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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1499-1514页

作者： Shenglei Shi Jiankui chen State Key Laboratory of Digital Manufacturing Equipment and technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

The sampling process is very inefficient for sam-pling-based motion planning algorithms that excess random sam-ples are generated in the planning *** this paper,we pro-pose an adaptive space expansion(ASE)approach which belongs to the informed sampling category to improve the sampling effi-ciency for quickly finding a feasible *** ASE method enlarges the search space gradually and restrains the sampling process in a sequence of small hyper-ellipsoid ring subsets to avoid exploring the unnecessary ***,for a con-structed small hyper-ellipsoid ring subset,if the algorithm cannot find a feasible path in it,then the subset is ***,the ASE method successively does space exploring and space expan-sion until the final path has been ***,we present a particular construction method of the hyper-ellipsoid ring that uniform random samples can be directly generated in *** last,we present a feasible motion planner BiASE and an asymptoti-cally optimal motion planner BiASE*using the bidirectional exploring method and the ASE *** demon-strate that the computation speed is much faster than that of the state-of-the-art *** source codes are available at https://***/shshlei/ompl.

关键词： Adaptive space expansion(ASE) hyper-ellipsoid ring informed sampling motion planning.

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State-of-the-art on the anchorage performance of rock bolts subjected to shear load

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International Journal of Coal Science & technology 2024年第1期11卷 1-30页

作者： Yu chen Haodong Xiao school of Civil Engineering Sun Yat-sen UniversityZhuhai519082China

Rock bolts are extensively utilized in underground engineering as a means of offering support and stability to rock masses in tunnels,mines,and other underground *** environments of high ground stress,faults or weak zones can frequently arise in rock formations,presenting a significant challenge for engineering and potentially leading to underground engineering *** bolts serve as a crucial structural element for the transmission of tensile stress and are capable of withstanding shear loads to prevent sliding of weak zones within rock ***,a complete understanding of the behavior of rock bolts subjected to shear loads is *** paper presents a state-of-the-art review of the research progress of rock bolts subjected to shear load in three categories:experiment,numerical simulation,and analytical *** review focuses on the research studies and developments in this area since the 1970s,providing a comprehensive overview of numerous factors that influence the anchorage performance of rock *** factors include the diameter and angle of the rock bolt installation,rock strength,grouting material,bolt material,borehole diameter,rock bolt preload,normal stress,joint surface roughness and joint expansion *** paper reviews the improvement of mechanical parameter setting in numerical simulation of rock bolt ***,it delves into the optimization of the analytical model concerning rock bolt shear theory,approached from the perspectives of both Elastic foundation beam theory coupled with Elastoplasticity theory and Structural mechanic *** significance of this review lies in its ability to provide insights into the mechanical behavior of rock *** paper also highlights the limitations of current research and guidelines for further research of rock bolts.

关键词： Rock bolt Shear load Shear test Numerical simulation Analytical model

Brain-inspired chaotic spiking backpropagation

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National Science Review 2024年第6期11卷 169-179页

作者： Zijian Wang Peng Tao Luonan chen Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Guangdong Institute of Intelligence Science and technology Pazhou Laboratory(Huangpu)

Spiking neural networks(SNNs) have superior energy efficiency due to their spiking signal transmission,which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation(CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets(***-CIFAR10 and DVS-Gesture) and large-scale static data sets(e.g. CIFAR100 and Image Net) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics,consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.

关键词： spiking neural networks surrogate gradient local minima backpropagation brain-inspired learning chaos

Rethinking attribute localization for zero-shot learning

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Science China(Information Sciences) 2024年第7期67卷 184-196页

作者： Shuhuang chen Shiming chen Guo-Sen XIE Xiangbo SHU Xinge YOU Xuelong LI school of Electronic Information and Communication Huazhong University of Science and Technology school of Computer Science and Engineering Nanjing University of Science and Technology school of artificial Intelligence Optics and Electronics Northwestern Polytechnical University

Recent advancements in attribute localization have showcased its potential in discovering the intrinsic semantic knowledge for visual feature representations, thereby facilitating significant visual-semantic interactions essential for zero-shot learning(ZSL). However, the majority of existing attribute localization methods heavily rely on classification constraints, resulting in accurate localization of only a few attributes while neglecting the rest important attributes associated with other classes. This limitation hinders the discovery of the intrinsic semantic relationships between attributes and visual features across all *** address this problem, we propose a novel attribute localization refinement(ALR) module designed to enhance the model's ability to accurately localize all attributes. Essentially, we enhance weak discriminant attributes by grouping them and introduce weighted attribute regression to standardize the mapping values of semantic attributes. This module can be flexibly combined with existing attribute localization *** experiments show that when combined with the ALR module, the localization errors in existing methods are corrected, and state-of-the-art classification performance is achieved.

关键词： zero-shot learning attention mechanism attribute localization image classification

HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin chen Cen chen Heshi WANG Jing LIU Zhongyao chenG Xiaofeng ZOU school of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology school of Computer Science and technology Wuhan University of Science and Technology school of Future technology South China University of Technology Shenzhen Research Institute of Hunan university school of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

Modulating Co-Co bonds average length in Co_(0.85)Se_(1-x)S_(x) to enhance conversion reaction for potassium storage

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Journal of Energy Chemistry 2024年第4期91卷 111-121页

作者： Daming chen Yuchun Liu Pan Feng Xiao Tao Zhiquan Huang Xiyu Zhang Min Zhou Jian chen Jiangsu Key Laboratory of Advanced Metallic Materials School of Materials Science and EngineeringSoutheast UniversityNanjing 211189JiangsuChina Hefei National Laboratory for Physical Sciences at the Microscale School of Chemistry and Materials ScienceUniversity of Science and Technology of ChinaHefei 230026AnhuiChina

While alloying transition metal chalcogenides(TMCs)with other chalcogen elements can effectively improve their conductivity and electrochemical properties,the optimal alloying content is still *** this study,we study the influence of dopant concentration on the chemical bonds in TMC and reveal the associated stepwise conversion reaction mechanism for potassium ion *** to density function theory calculations,appropriate S-doping in Co0.85Se(Co_(0.85)Se_(1-x)S_(x))can reduce the average length of Co-Co bonds because of the electronegativity variation,which is thermodynamically favourable to the phase transition *** optimal Se/S ratio(x=0.12)for the conductivity has been obtained from experimental *** assembled as an anode in potassium-ion batteries(PIBs),the sample with optimized Se/S ratio exhibits extraordinary electrochemical *** rate performance(229.2 mA h g^(-1)at 10 A g^(-1))is superior to the state-of-the-art *** assembled with Prussian blue(PB)as a cathode,the pouch cell exhibits excellent performance,demonstrating its great potential for ***,the stepwise K+storage mechanism caused by the coexistence of S and Se is revealed by in-situ X-ray diffraction and ex-situ transmission electron microscopy ***,this work not only provides an effective strategy to enhance the electrochemical performance of transition metal chalcogenides but also reveals the underlying mechanism for the construction of advanced electrode materials.

关键词： Co_(0.85)Se_(1-x)S_(x) Co-Co bonds Phase transition reactions Optimal Se/s Potassium ion batteries

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An adaptive large neighborhood search for the multi-point dynamic aggregation problem

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Control Theory and technology 2024年第3期22卷 360-378页

作者： Shengyu Lu Bin Xin Jie chen Miao Guo school of Automation Beijing Institute of TechnologyBeijing 100081China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

The multi-point dynamic aggregation(MPDA)problem is a challenging real-world *** the MPDA problem,the demands of tasks keep changing with their inherent incremental rates,while a heterogeneous robot fleet is required to travel between these tasks to change the time-varying state of each *** robots are allowed to collaborate on the same task or work separately until all tasks are *** is challenging to generate an effective task execution plan due to the tight coupling between robots abilities and tasks'incremental rates,and the complexity of robot *** effectiveness consideration,we use the variable length encoding to avoid redundancy in the solution *** creatively use the adaptive large neighborhood search(ALNS)framework to solve the MPDA *** the proposed algorithm,high-quality initial solutions are generated through multiple problem-specific solution construction *** heuristics are also used to fix the broken solution in the novel integrated decoding-construction repair process of the ALNS *** results of statistical analysis by the Wilcoxon rank-sum test demonstrate that the proposed ALNS can obtain better task execution plans than some state-of-the-art algorithms in most MPDA instances.

关键词： Adaptive large neighborhood search(ALNS) Multi-point dynamic aggregation(MPDA) Heuristic solution construction Multi-robot collaboration

Ultrahigh performance passive radiative cooling by hybrid polar dielectric metasurface thermal emitters

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Opto-Electronic Advances 2024年第4期7卷 17-25页

作者： Yinan Zhang Yinggang chen Tong Wang Qian Zhu Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Real-world passive radiative cooling requires highly emissive,selective,and omnidirectional thermal emitters to maintain the radiative cooler at a certain temperature below the ambient temperature while maximizing the net cooling *** various selective thermal emitters have been demonstrated,it is still challenging to achieve these conditions sim-ultaneously because of the extreme difficulty in controlling thermal emission of photonic structures in *** we demonstrated hybrid polar dielectric metasurface thermal emitters with machine learning inverse design,en-abling a high emissivity of~0.92 within the atmospheric transparency window 8-13μm,a large spectral selectivity of~1.8 and a wide emission angle up to 80 degrees,*** selective and omnidirectional thermal emitter has led to a new record of temperature reduction as large as~15.4°C under strong solar irradiation of~800 W/m2,signific-antly surpassing the state-of-the-art *** designed structures also show great potential in tackling the urban heat island effect,with modelling results suggesting a large energy saving and deployment area *** research will make significant impact on passive radiative cooling,thermal energy photonics and tackling global climate change.

关键词： radiative cooling dielectric metasurfaces machine learning thermal emitters

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A Quarterly High RFM Mining Algorithm for Big Data Management

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Computers, Materials & Continua 2024年第9期80卷 4341-4360页

作者： Cuiwei Peng Jiahui chen Shicheng Wan Guotao Xu school of International Education Guangdong University of TechnologyGuangzhou510006China school of Computer Science and technology Guangdong University of TechnologyGuangzhou510006China Business Administration South China University of TechnologyGuangzhou510641China Guangzhou Institute of technology Xidian UniversityGuangzhou710071China

In today’s highly competitive retail industry,offline stores face increasing pressure on *** hope to improve their ability in shelf management with the help of big data *** this,on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase ***(recency,frequency,andmonetary)patternmining is a powerful tool to evaluate the value of customer ***,the existing RFM patternmining algorithms do not consider the quarterly nature of goods,resulting in unreasonable shelf availability and difficulty in *** solve this problem,we propose a quarterly RFM mining algorithmfor On-shelf products named *** algorithmmines the high recency,high frequency,and high monetary patterns and considers the period of the on-shelf goods in quarterly *** conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s *** with the state-of-the-art RFM mining algorithm,our algorithm can identify more patterns and performs well in terms of precision,recall,and F1-score,with the recall rate nearing 100%.Also,the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining ***,we analyze the sales trends of products in different quarters and seasonal *** analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.

关键词： Data mining recency pattern high-utility itemset RFM pattern mining on-shelf management