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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Equilibrium analysis of networked game under best-response dynamics

Equilibrium analysis of networked game under best-response d...

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第43届中国控制会议

作者： Yu Xia Qi Su Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Both coordination,a behavioral pattern where individuals adopt the same behavior as the majority,and anticoordination,a behavioral pattern where individuals choose a behavior different from the majority,are prevalent in social and engineering *** include the spread of social norms or innovations for coordination and the division of labor and traffic congestion for *** the large-scale interacting systems driven by coordination and anticoordination is of great significance in understanding,predicting,and optimizing the evolutionary *** existing studies have primarily focused on analyzing the convergence of networked systems and evaluating the time to reach equilibrium,our investigation delves into all possible states of a networked system and scrutinizes all equilibrium ***,we observe that the number of equilibrium states increases exponentially with the network *** for network structure properties,homogeneous networks exhibit more equilibrium states than heterogeneous ***,for specific behavioral switching thresholds,the number of equilibrium states is surprisingly higher than at neighboring switching *** work reveals the intricate interplay between evolutionary dynamics,system structure,and behavioral switching thresholds.

关键词： Best-response dynamics Complex networks Game theory Equilibrium analysis

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A video archive management method based on blockchain technology 11

A video archive management method based on blockchain techno...

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Optoelectronic Imaging and Multimedia Technology XI 2024

作者： Wu, Rongteng College of Computer and Big Data Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

ISBN: (纸本)9781510682061

Video archives play a crucial role in various industries, from media and entertainment to security and surveillance. Managing these archives efficiently and securely is a growing challenge due to the increasing volume of video data being generated. In this paper, a video archive management method is presented to solve security and sensibility of the video files on blockchain. By encrypting video files and generating cryptographic hashes, the presented method can protect the integrity of the data and prevent unauthorized modifications. The method based on blockchain technology offers a compelling solution to the challenges faced in managing video archives. By combining the inherent security and transparency of blockchain with innovative data management algorithms, organizations can ensure the integrity, security, and accessibility of their video archives. © 2024 SPIE.

关键词： Data integrity

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An Enhanced Pythagorean Fuzzy Distance Method with Application in the Selection of Renewable Energy Source via MCDA

An Enhanced Pythagorean Fuzzy Distance Method with Applicati...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Terna, Anum Manasseh Ejegwa, Paul Augustine Feng, Yuming Joseph Sarwuan Tarka University Department of Mathematics Makurdi Nigeria Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing and Control Chongqing Wanzhou404100 China

ISBN: (纸本)9798331517021

Many complex decision-making processes have been prosecuted with the aid of Pythagorean fuzzy distance methods. This is because the theory of Pythagorean fuzzy sets has the ability to handle the vagueness and imprecision in decision-making. Various Pythagorean fuzzy distance methods have been constructed and applied to multiple criteria decision-making. Recently, Thakur et al. developed a Pythagorean fuzzy distance method based on Euler's number by considering an incomplete number of the parameters of Pythagorean fuzzy sets. This exclusion could render the result of the distance method unreliable in real-life applications. In this work, we augment the Thakur et al:'s Pythagorean fuzzy distance method by the inclusion of the complete parameters of Pythagorean fuzzy sets for an effective interpretation. The properties of the enhanced Pythagorean fuzzy distance method are described and the method satisfies the conditions of distance function. In addition, the new method is used to discuss the selection of renewable energy source based on multiple criteria decision-making approach. Furthermore, the enhanced method is compared with other existing Pythagorean fuzzy distance methods via multiple criteria decision-making approach to show the superiority of the augmented method. © 2024 IEEE.

关键词： multi-criteria decision analysis multiple criteria decision-making Pythagorean fuzzy distance method Pythagorean fuzzy set renewable energy source

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3D Machine Vision Enabled Shrinkage Pin Detection of Aviation Connectors in the Aircraft EWIS

3D Machine Vision Enabled Shrinkage Pin Detection of Aviatio...

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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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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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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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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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CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule Evaluation

CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Chen, Gong Liu, Jun Li, Chen-Qian Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350337020

Lung cancer is the most common malignant tumor worldwide, with high mortality rates. Pulmonary nodule is a common manifestation of lung cancer. Accurate segmentation and detection of pulmonary nodules from CT scans are essential for proper assessment of patient prognosis. However, this task remains challenging due to various factors, such as class imbalance and the need for detailed characterization of lung nodule segmentation. To address these issues, we propose a novel network, CPR-Net (Convolutional Pyramid Residuals-Net), that combines segmentation and benign-malignant classification for CT images of lung nodules. Our approach utilizes a PBMM module to expand the perception field and enhance the representation capability of the model, allowing it to learn more detailed information. We evaluate the classification performance using accuracy, precision, recall, F1 score, AUC, and the segmentation performance using the Dice coefficient. Experiments on both publicly available datasets and self-constructed datasets demonstrate that our proposed method in this paper outperforms other methods in terms of classification and segmentation performance under limited labeled data conditions. Moreover, Our results suggest that this model has great potential for improving lung nodule diagnosis in radiology for heterogeneous intranodal images. © 2023 IEEE.

关键词： class imbalance multi-task learning pulmonary nodule

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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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