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Using Soft computing and Computer Vision to Create and Control an Integrated Autonomous Robotic Manipulator Process 26

Using Soft Computing and Computer Vision to Create and Contr...

引用

26th international conference on Enterprise Information Systems, ICEIS 2024

作者： Rodrigues, João Antônio Toledo Anjos, Samuel Dos Silva, Mateus Coelho Câmara de Santos, Ricardo C. M Oliveira, Ricardo Augusto Rabelo Departmento de Computação - DECOM Universidade Federal de Ouro Preto - UFOP Ouro Preto Brazil Instituto Tecnológico Vale Universidade Federal de Ouro Preto - UFOP Ouro Preto Brazil

ISBN: (纸本)9789897586927

The development and control of an integrated autonomous robotic manipulation process requires a focus on the convergence of technologies such as soft computing, object detection, robotic arm engineering, and direct and inverse kinematics. For instance, the inverse kinematics issue can be targeted using soft computing instead of challenging mathematical applications. This paper explores using soft computing systems, an algorithm that produces approximate solutions to complex problems and phenomena. Thus, the use of soft computing proved valid, given the accuracy and speed of the claw. The soft computing technology is based on an evolutionary algorithm that allows us to create several points on a cartesian plane and mix them to implement inverse kinematics. Our results showed that using soft computing, which is different from the traditional way, leads to solid and functional results. The implementation involves integrating Arduino, Raspberry Pi 4.0, a PWM model PCA9685, a camera, and six servo motors to create a robotic arm. The system employs video streaming to transmit data to a local network, where the Raspberry Pi processes RGB to HSV images for object identification. The present work in this paper has experiments for the accuracy and speed of the claw to take a determinate object from the center and the side of the checkered board. Copyright © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Object detection

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convergence in Design: Bottle Opener and Lighter Integration for Enhanced Utility 15

Convergence in Design: Bottle Opener and Lighter Integration...

引用

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

作者： Gautam, Ahan Sharma, Kapil Setpal, Neeraj Patil, Manesh Avantika University Vishwanathpuram Lekoda Village Madhya Pradesh Ujjain456006 India Avantika University School of Design Madhya Pradesh Ujjain456006 India Avantika University Madhya Pradesh Ujjain456006 India

ISBN: (纸本)9798350370249

Today's engineering technology sectors are heavily focused on inspiring and motivating students to apply their theoretical knowledge in practical settings to meet the ever-increasing needs of the global technology industry. This essay investigates how a project weaved across the Engineering technology curriculum might be utilized to develop a product that can be sold in the current market to perform and simplify common domestic tasks. The primary purpose of the bottle openers currently on the market is to open particular types of bottles. If they are used for other purposes, they require separate items, which will limit their portability and practicality because more products will be needed to complete the same tasks. Therefore, the project's goal is to create a multipurpose bottle opener that can be used to open any kind of bottle that is on the market to solve this issue. This device will operate using fundamental mechanisms such as stress bending, cork plugs, and rack and pinion mechanisms. In their investigation, numerous researchers compared the forms and mechanisms. Although there are comparable works, in the present work casts a unique design and has a functionality difference which makes this project exclusive from other projects. This project yielded the optimum shape of a common product, a multifunctional bottle opener that can be used to open various bottle caps with varied styles. © 2024 IEEE.

关键词： Commerce

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Efficient Distributed Swarm Learning for Edge computing

Efficient Distributed Swarm Learning for Edge Computing

引用

IEEE international conference on Communications (IEEE ICC)

作者： Fan, Xin Wang, Yue Huo, Yan Tian, Zhi Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

ISBN: (纸本)9781538674628

Federated learning (FL) methods face major challenges including communication bottleneck, data heterogeneity and security concerns in edge IoT scenarios. In this paper, inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with non-independent and identically distributed (non-i.i.d.) data issues and Byzantine attacks, a very small amount of global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Further, we provide convergence analysis to theoretically demonstrate that the proposed CB-DSL is superior to the standard FL with better convergence behavior. In addition, to measure the effectiveness of the introduction of the globally shared dataset, we also evaluate the model divergence by deriving its upper bound. Numerical results verify that the proposed CB-DSL outperforms the existing benchmarks in terms of faster convergence speed, higher convergent accuracy, lower communication cost, and better robustness against non-i.i.d. data and Byzantine attacks(1).

关键词： Distributed swarm learning federated learning particle swarm optimization non-i.i.d. data convergence analysis model divergence analysis

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Exploring Possibilities And Methodologies for Big Data and 5G convergence

Exploring Possibilities And Methodologies for Big Data and 5...

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2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Alam, Intekhab Fiza, Samreen Sunil, M.P. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka 562112 India

ISBN: (纸本)9798350357769

The advantages and benefits of big data and cellular networks are discussed in this essay. First, a summary of their interactions, together with big data-driven and big data-assisted communication networks, is given. The erstwhile caters for big data characteristics like number, pace, and variation by utilizing diverse means such as transmission, storage, and processing in cellular networks in order to accommodate big data apps and services. In order to gather wireless big data and gain detailed information about the networks and users for better network management and scheduling, the latter makes use of big data methods. Two examples that focus on channel data collection and big data-aided peripheral information storage are offered that will further highlight the reciprocal advantages. The discussion concludes with a few intriguing open study questions. © 2023 IEEE.

关键词： 5G convergence Big Data Cellular Networks Cloud computing Data Analytics Data Collection Data Processing Data Storage and Data Security Data Transmission Edge computing Network Infrastructure Network Management Resource Allocation Wireless Communications

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DRL-Based AP Switch On/Off Scheme for Cell-Free Massive MIMO MEC Networks 14

DRL-Based AP Switch On/Off Scheme for Cell-Free Massive MIMO...

引用

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

作者： Suh, Hyunjoon Oh, Jintaek Kang, Shinhyeok Hwang, Taewon Yonsei University School of Electrical and Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

In this paper, we consider a cell-free massive multiple-intput multiple-output (MIMO)-enabled mobile edge computing (MEC) network where AP switch on/off method is adopted for energy saving. We formulate a system-wise energy minimization problem, which jointly optimizes on/off mode of APs, uplink transmit power, and offloading ratio. The problem is non-convex and thus hard to solve using optimization methods. To solve the problem, we first reformulate it as a Markov decision process (MDP) and then propose a deep reinforcement learning (DRL)-based scheme. Simulation results show that our proposed scheme can reduce the energy consumption significantly. © 2023 IEEE.

关键词： AP switch on/off cell-free massive MIMO deep reinforcement learning Mobile edge computing

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Parallel Artificial Rabbits Optimization Algorithm for Image Watermarking 15th

Parallel Artificial Rabbits Optimization Algorithm for Imag...

引用

15th international conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Gao, Han Zheng, Weimin Zhu, Minghui Shandong University of Science and Technology Shandong Qingdao266500 China

ISBN: (纸本)9789819700677

This paper proposes an improved blind watermarking approach combined with a meta-heuristic algorithm. For the purpose of enhancing convergence accuracy in the original artificial rabbit optimization algorithm, a parallel artificial rabbit optimization algorithm is proposed. It prevents the algorithm from getting trapped in local optima by employing inter-group communication strategies. And a mixed-domain watermarking algorithm is proposed. In order to balance the invisibility and robustness of the watermarking, the improved artificial rabbit optimization algorithm is applied to the watermarking algorithm. The experiment proves that the improved algorithm has better convergence. accuracy, and can balance the performance of the blind watermarking algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image watermarking

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Communication-Aware Optimization of Microservice Scheduling on Edge computing 7th

Communication-Aware Optimization of Microservice Scheduling...

引用

7th international conference on Innovative computing, IC 2024

作者： Park, Hokun Yu, Heonchang Korea University Seoul Korea Republic of

ISBN: (纸本)9789819741816

Edge computing is well suited to environments that send massive data over the network, such as the Internet of Things. Kubernetes is used to manage services reliably in geographically distributed edge environments. However, the default Kubernetes placement policy does not consider the edge environment, which may lead to the degradation of service. In this paper, we propose a placement and replacement method that considers the edge environment. Compared to the default placement policy, proposed method improves the service performance by reducing the data transfer delay and the number of replacements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Flood Risk Simulation and Mitigation in Coastal Areas: An ArcGIS Pro and Deep Learning Approach 11

Flood Risk Simulation and Mitigation in Coastal Areas: An Ar...

引用

11th international conference on Soft computing and Machine Intelligence, ISCMI 2024

作者： Khurshedjon, Farkhodov Lee, Seok-Hwan Lim, Seon-Ja Kwon, Ki-Ryong Pukyong National University Dept. of Artificial Intelligence Convergence Busan Korea Republic of Dong-A University Dept. of Computer and Artificial Intelligence Engineering Busan Korea Republic of Pukyong National University Dept. of IT Convergence and Application Engineering Busan Korea Republic of

ISBN: (纸本)9798331518127

The integration of ArcGIS with Deep Learning models demonstrates a significant advancement in coastal flood prediction and prevention, offering enhanced accuracy and detail in flood risk mapping. Our suggested approach not only improves disaster preparedness and response but also holds promise for broader application across vulnerable coastal regions. Continued refinement and expansion of proposed model will further solidify its role as a critical tool in enhancing coastal flood resilience on a global scale. This study underscores the effectiveness of integrating ArcGIS with Deep Learning models for coastal flood prediction and prevention. The model - whether prioritizing precision, recall, or a balanced approach - offers unique advantages tailored to different application needs. The promising results highlight the potential of this integrated approach to significantly improve disaster preparedness and response in vulnerable coastal areas. Moving forward, further refinement and expansion of this methodology across diverse regions will strengthen its role in enhancing global coastal flood resilience. © 2024 IEEE.

关键词： Prediction models

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Enhanced Monarch Butterfly Optimization Algorithm with Rank-Based Selection and Random Jump Strategy for Constrained Engineering Problems

Enhanced Monarch Butterfly Optimization Algorithm with Rank-...

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Kathiroli, Panimalar Kanmani, S. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India Puducherry Technological University Department of Information Technology Puducherry India

ISBN: (纸本)9798350333244

Nature-inspired algorithms have become increasingly prevalent in engineering and mathematical optimization due to their efficacy and adaptability. Among these algorithms, the Monarch Butterfly Optimization Algorithm (MBOA) is a population-based metaheuristic, characterized by its simple implementation and the utilization of the Migration Operator (MO) and Butterfly Adjusting Rate (BAR) to guide the search path of butterflies. However, the algorithm can suffer from susceptibility to local optima traps, early convergence, and inferior performance due to a lack of random factors. To address these challenges, we propose an Enhanced Monarch Butterfly Optimization Algorithm (E-MBOA) that incorporates Ranking-based Selection (RBS) and a Random Jumping strategy. The RBS prioritizes the search direction based on individuals' ranks in the population, enhancing the search process by encouraging the exploration of feasible areas with lower objective function values. By employing the Random Jumping strategy, the process can effectively investigate the search space and avoid local optima, thus mitigating early convergence issues. The proposed model is assessed on a variety of mechanical and engineering design problems. Results are contrasted to other optimization approaches published in the literature, demonstrating that the proposed model offers faster convergence to global optima and effectively avoids local optima traps. E-MBOA's performance validates its feasibility and effectiveness as a powerful tool for resolving complex engineering optimization problems. © 2023 IEEE.

关键词： Constrained optimization

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Research on computing Offloading Methods Based on Edge computing and Reinforcement Learning in The Industrial Internet of Things

Research on Computing Offloading Methods Based on Edge Compu...

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international conference on Internet of Things and Machine Learning, IoTML 2023

作者： Wang, Xian Wan, Qiang Li, JianCheng Faculty of Intelligent Manufacturing Wuyi University Jiangmen529020 China School ofComputer Science and Engineering South China University of Technology Guangdong GuangZhou510000 China School of Computer Science and Technology Guangdong University of Technology GuangZhou528300 China

ISBN: (纸本)9781510671805

Most of the existing research focuses on a single MEC server scenario, and there are few studies on computing offloading and resource allocation in multi-MEC server scenarios. Therefore, this paper proposes a dynamic computation offloading and resource allocation algorithm based on hybrid decision-making deep reinforcement learning, which utilizes deep deterministic policy gradients and dueling dual deep Q-networks to enhance the Actor-Critic structure. By calling the Actor part of DDPG to deal with continuous power allocation, and then combining the Critc part of DDPG with D3QN, the discrete MID is associated with the MEC server to solve the mixed decision-making problem in multi-MEC server multi-user scenarios. From the simulation results, we can see that the proposed algorithm has faster convergence and better stability than baseline algorithms such as DQN. © 2023 SPIE.

关键词： Reinforcement learning

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