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2023 International Symposium of Electronics Design Automation, ISEDA 2023

作者： Chen, Haifeng Chen, Wangyong Chen, Jiahui Cai, Linlin School of Microelectronics Science and Technology Sun Yat-sen University Guangzhou510275 China Sun Vatsen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510275 China

ISBN: (纸本)9798350304510

Gate-All-Around Nanosheet FETs (NSFETs) are promising candidates for sub 5nm node, however, the aggressive scaling NSFETs are susceptible to the self-heating effect (SHE). A precise thermal model compatible with industry SPICE model, e.g. BSIMCMG, is critical to improve the yield of NSFET-based integrated circuit design and to reduce the development timeline of the advanced process. In this paper, we propose a geometry dependent multi-stage thermal RC network to accurately predict self-heating over a wide frequency range, from DC to THz, for the advanced NSFETs, which is validated by the transient thermal simulations based on 3-D Finite Element Modeling (FEM) method. The proposed compact thermal model enables to take the impacts of nanosheet width, number of stacks (gate fingers) and gate length into account, where the stage and model parameters of the thermal RC model are determined by Bayesian deconvolution and Particle Swarm Optimization (PSO) methods, respectively. The underlying physical understandings behind the geometry dependent thermal behaviors are discussed in detail. © 2023 IEEE.

关键词： Nanosheets

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Prediction of NOx Emission in SCR System of Thermal Power Plant Using Machine Learning Approaches

Prediction of NOx Emission in SCR System of Thermal Power Pl...

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第35届中国过程控制会议

作者： Sai Saing Lin Jingcheng Wang Shunyu Wu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

Effective prediction of Nitrogen Oxides(NOx) emissions in thermal power plants is essential for addressing environmental pollution control. The Selective Catalytic Reduction(SCR) system plays a pivotal role in mitigating NOx output, yet its performance can vary significantly due to operational conditions. This study investigates the prediction of NOx emissions in Selective Catalytic Reduction(SCR) systems within thermal power plants. Time delay mechanism is an essential factor for accurate modeling and prediction. Therefore, we incorporate a time delay of 120 samples into our feature engineering and analysis. Six predictive models, including five regression models and one Long Short-Term Memory(LSTM) neural network, are employed to analyze the temporal dynamics of NOx emissions. The regression models were selected for their varied methodologies and strengths in handling different types of data distributions and features, whereas the LSTM model was specifically utilized for its proficiency in processing sequential data, capturing long-term dependencies crucial for time-series forecasting. The performance of these models was evaluated using three statistical metrics: Root Mean Squared Error(RMSE), Mean Absolute Error(MAE), and the coefficient of determination(R2). Among the models, the Extra Trees regression model demonstrated superior accuracy in predicting NOx emissions, outperforming other models.

关键词： Thermal power plant Nitrogen Oxides emission SCR system Machine Learning

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Depth-Aware Fusion Method Based on Image and 4D Radar Spectrum for 3D Object Detection

Depth-Aware Fusion Method Based on Image and 4D Radar Spectr...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Sun, Yue Qian, Yeqiang Wang, Chunxiang Yang, Ming The Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University The Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9781665481090

Safety and reliability are crucial for the public acceptance of autonomous driving. To ensure accurate and reliable environmental perception, intelligent vehicles must exhibit accuracy and robustness in various environments. Millimeter-wave radar, known for its high penetration capability, can operate effectively in adverse weather conditions such as rain, snow, and fog. Traditional 3D millimeter-wave radars can only provide range, Doppler, and azimuth information for objects. Although the recent emergence of 4D millimeter-wave radars has added elevation resolution, the radar point clouds remain sparse due to Constant False Alarm Rate (CFAR) operations. In contrast, cameras offer rich semantic details but are sensitive to lighting and weather conditions. Hence, this paper leverages these two highly complementary and cost-effective sensors, 4D millimeter-wave radar and camera. By integrating 4D radar spectra with depth-aware camera images and employing attention mech-anisms, we fuse texture-rich images with depth-rich radar data in the Bird's Eye View (BEV) perspective, enhancing 3D object detection. Additionally, we propose using GAN-based networks to generate depth images from radar spectra in the absence of depth sensors, further improving detection accuracy. © 2024 IEEE.

关键词： Radar imaging

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Unequal Error Protection Rateless Codes for Spatial Audio Transmission 24

Unequal Error Protection Rateless Codes for Spatial Audio Tr...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Yin, Junpeng Fu, Yusun Huang, Haobo Tang, Jinhui Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Ministry of Education of China School of Electronic Information and Electrical Engineering The Key Laboratory of System Control and Information Processing Shanghai China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350363760

Spatial audio is an important component of the metaverse. With the rapid increase of the spatial resolution and the number of objects, the data volume of the spatial side information has grown larger. However, the low bit rate transmission of spatial side information in the object-based audio coding methods leads to the low-quality reconstruction of spatial side information with limited bandwidth. To address this issue, this paper constructs a cascade design of spatial audio coding and unequal error protection (UEP) rateless codes. Furthermore, the further protection of specific spatial side information is proposed for different scenarios, which effectively addresses the limitation of traditional spatial audio coding in side information protection. For the situation where the code length K of different importance levels is different, we propose an adaptive degree distribution (ADD), adding K to the selection probability as an influencing factor, which has better performance than other degree distribution for short code length. ADD is used to replace the multiple degree distribution strategy of the traditional unequal error protection scheme, and the block error rate (BER) is reduced nearly 10 times compared with the traditional scheme. © 2024 IEEE.

关键词： Audio signal processing

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Unlock the flexibility for cooperative operation of hydrogen providers under transportation delay

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Applied Energy 2025年 394卷

作者： Yang, Xu Yang, Bo Wang, Zhaojian Liu, Sicheng Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory for System Control and Information Processing Ministry of Education of China Shanghai200240 China

Hydrogen provides a pathway to the clean transition of the future energy mix, rendering hydrogen energy service providers (HESPs) an important role in hydrogen supply. Additionally, collaborative operation through vehicle-based hydrogen transportation exhibits great potential for mitigating source–load fluctuations of HESPs. However, current collaborations on hydrogen transportation primarily focus on route selection while neglecting delay risks, which may precipitate abrupt energy deficits during the scheduling of energy receivers. This work proposes a multi-HESP cooperative game that considers a deadline assignment mechanism to ensure the robustness of hydrogen delivery while fully unlocking the scheduling flexibility supported by the road network. Considering the transportation-time uncertainty, the distributionally robust joint chance constraint (DRJCC) is introduced to model delay uncertainties effectively and improve hydrogen delivery reliability. A customized approximation method is proposed to reformulate the DRJCC model into a tractable approximation with low conservatism while enhancing flexibility in decision-making under transportation delay uncertainty. Case studies verify that the proposed framework can improve the flexibility and enthusiasm of multi-HESP cooperation under the electricity–hydrogen transmission network. © 2025 Elsevier Ltd

关键词： Travel time

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Kalman Filter Adaptive Speed Tracking control for High-speed Trains with Noise Interference

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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IEEE International Conference of Intelligent Robotic and control Engineering (IRCE)

作者： Chunli Zhang Yuwei Xie Lei Yan Fucai Qian College of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively.

关键词： Resistance Filtering Noise Process control Interference Predictive models Mathematical models Kalman filters Adaptive control MATLAB

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control and Transmission Co-design for Industrial CPS Integrated with Time-Sensitive Networking

Control and Transmission Co-design for Industrial CPS Integr...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Lin, Meihan Xu, Qimin Lu, Xuanzhao Zhang, Jinglong Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Co-design of control and communication is critical to enhancing the control performance of industrial Cyber-Physical System (ICPS). However, the stochastic features of non-deterministic communication strategies in existing works limit control performance improvement. This paper proposes a centralized-control and deterministic-transmission co-design architecture for multi-subsystem ICPS over Time-Sensitive Network (TSN). Packet-wise no-wait transmission scheduling is devised to enable fine-grained scheduling for deterministic delays of the control system data. An integrated system model for the co-design architecture is constructed by integrating scheduling variables into the control system model, which enables simultaneous optimization of the control and transmission strategies for improving the feasible solution space and control performance. Co-design problem based on the integrated model is formulated as mixed-integer non-linear programming (MINLP) problem. A Tabu-search-based heuristic algorithm is proposed to obtain near-optimal solutions efficiently for the co-design problem. Simulation results demonstrate that the proposed method effectively improves control performance compared with best-effort transmission without co-design. © 2022 IEEE.

关键词： Scheduling

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A Method for Detecting the Reducers' Grease Failure Based on Vibration Signal Analysis

A Method for Detecting the Reducers' Grease Failure Based on...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Ditong Zhang, Hong Zheng, Ying Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan China Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350337754

Real time detection of the lubricating grease's effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task. © 2023 IEEE.

关键词： Support vector machines

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Nonconvex Noise-Tolerant Neural Model for Repetitive Motion of Omnidirectional Mobile Manipulators

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1766-1768页

作者： Zhongbo Sun Shijun Tang Jiliang Zhang Junzhi Yu the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE College of Information Science and Engineering Northeastern UniversityShenyang 110819China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

Dear Editor,Quadratic programming problems(QPs)receive a lot of attention in various fields of science computing and engineering applications,such as manipulator control[1].Recursive neural network(RNN)is considered t... 详细信息

关键词： directional programming hardware

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