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A Great Deluge Algorithm for University Course Timetabling

A Great Deluge Algorithm for University Course Timetabling

引用

2023 ieee international conference on Automatic control and intelligent systems, I2CACIS 2023

作者： Loke, Zi Xuan Goh, Say Leng Mountstephens, James Likoh, Jonathan Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan International Campus Labuan Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Preparatory Centre for Science and Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350321302

Here we propose a two-stage approach to address the University Course Timetabling Problem. In the first stage, we utilize an existing algorithm called Tabu Search with Sampling and Perturbation to generate a feasible solution, and in stage two, we use a Great Deluge algorithm to improve the quality of this feasible solution. The proposed methodology was tested on a recent instance of real university timetabling data from Universiti Malaysia Sabah. Experiments were conducted to determine the most suitable GD algorithm parameter values that resulted in optimal performance. Additionally, the performance of the GD algorithm was compared with that of a Genetic Algorithm and was found to achieve a lower minimum and lower average cost values. © 2023 ieee.

关键词： Tabu search

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Automatic Berthing Based on Reinforcement Learning and Feedback control 6

Automatic Berthing Based on Reinforcement Learning and Feedb...

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6th ieee international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Ding, Kaiming Ye, Jun Hu, Yi Dang, Jinglong Liu, Jialun Ocean and Energy Power Engineering Wuhan University of Technology School of Naval Architecture WuHan430063 China Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety Intelligent Transportation Systems Research Center WuHan430063 China

ISBN: (纸本)9798350325485

Berthing is one of the most critical and complex operations for ships. Application of automatic berthing control can improve the safety, efficiency, and sustainability of vessel maneuvering. Current studies on automatic berthing control systems are mostly based on optimal control theory or artificial neural networks. These control methods either have low environmental adaptability or need large amounts of data to support the training process. In this paper, a novel method for automatic ship berthing that combines reinforcement learning with feedback control is proposed. Such method can adapt to different ship and navigation environments by automatically adjusting control parameters. This simplifies the controller deployment on board and reduces the data requirement for agent training. Simulations show that the proposed methods could handle auto-berthing problem in various scenarios. © 2023 ieee.

关键词： Reinforcement learning

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Visual Design Of Artificial Pancreas Glucose control Method

Visual Design Of Artificial Pancreas Glucose Control Method

引用

2023 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2023

作者： Zhou, Ying Song, Rui Zhuo Xia, Li Na School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China

ISBN: (纸本)9798350309003

The number of diabetic patients is predicted to increase and the age of diabetic patients is slowly becoming younger. Diabetes is generally categorized into the following types: type I diabetes, type II diabetes, gestational diabetes, and other types of diabetes caused by pancreatic disease, endocrine disease, medications, and other factors. The traditional method of treating diabetes involves injecting insulin using a device outside the body. This method is physically and psychologically harmful to the diabetic. Therefore, this paper focuses on the glycemic control of patients with type I diabetes who have completely lost their insulin secretion, and whose pancreas is unable to secrete insulin. Therefore, it is necessary to use a blood glucose sensor to measure the blood glucose concentration in the patient's body. Then the measured blood glucose concentration in the diabetic patient's body is fed back to the computer terminal. Finally the controller is utilized for regulation. This paper focuses on the regulation of blood glucose in the body of type I diabetic patients and combines a fuzzy controller and a PID controller to regulate the blood glucose concentration in type I diabetic patients. In this paper, the blood glucose-insulin metabolism models of healthy people and diabetic patients are firstly established. Then the parameter values of the PID controller are adjusted through several trials to build a fuzzy rule base. Then the fuzzy controller is designed so that the diabetic's blood glucose can be successfully tracked to the healthy person's blood glucose changes. In this way, the purpose of regulating the blood glucose concentration of diabetic patients is realized. In addition, on the basis of realizing the control of blood glucose concentration. In this paper, the GUI toolbox of MATLAB is also used to design the visualization interface for the simulation process and the results. © 2023 ieee.

关键词： Insulin

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Design of Output Voltage control for Dual-Active Bridge of Solid-State Transformer in Indonesia

Design of Output Voltage Control for Dual-Active Bridge of S...

引用

2023 international conference on Smart-Green technology in Electrical and Information systems, ICSGTEIS 2023

作者： Ridwan, Muhammad Sidik, Yohan Fajar Sriyono Gausultan, Kevin Widyastuti, Rifka Wijaya, F. Danang Supriyadi, Guntur Wijaya, Lathief Pln Research Institute Transmission and Distribution Research Department Jakarta Indonesia Universitas Gadjah Mada Electrical Engineering Department Yogyakarta Indonesia Cyber University Information Technology Department Jakarta Indonesia

ISBN: (纸本)9798350382822

This article presents the design and simulation of a single-phase shift control system for a future smart transformer in Indonesia with a Solid-State Transformer (SST). The SST designed using three-stage topology, with a dual-active bridge (DAB) converter as a DC-to-DC converter connecting the rectifier to the inverter. This study applies a single-phase shift control for output voltage control for DAB in the SST. The controller is designed for singular and modular topologies of DAB. The modular design consists of 40 levels of DAB to transform medium voltage into low voltage. The control parameter is obtained by using PID tuner and then adjusted to gain better performance of dynamic conditions. The dynamic conditions include step and load response, with measuring the voltage and current responses. The simulation results show successful operation of the proposed control system, with stable voltage and current responses. © 2023 ieee.

关键词： DC-DC converters

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Resource Allocation and Optimization for Federated Learning in Vehicular Edge Caching

Resource Allocation and Optimization for Federated Learning ...

引用

2024 ieee international conference on Unmanned systems, ICUS 2024

作者： Yin, Chenlong Li, Xuebin Wang, Xiaowei Li, Yang Xu, Biao Bian, Yougang College of Mechanical and Vehicle Engineering Hunan University Changsha China Wuxi Intelligent Control Research Institute Hunan University Wuxi China

ISBN: (纸本)9798350384185

Edge caching enables vehicles to access content directly from roadside units (RSUs), which can effectively improve the user experience in the current era of content explosion. However, due to the limited cache capacity of RSUs, it is of great importance to cache hotspot content on RSUs and improve the hit rate of cached content. Many researchers employ federated learning to train models for content prediction, which protects user privacy and utilizes user information data. However, few studies have considered the distribution and aggregation of federated learning models in the context of restricted RSU communication resources. In this paper, we investigate the use of federated learning for edge caching and propose a Resource Allocation for Federation Learning in Content Caching (RAFLCC) scheme. The problem is modeled as an energy minimization problem under time constraints. Based on the modeled problem, a reasonable allocation of resources is achieved, allowing more vehicles to participate in federated learning training. The experimental results demonstrate that RAFLCC outperforms other baseline caching schemes. © 2024 ieee.

关键词： Federated learning

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Innovative Approach to Supernumerary Teeth Identification: CNN-Based intelligent Medical Auxiliary System for Occlusal Radiographs 11

Innovative Approach to Supernumerary Teeth Identification: C...

引用

11th ieee international conference on cyber Security and Cloud Computing, CSCloud 2024

作者： Chen, Tsung-Yi Wu, Hung-I Li, Zhi-Han Fu, Jui-An Chen, Chiung-An Li, Kuo-Chen Liong, Sze-Teng Chi, Tsun-Kuang Chen, Shih-Lun Feng Chia University Department of Electronic Engineering Taichung40724 Taiwan Chang Gung Memorial Hospital Department of Pediatric Dentistry Linkou Taoyuan City33305 Taiwan Ming Chi University of Technology Department of Electrical Engineering New Taipei City243303 Taiwan Chung Yuan Christian University Department of Information Management Taoyuan City320317 Taiwan Chung Yuan Christian University Department of Electronic Engineering Taoyuan City320314 Taiwan

ISBN: (纸本)9798350376982

Supernumerary teeth (ST) not only commonly obstruct adjacent permanent teeth, causing ectopic eruption, rotation, and root absorption but also present the potential for cystic lesions extending into the nasal cavity. Early intervention is crucial to avoid severe complications like periodontal abscesses and severe dental caries. However, ST may occur in any oral region with variable shapes and growth directions, posing a significant challenge for image recognition. This prevalent dental condition in the Asian population (3-5%) lacks sufficient representation in AI-assisted systems. Given this, the study suggests a system reliant on Convolutional Neural Network (CNN) for identifying supernumerary teeth in occlusal radiographs. The algorithm incorporates feature enhancement and AI integration methods. Additionally, the study introduces a color mapping approach to address the variability in ST shape and growth direction, significantly improving recognition accuracy from 63.16% to 76.32%. The result of this research demonstrates an improvement of 18.43% in accuracy and 78.95% in recall rate for supernumerary tooth detection compared to existing technology. This study successfully addresses the challenge of identifying supernumerary teeth, which were previously difficult to recognize. To ensure compliance with ethical and regulatory standards, the proposal has obtained certification from the Institutional Review Board (IRB) with the reference number 202400084B0. © 2024 ieee.

关键词： Convolutional neural networks

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Moving Towards Autonomous control and Perception systems via Vehicular Cloud Computing (VCC): A Review

Moving Towards Autonomous Control and Perception Systems via...

引用

2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Gupta, Suhas Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350357769

The exploration on vehicular systems administration, which is critical for normalization, viable traffic the executives, security, and infotainment, has invigorated significant examination. Astute Transportation Frameworks (ITS) advance when vehicles are furnished with different correspondence frameworks, processing assets, stockpiling, and improved detecting capacities. Vehicular Distributed computing (VCC) is a significant arrangement that uses vehicle assets to address challenges in vehicular organizations and empower quick direction. This paper gives a far reaching and exceptional outline of VCC, including an order framework that underlines its purposes, cloud designs, key administration, between cloud correspondence, and protection/security issues. In view of a careful assessment of existing writing, we recommend a VCC system, which we contrast with conventional Distributed computing (CC) and examine unsettled research points. As per writing, VCC is a pragmatic and financially beneficial change that drives 'keen vehicular organizations towards independent traffic, high level vehicle control, and refined detecting frameworks.' © 2023 ieee.

关键词： automobile networks Cloud computing for vehicles control of road vehicles transportation systems with intelligence cloud processing

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A Review on Crop Yield Prediction using Deep Learning 8

A Review on Crop Yield Prediction using Deep Learning

引用

8th international conference on Inventive systems and control, ICISC 202

作者： Verma, Sheily Singh, Shubham CUC-Ulster University Department of Computer Science/ Cyber Security Doha Qatar Era Smith Technology Noida India

ISBN: (纸本)9798350386578

The integration of cutting-edge technology into agricultural practices has catalyzed a transformative shift in global agriculture. This research study focuses on the revolutionary impact of deep learning approaches on crop yield prediction, a critical aspect of sustainable agriculture amid increasing global food demands. Traditional methods for crop yield estimation are examined initially, revealing limitations in accuracy and adaptability to diverse agroclimatic conditions. Subsequently, the rise of deep learning is explored, highlighting its ability to autonomously extract complex patterns from vast agricultural datasets. This research study provides a comprehensive classification and analysis of deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and attention mechanisms, in the context of agricultural yield prediction. This study discusses about the incorporation of sensor-based information, satellite imagery, and climate data into deep learning models, emphasizing the synergy among the data sources to enhance the prediction accuracy. Additionally, this research study addresses the challenges related to data quality, model interpretability, and scalability, proposing solutions and future research directions. By illuminating the transformative role of deep learning in crop yield prediction, this research study equips researchers, practitioners, and policymakers with valuable insights to advance sustainable agriculture and ensure global food security amidst changing climatic patterns and demographic trends. © 2024 ieee.

关键词： crop yield prediction Recurrent Neural Networks (RNNs) Root Mean Square Error (RMSE)

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A False Data Injection Attack on Data-Driven Strategies in Smart Grid Using GAN 36th

A False Data Injection Attack on Data-Driven Strategies in...

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36th international conference on Industrial, Engineering and Other Applications of Applied intelligent systems, IEA/AIE 2023

作者： Dash, Smruti P. Khandeparkar, Kedar V. Department of Computer Science and Engineering Indian Institute of Technology Dharwad Dharwad India

ISBN: (纸本)9783031368219

The smart grid is a critical cyber-physical infrastructure;attackers may exploit vulnerabilities to launch cyber attacks. The smart grid control system relies heavily on the communication infrastructure among sensors, actuators, and control systems, making it vulnerable to cyber-attacks. We propose a method for injecting a false data injection attack (FDIA) into the smart grid using generative adversarial networks (GAN). A sample of disturbance vectors generated using deep temporal convolutional GAN (DTCGAN) is superimposed on the original phasor measurement unit (PMU) measurements to generate compromised data. The performance results show a significant impact of the developed attack on data-driven methods for grid monitoring. Specifically, we demonstrated the attack on a transient stability application. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network security

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Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses 10

Joint Action is a Framework for Understanding Partnerships B...

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10th ieee RAS/EMBS international conference for Biomedical Robotics and Biomechatronics (BioRob)

作者： Dawson, Michael R. Parker, Adam S. R. Williams, Heather E. Shehata, Ahmed W. Hebert, Jacqueline S. Chapman, Craig S. Pilarski, Patrick M. Univ Alberta Dept Med Edmonton AB Canada Alberta Machine Intelligence Inst Amii Edmonton AB Canada Univ Alberta Fac Rehabil Med Edmonton AB Canada Univ Alberta Dept Biomed Engn Edmonton AB Canada Univ Alberta Fac Kinesiol Sport & Recreat Edmonton AB Canada

ISBN: (纸本)9798350386523;9798350386530

This work contributes a conceptual analysis of upper-limb prosthesis control methods;the goal of this work is to deliver new insight into the design of future biomechatronic systems intended for human interaction. Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by a user's robotic limb. However, controlling multiple degrees of freedom via muscle-generated (myoelectric) signals remains challenging for individuals with limb difference. To address this issue, various machine learning controllers have been developed to better predict a user's movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. We here suggest that one possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of human-prosthesis joint action. The field of joint action has been commonly applied to two human partners who work jointly together to effect coordinated change in their shared environment. Using a joint action framework also provides opportunities to understand the interactions between human and machine partners: how each represents the other's goal, their monitoring and prediction of each other's actions, the communication between them, and their ability to adapt to each other. In this work, we survey three different prosthesis controllers-proportional electromyography with sequential switching, pattern recognition, and adaptive switching-in terms of how they present the hallmarks of joint action. The results of this comparison contribute a new perspective for understanding how existing myoelectric systems relate to each other, along with two concrete recommendations for how to improve these systems via additional capacity for prediction learning and coordination smoothing.

关键词： Multi agent systems

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