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2023 International Conference on System Science and engineering, ICSSE 2023

作者： Thanh, Le Tien Lam, Le Hoang Nguyen, Thanh Nha Tran, Duc Thien Ho Chi Minh City University of Technology and Education Automation Control Department Ho Chi Minh City70000 Viet Nam Ho Chi Minh City University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City70000 Viet Nam

ISBN: (纸本)9798350322941

To address the issue of plastic waste, a robot using deep learning technology for visual recognition to classify plastic waste has been developed. This system includes a 3DOF robot arm, a conveyor, a camera, an electrical cabinet, and a computer. The object detection component of the system is designed using transfer learning with a pre-trained YOLOv5 model to ensure the system operates in real time. Selecting the best model by evaluating and comparing the results of models trained using labeling by bounding box and polygon methods. Then, the real-world coordinates for the origin of the robot arm are determined by utilizing matrices obtained from MATLAB through chessboard images. The computer processes the data and transmits commands to the robot arm system and conveyor, which is controlled by a PLC and 3 different Servo Drivers, for object sorting on the conveyor. The best-performing model has a Precision of 92.1% and a Recall of 87.3%, and the success rate of picking up an object is 91.5%. While the experimental results indicate complete stability in inter-device connectivity, implementing it would necessitate hardware improvements to leverage its potential. © 2023 IEEE.

关键词： Deep learning

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A Novel Screening Framework for Lymph Node Metastasis in Colorectal Cancer Based on Deep Learning Approaches 2022

A Novel Screening Framework for Lymph Node Metastasis in Col...

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7th International Conference on Multimedia and Image Processing, ICMIP 2022

作者： Liu, Yeming Li, Fulong Yu, Haitao Zhang, Zhiyong Li, Huiyan Han, Chunxiao Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Pathology Tangshan Gongren Hospital Tangshan063000 China

ISBN: (纸本)9781450387408

As a diagnostic criterion for cancer, histopathology image analysis is quite critical for the subsequent therapeutic treatment of patients. Nowadays, the diagnosis is mainly depended on manually which is less precise and low-Accuracy. To address the problem, we propose a novel screening framework combined image preprocess and AI approaches for the automatic detection of lymph node metastasis of colorectal cancer. First calculates the Histogram of Oriented Gradient (HOG) and Gray Level Cooccurrence Matrix (GLCM) of high-resolution digital images transformed from pathological sections. Statistical analysis show that Support Vector Machine (SVM) can be used to automatically identify cancerous areas. We further introduce deep learning models Convolutional Neural Network (CNN) into our framework, taking preprocessed images as inputs. The screening results demonstrate that the highest overlapping ratio can be achieved compared with manually annotation areas is 93.09% got by CNN, while another approaches SVM get an accuracy of 83.75%. The combination of image preprocess and deep learning can effectively improve the efficiency of lymph node metastasis screening in colorectal cancer and has great significance for the further development of Computer Aided Diagnosis (CAD) systems. © 2022 ACM.

关键词： Support vector machines

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A three-dimensional force estimation method for the cable-driven soft robot based on monocular images

arXiv

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arXiv 2024年

作者： Zhu, Xiaohan Bu, Ran Li, Zhen Xu, Fan Wang, Hesheng School of Electronic Information and Electrical Engineering The Department of Automation Shanghai Jiao Tong University China Artificial Intelligence Research Institute School of Information and Control Engineering China University of Mining and Technology China

Soft manipulators are known for their superiority in coping with high-safety-demanding interaction tasks, e.g., robot-assisted surgeries, elderly caring, etc. Yet the challenges residing in real-time contact feedback have hindered further applications in precise manipulation. This paper proposes an end-to-end network to estimate the 3D contact force of the soft robot, with the aim of enhancing its capabilities in interactive tasks. The presented method features directly utilizing monocular images fused with multidimensional actuation information as the network inputs. This approach simplifies the preprocessing of raw data compared to related studies that utilize 3D shape information for network inputs, consequently reducing configuration reconstruction errors. The unified feature representation module is devised to elevate low-dimensional features from the system’s actuation signals to the same level as image features, facilitating smoother integration of multimodal information. The proposed method has been experimentally validated in the soft robot testbed, achieving satisfying accuracy in 3D force estimation (with a mean relative error of 0.84% compared to the best-reported result of 2.2% in the related works). Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic surgery

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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Event-Triggered Neural Asynchronous control of Nonhomogeneous Markov Jump Power Systems with Hybrid Cyberattacks and Unknown States

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IEEE Transactions on Power Systems 2025年

作者： Luo, Wende Chen, Haiyang Zong, Guangdeng Zhao, Xudong Qufu Normal University School of Engineering Rizhao276826 China Tiangong University School of Control Science and Engineering Tianjin300387 China Nanjing Institute of Technology School of Automation Nanjing211167 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

This paper studies the observer-based eventtriggered neural asynchronous control problem of nonhomogeneous Markov jump power systems (MJPSs) under hybrid cyberattacks. Both periodic denial-of-service attacks and deception attacks (DAs) are involved because of the openness of networks. An event-triggered mechanism is established to ease the network load. Besides, neural network (NN) approaches are adopted to suppress the adverse effects of DAs on nonhomogeneous MJPSs, and the hidden Markov model (HMM) is employed to capture the asynchronous phenomenon between the system mode and the controller mode. With NN approaches and HMM, an observer-based NN asynchronous controller is designed. By constructing the mode- and parameter-dependent Lyapunov function, sufficient criteria ensuring boundedness in probability for nonhomogeneous MJPSs are obtained. Then, a design algorithm is shown to simultaneously obtain the controller gains, observer gains, and event-triggering weight matrices based on the established conditions. Finally, the single-machine infinitebus power system and 3-machine 6-bus power system are applied to justify the validity of the proposed algorithm. © 1969-2012 IEEE.

关键词： Lyapunov functions

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Informer-LSTM-EWMA for Panax Notoginseng with Prediction of Early Intervention Time

Informer-LSTM-EWMA for Panax Notoginseng with Prediction of ...

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Data Driven control and Learning Systems (DDCLS)

作者： Jiahui Ye Chunxi Yang Xiufeng Zhang Jin Zhang Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Panax notoginseng is one of economic crop with significant medicinal value in southwest China. However, traditional extensive fertilization and irrigation method usually damages the soil, making it difficult to achieve sustainable cultivation of Panax notoginseng. In order to assess the growth status of Panax notoginseng and take timely action, this paper proposed an Informer-LSTM-EWMA model to predict the growth trend of Panax notoginseng in cultivation fields. In the experimental greenhouse, we set up two experimental fields A and B to verify the feasibility of the proposed method. Firstly, wavelet denoising is utilized on the date collected by the sensor. Then, Informer and Long short-term Memory (LSTM) are used to train the denoised data to obtain data prediction. Further process the predicted data through Exponential Weighted Moving Average (EWMA) for trend prediction. Finally, the early intervention time is obtained by searching for the maximum value. The results indicate that the model can predict early intervention time and its predictive performance is far better than that of a single model.

关键词： Learning systems Economics Irrigation Noise reduction Crops Predictive models Soil

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Hierarchical control of mixed traffic flow for mobility and energy economy promotion at signalized intersection

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Energy 2025年 329卷

作者： Shen, Shiquan Liu, Huan Li, Zongdian Wang, Qingwang Zhang, Huawei He, Huafeng Chen, Zheng Faculty of Transportation Engineering Kunming University of Science and Technology Kunming650500 China Department of Electrical and Electronic Engineering Tokyo Institute of Technology Tokyo1528550 Japan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Vocational College of Transportation Kunming650300 China

The emergence of vehicle-to-infrastructure and vehicle-to-vehicle wireless communication offers alternative perspectives to improve traffic mobility in urban road traffic network, but most of the existing research mainly focuses on fully automated scenarios or assume that all vehicles have communication capabilities. To compensate the research shortcomings, this paper considers a mixed traffic flow consisting of various vehicles with different levels of intelligence, and proposes a hierarchical optimal control strategy for isolated signalized intersections in rural roads to maximize traffic mobility and vehicle energy efficiency by controlling only a few connected autonomous vehicles (CAVs). In the upper layer, the mixed traffic flow near the intersection is dynamically divided into multiple "1+m" mixed platoons composed of one leading CAV and m following vehicles. In the lower layer, an optimal control framework for the mixed platoon system is established to optimize the velocity trajectory of the leading CAV, with the goal of enhancing energy efficiency, driving comfort and traffic mobility of the entire platoon. The simulation results demonstrate that the proposed strategy is suitable for any random mixed traffic scenarios and provides significant benefits in terms of traffic mobility and energy efficiency in almost all scenarios. © 2025 Elsevier Ltd

关键词： Optimal control systems

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The Metal Polishing System for Finishing Shiny Metal Surfaces by Free Abrasive Polishing

The Metal Polishing System for Finishing Shiny Metal Surface...

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2023 International Conference on System Science and engineering, ICSSE 2023

作者： Lam, Le Hoang Truong, Nguyen Hoang Huy, Nguyen Minh Thanh, Truong Quoc Tran, Duc Thien Ho Chi Minh City University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City70000 Viet Nam HCMC University of Technology Faculty of Mechanical Engineering Ho Chi Minh City70000 Viet Nam Ho Chi Minh City University of Technology and Education Automation Control Department Ho Chi Minh City70000 Viet Nam

ISBN: (纸本)9798350322941

This study investigates a force control method for free abrasive polishing to achieve a shiny metal surface finish. Despite the potential of this technique, this method is not widely used in metal surface polishing due to challenges in controlling the force during movement. To address the force control issue, we proposed an approach that uses the feedback force value indirectly calculated through the current value of the AC driver and applies it in a closed-loop control system. The proposed method is evaluated experimentally by polishing different metal pieces with varying particle sizes and movement speeds. The experimental results demonstrate that the proposed force control method effectively maintains a constant force during the polishing process, which leads to an improved surface finish. They also show that decreasing particle size and movement speed can improve the surface finish, while the contact force has a limited effect. These findings contribute to a better understanding of the relationship between force, movement speed, and surface finish quality in metal polishing. © 2023 IEEE.

关键词： Force control

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Average Communication Rate for Networked Event-Triggered Stochastic control Systems

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Zengjie Liu, Qingchen Mamduhi, Mohammad H. Hirche, Sandra Eindhoven University of Technology Department of Electrical Engineering Netherlands University of Science and Technology of China Department of Automation Hefei China University of Birmingham School of Computer Science United Kingdom Technical University of Munich Chair of Information-Oriented Control Munich Germany

Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes. For a NET-SCS, the propagation of nonlinear statistics of the network communication status brought up by deterministic thresholds makes the precise computation of ACR difficult. Previous work used to over-simplify the computation using a Gaussian distribution without incorporating this nonlinearity, leading to sacrificed precision. This paper proposes a novel approach to calculate the exact ACR for a NET-SCS using a recursive model. We use theoretical analysis and a numerical study to qualitatively evaluate the deviation gap of the conventional approach that ignores the side information. The accuracy of our proposed method, alongside its comparison with the simplified results of the conventional approach, is validated by experimental studies. Our work is promising to benefit the efficient resource planning of networked control systems with limited communication resources by providing accurate ACR computation. © 2025 IEEE.

关键词： Networked control systems

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Online Residual Learning from Offline Experts for Pedestrian Tracking

Online Residual Learning from Offline Experts for Pedestrian...

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IEEE Conference on Decision and control

作者： Anastasios Vlachos Anastasios Tsiamis Aren Karapetyan Efe C. Balta John Lygeros Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich Switzerland Control and Automation Group inspire AG 8005 Zürich Switzerland and with the Automatic Control Laboratory

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predictions generated before or at the beginning of the prediction horizon. We augment every offline prediction by learning their respective residual error concerning the true target state online, using the recursive least squares algorithm. At a higher level, we treat the augmented lower-level predictors as experts, adopting the Prediction with Expert Advice framework. We utilize an adaptive softmax weighting scheme to form an aggregate prediction and provide guarantees for ORL in terms of regret. We employ ORL to boost performance in the setting of online pedestrian trajectory prediction. Based on data from the Stanford Drone Dataset, we show that ORL can demonstrate best-of-both-worlds performance.

关键词： Pedestrians Aggregates Prediction algorithms Trajectory Drones

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