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15th International symposium on computational intelligence and Design, ISCID 2022

ISBN: (纸本)9781665456166

The proceedings contain 65 papers. The topics discussed include: a novel differential evolution sparrow search algorithm;an optimal differential vector incorporated whale optimizer;combinational indoor layout algorithm;research on the impact of sentimental characteristics on government responses;research on the fusion influence of participant by fusing the text sentiment feature;multi-population genetic algorithm for seamless steel tube blank design considering delivery time;comparison of temperature control time prediction models for wide and thick plates based on machine learning;study on the intelligent and innovative design of traditional graphic semantics in China;algorithm comparison for the order grouping problem with delivery factors;a comparative study on the emblem design of Chinese and western universities;link prediction on dynamic heterogeneous graphs via GNN with multiple attention mechanisms;image reconstruction based on multi-channel parallel magnetic resonance imaging technology;and hyper-heuristic genetic algorithm for hybrid flow shop scheduling problem with unrelated parallel machines.

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Exploring Innovations in Autonomous Robotics and Mechatronics: Application in Manufacturing and Healthcare 5

Exploring Innovations in Autonomous Robotics and Mechatronic...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Shalini, T.Grace Ganeshabu, L. Chemudugunta, Priyanka Yamsani, Nagendar Kumar, Ch.Ashok Athiraja, A. School of Computing Department of Computational Intelligence Chengalpattu Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Mevalurkuppam Rajalakshmi Nagar Tamil Nadu Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India Sr University Department of School of Computer Science and Artificial Engineering Warangal India Malla Reddy Engineering College Department of Mechanical Engineering Main Campus Hyderabad Telangana Maisammaguda India Telangana Secunderabad India

ISBN: (纸本)9798331504403

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics. © 2024 ieee.

关键词： Deep reinforcement learning

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Carbon Price Forecasting with Quantile Regression and Feature Selection 7

Carbon Price Forecasting with Quantile Regression and Featur...

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2023 7th International symposium on Computer Science and Intelligent control, ISCSIC 2023

作者： Pang, Tianqi Tan, Kehui Fan, Chenyou South China Normal University School of Artificial Intelligence Guangzhou China

ISBN: (纸本)9798350342987

Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on the carbon price. Due to its volatility and nonlinearity, predicting accurate carbon prices is generally a difficult task. In this study, we propose to improve carbon price forecasting with several novel practices. First, we collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. Then we select the most significant factors and disclose their optimal grouping for explainability. Finally, we use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions. We demonstrate through extensive experimental studies that our proposed methods outperform existing ones. Also, our quantile predictions provide a complete profile of future prices at different levels, which better describes the distributions of the carbon market. © 2023 ieee.

关键词： Carbon

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Research and Analysis of Gesture Recognition Experimentation Based on YOLOv5

Research and Analysis of Gesture Recognition Experimentation...

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ieee Information Technology, Networking, Electronic and automation control Conference

作者： Xindi Wang Peng Wang Chifeng College of Mathematics and Computer Science Inner Mongolia China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

As object detection technology continues to evolve, research topics in computer vision are becoming increasingly diverse. The YOLOv5 object detection model offers robust support for research in this field. This article discusses the development and current state of gesture recognition technology applications, and employs YOLOv5 technology to train a model that can recognize simple gestures. The article identifies some issues encountered in the experiments, analyzes their causes, and offers corresponding solutions. The objective is to demonstrate the feasibility of artificial intelligence technologies, represented by object detection models, in intelligentizing sign language communication for deaf and mute individuals.

关键词： YOLO Sign language Computer vision automation computational modeling Information technology Artificial intelligence

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Conception of a formal model-based methodology to support railway engineers in the specification and verification of interlocking systems 16

Conception of a formal model-based methodology to support ra...

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16th ieee International symposium on Applied computational intelligence and Informatics, SACI 2022

作者： Lukacs, Gabor Bartha, Tamas Budapest University of Technology and Economics Dept. of Control for Transportation and Vehicle Systems Budapest Hungary

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

The use of formal modeling is gaining popularity in the development of safety-critical transport applications, in particular railway interlocking systems, due to its ability to specify the functionality of systems using mathematically precise logical rules. The goal of the research described here is to con-ceptualize a methodology that provides a specification/verification environment supporting the developers (domain engineers) in the construction and verification of formal specifications. The aim of the methodology is to decrease the need for mathematical-computer science background/knowledge at the system engineering level. The proposed approach includes a set of well-known and widely used methods, techniques, and tools to specify and verify the functionality related to the development of railway interlocking systems, such as structured and object-oriented formalisms (e.g., the Unified Modeling Language), model-driven development, model checking, etc. The application of the methodology facilitates the construction of correct, complete, consistent, and verifiable functional specifications of a given component. This in turn brings a significant improvement of quality, and distributes the development costs more evenly among the related life-cycle phases. © 2022 ieee.

关键词： Model checking

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Why Fuzzy control Is Often More Robust (and Smoother): A Theoretical Explanation

Why Fuzzy Control Is Often More Robust (and Smoother): A The...

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ieee symposium Series on computational intelligence (SSCI)

作者： Orsolya Csiszár Gábor Csiszár Olga Kosheleva Martine Ceberio Vladik Kreinovich Research Center for Complex Systems Aalen University Institute of Applied Mathematics John von Neumann Faculty of Informatics Óbuda University Biomatics and Applied AI Institute John von Neumann Faculty of Informatics Óbuda University Department of Teacher Education University of Texas at El Paso El Paso Texas USA Department of Computer Science University of Texas at El Paso El Paso Texas USA

In many practical situations, practitioners use easier-to-compute fuzzy control to approximate the more-difficult-to-compute optimal control. As expected, for many characteristics, this approximate control is slightly worse than the optimal control it approximates. However, with respect to robustness or smoothness, the approximating fuzzy control is often better than the original one. In this paper, we provide a theoretical explanation for this somewhat mysterious empirical phenomenon.

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A History Information-Based A* Algorithm for the Path Planning with Multiple Alternative Destinations

A History Information-Based A* Algorithm for the Path Planni...

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ieee International symposium on Industrial Electronics (ISIE)

作者： Ke Wang Wei Liang Huaguang Shi Jialin Zhang Qi Wang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

As a cornerstone technology in automation, path planning plays a crucial role across various domains, including logistics, autonomous vehicles, and robotics. A novel path planning challenge, focusing on routes with multiple alternative destinations, has emerged as a focal point in industrial settings. Traditional approaches such as breadth-first and heuristic searches lack specific optimizations for this challenge, resulting in suboptimal efficiency. This paper introduces a new approach to the path planning problem with multiple alternative destinations that utilizes historical data. It features three heuristic strategies: leveraging historical information for search acceleration, pruning based on the length of the current shortest path, and searching in ascending order of the Manhattan distance. These strategies enhance the search efficiency by leveraging historical information for faster searches and pruning based on real-time search outcomes. Simulation results show that the proposed algorithm outperforms the existing works in terms of the computational cost.

关键词： Service robots Navigation Simulation Focusing Path planning Real-time systems computational efficiency

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Cost Minimization Strategy for UAVs Offload Calculation in MEC Environment 34

Cost Minimization Strategy for UAVs Offload Calculation in M...

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34th Chinese control and Decision Conference (CCDC)

作者： Mou, Shuhan Jiang, Mingquan Wu, Zhenghuang Dong, Tao Li, JunHao Zhang, Dehua Henan Univ Sch Comp & Informat Engn Kaifeng 475000 Peoples R China Henan Univ Sch Artificial Intelligence Zhengzhou 450046 Peoples R China

ISBN: (纸本)9781665478960

To meet the cost minimization requirement of computational offloading for UAVs in Mobile Edge Computing(MEC) environment, this paper proposes a cost minimization strategy based on the improved DDQN optimization algorithm by time delays, energy consumption and computational offloading model. Aiming at the difficult problem of MEC server resource allocation in the model, this paper uses a dichotomous approximation solution strategy for optimal resource allocation, based on which an action screening strategy is adopted to avoid the dimensional disaster problem that may occur in the state space of DDQN, and finally priority is introduced on the empirical replay pool sampling, which is used to improve the convergence speed of the algorithm. The simulation experimental results show that the algorithm effectively reduces the overall system energy consumption and time delays, improves the task offloading success rate, and achieves good stability compared with several other classical algorithms.

关键词： MEC UAVs Cost minimization DDQN Time delay Energy consumption

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Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA

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ieee ROBOTICS AND automation LETTERS 2021年第2期6卷 2335-2342页

作者： Plancher, Brian Neuman, Sabrina M. Bourgeat, Thomas Kuindersma, Scott Devadas, Srinivas Reddi, Vijay Janapa Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02478 USA MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Boston Dynam Waltham MA 02451 USA

Computing the gradient of rigid body dynamics is a central operation in many state-of-the-art planning and control algorithms in robotics. Parallel computing platforms such as GPUs and FPGAs can offer performance gains for algorithms with hardware-compatible computational structures. In this letter, we detail the designs of three faster than state-of-the-art implementations of the gradient of rigid body dynamics on a CPU, GPU, and FPGA. Our optimized FPGA and GPU implementations provide as much as a 3.0x end-to-end speedup over our optimized CPU implementation by refactoring the algorithm to exploit its computational features, e.g., parallelism at different granularities. We also find that the relative performance across hardware platforms depends on the number of parallel gradient evaluations required.

关键词： Computer architecture for robotics and automation hardware-software integration in robotics dynamics

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Local Search Enhanced Multi-Objective Evolutionary Algorithm for Fuzzy Flexible Job Shop Scheduling

Local Search Enhanced Multi-Objective Evolutionary Algorithm...

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ieee symposium Series on computational intelligence (SSCI)

作者： Xuwei Zhang Ziyan Zhao Shixin Liu The State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang China

The uncertainty in actual manufacturing systems often manifests as uncertain processing times, especially in flexible manufacturing systems. This work proposes a Decomposition-based Evolutionary Algorithm with Local Search (DLSEA) to solve flexible scheduling with fuzzy processing times by minimizing makespan and total machine workload. Considering the different scales of objectives, two normalization methods are employed on subpopulations, respectively, aiming to mitigate the potential detrimental effects of a single normalization method. This work also introduces a local search method to enhance the performance of DLSEA. The proposed DLSEA is compared with four state-of-the-art algorithms on two series of cases. The experimental results show that DLSEA exhibits superior search capabilities.

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