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检索条件"任意字段=3rd International Conference on Intelligent Robotics and Control Engineering, IRCE 2020"

共 1399 条记录，以下是1-10 订阅

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irce 2020 - 2020 3rd international conference on intelligent robotics and control engineering

IRCE 2020 - 2020 3rd International Conference on Intelligent...

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3rd international conference on intelligent robotics and control engineering, irce 2020

ISBN: (纸本)9781728189727

The proceedings contain 21 papers. The topics discussed include: research on the computer synchronization of the fault-tolerant system;prevention of ant mills in pheromone-based search algorithm for robot swarms;texture gradient and deep features fusion-based image scene geometry recognition system using extreme learning machine;research on detection and tracking of moving vehicles in complex environment based on real-time surveillance video;application of surface reconstruction for car undercarriage inspection;multi-level feature fusion mechanism for single image super-resolution;parameters selection for blurred image matching;an improved intelligent scissors algorithm for the segmentation of vessels segments in coronary angiography imaging;the challenges and opportunities of artificial intelligence for trustworthy robots and autonomous systems;and trajectory tracking of a nonholonomic mobile robot using optimal cascade sliding mode controller.

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2020 The 3rd international conference on intelligent robotics and control engineering

2020 The 3rd International Conference on Intelligent Robotic...

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IEEE international conference of intelligent Robotic and control engineering (irce)

Presents the title page of the proceedings record.

ISBN: (数字)9781728189727

ISBN: (纸本)9781728189734

Presents the title page of the proceedings record.

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A Self-Aware Robotic Machining Architecture Based on Physics-Informed Neural Networks 3

A Self-Aware Robotic Machining Architecture Based on Physics...

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3rd international conference on Mechatronics, control and robotics, ICMCR 2025

作者： Zhang, Yuanhui Wang, Mingfeng Wu, Kai South China University of Technology Shien-Ming Wu School of Intelligent Engineering Guangzhou China Engineering Brunel University Department of Mechanical and Aerospace London United Kingdom

ISBN: (纸本)9798331544546

Industrial robots have been widely used in various industrial scenarios due to their flexibility and expandability. However, robots are susceptible to instability at high speeds and load conditions due to their low stiffness and dynamic characteristics variation with posture. Enhancing the processing stability of industrial robots through data-driven modeling and physical modeling approaches suffers from different drawbacks, such as solution complexity and weak interpretability. With the emergence of physics-informed neural networks (PINNs), new methodologies can be developed to enhance the self-aware processing of robots. In this paper, typical industry application scenarios and dynamics of robotics as well as PINNs are introduced and analyzed, and a framework and method based on PINNs are proposed to enhance the self-aware operation of industrial robots. This framework and methodology contribute to the researcher's efforts to apply PINNs more intensively to robot operation in the future to improve the stability and intelligence of robot operation. © 2025 IEEE.

关键词： Industrial robots

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Development of a Soft Gripper Using Kirigami-Inspired Techniques 3

Development of a Soft Gripper Using Kirigami-Inspired Techni...

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3rd international conference on Mechatronics, control and robotics, ICMCR 2025

作者： Gomes, Efrem Olivio Chang, Shyang-Jye Saputra, Ilham Chang, Hsu-Feng National Yunlin University of Science and Technology Department of Mechanical Engineering Douliu64002 Taiwan Vocational School Universitas Gadjah Mada Department of Mechanical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798331544546

This study introduces a soft robotic gripper inspired by Kirigami, designed to improve flexibility and adaptability for handling delicate or irregular objects. The gripper, fabricated using silicone elastomers with strategic Kirigami cuts, allows significant deformation while maintaining structural integrity. Two models were tested on various objects. Model 1 showed superior performance, while Model 2, designed for larger objects, struggled with smaller and heavier items. The gripper demonstrated excellent adaptability, securely gripping diverse objects by conforming to complex geometries. Its potential for gentle handling makes it suitable for biomedical and agricultural applications. Durability tests revealed that the 1.5 mm thickness gripper maintained performance after over 100 cycles, outperforming the 1.0 mm version, which exhibited loosening after fewer uses. Compared to conventional grippers, the Kirigamiinspired design showed enhanced flexibility, precision, and durability, making it ideal for automation tasks. Future refinements in materials and design will further improve its performance. © 2025 IEEE.

关键词： intelligent robots

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Neural Contextual Adaptation (NCA): For Unsupervised Learning in Dynamic Environments 3

Neural Contextual Adaptation (NCA): For Unsupervised Learnin...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Praveen Kumar, G.D. Sanjan, S. Suraj, P. Rohan, U.A. Srinivas, R. Thanush, V. Visvesvaraya Technological University P.E.S College of Engineering Department of Computer Science and Engineering Mandya India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In recent years, artificial intelligence (AI) has made significant strides in various domains, primarily driven by supervised learning techniques requiring vast amounts of labeled data. However, real-world applications, particularly those involving rapidly changing environments, demand models capable of autonomous adaptation without the need for extensive labeled datasets. In this paper, we present a novel framework for unsupervised learning that allows AI models to dynamically adapt in real-time to changing environments. Our approach leverages a combination of self-organizing maps and contrastive predictive coding to enhance model flexibility and adaptability across diverse settings. We introduce an architecture that incorporates temporal data processing and on-the-fly adaptation mechanisms, enabling the model to learn and generalize from evolving data distributions. Through extensive practical assessments and experimentation, we demonstrate our model's ability to outperform existing unsupervised approaches in real-time object detection and decision-making scenarios. The results, which show a significant reduction in model retraining time and increased adaptability to novel environments, suggest that our framework can be applied to various fields including robotics, autonomous vehicles, and dynamic control systems. © 2025 IEEE.

关键词： Self-supervised learning

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Design and implementation of a Raspberry Pi-driven intelligent cart driving system 3

Design and implementation of a Raspberry Pi-driven intellige...

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3rd international conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Wu, Lin Cheng, Jinfeng Huang, Haoran Chang, Ho-Hsuan Zeng, Miaowang DGUT-CNAM Institute Dongguan University of Technology Dongguan523106 China

ISBN: (纸本)9781510688179

The rapid advancement of science and technology has underscored the significant potential of intelligent driving systems in the fields of robotics and automotive industries. We focus on the design and implementation of intelligent trolley driving system based on Raspberry Pi driver in this paper, in which a system that utilizes Raspberry Pi as its central control platform, leveraging its robust computational capabilities and diverse interface resources. By integrating the OpenCV computer vision library, the proposed system performs real-time environmental image acquisition and processing, accurately identifying and locating obstacles. In this study the hardware configuration is meticulously detailed, encompassing the selection and integration of Raspberry Pi components and sensor arrangements. The software design process is thoroughly explored, covering image acquisition algorithms, obstacle recognition and localization techniques, and the development of driving strategies. The rigorous testing demonstrated the system's ability to make swift and precise decisions across various environmental conditions, implementing effective problem-solving measures with high stability and reliability. Finally, we expect that this approach offers a viable technical solution for applications in related fields, contributing to the development of autonomous systems and intelligent transportation technologies. © The Authors.

关键词： Automotive industry

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Detection and Mitigation of DDOS Attack in SDN using Feature based RF & MLP Approach 3

Detection and Mitigation of DDOS Attack in SDN using Feature...

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3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saranraj, K. Sumugar, M. Anantha Moorthy, R. Gowthami, N. Tamilselvi, S. National Engineering College Kovilpatti India

ISBN: (纸本)9798331527549

Software Defined Networking (SDN) is an architecture that decouples the control plane from the data plane, allowing for centralized oversight and flexible configuration of network resources via software. However, this centralization of the controller makes it especially susceptible to Distributed Denial of Service (DDoS) attacks. To effectively prevent and mitigate such attacks, deploying a robust machine learning model is crucial. In this project, the Genetic Algorithm and Chimp Optimization Algorithm were employed as optimal feature selection methods to identify and extract the most representative and distinguishing features from the SDN_DDoS_2020 dataset, which contains ICMP, TCP, and UDP attack traffic (sourced from Mendeley). The refined feature subset was then used to train and evaluate the performance of two machine learning algorithms commonly used are Random Forest (RF) and Multilayer Perceptron (MLP).Furthermore, a dedicated mitigation model was developed to block or redirect the flood requests from TCP, UDP, and ICMP attack traffic. Among the models tested, the combination of the Chimp Optimization Algorithm with the MLP classifier demonstrated superior performance, achieving an impressive accuracy of 98.68%. Notably, this model also exhibited a low false alarm rate of 0.74 compared to all other models, enhancing its reliability in real world scenarios where accurate attack detection and minimal false positives are essential. © 2025 IEEE.

关键词： Genetic algorithms

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Research on the Conflict Behavior of Internal and External Container Trucks in Smart Port 3rd

Research on the Conflict Behavior of Internal and External C...

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3rd international conference on Information control, Electrical engineering, and Rail Transit, ICEERT 2023

作者： Ma, Xiaoxu Liu, Suri Wang, Wenyuan State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Liaoning Dalian116000 China

ISBN: (纸本)9789819788903

The intelligent transformation of traditional container terminals has become one of the main trends in the construction of smart ports. Among them, upgrading the traditional manned container trucks to unmanned ones and using the corresponding traffic control system to realize the unmanned horizontal transportation of the wharf is the key link of infrastructure transformation. As a new traffic phenomenon, the traffic conflict between manned trucks and unmanned trucks in ports has become a key bottleneck restricting the intelligent transformation and upgrading of ports. Based on the comprehensive analysis of the driving logic of the internal and external trucks in the smart port, this paper summarizes the conflict handling logic of the two in the typical traffic conflict scenarios in the port area. The simulation optimization evaluation modeling software AnyLogic is used to establish the port traffic conflict model based on the manned and unmanned trucks, and the impact of the two types of truck conflict behavior on the overall traffic of the port area is quantitatively analyzed according to the simulation results. The truck conflict model proposed in this paper can provide a reference for the transformation design of traditional container terminals to automated terminals, and provide decision support for the planning and layout of the port network under the background of improving smart ports. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bottles

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AI Enabled Swarm Application for Fire-Fighting Bots Over Web-Bluetooth APIs 3rd

AI Enabled Swarm Application for Fire-Fighting Bots Over Web...

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3rd international conference on intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Bhattacharya, Debasish Ali, Israj Custom Software Engineering Senior Analyst Accenture Solutions Pvt. Ltd PunePDC5 India School of Electronics KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9789819783540

According to estimates by NCRB from 2019–2020, fires and a lack of timely and adequate fire protection result in around 20,366 deaths annually in India ( G. Litta, R. Di Rienzo, R. Morello, R. Roncella, F. Baronti, and R. Saletti, "Flexible platform with wireless interface for DC-motor remote control," in 2018 IEEE international conference on Industrial Electronics for Sustainable Energy Systems (IESES), 2018, vol. 2018-Janua, pp. 509–514.). Due to the intense heat, firefighters frequently pass away while putting out the flames. Therefore, a technological solution will make it easier to prevent the loss of life from such dangerous activities. Here, we have created a robot to put out the fire utilizing AI-enabled Bluetooth-based automated techniques hosted in the Cloud in order to decrease these incidents. We can design a model-based solution for this specific issue thanks to the extensive advancements in Bluetooth technology, Web-Bluetooth APIs, and IR sensors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fires

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Minimizing Average Job Completion Time for Multi-resource Queuing Computing Systems Using Drift-Plus-Penalty 3rd

Minimizing Average Job Completion Time for Multi-resource Q...

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3rd international conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Chen, Chuanxu Li, Ruiting Guan, Quansheng Shi, Zhan Zeng, Ying Zheng, Jianping School of Electronic and Information Engineering South China University of Technology Guangzhou China Electric Power Dispatch and Control Center Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (纸本)9789819616978

The Lyapunov drift-based scheduling algorithms control the queue length to stabilize the system and maximize the throughput for multi-resource multi-queue cloud/edge systems. However, these algorithms often ignore the heterogeneity in job length, thus leading to a long delay for job processing. To this end, this paper takes the job completion time (JCT) as a penalty in the drift-plus-penalty expression to minimize the average job competition time, and proposes a Lyapunov drift-plus-time (LDPT) based scheduling algorithm for multi-resource queuing computing systems. The optimality of LDPT with respect to average JCT and queue length is analyzed. By using the Amazon EC2 to simulate, the results show that LDPT can improve average JCT and average queuing time fairness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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