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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Zhang, Fucheng Tian, Pinfang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-TimeIndustrial Systems Wuhan China

ISBN: (纸本)9798350394375

In the current field of object detection, the YOLOv7 model demonstrates significant advantages in terms of both detection speed and accuracy. However, the model shows deficiencies in focussing on key information, and its loss function's sensitivity to bounding boxes results in suboptimal performance in densely populated environments and small target detection scenarios. To address these issues, this study proposes a mask target detection method based on YOLOv7. By integrating a channel attention mechanism into the YOLOv7 model, this method enhances the model's focus on important information in images, effectively reducing interference from background factors. Additionally, the method incorporates Normalized Wasserstein Distance (NWD) into the loss function to optimize the model's evaluation of small target detection. Experimental verification shows that the improved model maintains its original detection speed while increasing the overall accuracy to 92.1%, a 2.9% improvement over the original YOLOv7 model. It achieves a 3.3% increase in the mAP.5 metric. This indicates that the optimised YOLOv7 model has superior detection capabilities, allowing it to perform mask-wearing detection tasks more rapidly and accurately in natural environments. © 2024 IEEE.

关键词： Object detection

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of systems science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Data-Driven Control of Distributed Event-Triggered Network systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic Control University of StuttgartStuttgart 70550Germany the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

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Trajectory tracking control of autonomous heavy-duty mining dump trucks with uncertain dynamic characteristics

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science China(Information sciences) 2023年第10期66卷 246-262页

作者： Liang CHEN Zhaobo QIN Manjiang HU Hongbo GAO Yougang BIAN Biao XU Xiaoyan PENG State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Wuxi Intelligent Control Research Institute of Hunan University Department of Automation University of Science and Technology of China

The proportion of mines using autonomous mining trucks is still very low at present. To promote the development of intelligent mining, it is urgent to proceed with the drive-by-wire modification to common mining trucks and design a motion control algorithm considering uncertain dynamic *** paper proposes a trajectory tracking control method for autonomous heavy-duty mining dump trucks(AHMDTs) with uncertain dynamic characteristics. In this method, a driving/braking force compensation algorithm based on an inverse dynamic model is designed to guarantee accurate longitudinal control with less control gain tuning. Using road curvatures, a modified rear-wheel position feedback control method is proposed to deal with reverse-path tracking, which can simultaneously reduce the lateral error and yaw angle error. A modified Stanley controller considering the collaborative preview based on speed and curvature is constructed to achieve accurate path tracking in the forward gear. Moreover, the proposed method focuses on the practice of trajectory tracking control in an open-pit mine condition with the adverse effects caused by uncertain vehicle dynamics, huge variable load, and large actuator lag. Real vehicle tests show that the proposed methodology can control AHMDTs with a low tracking error.

关键词： vehicle control trajectory tracking control driving/braking force compensation rear-wheel position feedback

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Analog parallel processor for broadband multifunctional integrated system based on silicon photonic platform

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Light(science & Applications) 2025年第3期14卷 746-756页

作者： Na Qian Defu Zhou Haowen Shu Ming Zhang Xingjun Wang Daoxin Dai Xiao Deng Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBejingChina State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZhejiang UniversityHangzhouChina

Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial ***,broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic ***,we report an analog parallel processor(APP)based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog *** APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2N parallel *** photonic analog parallelism,data rate and data volume in each sequence are simultaneously compressed,which mitigates the requirement on each parallel computing ***,the fusion of the outputs from each computing core is equivalent to directly processing broadband *** the proof-of-concept experiment,two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated *** bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is *** wireless communication rate of 8 Gbit/s is also *** the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture,we anticipate that the proposed APP will accelerate the development of powerful optoelectronic processors as critical support for applications such as satellite networks and intelligent driving.

关键词： analog parallel processor sharing hardware platform multifunctional integrated systems establish full cooperation different functionalities analog parallel processor app based silicon photonic platform electronic circuitryherewe broadband signal

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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LLM4CP:Adapting Large Language Models for Channel Prediction

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Journal of Communications and Information Networks 2024年第2期9卷 113-125页

作者： Boxun Liu Xuanyu Liu Shijian Gao Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Department of Electronic and Computer Engineering and Department of Civil and Environmental Engineering The Hong Kong University of Science and TechnologyHong KongChina

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

关键词： channel prediction massive multi-input multi-output(m-MIMO) large language models(LLMs) fine-tuning time-series

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Hints from Hertz: Dynamic Frequency Scaling Side-Channel Analysis of Number Theoretic Transform in Lattice-Based KEMs

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded systems 2024年第3期2024卷 200-223页

作者： Yu, Tianrun Cheng, Chi Yang, Zilong Wang, Yingchen Pan, Yanbin Weng, Jian Hubei Key Laboratory of Intelligent Geo-Information Processing School of Computer Science China University of Geosciences Wuhan China State Key Laboratory of Integrated Services Networks Xidian University Xian China The University of Texas at Austin AustinTX United States Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China College of Information Science and Technology Jinan University Guangzhou China

Number Theoretic Transform (NTT) has been widely used in accelerating computations in lattice-based cryptography. However, attackers can potentially launch power analysis targeting the NTT because it is one of the most time-consuming parts of the implementation. This extended time frame provides a natural window of opportunity for attackers. In this paper, we investigate the first CPU frequency leakage (Hertzbleed-like) attacks against NTT in lattice-based KEMs. Our key observation is that different inputs to NTT incur different Hamming weights in its output and intermediate layers. By measuring the CPU frequency during the execution of NTT, we propose a simple yet effective attack idea to find the input to NTT that triggers NTT processing data with significantly low Hamming weight. We further apply our attack idea to real-world applications that are built upon NTT: CPA-secure Kyber without Compression and Decompression functions, and CCA-secure NTTRU. This leads us to extract information or frequency hints about the secret key. Integrating these hints into the LWE-estimator framework, we estimate a minimum of 35% security loss caused by the leakage. The frequency and timing measurements on the Reference and AVX2 implementations of NTT in both Kyber and NTTRU align well with our theoretical analysis, confirming the existence of frequency side-channel leakage in NTT. It is important to emphasize that our observation is not limited to a specific implementation but rather the algorithm on which NTT is based. Therefore, our results call for more attention to the analysis of power leakage against NTT in lattice-based cryptography. © 2024, Ruhr-University of Bochum. All rights reserved.

关键词： Side channel attack

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