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6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： ZiJun Nie YongQi Hu ZiZheng Yu RuJia Li ZhiJian Yin HuaLin Zhan Zhen Yang School of Communications and Electronics Jiangxi Science and Technology Normal University Nanchang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Considering smoke detection in a complex scene, high smoke object error rate and low detection efficiency, an improved YOLOV5 for smoke detection is proposed in this paper. To improve the effectiveness of smoke detection algorithm for smoke targets in complex scenes. First, a new lightweight convolution technique GSConv is introduced to replace the Conv layer in the neck layer for feature extraction, and the original C3 module is replaced by Cross Stage Partial Network (GSCSP) module VoVGSCSP. This improvement reduces the computation complexity of the algorithm while maintaining the detection accuracy. Second, CIoU is replaced by SIoU as the regression function of the prediction box to improve the prediction accuracy of the prediction box and reduce the rate of missed detection of the target. Finally, to improve the performance of the deep network, Dynamic ReLU is introduced as an activation function (dynamically adjusting the ReLU parameters depending on the input data). Experiments on the SM-dataset (smoke detection dataset) show that the improved model improves the accuracy and precision of target smoke detection compared to the YOLOV5 model. Its precision is increased by 1.8%, mAP@0.5 is increased by 1.0%, the complexity of the model is reduced by 23.8%. The improved algorithm proposed in this paper can effectively extract smoke features and more suitable for smoke detection tasks in complex scenes.

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Dynamic Template Update Mechanism for Visual Object Tracking

Dynamic Template Update Mechanism for Visual Object Tracking

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6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： YongQi Hu ZhongWang Cai ZiJun Nie ZiZheng Yu Ying Cao Zhijian Yin Zhen Yang School of Communications and Electronics Jiangxi Science and Technology Normal University Nanchang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Given the requirements for robust target classification and accurate target state estimation in visual tracking, SiamFC++ proposes a set of practical guidelines for designing high-performance general-purpose trackers by considering the special nature of visual tracking problems. Inspired by dynamic modules, We propose an empirical method for integrating a dynamic module into the image input, which is concatenated with the template module after feature maps are extracted by the backbone network. Since the position and shape of the object can change significantly within a video sequence, the added dynamic module can better focus on the target region of the feature map to obtain better similarity maps. Extensive experiments and comparisons demonstrate that our simple and effective method achieves reliable results on the benchmarks of LaSOT, TrackingNet, and GOT-10K and provides a significant speed advantage in real-time.

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Digital Twin Based Sintering Proportioning: Improved Whale Optimization Method

Digital Twin Based Sintering Proportioning: Improved Whale O...

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Chinese Automation Congress (CAC)

作者： Wei Liu Xuda Ding Xuehan Bai Cailian Chen Xingping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P.R. China

Sintering proportion optimization plays a crucial role in determining the final product quality of sintered iron ore. However, the traditional approach of separating the optimization processes between the procurement and technical departments often fails to achieve the global optimum. In this paper, We propose a joint optimization approach for sintering proportioning based on digital twin (DT) technology. To tackle the complex constraints of this joint optimization problem, we develop a multiple improved whale optimization algorithm (MIWOA) that overcomes the issue of getting trapped in local optima. The proposed MIWOA incorporates three strategies, including a nonlinear convergence factor, Levy flight, and Gaussian mutation. Besides, the numerical simulations are conducted in order to analyze the influence of the convergence factors. Finally, various related approaches for sintering proportioning optimization are tested, and DT-based MIWOA achieved the best performance.

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Periodicity in dynamical games driven by the Hedge algorithm and myopic best response 63

Periodicity in dynamical games driven by the Hedge algorithm...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Guo, Xinxiang Mu, Yifen Yang, Xiaoguang University of Chinese Academy of Sciences The Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences Beijing100190 China Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences The Key Laboratory of Systems and Control Beijing100190 China Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences The Key Laboratory of Management Decision and Information System Beijing100190 China

ISBN: (纸本)9798350316339

In this paper, we consider the n × n two-payer zero-sum repeated game in which one player (player X) employs the popular Hedge (also called multiplicative weights update) learning algorithm while the other player (player Y) adopts the myopic best response. The theoretical analysis on the dynamics of such game system is still rare, which is however of promising interests. We investigate the dynamics of such Hedge-myopic system by defining a metric Q(xt), which measures the distance between the stage strategy xt and Nash Equilibrium (NE) strategy of player X. We analyze the trend of Q(xt) and prove that it is bounded and can only take finite values on the evolutionary path when payoffs are all rational numbers and the game has an interior NE. Based on this, we prove that the stage strategy sequence of both players are periodic after finite stages and the time-averaged strategy of player Y within one period is an exact NE strategy. © 2024 IEEE.

关键词： Consensus algorithm

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A Method for Compressed Storage and Retrieval of Triplet Data Based on Cuckoo Hashing 21

A Method for Compressed Storage and Retrieval of Triplet Dat...

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21st International Computer Conference on Wavelet Active Media Technology and information processing, ICCWAMTIP 2024

作者： Pengcheng, Liu Qinzhi Shibin, Zhang Yan, Chang Lili, Yan School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China

ISBN: (纸本)9798331519254

This paper proposes a method for compressing and retrieving triple data based on optimized cuckoo hash. Firstly, the optimized cuckoo hash algorithm is utilized to quickly establish node indexes. Then, the relationship information of triples is stored in the form of a sparse matrix according to the relationship type. Finally, the double compression algorithm k2-Rel is employed to compress the relationship attribute matrix of stored triple information, aiming to reduce storage space and lower storage costs. Experimental results demonstrate that the optimized cuckoo hash algorithm significantly reduces the time for building node indexes. Furthermore, utilizing the k2-Rel compression algorithm for compressing the relationship information between nodes substantially decreases storage space, ultimately achieving efficient storage and retrieval of triple data. © 2024 IEEE.

关键词： Cuckoo hash Data compression Knowledge graph Triple data

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Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine

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Chinese Journal of Electronics 2020年第4期29卷 786-792页

作者： SUN Yongkui CAO Yuan XIE Guo WEN Tao School of Electronic and Information Engineering Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Railway point machines(RPMS) are one of the key equipments in the railway system to switch different routes for the *** monitoring for RPMs is a vital measure to keep train operation safe and *** convenience and low cost into consideration, a novel intelligent condition monitoring method for RPMs based on sound analysis is ***-domain and frequency-domain features are obtained,and normalized using z-score standardization method to eliminate the influences of different *** particle swarm optimization(BPSO) is utilized to select the most significant discrimination feature *** effects of the selected optimal features are verified using Support vector machine(SVM), 1-Nearest neighbor(1 NN), Random forest(RF), and Naive Bayes(NB).Experiment results indicate SVM performs best on identification accuracy and computing cost compared with the other three *** identification accuracies on normal switching and reverse switching processes reach100% and 99.67%, respectively, indicating the feasibility of the proposed method.

关键词： Condition monitoring Sound analysis Railway point machines(RPMs) Binary particle swarm optimization(BPSO) Support vector machine(SVM)

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Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tian, Zhuojun Zhang, Zhaoyang Li, Yiwei Bennis, Mehdi Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Oulu Center for Wireless Communications Oulu90014 Finland State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing Hangzhou310027 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China

To jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a distributed strong lottery ticket hypothesis (DSLTH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represented as the ‌Hadamard product of global real-valued parameters and a personalized binary mask for pruning. The local model is learned by updating and fusing the personalized binary masks while the real-valued parameters are fixed among different agents. To further reduce the complexity of hardware implementation, we incorporate a group sparse regularization term in the loss function, enabling the learned local model to achieve structured sparsity. Then, a binary mask aggregation algorithm is designed by introducing an intermediate aggregation tensor and adding a personalized fine-tuning step in each iteration, which constrains model updates towards the local data distribution. The proposed method effectively leverages the relativity among agents while meeting personalized requirements in heterogeneous node conditions. We also provide a theoretical proof for the DSLTH, establishing it as the foundation of the proposed method. Numerical simulations confirm the validity of the DSLTH and demonstrate the effectiveness of the proposed algorithm. © 2015 IEEE.

关键词： Iterative methods

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A Novel Modulation Recognition Algorithm for Satellite Communication and TT&C Signals 2

A Novel Modulation Recognition Algorithm for Satellite Commu...

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2nd International Signal processing, Communications and Engineering Management Conference, ISPCEM 2022

作者： Zhang, Dongwei Liang, Xiaolong Huang, Canyu Shang, Yaobo Air Traffic Control and Navigation School Air Force Engineering University Shaanxi Key Laboratory of Meta-Synthesis for Electronic and Information System Xi'an China

ISBN: (纸本)9781665492713

Electromagnetic signal modulation mode is one of the key elements of battlefield technical parameter information. This paper proposes an efficient modulation recognition algorithm for satellite communication and TT&C signals based on decision tree, which can accurately identify eight satellite modulation modes, including BPSK, QPSK, 2FSK, 4FSK, 16QAM, BPSK/PM, QPSK/PM and 2FSK/PM. The spectrum characteristics of the modulation methods commonly used in satellite communication and TT&C signals are firstly analyzed, and the main differences between and within categories of modulation methods of satellite signals are summarized;Then, rational feature parameters are selected and designed, and a complete modulation recognition process is proposed based on these parameters;Finally, the correctness and performance of the proposed recognition algorithm are evaluated by numerical simulation. The simulation results show that when the SNR (signal-to-noise ratio) of the proposed algorithm is more than 4dB, the modulation recognition accuracy of the eight satellite communication/TT&C signals can reach above 95%. © 2022 IEEE.

关键词： Quadrature phase shift keying

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Silicon Content control for Blast Furnace: Model-free DDQN-ICM Method

Silicon Content Control for Blast Furnace: Model-free DDQN-I...

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Chinese Automation Congress (CAC)

作者： Baocong Zhang Xuda Ding Wei Liu Cailian Chen Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P.R. China

Silicon content control is critical to ensure the quality of molten iron and the stable operation of the blast furnace. The complex reactions and large uncertainty of the blast furnace make it hard to control. Several predictive control-based methods were already studied in Silicon content control. However, the requirements of prediction model design and short predictive horizon limit the generalization and long-term optimization of the methods. To tackle these issues, we propose a deep double Q-learning (DDQN) and intrinsic curiosity module (ICM) based Silicon content control method. First, the deep Q-learning framework employed in our approach eliminates the need for a prediction model and supports long-range simulations, thereby enhancing algorithm generalization and long-term optimization capabilities. Second, DDQN and ICM help to avoid the over-optimistic of action taking and improve exploration efficiency to tackle the large uncertainty of the blast furnace. Third, to ensure operational safety, we have designed a regulation strategy that incorporates ε-greedy exploration and ICM, mitigating the risks posed by exploration actions. Finally, we validate the effectiveness of our method using on-site data, demonstrating that DDQN-ICM achieves superior performance in terms of tracking accuracy and disturbance rejection.

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Research of Drawing Data Asset Graph Based on Force-Oriented Algorithm 21

Research of Drawing Data Asset Graph Based on Force-Oriented...

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21st International Computer Conference on Wavelet Active Media Technology and information processing, ICCWAMTIP 2024

作者： Sihan, Huang Zhi, Qin Shibin, Zhang Yan, Chang Lili, Yan School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China

ISBN: (纸本)9798331519254

With the rapid development of the new energy vehicle industry, the data volume in the industry is growing explosively. How to effectively visualize and analyze these data has become a focus of attention in the industry. As an intuitive way of data organization and presentation, asset graphs can display complex data relationships in a graphical form, which helps to quickly understand the information behind the data. This paper proposes a method for drawing data asset graphs based on the force-oriented algorithm, specifically tailored to the data characteristics of the new energy vehicle industry. The method improves the existing force-oriented algorithms through feature-enhanced node importance assessment, customized force model construction, multi-level community division and clustering, and node layout optimization. Experimental verification has shown that this method can effectively visualize new energy vehicle data, with certain advantages in layout quality and layout efficiency. © 2024 IEEE.

关键词： Asset Graph Data Visualization Force-Oriented Algorithm New Energy Vehicles

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