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arXiv 2025年

作者： Sánchez-Arriaga, Néstor Eduardo Canzini, Ethan Espley-Plumb, Nathan John Farnsworth, Michael Pope, Simon Leyland, Adrian Tiwari, Ashutosh Department of Automatic Control & Systems Engineering University of Sheffield Sheffield United Kingdom Department of Materials Science & Engineering University of Sheffield Sheffield United Kingdom Airbus Robotics Airbus UK Chester United Kingdom

Thin-film inspection on large-area substrates in coating manufacture remains a critical parameter to ensure product quality;however, extending the inspection process precisely over a large area presents major challenges, due to the limitations of the available inspection equipment. An additional manipulation problem arises when automating the inspection process, as the silicon wafer requires movement constraints to ensure accurate measurements and to prevent damage. Furthermore, there are other increasingly important large-area industrial applications, such as Roll-to-Roll (R2R) manufacturing where coating thickness inspection introduces additional challenges. This paper presents an autonomous inspection system using a robotic manipulator with a novel learned constraint manifold to control a wafer to its calibration point, and a novel multi-sensor array with high potential for scalability into large substrate areas. We demonstrate that the manipulator can perform required motions whilst adhering to movement constraints. We further demonstrate that the sensor array can perform thickness measurements statically with an error of 0.5◦ from the calibration point whilst monitoring the RMSE and R2 over 1406 data points. These features are potentially useful for detecting displacement variations in R2R manufacturing processes. © 2025, CC BY-NC-ND.

关键词： Silicon wafers

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Scalable and Low-Cost Remote Lab Platforms: Teaching Industrial Robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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EQUILIBRIA AND STABILITY OF ONE MESSENGER RNA AND TWO MICRO RNA DYNAMICS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2022年第4期84卷 3-8页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

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Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale

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Mathematical Biosciences and engineering 2024年第3期21卷 3838-3859页

作者： Lu, Yu Liu, Tongqiang Sheng, Quan Zhang, Yutao Shi, Haifeng Jiao, Zhuqing School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China Department of Nephrology The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University Changzhou213003 China Department of Radiology The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University Changzhou213003 China School of Computer Science and Artificial Intelligence Changzhou University Changzhou213164 China

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients. © 2024 the Author(s).

关键词： Forecasting

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Person Detection and Tracking Using UAV and Neural Networks

Person Detection and Tracking Using UAV and Neural Networks

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Mediterranean Conference on control and Automation (MED)

作者： Andrei-Stelian Stan Loretta Ichim Valentin-Petrisor Parvu Dan Popescu Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania Faculty of Aerospace Engineering University Politehnica of Bucharest Bucharest Romania

With the recent advancements in drone technology, there has been an increase in the development of human detection and tracking techniques for various applications, especially near borders. In this research, we propose methods to enhance people detection performance in diverse outdoor scenarios. Our dataset design includes a wide range of lighting and color changes, different target distances, angles, and postures. The experimental data consists of images taken in various environmental situations, such as changing the drone’s flight height and capturing pictures in intensive light. To evaluate the performance of our proposed method, we enhanced the generic YOLOv5 model using the gathered data, and calculated key performance indicators, including loss functions, recall, accuracy, and mAP50. We compared the performance of our enhanced model against the standard YOLOv5 model and its versions on the same testing set.

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MPPT techniques application and comparison for photovoltaic panels 23

MPPT techniques application and comparison for photovoltaic ...

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Dobrea, Marius-Alexandru Arghira, Nicoleta Vasluianu, Mihaela Neculoiu, Giorgian Moldoveanu, Ana-Maria Cristiana University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering Faculty of Hidrotechnics Bucharest Romania

ISBN: (纸本)9781665439398

Photovoltaic (PV) panel modelling and control is very important in renewable energy systems. Due to it variability, PV panel generation power should be maximized for the given climate conditions. This paper considers three Maximum Power Point Tracking (MPPT) algorithms: Perturb Observe, Incremental Conductance and Fuzzy Logic. After a clear description for each algorithm, modelling, testing and validation is conducted on real experimental data for a 250W PV panel. Over one day period, a comparison between the 3 control MPPT algorithms show that Perturb Observe gives the best results, close to Incremental Conductance algorithm application. © 2021 IEEE.

关键词： Fuzzy logic

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Multiclassification Model of Histopathological Breast Cancer Based on Deep Neural Network 19

Multiclassification Model of Histopathological Breast Cancer...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Mohammed, Faris E. Zghal, Nadia Smaoui Aissa, Dalinda Ben El-Gayar, M.M. Tunis University Microwave Electronics Research Laboratory Faculty of Science Tunisia Enis Control and Energy Management Research Laboratory Sfax Tunisia New Mansoura University Faculty of Computer Science and Engineering Department of Computer Science Egypt

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Breast cancer is one of the most important diseases that lead to death, according to the reports of the World Health Organization. Reports also indicated that breast cancer affects women more than men. Late or wrong diagnosis leads to a deterioration in the patient's condition and may lead to death. Therefore, automated multiple classifications using machine learning of breast cancer from histopathological images plays a major role in the early diagnosis of the disease. Multiple classifications of breast cancer is the identification of tumors such as Carcinoma, Fibroadenoma, Cysts, etc. However, this type of classification faces many challenges such as extracting subtle differences between the binary classes of tumors and whether they are benign or malignant, strong coherence of tumor cells and widespread color heterogeneity. Therefore, we propose in this paper the use of a sophisticated model that works on semantic segmentation and extracting distinctive patterns and classifying them using a deep neural network. The structured deep learning model performed impressively (average accuracy of 98.7%) on a large-scale data set, demonstrating the power of our method in providing an effective tool for classifying multiple breast cancers in clinical settings. © 2022 IEEE.

关键词： Semantic Segmentation

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Fractional Order vs Integer Order Transfer Function Models of a Pendulum

Fractional Order vs Integer Order Transfer Function Models o...

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International Conference on Automation, AUTOMATION 2021

作者： Oprzędkiewicz, Krzysztof Rosól, Maciej Żegleń-Wlodarczyk, Jakub Department of Automatic Control and Robotics Faculty of Electrotechnics Automatic Control Informatics and Biomedical Engineering AGH University of Science and Technology al. A Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783030748920

The paper presents the comparison of fractional order to integer order transfer function models of physical pendulum. The considered transfer functions describe the behaviour of the system around the lower, stable equilibrium point. Fractional order models are built using ORA approximation. Models were verified using real experimental data. Results of tests show that the fractional order transfer function can correctly describe the physical pendulum. Integer order models are a little bit more accurate, but they require more parameters to identify. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Linear systems

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A Discrete, Fractional Order, Memory-Effective State Space Model of a RLC Circuit

A Discrete, Fractional Order, Memory-Effective State Space M...

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International Conference on Automation, AUTOMATION 2021

作者： Oprzędkiewicz, Krzysztof Department of Automatic Control and Robotics Faculty of Electrotechnics Automatic Control Informatics and Biomedical Engineering AGH University of Science and Technology al. A Mickiewicza 30 Krakow30-059 Poland

The paper presents a new, discrete, memory-effective state-space model of fractional RLC network. For the presented model its stability, positivity and reaction to different inital functions are analysed. Results are ... 详细信息

ISBN: (纸本)9783030748920

关键词： Timing circuits

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Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning

Microservice Deployment for Satellite Edge AI Inference via ...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zhiyong Yu Hei Victor Cheng Zhanpeng Yang Xin Liu Yuning Jiang Yong Zhou Yuanming Shi School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Aarhus University Denmark Automatic Control Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices’ communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time.

关键词： Satellites Accuracy Heuristic algorithms Satellite broadcasting Microservice architectures Low earth orbit satellites Edge AI computer architecture Resource management Optimization

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