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第43届中国控制会议

作者： Yao Wang Cheng Ren Jiaxin Xu Cailian Chen Xinping Guan Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aviation connectors are responsible for information and energy transmission in the aircraft electrical wiring interconnection system(EWIS).Even a tiny shrinkage of any connector pin may lead to a critical impact on the reliability and stability of the ***,the current detection method of connector pin shrinkage mainly relies on manual visual inspection,which is prone to missed and false ***,traditional 2D machine vision methods face challenges in dealing with fine pins and deep calibers of *** address this issue,this paper designs a defect detection device and proposes a 3D-enabled shrinkage pin detection(3D-SPD) *** the device,a 4-degree-of-freedom(4-DOF) support mechanism is designed to collect the connector point cloud based on the imaging principle of a 3D structured light ***,a preprocessed method is presented to extract defective regions from the raw point ***,3D-SPD algorithm can automatically select the reference plane and detect the pin position,enabling online detection of connector shrinkage *** results show that 3D-SPD algorithm is effective in detecting shrinkage pin defects on various types of aviation connectors,with an accuracy rate of 98.62%and a shrinkage pin detection rate of 100%.

关键词： Aviation Connector EWIS 3D Machine Vision Defect Detection Shrinkage Pin

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Multi-Objective Team Personality Tourism Route Design Based on Fuzzy APACA

Multi-Objective Team Personality Tourism Route Design Based ...

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Emerging Technology (INCET), International Conference for

作者： Xiaoling Song Huanhuan Peng Key Laboratory of Intelligent Information Processing Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In order to solve the challenge of multi-objective team personalized tourism route design, in view of the shortcomings of the existing divide and conquer algorithms, this study proposes an innovative personalized tourism route design method based on fuzzy APACA. This new scheme uses the principles of gradient descent theory to accurately identify and locate key influencing factors, and accordingly carries out a sensible classification of indicators to reduce possible interference. At the same time, by using the unique mechanism of fuzzy APACA, this scheme cleverly constructs the design strategy of tourism routes. The empirical results show that this scheme shows a significant improvement compared with the traditional divide and conquer algorithm in terms of key performance indicators such as the accuracy of personalized tourism route design and the processing efficiency of key factors, showing its obvious strong advantages. In the multi-objective team, the personalized tourism route design plays a crucial role, which can accurately predict and optimize the growth trend and output results of the multi-objective team's personalized tourism route design. However, in the face of complex simulation tasks, traditional divide-and-conquer algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of fuzzy APACA optimization for the design of personalized tourism routes, and accurately controls the influencing parameters through gradient descent theory, which is used as the road map for index allocation, and then uses fuzzy APACA to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the design of multi-

关键词： Accuracy Roads key performance indicator Interference Prediction algorithms Market research Classification algorithms

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SPARC: Prediction-Based Safe control for Coupled controllable and Uncontrollable Agents with Conformal Predictions

arXiv

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arXiv 2024年

作者： Wang, Shuqi Wang, Siqi Li, Shaoyuan Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China

We investigate the problem of safe control synthesis for autonomous systems operating in environments with uncontrollable agents whose dynamics are unknown but coupled with those of the controlled system. This scenario naturally arises in various applications, such as autonomous driving and human-robot collaboration, where the behavior of uncontrollable agents, such as pedestrians or human operators, cannot be directly controlled but is influenced by the actions or the states of the autonomous vehicle or robot. In this paper, we present SPARC (Safe Prediction-Based Robust controller for Coupled Agents), a new framework designed to ensure safe control in the presence of uncontrollable agents that are coupled with the controlled system. Particularly, we introduce a joint distribution-based approach to account for the coupled dynamics of the controlled system and uncontrollable agents. Then, SPARC leverages conformal prediction to quantify the uncertainty in data-driven prediction of the agent behavior. By integrating the control barrier function (CBF), SPARC provides provable safety guarantees at a high confidence level. We illustrate our framework with a case study of an autonomous driving scenario with walking pedestrians. Copyright © 2024, The Authors. All rights reserved.

关键词： Human robot interaction

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Multi-objective Dynamic AGV Scheduling Method Based on Deep Reinforcement Learning

Multi-objective Dynamic AGV Scheduling Method Based on Deep ...

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Asian control Conference

作者： Gaoshang Wang Yuanyuan Zou Shaoyuan Li Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education Shanghai

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In industrial production, automated guided vehicles (AGVs) are widely used for material transfer and workpiece transportation to improve production efficiency. The growing demands of dynamic orders and switching production bring great challenges to the dynamic scheduling problem of AGV systems. To address the dynamic tasks with different optimization objective weight factors, a self-attention based multi-objective reinforcement learning (SAMORL) AGV dynamic scheduling method is proposed in this paper. At each rescheduling point, the self-attention based multi-objective deep dueling double Q-network (SAMOD3QN) is utilized to estimate the Q-values of task allocation actions on each objective respectively. Then in the action choosing process, the Q-values on different objectives are weighted according to the given weight factors. In this way, the task allocation policy achieves quick adjustment to different optimization objectives. Furthermore, the beam search algorithm is utilized to expand the search space of the optimal action trajectory according to the cumulative reward and estimated Q-value. The effectiveness and adaptability of the proposed dynamic scheduling method is illustrated by test examples based on stochastically inserted tasks.

关键词： Remotely guided vehicles Job shop scheduling Transportation Production Switches Dynamic scheduling Trajectory

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Geometry Dependent Multi-stage Compact Thermal Model for Advanced Nanosheet FETs

Geometry Dependent Multi-stage Compact Thermal Model for Adv...

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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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