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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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FPGA-based Real-Time High-resolution Image Processing for rocket on-board processing 17

FPGA-based Real-Time High-resolution Image Processing for ro...

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17th IEEE International Conference on computer Research and Development, ICCRD 2025

作者： Zhou, Hui Wang, Xiaofeng Xie, Yujia Cao, Yongxiang Tang, Yu Huo, Yongchun Wen, Huan Zhao, Xiongbo Li, Chaoran National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute Beijing China Beihang University Computer Science and Engineering Beijing China Beihang University National Superior College for Engineers Beijing China

ISBN: (纸本)9798331531881

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-Time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-The-Art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-To-end computation time for

关键词： Image enhancement

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FPGA-based Real-time High-resolution Image Processing for rocket on-board processing

FPGA-based Real-time High-resolution Image Processing for ro...

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International Conference on computer Research and Development (ICCRD)

作者： Hui Zhou Xiaofeng Wang YuJia Xie Yongxiang Cao Yu Tang Yongchun Huo Huan Wen Xiongbo Zhao Chaoran Li Beijing Aerospace Automatic Control Institute National key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China Computer Science and Engineering Beihang University Beijing China National Superior College for Engineers Beihang University Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-the-art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-to-end computation time for

关键词： YOLO Rockets Power demand Convolution Multicore processing Reviews Scalability Accelerator architectures Real-time systems Field programmable gate arrays

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Finite-time robust performance improvement for non-linear systems in the presence of input non-linearity and external disturbance

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IET control Theory and Applications 2023年第6期17卷 672-682页

作者： Wang, Haoping Ghaffari, Valiollah Mobayen, Saleh Bartoszewicz, Andrzej Fekih, Afef Assawinchaichote, Wudhichai School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering Persian Gulf University Bushehr Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute of Automatic Control Lodz University of Technology Lodz Poland Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The main focus of this research is on improving the performance of dynamic systems with actuator non-linearities and time-varying disturbances. To this end, using the concept of finite-time stability, a novel observer is presented to estimate the disturbances in systems with input non-linearities. Then, introducing an innovative sliding manifold, a robust observer-based technique is derived to guarantee the finite-time stability of the sliding surface. Hence, the closed-loop system would be stable in the presence of hard non-linearities and time-varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. © 2022 The Authors. IET control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Closed loop systems

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ML-based Expert Products Scoring System

ML-based Expert Products Scoring System

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Progress in Applied Electrical Engineering (PAEE)

作者： Patryk Mendoń Mariusz Pelc Aleksandra Kawala-Sterniuk Mariusz Gola Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Mathematics Physics and Computer Science University of Opole Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

In this paper the authors propose a system architecture which allows product scoring based on sentiment analysis and utilising Machine Learning (ML) and Expert systems technologies. This solution presented in this paper allows decoupling the AI-based part, which is very capable in sentiment analysis but is lacking some flexibility to actually put the sentiment analysis results in a wider context to make a better sense as to what the results actually mean. This task in our solution is performed by the Expert system where the expert may provide some decision making rules in order to determine the final system *** results confirm discrepancy between reviews and star ratings.

关键词： Knowledge engineering Fuzzy logic Sentiment analysis Reviews Systems architecture Stars Machine learning Cognition Expert systems System analysis and design

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Tuning the feedback controller gains is a simple way to improve autonomous driving performance

arXiv

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arXiv 2024年

作者： Liang, Wenyu Baldivieso, Pablo R. Drummond, Ross Shin, Donghwan Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Dept. of Computer Science University of Sheffield Sheffield United Kingdom

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle’s closed loop dynamics. The aim of this paper is to highlight the limitations of this approach;it is shown that retuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness. © 2024, CC BY.

关键词： Feedback control

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Comparative Study of Neural Networks Used in Halyomorpha Halys Detection*

Comparative Study of Neural Networks Used in Halyomorpha Hal...

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Mediterranean Conference on control and Automation (MED)

作者： Dan Popescu Loretta Ichim Mihai Dimoiu Raluca Trufelea Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

The paper’s purpose was to investigate some methods based on neural networks for the detection and classification of harmful insects for agriculture as the Halyomorpha Halys. The implementation of different object detection networks for image categorization was analyzed. Images from the Maryland Biodiversity database were used for neural network training and testing. Rotation, scaling, blurring, mirroring, and other techniques were employed for data augmentation. For the detection and classification of Halyomorpha Halys, some neural networks that include multiple smaller networks were implemented and investigated. The networks used are the following: YOLOv5s, SSD with different backbones such as MobileNet V1, MobileNet V2, and ResNet-50, Faster R-CNN with ResNet-50 backbone, and EfficientDet-D0. Moreover, neural networks were evaluated and compared based on performance metrics such as accuracy and time. Performances like accuracy between 0.49 – 0.86 and time between 36 ms – 55 ms were obtained. The best results were obtained for YOLOv5s, in terms of accuracy, and EfficientDet-D0, in terms of time.

关键词： Training Insects Neural networks Transfer learning Object detection Real-time systems Agriculture

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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IEEE International Conference on Big Data

作者： Krzysztof Hanzel Damian Grzechca Adam Ziebinski Lukas Chruszczyk Artur Janus Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Distributed Systems and Informatics Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Student of Electrical Engineering Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.

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Interoperability Framework for Cyber-Physical Systems Based Capabilities

Interoperability Framework for Cyber-Physical Systems Based ...

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International Conference on control Systems and computer science (CSCS)

作者： Eugen Pop Miruna-Elena Iliuță Nicolae Constantin Daniela Gîfu Alexandru Dumitrascu Faculty of Automatic Control and Computers University “Politehnica” of Bucharest Romania Institute of Computer Science Romanian Academy Iasi – Branch Romania

Cyber-Physical Systems represent digital systems based mainly on the interconnection between the physical world and cyberspace. Physical processes are controlled and monitored through networked computers. Feedback loops between the physical and computing environment are involved, including sensors and actuators. Thus, the CPS design involves the analysis of the interrelated dynamical physical processes, computation resources, networks, and software. CPS is application-oriented, by its nature so the necessity of available services composition appears frequently. The CPS services, resources and capabilities can be placed at the system and node level. In this context, the Communication Cost is an important performance criteria for accessing their resources, even in a composite form. In this paper an interoperability framework, for Cyber-Physical Systems capabilities, using REST and SOAP web services is presented. The interoperability capabilities of the framework are ensured by both of the convergence of IP and non-IP networks for services access and by the services composition facilities. For a scenario of multiuser access at the composed services, the interoperability framework response time is evaluated, because it brings an important contribution in the determination of Communications Cost, in a QoS approach.

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Target Localization of Short Vertical Line Array Based on Least Squares Method

Target Localization of Short Vertical Line Array Based on Le...

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International Conference on computer Network, Electronic and Automation (ICCNEA)

作者： Shaochun Qu Zhipeng Cai Jing Ma School of Computer Science and Engineering Xi’an Technological University Xi’an China North Automatic Control Technology Institute Taiyuan China

In the shallow sea environment, array of small array elements may not be able to accurately locate targets due to undersampling. To address this issue, the paper proposed a method called sound field extension based on the reception of array of small array elements, which generates a virtual measurement field of a vertical array with lager number. The method utilizes the extended acoustic field for VTRM. This approach significantly improves the focusing and positioning performance of the TRM. The key implementation lies in estimating the normal wave mode coefficients from the acoustic field acquired by the short vertical line array. The paper uses the least squares method to estimate the mode coefficients. Experimental results demonstrate that compared to conventional time processing methods for an array of small array elements, the VTRM method using the a vertical array with a large number of elements can effectively enhance targeting and focusing performance of the short array. For array of small array elements with 21 elements, the SNR (Signal Interference to Noise Ratio) effectively improves by 4.32 dB.

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