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Quantization-based Optimization with Perspective of Quantum Mechanics 14

Quantization-based Optimization with Perspective of Quantum ...

引用

14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Seok, Jinwuk Cho, Changsik Artificial Intelligence Research Laboratory Electronics and Telecommunications Research Institute Ai Computing System Sw Research Section Future Computing Research Division Daejeon Korea Republic of

ISBN: (纸本)9798350313277

Statistical and stochastic analysis based on thermodynamics has been the main analysis framework for stochastic global optimization. Recently, with the appearance of quantum annealing or quantum tunneling algorithms for global optimization, we require a new research framework for global optimization algorithms. In this paper, we provide the analysis for quantization-based optimization based on the Schrödinger equation to reveal what property in quantum mechanics enables global optimization. We present that the tunneling effect derived by the Schrödinger equation in quantization-based optimization enables to escape of a local minimum. Additionally, we confirm that this tunneling effect is the same property included in quantum mechanics-based global optimization. Experiments with standard multi-modal benchmark functions represent that the proposed analysis is valid. © 2023 IEEE.

关键词： Optimization Quantization Quantum Mechanics Schrödinger Equation

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A Lightweight Certificateless Authentication and Key Agreement Protocol for Cloud Environments 15

A Lightweight Certificateless Authentication and Key Agreeme...

引用

15th international conference on Communication Software and Networks, ICCSN 2023

作者： Niu, Jingyu Wang, Yubo Ning, Zhenhu Wang, Yang Wang, Shuaihu Zhu, Zhenbo Beijing University of Technology Faculty of Information Technology Beijing China Education Technology Co. Beijing China

ISBN: (纸本)9798350329360

With the trend of multi-domain coexistence and multi-cloud convergence in cloud computing, the security of massive device access is facing severe challenges. The essential security requirement for connection between terminal devices and cloud servers is authentication and the establishment of the session key. It is incredibly challenging to design a two-party mutual authentication and agreement protocol. In this paper, we propose a lightweight certificateless authentication and key agreement protocol, which combines elliptic curve cryptography and trusted computing to achieve mutual authentication between internet of things devices and cloud servers. We analyze the potential attacks in the protocol and confirm the accuracy and security of the protocol using formal analysis methods. Finally, we compare the protocol against different protocols in terms of computation and communication overhead and demonstrate that our protocol is better and more effective. © 2023 IEEE.

关键词： Cloud computing

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A Finite time control method for flexible manipulator based on dynamic sliding mode

A Finite time control method for flexible manipulator based ...

引用

2023 international conference on Power, Communication, computing and Networking Technologies, PCCNT 2023

作者： Long, Yecheng Hu, Yunliang Huang, Yiyang Liang, Zhifeng Shi, Mengxin Zhuhai Power Supply Bureau of Guangdong Power Grid Corporation Zhu Hai China Shenzhen Aerospace Scienceand Technology Innovation Research Institute ShenZhen China School of Aerospace Science and Technology Xidian University Xi'an China

ISBN: (纸本)9781450399951

In order to deal with the defect of slow convergence rate and singularity of classical sliding mode control and finite time control in flexible robotic arm control problems, a finite time control method based on dynamic sliding mode surface is designed to realize the tracking of reference signals. Firstly, the classical sliding mode surface is improved by introducing the dynamic sliding mode means to update the sliding mode parameters in real time to achieve the finite time convergence of the system on the sliding mode surface, and the magnified sliding mode parameters are updated in real time based on the parametric number of the error angle and the fractional order convergence rate of the system is achieved. Then the finite-time controller is designed based on the fractional-order state feedback and the said sliding surface to realize the tracking of the system for the reference trajectory. Finally, the validity and reliability of the proposed method are verified by physical simulation. © 2023 ACM.

关键词： Flexible manipulators

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Multi-Objective Task Scheduling in Cloud Environments using Dynamic Velocity-Adaptive Particle Swarm Optimization

Multi-Objective Task Scheduling in Cloud Environments using ...

引用

2024 OPJU international technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, Ravi Vardhan, Manu Natinal Institute of Technology Dept. of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350373783

In the realm of cloud computing, tackling the multi-objective task scheduling problem means finding the best way to assign tasks to virtual machines, taking into account conflicting goals like minimizing execution time, load balance and cost. This intricate task demands smart algorithms to strike a balance between these competing objectives, ensuring the most efficient use of resources and meeting user expectations in the cloud. Different optimization techniques, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been explored to handle this challenge and optimize task scheduling in the cloud environment. In classical PSO, particles can move at varying speeds in each iteration. In this study, we apply Velocity Stagnation Particle Swarm Optimization (VSPSO) variants where velocity stagnation applied during particles movement. This approach provides particles with a third movement option, enabling them to maintain the same velocity as in the previous iteration. VSPSO has a greater capacity to balance the exploration and exploitation. VSPSO adjusts the initial term of the PSO velocity equation to prevent premature convergence. Furthermore, the population of VSPSO is split into two swarms to ensure *** the inherent capabilities of PSO, VSPSO algorithm dynamically updates particle velocities to attain 5-6% total execution time 6-10% reduced cost and optimal balance load. Through meticulous comparisons with other algorithms, including Genetic Algorithm and Ant Colony Optimization, this study scrutinizes the effectiveness of our enhanced PSO approach. The overarching goal is to push the boundaries of multi-objective task scheduling in cloud computing by attaining minimal execution time, reduce cost and optimal balance load on each virtual machines. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Fast and Simple Method for Sleep Breathing Cycle Segmentation in Time Domain 18

A Fast and Simple Method for Sleep Breathing Cycle Segmentat...

引用

18th international conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Park, Rayoung Lee, Heonzoo Nguyen, Tan Loc Kim, Sejin Won, Yonggwan Chonnam National University Eng.Bio-IT Fusion Technology Center Dept. of ICT Convergence System Gwangju Korea Republic of Chonnam National University Dept. of ICT Convergence System Eng. Gwangju Korea Republic of AITOMATIC Inc Palo AltoCA United States

ISBN: (纸本)9798350331011

An automatic sleep respiratory cycle segmentation method in time-domain is introduced. Unlike the methods using spectral analysis that require high computational process such as Fourier transform, our approach uses a nonoverlapping window for simple computation in time domain, which is suitable for low power computing systems. Typical sleep breathing sound files over 6-hour length were used to evaluate our method, and the experimental results showed that our method demonstrated the accuracy over 95% and possible improvement with further investigation on parameter optimization for the steps by close examination of the signal characteristics. © 2024 IEEE.

关键词： Spectrum analysis

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On Predicting Internal Humidity Missing in Emergency Rooms Using Environmental Data from Korea Meteorological Administration 16

On Predicting Internal Humidity Missing in Emergency Rooms U...

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international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart Data, SmartData and Congress on Cybermatics (Cybermatics)

作者： Lee, Bumsik Yeom, Gwangrae Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence Suwon Gyeonggi Do South Korea Gfyhealth AI Res Ctr Seongnam Si Gyeonggi Do South Korea

ISBN: (纸本)9798350309461

This study addresses the issue of missing environmental data estimates collected from sensors within ER (Emergency Room). Ignoring missing in data during analysis can introduce bias and lead to incorrect conclusions. There are various methods to tackle this problem, such as deletion, statistical imputation, machine learning-based imputation, and generative imputation. In particular, compensating for missing data requires a multi-pronged approach to predicting methods. In this paper by combining external data from the Korea Meteorological Administration with internal ER's internal environmental data, we estimated the missing variable of internal humidity. It integrates external data with the internal data to analyze correlations and predict missing values. The analysis revealed a strong correlation between internal humidity and external temperature. There are two types of missing values: short-term and long-term. We focused on addressing long-term missing values using machine learning. The outcomes of this paper can serve as a valuable resource for enhancing safety and the overall environment within emergency departments. Consequently, this research is anticipated to enhance the emergency department's environment, ultimately contributing to the safety and comfort of patients in critical situations. Additionally, it can furnish ER operators with vital information for decision-making, and afford patients an improved experience.

关键词： Time-serial Data Missing Value Environmental Sensor Emergency Room Regression Imputation

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Dynamic event-triggered adaptive positioning control of unmanned surface vehicles with system uncertainties 4

Dynamic event-triggered adaptive positioning control of unma...

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2024 4th international conference on Mechatronics, Automation and Intelligent Control, MAIC 2024

作者： Liu, Caiyun Li, Xingxin Chen, Xinghua Liu, Qian Zheng, Zhenming College of Automation Harbin University of Science and Technology Heilongjiang Harbin150080 China College of Intelligent Systems Science and Engineering Harbin Engineering University Heilongjiang Harbin150001 China Beijing Institute of Control and Electronic Technology Beijing100000 China Zhejiang Huadong Geotechnical Investigation & Design Institute Co. Ltd. Zhejiang Hangzhou310030 China

To solve the problems of unmanned surface vehicles (USVs), such as limited network bandwidth and computing resources, a finite-time command filtering backstepping (FTCFB) robust adaptive dynamic positioning control method with dynamic event-triggered mechanisms (DETM) for unmanned surface vehicles (USVs) is proposed. It addresses thruster dynamics, system uncertainties, and unknown external disturbances, ensuring finite-time convergence while mitigating issues like communication congestion and actuator wear. Adaptive neural networks are utilized to estimate the system uncertainties, and a DETM-based FTCFB control law, validated with Lyapunov stability theory, guarantees finite-time convergence of tracking and parameter estimation errors. Numerical simulations confirm the method’s effectiveness. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Unmanned surface vehicles

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Position Control of Mobile Robot Based on Deep Reinforcement Learning 2

Position Control of Mobile Robot Based on Deep Reinforcement...

引用

2nd international conference on Informatics, Networking, and computing, ICINC 2023

作者： Hu, Guobin Chai, Hui Pu, Xi Guo, Min College of Mechanical Electronical and Engineering Wuhan University of Technology Wuhan China School of Control Science and Engineering Shandong University Ji’nan China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University of Technology Beijing China

ISBN: (纸本)9781510674745

In the field of mobile robot control, the utilization of reinforcement learning methods often faces the challenge of sparse rewards, resulting in suboptimal control performance. This paper proposes an approach that leverages the Diversity is All You Need (DIAYN) framework to dynamically generate reward functions and enhance the policy network weights of reinforcement learning algorithms. A comparative analysis is conducted with traditional reinforcement learning algorithms, namely Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), and behavior-based robotic and proportional-integral-derivative (BBR_PID) planning control algorithm. The results obtained from the CoppeliaSim simulation experiment demonstrate that, under identical training conditions, the DIAYN-based DDPG algorithm exhibits superior learning capability and achieves faster convergence to optimal actions. Consequently, the mobile robot can reach the target point more efficiently and with greater stability. © 2024 SPIE.

关键词： Reinforcement learning

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Optimal Node Deployment and Coverage in Next Generation Wireless Sensor Networks Applications 15

Optimal Node Deployment and Coverage in Next Generation Wire...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mandli, Rami Reddy Chandra, M. L. Ravi Dilli, Ravilla Jawaharlal Nehru Technological University Electronics and Communication Engineering Ananthapuramu India Srinivasa Ramanujan Institute of Technology Electronics and Communication Engineering Andhra Pradesh Ananthapuramu India Manipal Institute of Technology Manipal Academy of Higher Education Electronics and Communication Engineering Udupi Karnataka Manipal576104 India

ISBN: (纸本)9798350370249

Sensor nodes deployment is very critical in wireless sensor networks (WSNs) to cover a set of predetermined locations called 'targets' in the region of interest (ROI), and to enhance the coverage area, optimal position of sensor nodes in ROI is essential. The aim of sensor nodes deployment and coverage in WSNs is to compute sensor node's locations in the given network terrain such that they effectively detect the events as well as maximize the network lifetime. Battery depletion at sensor nodes creates coverage holes in ROI and it mitigates the network coverage. The objective of this paper is to define optimal locations of sensor nodes prior to their deployment and enhance the coverage area in the given geographical location using an improved particle swarm optimization (IPSO) algorithm. The experimental results showed superior coverage and faster convergence rate with the IPSO algorithm-than random deployments and grid-based deployments of sensor nodes. © 2024 IEEE.

关键词： Network lifetime node coverage particle swarm optimization sensor node deployment wireless sensor networks

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Breast Cancer Stage Identification Using Machine Learning 11th

Breast Cancer Stage Identification Using Machine Learning

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11th EAI international conference on Advancement of Science and technology, ICAST 2023

作者： Arega, Birtukan Shegaw Mengsitu, Tesfahunegn Minwuyelet Faculty of Computing Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Information Convergence Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9783031641503

This paper presents a machine learning model for determining the stages of breast cancer. The model normalizes images, eliminates noise, and removes artifacts through preprocessing, segmentation, and classification. Patching is used to extract the region of interest (ROI), while multi-thresholding is used for segmentation. The suggested models, including CNN-KNN, VGGNet-16, CNN-naïve Bayes, and CNN-SVM, were tested on a dataset of images from the CBIS-DDSM database. Experiments were conducted using full mammogram images and segmented ROI images, resulting in accuracies of 39%, 85%, 86%, and 91%, respectively. The CNN-SVM model achieved a 6% increase in classification accuracy compared to the state-of-the-art VGGNet-16 model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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