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Consensus MPC-Based hierarchical control of Islanded AC Microgrid 3

Consensus MPC-Based hierarchical control of Islanded AC Micr...

引用

3rd international conference on Communication, computing and Digital Systems, C-CODE 2023

作者： Khan, Zawar Aamir, Muhammad Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Electrical & Computer Engineering Department Haripur Pakistan

ISBN: (纸本)9798350332391

In this paper, the consensus model predictive control (MPC)-based hierarchical control scheme is presented for the primary and secondary layers of an islanded AC microgrid (MG). The secondary control is designed on the tracker consensus method, in this way a distributed controller in collaboration with the finite time observer will set the referenced values of voltage magnitude and frequency by significantly speeding up the method of synchronous convergence. Furthermore, the primary control has been implemented for each DG separately and comprises Finite Control Set-Model Predictive control (FCS-MPC), and droops control. FCS-MPC regulates PCC reference voltages for each DG unit. Droop Control regulates power exchange within and among DG units. In the proposed hierarchical control approach, information sharing is accomplished by a neighbouring node creating a sparse communication network while fully eliminating the requirement for a centralized controller. The controller effectiveness have been validated against unbalanced load, transients, and load plug-and-play capabilities. The simulation result shows that MG operates effectively using FCS-MPC for parallel inverters of DG units, and the consensus algorithm based distributed secondary controller in MG effectively delivers the anticipated power, voltage, and frequency. © 2023 IEEE.

关键词： Model predictive control

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Design of an Early Learning Car for Garbage Sorting Based on Object Detection 3

Design of an Early Learning Car for Garbage Sorting Based on...

引用

3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Hu, Yuhao Dai, Zhihao Jiang, Zhenxiao Ji, Jiawei Yan, Yuxuan Wang, Junjie Li, Juanjuan School of Electronics and Information Technology Soochow University China

ISBN: (纸本)9798350343922

China has comprehensively launched the work of living rubbish classification in cities at the prefecture level and above across the country since 2019, and rubbish classification has begun to be integrated into the daily life of all people. However, due to the lack of awareness of rubbish classification for a long time, it has become a habit for adult groups not to classify rubbish, so the education of rubbish classification for young children is particularly important. In this thesis, an intelligent trolley control system is designed in the context of the implementation of the waste classification policy, aiming to improve the efficiency and accuracy of waste classification in the future by means of waste classification education for young children. The system is based on STM32, K210 and LD3320 control. Firstly, a lightened yolov2 target detection model is used to detect the gesture card teaching aids, and then Kalman filtering is applied to the detection results to achieve the tracking of the detected target on the image and to improve the stability of target detection. Then, PID control algorithm is used to achieve motion control of the trolley so that it can advance to the vicinity of the gesture sign as expected. Finally, the LD3320 recognises the voice of the toddler and opens the corresponding category of bins to achieve trash classification. The experimental results show that the intelligent cart system has good accuracy and stability in target detection and dynamic tracking, and high accuracy in speech recognition, which demonstrates good application prospects in young children's rubbish classification education, and promises to effectively improve the efficiency of rubbish classification and young children's participation, which is of positive significance for the promotion of the implementation of rubbish classification policy. © 2023 IEEE.

关键词： Three term control systems

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Optimal Finite- Time Prescribed Performance of Servo Pneumatic Positioning with PID Control Tuning using an Evolutionary Mating Algorithm 13

Optimal Finite- Time Prescribed Performance of Servo Pneumat...

引用

13th IEEE international conference on Control System, computing and Engineering, ICCSCE 2023

作者： Irawan, Addie Sulaiman, Mohd Herwan Azahar, Mohd Iskandar Putra Universiti Malaysia Pahang Faculty of Electrical & Electronics Engineering Technology Pahang Pekan26600 Malaysia

ISBN: (纸本)9798350323184

This paper presents an optimum tuning on finite-time prescribed performance with PID (FT-PPC-PID) controller using the Evolutionary Mating Algorithm (EMA) approach for a pneumatic servo system's (PSS) rod-piston positioning. The design objective is to optimize the convergence rate and finite time of the prescribed performance function in error transformation in parallel with PID controller's gains. The multi-step input trajectory on the PPVDC model plant was used for simulations with specific load and random noise as disturbances. The results demonstrate that the controller optimized with EMA outperforms the same controller optimized with other methods in achieving dynamic multi-step positioning of the rod-piston. This highlights the significant enhancement in overall performance of PPVDC positioning, including the stability of its internal system, through the EMA-optimized finite-time prescribed performance controller with PID. © 2023 IEEE.

关键词： Pneumatic actuators

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Research on Cloud computing Task Scheduling Based on Swarm Intelligence Algorithm

Research on Cloud Computing Task Scheduling Based on Swarm I...

引用

international conference on Business Intelligence and Information technology, BIIT 2023

作者： Zhang, Biying Zhang, Lei Zhang, Bowen Harbin University of Commerce Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin150028 China

ISBN: (纸本)9789819739790

As an important research topic in the field of cloud computing, the task scheduling problem has been deeply discussed by the majority of researchers. The overall efficiency of traditional algorithms is low. In the face of the increasing amount of data at present, it is a bit powerless. Aiming at the problem of the completion time in the task scheduling of cloud computing, this paper uses the particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA) in the swarm intelligence algorithm to fuse and adds three kinds of behavior of artificial fish swarm algorithm in the optimization process of particle swarm algorithm, so that the fusion algorithm (AFPSO) not only retains the characteristics of rapid convergence of particle swarm algorithm but also has the characteristics of artificial fish swarm algorithm which does not always stay in the local best position. Experiments show that the fused algorithm has better performance in task scheduling completion time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Scheduling algorithms

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Transforming Agriculture: Navigating the Challenges and Embracing the Opportunities of Artificial Intelligence of Things

Transforming Agriculture: Navigating the Challenges and Embr...

引用

2023 IEEE international conference on Agrosystem Engineering, technology and Applications, AGRETA 2023

作者： Leong, Ying Mei Lim, Ean Heng Subri, Nor Fatiha Binti Jalil, Norazira Binti A Quest International University Faculty of Computing and Engineering Ipoh Malaysia Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Malaysia

ISBN: (纸本)9798350347333

Agriculture faces significant challenges, including climate change, limited resources, and population growth, necessitating innovative solutions to ensure sustainable productivity and food security. Artificial Intelligence of Things (AIoT), a convergence of Al and IoT technologies, offers transformative potential for agriculture by optimizing resource utilization, improving production management, and reducing labor dependency. This paper presents an overview of AIoT applications in agriculture, highlighting its benefits, opportunities, and challenges. AIoT's benefits include increased efficiency and productivity through automation, reduced labor dependency with AI-powered robots, improved decisionmaking by analyzing real-time data, and enhanced sustainability by optimizing resource use. Opportunities for AIoT in agriculture encompass precision farming, predictive analytics, autonomous farming, resource optimization, supply chain efficiency, and decision support systems. However, implementing AIoT in agriculture faces challenges such as data quality, connectivity, cost, privacy, and user adoption. Addressing these challenges is essential to harness AIoT's potential fully. Future research directions include developing advanced Al algorithms, exploring edge computing, promoting interoperability and standards, and investigating AIoT's role in climate resilience and resource management. Research should also focus on AIoT adoption and usability, social and ethical implications, and AIoT-based supply chain integration. AIoT offers the opportunity to revolutionize agriculture, address challenges and unlock opportunities for sustainable productivity and food security. By adopting AIoT technologies and following relevant research directions, the agricultural sector can overcome challenges and achieve a resilient and food-secure future. © 2023 IEEE.

关键词： Artificial intelligence

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Design and Implementation of Low-Cost Fog computing Architecture for IoT-Based Applications 12

Design and Implementation of Low-Cost Fog Computing Architec...

引用

12th international conference on ICT convergence (ICTC) - Beyond the Pandemic Era with ICT convergence Innovation

作者： Zainudin, Ahmad Nwakanma, Cosmas Ifeanyi Kim, Dong-Seong Lee, Jae-Min Kumoh Natl Inst Technol Dept Elect Engn Gumi 39177 South Korea Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi 39177 South Korea

ISBN: (纸本)9781665423830

Internet of Things (IoT), currently cloud IoT infrastructure is shifting toward fog computing through the task offloading process. Fog computing provides computing capabilities that bring closer to the IoT devices. By task offloading shifting to the fog, nodes can avoid high network congestion. Fog computing can improve the quality of service, especially for delay-sensitive applications by enabling low latency requirements. In the IoT infrastructure, low-cost devices are used for less expensive infrastructure deployments. Raspberry pi is used for computing infrastructure with cheaper development. A small single-board computer not only has high computing capacity but also allows high portability.

关键词： fog computing task offloading low-cost raspberry pi smart home

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Research on Multi-objective Green Vehicle Path Optimization Based on Whale Algorithm

Research on Multi-objective Green Vehicle Path Optimization ...

引用

2023 international conference on Networking, Informatics and computing, ICNETIC 2023

作者： Zhou, Jiaxu Beijing Technology and Business University School of E-Business and Logistics Beijing100000 China

ISBN: (纸本)9798350313314

With the increasing competition in the logistics industry, it is extremely important to improve customer satisfaction for the development of modern logistics enterprises. At the same time, driven by the concept of low-carbon economy, energy conservation and emission reduction in logistics and transportation are increasingly valued. In the existing research on green vehicle routes, the main way to reduce fuel consumption and pollution emissions is still to reduce repeated transportation, and the shortest distance is often used as the selection criterion to select a transportation route between two transportation nodes. However, the running speed and emission of vehicles under different road conditions will also be significantly different, and the difference of road conditions is significantly reflected in the road grade. In order to realize the rational planning of logistics distribution vehicle route, reduce the distance and cost of logistics distribution, an improved Whale optimization algorithm (WOA) is proposed to solve the mathematical model of logistics distribution vehicle route planning. In terms of algorithm, this paper chooses WOA with simple search mechanism and few parameter settings to solve VRP (VRP). The improved WOA (GWOA) is used for solution research, and compared with the basic WOA, GA and ACO algorithms. The experimental results show that GWOA has better global optimization ability and faster convergence characteristics in solving the optimal path, shortest distance and maximum satisfaction. © 2023 IEEE.

关键词： Global optimization

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Preliminary Study of AI·Data Platform for Medical AI Semiconductor Research and Training

Preliminary Study of AI·Data Platform for Medical AI Semico...

引用

2024 IEEE international conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Urtnasan, Erdenebayar Ha, Sungwook Lee, Nam Yeong Cho, Sang Wan Kim, Jaesoo Yonsei Insititute of AI Data Convergence Science Yonsei University Mirae Campus Wonju Korea Republic of Yonsei University Department of AI Semiconductor Mirae Campus Wonju Korea Republic of Yonsei University Division of Semiconductor System Mirae Campus Wonju Korea Republic of

ISBN: (纸本)9798331530839

In this study, we studied the implementation of an artificial intelligence (AI) data platform to support medical AI semiconductor research and training for fostering AI semiconductor experts. The proposed AI data platform has two parts to implementing comprehensive and adaptive infrastructure for AI semiconductor research and training. First, the medical AI solution section that deals with data platform and AI platform where is medical data and high-performance computing environment for medical AI solution development. Next is the AI semiconductor section where a diverse infrastructure to built-up the AI semiconductor design platform provides fabless AI semiconductor design and verification, thereby providing the outstanding training environments. Finally, we achieved comprehensive infrastructure for medical AI semiconductor research and training based on AI data platform. © 2024 IEEE.

关键词： AI data platform AI semiconductor AI solution Medical AI Real world data

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Research on Handwritten Test Paper Score Recognition Algorithm Based on LSTM Model 7

Research on Handwritten Test Paper Score Recognition Algorit...

引用

7th international conference on Algorithms, computing and Systems, ICACS 2023

作者： Yan, Jun Yi, Jiliang Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798400709098

In order to solve the problems that the excessive parameters of the network model in the existing handwritten character recognition methods increase the consumption of computing and storage resources, the target recognition rate is difficult to meet the real-time requirements, and it is easy to cause parameter explosion, gradient loss and over-fitting. Based on the traditional digital image processing module, a long-term and short-term memory network (LSTM) model is embedded to identify the handwritten score template of a specific test paper. This method performs normalization, noise reduction, tilt correction, and binarization preprocessing on the handwritten digital test paper image. The Hough line detection is used to cut the horizontal line, and the LSD algorithm erases the vertical line to obtain the target image. Finally, a new digital recognition algorithm is constructed based on the LSTM model. Through experimental testing and verification, the results show that the method effectively improves the rate of recognizing handwritten digit scores in pictures, reduces the number of model parameters and resource consumption, accelerates the convergence speed of the model, and meets the actual use requirements of identifying specific test paper picture templates under certain conditions. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Noise abatement

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Exploring the Potential of OCR Integration for Object Detection in Invoices 27

Exploring the Potential of OCR Integration for Object Detect...

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27th international conference on System Theory, Control and computing, ICSTCC 2023

作者： Bulzan, Andrei-Ştefan Cernazanu-Glăvăn, Cosmin Marcu, Marius-George Department of Computer and Information Technology Universitatea Politehnică Timisoara Timisoara Romania

ISBN: (纸本)9798350337983

This paper investigates the impact of incorporating Optical Character Recognition (OCR) information into object detection models for extracting key information fields from invoices. We propose a method that adds a fourth channel to the input images, representing text presence, derived from two OCR models. Our experiments show that while larger models do not benefit from the additional text localization information, smaller models exhibit significant accuracy improvements and accelerated learning. In particular, we observe a substantially higher mean average precision (mAP) by epoch 20 out of 100 when including the fourth OCR information channel. This research demonstrates the potential benefits of incorporating OCR information into object detection models, particularly for smaller models with limited resources, by enhancing not only accuracy but also the speed of convergence during training. © 2023 IEEE.

关键词： Object detection

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