resource management. Our resource allocation strategies (PDRA and TLAA) can effectively improve resource utilization and reduce job completion time. CERR can reduce the impact of resource revocation on batch jobs and perform better than existing resource revocation strategies. © 2021 John Wiley & Sons Ltd.

关键词： Resource allocation

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