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Design, Automation and Test in Europe Conference and Exhibition

作者： Lingfeng Zhou Shanlin Xiao Huiyao Wang Jinghai Wang Zeyang Xu Bohan Wang Zhiyi Yu School of Microelectronics Science and Technology Sun Yat-sen University China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Asynchronous circuits have recently become more popular in Internet of Things (IoT) and neural network chips because of their potential low power consumption. However, due to the lack of Electronic Design Automation (EDA) tools, the asynchronous circuits design efficiency remains low and faces challenges in large-scale applications. This paper proposes a new asynchronous circuits design flow using traditional EDA tools, and applies a new backward delay propagation constraint (BDPC) method. In this method, control paths and data paths are tightly coupled and analyzed together to improve the accuracy of static timing analysis. Compared to previous works, the proposed design flow and constraint method offer significant advantages in terms of accuracy and efficiency. To verify this flow, an asynchronous RISC-V processor was implemented on TSMC 65nm process. Compared to synchronous version, asynchronous processor achieves a power optimization of 17.4 % while main-taining the same speed and area.

关键词： Electric potential Power demand Design automation Neural networks Delays Internet of Things Optimization

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Association Testing for High-Dimensional Multiple Response Regression

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Journal of systems Science & Complexity 2023年第4期36卷 1680-1696页

作者： WANG Jinjuan JIANG Zhenzhen LIU Hongzhi MENG Zhen School of Mathematics and Statistics Beijing Institute of TechnologyBeijing 100081China LSC Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Science University of Chinese Academy of SciencesBeijing 100190China Institute of Food Science and Technology Chinese Academy of Agricultural Sciences/Comprehensive Key Laboratory of Agricultural Products Processing and Quality ControlMinistry of AgricultureBeijingChina School of Statistics Capital University of Economics and BusinessBeijing 100070China

Multiple response regression model is commonly employed to investigate the relationship between multiple outcomes and a set of potential predictors,where single-response analysis and multivariate analysis of variance(MANOVA)are two frequently used methods for association ***,both methods have their own *** basis of the former method is independence of multiple responses and the latter one assumes that multiple responses are normally *** this work,the authors propose a test statistic for multiple response association analysis in high-dimensional situations based on F *** is free of normal distribution assumption and the asymptotic normal distribution is obtained under some regular *** computer simulations and four real data applications show its superiority over single-response analysis and MANOVA methods.

关键词： Association analysis high-dimensional multiple response multiple response regression non normality

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Detecting Defects of Wooden Boards by Improved YOLOv4-tiny Algorithm 17th

Detecting Defects of Wooden Boards by Improved YOLOv4-tiny A...

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Qiu, Yang Ai, Zhijie Lin, Ye Xu, Zhezhuang Liu, Xinxiang College of Electrical Engineering and Automation Fuzhou University Fuzhou350000 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9789811663192

Wooden boards are widely used as raw materials for furniture. Detecting defects on the wooden is significant to improve the quality of products. In this paper, we propose an improved YOLOv4-tiny target detection algorithm with feature extraction and utilization tailored for wood defect recognition. The proposed algorithm replace the residual network of the original algorithm with the Res2Net network to enhance the feature extraction ability, and increase the different scales to improve the prediction accuracy. A testbed is deployed to collect the images of wooden boards, and the performance of the proposed algorithm is evaluated on the testbed. Compared with the original YOLOv4-tiny algorithm, the mean average precision for detecting the three types of defects deadknot, insectpest and crack was 80.06% which was an increase of 5%. Compared with other methods of wood defect detection, the proposed algorithm is more accurate and faster. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Testbeds

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Quality Prediction of Injection Molding Process Using CNN and Parallel-Cascade LSTMs 43

Quality Prediction of Injection Molding Process Using CNN an...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Yibo Li, Dewei Zhou, Yuanqiang Gao, Furong Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai201100 China Tongji University College of Electronic and Information Engineering Shanghai201804 China Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Guangzhou Hkust Fok Ying Tung Research Institute Department of Chemical and Biological Engineering Guangzhou511458 China

ISBN: (纸本)9789887581581

Quality prediction of plastic products has always been an important research topic in the injection molding production process. However, existing quality prediction methods struggle to handle both high-frequency data and statistical data generated during the injection molding production process. This paper introduces a novel quality prediction model for injection molding production, utilizing a combination of Convolutional Neural Networks (CNN) and Parallel-Cascade Long Short-Term Memory (PCLSTM) networks. The PCLSTM network is designed to handle the complexities of high-frequency data in injection molding, overcoming the limitations of traditional LSTM methods in processing long sequence data. By segmenting data into stages and utilizing a cascade mechanism, the PCLSTM architecture efficiently captures important temporal dynamics. Additionally, the CNN network is designed to extract statistical data features. The quality prediction results are obtained by fusing two types of data features and then passing them through a fully connected network. Experimental results on real production datasets show that the CNN-PCLSTM model outperforms existing models in prediction accuracy and training convergence speed. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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Quick Routing Response to Link Failure in Low-Earth Orbit Satellite Networks 8

Quick Routing Response to Link Failure in Low-Earth Orbit Sa...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Lu, Zhiping Zhi, Ruxin Ma, Weiguo School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing China Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665450515

Low earth orbit satellite communication systems are attractive for provisioning global, high-speed and low latency Internet access services. In order to achieve global continuous multiple coverage, the number of satellites is tremendous. But Low earth orbit satellite networks are prone to instability due to unpredictable link failures and frequent topology changes. Therefore, in this paper, a novel routing schemes with quick response to link failures is first proposed. Based on Dijkstra algorithm, a scalable routing algorithm is proposed to minimize the end-to-end transmission delay. It takes advantage of the predictable constellation trajectory, and changes dynamically according to the changes of network topology. It provides the shortest routing path and an alternative path simultaneously. When a link failure is detected by one satellite, notification packets will be sent to its neighbors for adjustment to the alternative routing path. Notification packets also will be sent to the head node for the recalculation of routing table. Finally, extensive simulations have been conducted, and the results show that the proposed scheme is able to produce desired performance. © 2022 IEEE.

关键词： Routing algorithms

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The CEEMDAN-ps-gcGRU Model On Water Pressure Prediction With Strong Irregularity

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IFAC-PapersOnLine 2023年第2期56卷 7108-7113页

作者： Tzuyi Kan Jingcheng Wang Shunyu Wu Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

As for urban water dispatch, multiple steps of water pressure prediction are necessary. Due to the strong irregularity of water pressure, both machine learning and original deep learning methods can't accurately predict water pressure. In order to predict the water pressure with significant irregularity, this paper has developed a prediction model. The CEEMDAN approach seeks to reduce the noise of the raw water pressure data, which is directly recorded by sensors. The extraction of pump state signals has the ability to introduce extra useful information to the training model. Gradient Correction Methodology is a new strategy for increasing the precision of water pressure prediction in multi-step models. It could alleviate the gradient disappearing issue for long horizon prediction. In general, the designed prediction model on water pressure performs better than other models with long step sizes.

关键词： CEEMDAN states of pump signals Gradient Correction GRU

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A Reinforcement Learning Mission Supervisor with Memory for Human-multi-robot Coordination systems 17th

A Reinforcement Learning Mission Supervisor with Memory for ...

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Mo, Zhibin Zhang, Zhenyi Chen, Yutao Huang, Jie School of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China

ISBN: (纸本)9789811663192

In this paper, a novel reinforcement learning mission supervisor (RLMS) with memory is proposed for human-multi-robot coordination systems (HMRCS). The existing HMRCS are known to suffer from long decision waiting time and large mission error caused by repeated human intervention, restricting the autonomy of multi-robot systems. The proposed supervisor elaborately integrates deep-Q-network (DQN) and long-short-term memory (LSTM) knowledge base within the null-space based behavioral control (NSBC) framework, so as to achieve optimal adjustment strategy of the behavioral priority in the presence of mission conflicts, and to reduce the frequency of human intervention. In particular, the proposed RLMS with memory first memorize human intervention history when robot systems are not confident in decision making when encountering emergencies, and then reload the history information when encountering the same situation that have been tackled by human previously. Simulation demonstrates the effectiveness of proposed RLMS with memory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 123-134页

作者： Yanni Wan Jiahu Qin Xinghuo Yu Tao Yang Yu Kang Department of Automation University of Science and Technology of ChinaHefei 230027China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China School of Engineering RMIT UniversityVIC 3000Australia State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China Department of Automation State Key Laboratory of Fire ScienceInstitute of Advanced TechnologyUniversity of Science and Technology of ChinaHefei 230027China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of SciencesBeijing 100190China

This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both *** PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption *** challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the *** address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’*** proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price *** comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.

关键词： Demand response management(DRM) Markovian decision process(MDP) Monte Carlo simulation reinforcement learning(RL) smart grid

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Hybrid signal fusion for target detection in distributed PA-MIMO radar systems on moving platforms

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhou, Dingsen Yang, Minglei Lian, Hao Greco, Maria Sabrina Gini, Fulvio Xidian University National Key Laboratory of Radar Signal Processing Shaanxi Xi'an710071 China Science and Technology on Underwater Test and Control Laboratory Dalian116013 China University of Pisa Department of Information Engineering Pisa56122 Italy

Distributed phased multiple-input multiple-output (PA-MIMO) radar systems on moving platforms combine the spatial diversity of MIMO radars with the coherent gain of phased-array radars, making them ideal for dynamic environments. However, challenges such as motion-induced channel variability, Doppler shifts, and complex parameter estimation often hinder target detection. Traditional methods like non-coherent fusion (NCF) and coherent fusion (CF) struggle to adapt to such variability, leading to suboptimal performance. In this paper, we propose a hybrid signal fusion (HSF) scheme that integrates joint space-time-phase compensation (JSTPC) with a compressed fusion network. The JSTPC module mitigates motion-induced phase errors and ensures precise alignment of received signals, while the compressed fusion network combines CF and NCF stages, leveraging their respective strengths to adaptively manage channel correlation variability. We also develop a joint parameter estimation algorithm to accurately extract direction of departure, direction of arrival, and Doppler frequency, thereby ensuring precise signal reconstruction and enhancing the detection process by estimating key parameters in real time. Theoretical analyses of detection thresholds and probabilities, derived using moment-matching techniques, further validate the reliability of the proposed scheme. Monte Carlo simulations and theoretical analyses for diverse radar configurations and channel correlation scenarios show that the proposed HSF scheme achieves 2 dB detection gain improvement over NCF. © 2025 IEEE.

关键词： Frequency estimation

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Bandstop Cavity Filter Based Decoupling Method for 1×2 MIMO Slot Antennas

Bandstop Cavity Filter Based Decoupling Method for 1×2 MIMO...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Mingqi Li Nan Yang Kai Lu Peng Fei Hu Yunliang Long School of Electronics and Information Technology (School of Microelectronics) and Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou China State Key Laboratory of Millimeter Waves Southeast University Nanjing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

In this paper, a bandstop cavity filter is presented for decoupling of 1×2 MIMO (multiple-input multiple-output) slot antennas. A cavity is formed by placing two rectangular waveguides (RWGs) side by side with shared narrow wall removed. By fine tuning the cavity size, the TE 120 and TE 130 modes of the cavity can be excited to obtain two transmission zeros (TZs) through their bypass coupling with non-resonating TE 10 mode, thus forming a bandstop filter. In this paper, this bandstop filter is used to feed and decouple 1×2 45° polarized slot antennas. Because of the bandstop characteristic of the cavity filter, the isolation between two feeding ports can be high in nature, suitable for MIMO applications. Moreover, the field distribution in cavity is asymmetric due to field superposition, so only one slot antenna can radiate, and the radiation patterns are also restored. To verify the effectiveness of this method, a prototype is designed. Simulated results show that isolation is higher than 35 dB in band of interest and typical unidirectional radiation patterns are also obtained.

关键词： Couplings Slot antennas Transmitting antennas Prototypes Rectangular waveguides Antenna feeds Filtering theory

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