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Securing Data in Multimode Fibers by Exploiting Mode-Dependent Light Propagation Effects

Research 2023年第3期2023卷 669-682页

作者： Stefan Rothe Karl-Ludwig Besser David Krause Robert Kuschmierz Nektarios Koukourakis Eduard Jorswieck Jürgen W.Czarske TU Dresden Faculty of Electrical and Computer EngineeringLaboratory of Measurement and Sensor System Technique01062 DresdenGermany TU Braunschweig Faculty of Electrical EngineeringInformation TechnologyPhysicsInstitute for Communications Technology38106 BraunschweigGermany TU Dresden Faculty of Physics01062 DresdenGermany

Multimode fibers hold great promise to advance data rates in optical communications but come with the challenge to compensate for modal crosstalk and mode-dependent losses,resulting in strong *** holographic measurement of the transmission matrix enables not only correcting distortions but also harnessing these effects for creating a confidential data connection between legitimate communication parties,Alice and *** feasibility of this physical-layer-security-based approach is demonstrated experimentally for the first time on a multimode fiber link to which the eavesdropper Eve is physically *** the proper structured light field is launched at Alice’s side,the message can be delivered to Bob,and,simultaneously,the decipherment for an illegitimate wiretapper Eve is *** a real communication scenario,we implement wiretap codes and demonstrate confidentiality by quantifying the level of *** to an uncoded data transmission,the amount of securely exchanged data is enhanced by a factor of *** complex light transportation phenomena that have long been considered limiting and have restricted the widespread use of multimode fiber are exploited for opening new perspectives on information security in spatial multiplexing communication systems.

关键词： distortion destroyed holographic

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing 63

Network-Aware and Welfare-Maximizing Dynamic Pricing for Ene...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Alahmed, Ahmed S. Cavraro, Guido Bernstein, Andrey Tong, Lang Cornell University School of Electrical and Computer Engineering IthacaNY United States National Renewable Energy Laboratory Power System Engineering Center GoldenCO United States

ISBN: (纸本)9798350316339

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage. © 2024 IEEE.

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Data quantity governance for machine learning in materials science

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National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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Dynamic event-triggered formation control of second-order nonholonomic systems

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Journal of systems engineering and Electronics 2023年第2期34卷 501-514页

作者： WANG Xiaoyu SUN Sijia XIAO Feng YU Mei School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

In this paper,the formation control problem of secondorder nonholonomic mobile robot systems is investigated in a dynamic event-triggered ***-triggered control protocols combined with persistent excitation(PE)conditions are *** event-detecting processes,an inactive time is introduced after each sampling instant,which can ensure a positive minimum sampling *** increase the flexibility of the event-triggered scheme,internal dynamic variables are included in event-triggering ***,the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive *** addition,event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation(low-pass)*** asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.

关键词： nonholonomic system dynamic event-triggered control consensus-based formation

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A CNN-LSTM Network for Classification of Attention Deficit Hyperactivity Disorder from EEG Data 41

A CNN-LSTM Network for Classification of Attention Deficit H...

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第41届中国控制会议

作者： Yaru Zhang Xiangxin Tan Yue Gu Key Laboratory of Computer Vision and System (Ministry of Education) School of Computer Science and EngineeringTianjin University of Technology Engineering Research Center of Learning-Based Intelligent system Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Attention deficit hyperactivity disorder(ADHD) is one of the most common neuropsychiatric disorders in children,with core symptoms of inattention,impulsivity,and *** a result,early detection and treatment are *** this point,ADHD can be diagnosed using an electroencephalogram(EEG).This study proposes a model for identifying the EEG of children with *** proposed model is made up of three parts:temporal convolutional blocks,spatial convolutional blocks,and Long Short Term Memory(LSTM),and because EEG contains more temporal information than other data,we build a model that can extract more temporal *** study included 24 subjects,including 12 children with ADHD and12 normal *** experimental results show that our proposed model outperforms Support Vector Machine(SVM) and EEGNet,with our model achieving correct classification rates of 92.29%,92.76%,and 90.91% on the three backs,respectively,and 94.25% on the full data,demonstrating the model's feasibility and applicability to the ADHD recognition problem.

关键词： Attention Deficit Hyperactivity Disorder Electroencephalogram Deep learning Classification

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SAM-COD+: SAM-Guided Unified Framework for Weakly-Supervised Camouflaged Object Detection

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IEEE Transactions on Circuits and systems for Video Technology 2025年第5期35卷 4635-4647页

作者： Chen, Huafeng Wei, Pengxu Guo, Guangqian Gao, Shan Northwestern Polytechnical University Unmanned System Research Institute Xi’an710072 China National Key Laboratory of Unmanned Aerial Vehicle Technology Xi’an710072 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Peng Cheng Laboratory Shenzhen518066 China

Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods and struggle to simultaneously support all the existing types of camouflaged object labels, including scribbles, bounding boxes, and points. Even for Segment Anything Model (SAM), it is still problematic to handle the weakly-supervised COD and it typically encounters challenges of prompt compatibility of the scribble labels, extreme response, semantically erroneous response, and unstable feature representations, producing unsatisfactory results in camouflaged scenes. To mitigate these issues, we propose a unified COD framework in this paper, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels. Our SAM-COD employs a prompt adapter to handle scribbles as prompts based on SAM. Meanwhile, we introduce response filter and semantic matcher modules to improve the quality of the masks obtained by SAM under COD prompts. To alleviate the negative impacts of inaccurate mask predictions, a new strategy of prompt-adaptive knowledge distillation is utilized to ensure a reliable feature representation. To validate the effectiveness of our approach, we have conducted extensive empirical experiments on three mainstream COD benchmarks. The results demonstrate the superiority of our method against state-of-the-art weakly-supervised and even fully-supervised methods. Our source codes and trained models will be publicly released. © 1991-2012 IEEE.

关键词： Object recognition

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of computer Science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Learning to control the complex light propagation through few-mode fiber without a reference wave

Learning to control the complex light propagation through fe...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Rothe, Stefan Krause, David Zhang, Qian Pohle, Dennis Koukourakis, Nektarios Czarske, Jürgen W. Technische Universitat Dresden Faculty of Electrical and Computer Engineering Laboratory of Measurement and Sensor System Technique Dresden01062 Germany

We show reference-less transmission matrix measurements using a neural network operating with up to 23 modes in real-time. We use synthetic training data enabling optical predistortion through an SLM for light control... 详细信息

ISBN: (纸本)9781957171258

关键词： Light propagation

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Deep Learning-based Mortality Prediction of COVID-19 Patients with Cardiovascular Disease 9

Deep Learning-based Mortality Prediction of COVID-19 Patient...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zheng, Huilin Liang, Yingyi Zhang, Daming Shi, Kai Dong, Luxi Xie, Xiaolan Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology College of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350391367

Coronavirus disease 2019 (COVID-19) is one of the most dangerous diseases in recorded human history due to its high contagiousness and rapid dissemination. Moreover, people who already have medical issues like cardiovascular disease (CVD) have a higher risk of getting seriously ill. Therefore, this study proposes a deep learning-based prediction model for the mortality of COVID-19 patients with CVD. The research substances are introduced as follows. First, we obtain the original dataset from the Kaggle website provided by the Mexican government, and extract our experimental data. After that, we develop a deep learning-based prediction model for the mortality of COVID-19 patients with CVD while applying a 5-fold cross-validation approach for the model training and validation. Finally, we evaluate the performance of the proposed model and also compare it with many common machine learning models. In our experimental results, the proposed model achieved higher performance than all of the other models. It facilitates decision-making and provides an accurate mortality prediction for COVID-19 patients with CVD. © 2024 IEEE.

关键词： COVID-19

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