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arXiv 2023年

作者： Li, Mengmou Tanaka, Taichi Carnerero, A. Daniel Wasa, Yasuaki Hirata, Kenji Fujisaki, Yasumasa Ushifusa, Yoshiaki Hatanaka, Takeshi Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Electrical Engineering and Bioscience School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan Department of Electrical and Electronic Engineering School of Engineering University of Toyama Toyama930-8555 Japan Department of Information and Physical Sciences Graduate School of Information Science and Technology Osaka University Osaka565 0871 Japan Faculty of Economics and Business Administration The University of Kitakyushu Fukuoka802-8577 Japan

In this research, we investigate Net-Zero Energy Houses (ZEH), which harness regionally produced electricity from photovoltaic (PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This paper examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. We propose two potential scenarios: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing;the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, we apply the Monte Carlo sample average approximation (SAA) to the problems. To demonstrate the practical applicability of these models, we implement the proposed scenarios in the Jono neighborhood in Kitakyushu, Japan. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Designing of A Plastic Garbage Robot With Vision-Based Deep Learning Applications

Designing of A Plastic Garbage Robot With Vision-Based Deep ...

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International Conference on System Science and engineering (ICSSE)

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

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.

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Intelligent Networked control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

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Computer Systems Science & engineering 2023年第10期47卷 721-739页

作者： Mohamed Esmail Karar Amged Sayed A.Mahmoud College of Computing and Information Technology(CCIT) Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportP.O.Box 12577Smart VillageSmart Village-B 2401Egypt

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability *** paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status *** infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good *** developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)*** networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs *** avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F *** simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy ***,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

关键词： Intelligent medical systems telemedicine fuzzy control teaching learning-based optimization

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Delivering Improved Care through Remote Health Monitoring Systems by Using Microservices. Case Study: Fall Detection Encompassed into Connected Care 24

Delivering Improved Care through Remote Health Monitoring Sy...

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24th International Conference on control Systems and Computer Science, CSCS 2023

作者： Ianculescu, Marilena Alexandru, Adriana Giura, Ionut-Emanuel Vasilevschi, Ana-Mihaela National Institute for Research and Development in Informatics Communication Digital Applications and Systems Department Bucharest Romania University Politehnica of Bucharest Doctoral School of Automatic Control and Computers Bucharest Romania Counselors Department National Institute for Research and Development in Informatics Târgovişte Romania Faculty of Electrical Engineering Electronics and Information Technology Târgovişte Romania Valahia University of Târgovişte Târgovişte Romania

ISBN: (纸本)9798350313390

Based on today's modern technologies, patient care can be provided remotely, in a connected way, being, at the same time, personalized, patient-centered and proactive. Such an approach can be achieved using Remote Health Monitoring Systems (RHMS). In case of elderly people, dramatic permanent deteriorations of their health status can be caused by falls. The seniors' health and life style monitoring and the detection and/or prevention of falls can be done by such RHMS, an example being the RO-SmartAgeing system developed inside 'Non-invasive Monitoring System and Health Assessment of the Elderly in a Smart Environment (RO-SmartAgeing)' research project. The paper presents the problem of using microservices for optimizing the architecture and functionalities of RO-SmartAgeing system, and the way in which the problems of ensuring the security of patient data can be solved. © 2023 IEEE.

关键词： Fall detection

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MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis

arXiv

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arXiv 2024年

作者： Liu, Shuhong Deng, Tianchen Zhou, Heng Li, Liuzhuozheng Wang, Hongyu Wang, Danwei Li, Mingrui Department of Information Science and Technology Department of Complexity Science and Engineering The University of Tokyo Tokyo113-8654 Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Mechanical Engineering Columbia University New York10027 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Dalian University of Technology Dalian116024 China

Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems. © 2024, CC BY.

关键词： Gaussian distribution

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Neural-LAFC: A Neural Learning Adaptive Flight controller for Tailless Flapping-Wing Robots Under Uncertain Wind Disturbance Conditions

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Guo, Qingcheng Zhang, Yichen Mou, Jiawang Liu, Wu Wu, Xiaosheng Lu, Jun-Guo Shanghai Jiao Tong University Department of Automation China Key Laboratory of System Control and Information Processing Ministry of Education of China China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai200240 China Shanghai Jiao Tong University National Key Laboratory of Advanced Micro and Nano Manufacture Technology China Shanghai Jiao Tong University Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai200240 China Designing Institute of Hubei Space Technology Academy Wuhan China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

Enabling bio-inspired flapping-wing robots (FWRs) to maintain stable flight in windy environments, similar to real flying creatures, is of significant importance. In this work, a neural learning-based adaptive flight controller (Neural-LAFC) is proposed for the attitude and position control of a tailless FWR under uncertain wind conditions. The key approach involves employing a deep neural network (DNN) that incorporates not only attitude information but also instantaneous flapping angles of the wings to learn the aerodynamic torques that cannot be accurately modeled. The training data are obtained from flight experiments conducted with the FWR in a wind tunnel environment. Subsequently, a composite adaptive law is presented to update a set of linear coefficients. These linear coefficients are utilized to blend the pre-trained basis functions to represent the aerodynamic torques. The tracking errors are shown to exponentially converge to a bounded region, as guaranteed by a Lyapunov function. Based on the learning-based attitude controller, we also develop an adaptive controller for position control. Notably, apart from the training phase, the FWR operates without prior knowledge of wind speed. Experimental results demonstrate that the Neural-LAFC successfully achieves the desired attitude stabilization, target point tracking, and path tracking of the FWR in windy conditions. The proposed control scheme also outperforms baseline controllers in wind disturbance suppression, reducing the root mean square position errors by more than 40% when the FWR is subjected to a horizontal wind within the speed of 180 {cm s{-1}}. © 1965-2011 IEEE.

关键词： Wind tunnels

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The Optimized Model of Hybrid Grid Connected System for Power Generation of The University of Lahore, Pakistan

The Optimized Model of Hybrid Grid Connected System for Powe...

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electrical engineering and Sustainable Technologies (ICEEST), International Conference on

作者： Muhammad Ahmad Khan Muhammad Zain Yousaf Ali Raza Muhammad Faizan Tahir Mazhar Ali Abdul Qadeer School of Electrical and Information Engineering Tianjin University Tianjin China School of Electrical and Information Engineering Hubei University of Automotive Technology Shiyan China Department of Electrical Engineering University of Engineering and Technology Lahore Lahore Pakistan School of Electric Power South China University of Technology Guangzhou China School of Electrical and Electronic Information Control Engineering Tianjin University Tianjin China School of Electrical Engineering and Automation Wuhan University Wuhan China

Energy is essential for a country's economic sector to be stable and prospering, but unfortunately, Pakistan's energy crisis is the country's biggest roadblock to increasing its economy. Industries need a constant supply; even minor supply delays can cost businesses millions of dollars. The primary reason for load shedding in Pakistan is the country's reliance on costly fossil fuels, whose prices are volatile and continue to increase. Pakistan has great potential for Renewable Energy Sources (RESs) such as wind, solar, micro-hydro/canal fall, biomass, biogas, tidal/ocean, geothermal, and biodiesel. This study aims to contribute to ongoing research on hybrid grid-connected systems by highlighting the optimal modeling and sizing and considering numerous techno-economic aspects such as system stability, capacity shortage constraint, Battery State of Charge (SoC), Photovoltaic (PV) power production, Net Present Cost (NPC), and PV array monitoring systems. The Hybrid Optimization Model for Electric Renewables (HOMER) software is used to analyze the six different scenarios employing the techno-economic factors, sensitivity analysis, and environmental consequences. The proposed system shows a significant decrease in energy costs and environmental emissions.

关键词： Costs Sensitivity analysis Production Power system stability Reliability engineering Hybrid power systems Software

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Time Delay Estimation with Fixed-time SMC Scheme for Nonlinear Dynamics

Time Delay Estimation with Fixed-time SMC Scheme for Nonline...

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2024 International Conference on control, automation and Diagnosis, ICCAD 2024

作者： Ahmed, Saim Azar, Ahmad Taher Fekik, Arezki Wang, Haoping Njima, Chakib Ben Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia Riyadh11586 Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Benha Egypt University Akli Mohand Oulhadj-Bouria Department of Electrical Engineering Rue Drissi Yahia Bouira 10000 Algeria Nantes Université École Centrale Nantes Cnrs LS2N Umr 6004 NantesF-44000 France Nanjing University of Science and Technology School of Automation China University of Sousse Tunisia

ISBN: (纸本)9798350361025

Time delay estimation using fixed-time terminal sliding mode control (TDEFxSMC) for unknown nonlinear systems with uncertainty and external disturbances is presented in this study. Firstly, the notion of fixed-time terminal sliding mode control (FxSMC) is outlined and thoroughly examined. Using the FxSMC scheme, nonsingular fixed-time control, non-chatter control inputs, and high performance tracking positions have been achieved. To estimate the unknown dynamics, proposed method incorporates a TDE scheme. The Lyapunov theorem is applied to find whether the proposed system can achieve its stability in a fixed-time. The effectiveness of the suggested method is evaluated and demonstrated by presenting the results of the proposed method implemented to the nonlinear robot dynamics. © 2024 IEEE.

关键词： Dynamical systems

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Takieldeen, Ali E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Automatic-Scheduling-Integrated Computerized Maintenance Management System for Small and Medium Manufacturing Plant

Automatic-Scheduling-Integrated Computerized Maintenance Man...

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International Conference on Green technology and Sustainable Development (GTSD)

作者： Ngoc-Huy Tran Xuan-Thuyen Le Department of Control & Automation Faculty of Electrical & Electronics Engineering Ho Chi Minh City University of Technology (HCMUT) VNU-HCM Ho Chi Minh City Vietnam Innovation F abLab Ho Chi Minh City University of Technology (HCMUT) VNU-HCM Ho Chi Minh City Vietnam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

Since the onset of the Fourth Industrial Revolution, modern industrial plants have become increasingly complex, leading to a growing demand for specialized information systems to manage their operations. Computerized Maintenance Management System (CMMS) is a popular tool which can be used to manage physical assets maintenance in a factory. While the current implementations of CMMS in small and medium specialized manufacturing plants improves many aspects of maintenance operations such as increased productivity, better planning, there are many enhancements that could be made in term of efficiency, extensibility, … This paper proposes a CMMS solution that, in addition to typical CMMS main features, focus on creating an open interface following ISA-95 for easy integration into other manufacturing management systems and enterprise level software, as well as improving user productivity through the use of automatic scheduling based on Tabu Search algorithm. The results from a plastic manufacturing plant indicate that the proposed solution can significantly reduce scheduling efforts while maintaining scheduling constraints and quality.

关键词： Productivity Job shop scheduling Processor scheduling Software algorithms Software Maintenance Sustainable development

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