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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

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MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search

MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Sea...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Cioflan, Cristian Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781728190778

Neural Architecture Search (NAS) has proved effective in offering outperforming alternatives to handcrafted neural networks. In this paper we analyse the benefits of NAS for image classification tasks under strict computational constraints. Our aim is to automate the design of highly efficient deep neural networks, capable of offering fast and accurate predictions and that could be deployed on a low-memory, low-power system-on-chip. The task thus becomes a three-party trade-off between accuracy, computational complexity, and memory requirements. To address this concern, we propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS). We employ a one-shot architecture search approach in order to obtain a reduced search cost and we focus on an anytime prediction setting. Through the usage of multiple-scaled features and early classifiers, we achieved state-of-the-art results in terms of accuracy-speed trade-off.

关键词： Deep learning Costs automation Conferences Neural networks Memory management System-on-chip

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Collision-free Coverage Path Planning for the Variable-speed Curvature-constrained Robot

Collision-free Coverage Path Planning for the Variable-speed...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Lin Li Dianxi Shi Songchang Jin Yixuan Sun Xing Zhou Shaowu Yang Hengzhu Liu College of Computer National University of Defense Technology Changsha Hunan China Tianjin Artificial Intelligence Innovation Center (TAIIC) Tianjin China Artificial Intelligence Research Center (AIRC) Defense Innovation Institute Beijing China College of Intelligence Science and Technology National University of Defense Technology Changsha Hunan China

Dubins coverage has been extensively researched to address the coverage path planning (CPP) problem of a known environment for the curvature-constrained robot. However, its fixed-speed assumption prevents the robot from accelerating to reduce the time and limits its flexibility to avoid obstacles. Therefore, this paper presents a collision-free CPP approach (CFC) for the obstacle-constrained environment, which enhances time efficiency by constructing the variable-speed Dubins paths and ensures robot safety by building a risk potential surface for representing the possibility of collision. Furthermore, CFC models the CPP problem as an asymmetric traveling salesman problem (ATSP) and utilizes a graph pruning strategy to reduce the computational cost. Comparison tests with other Dubins coverage methods demonstrate that CFC provides shorter coverage times and better runtimes than the other Dubins coverage methods while preventing collision risk between the robot and obstacles. Physical experiments in a laboratory setting demonstrate the applicability of CFC to the physical robot.

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Efficient Transformer-Based Visual Tracking for Edge Computing Devices 9th

Efficient Transformer-Based Visual Tracking for Edge Computi...

引用

9th international symposium on Artificial intelligence and robotics, ISAIR 2024

作者： Xu, Yixin Xu, Tianyang Zhang, Wenkang Wu, Jinhui Cheng, Zinan Yang, Wankou School of Automation Southeast University Nanjing210096 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China School of Cyber Science and Engineering Southeast University Nanjing210096 China Jiangsu Automation Research Institute Lianyungang222002 China Advanced Ocean Institute of Southeast University Nantong226010 China

ISBN: (纸本)9789819629138

In recent years, transformer-based visual tracking models have shown significant advancements by leveraging global feature representations to improve information interaction. However, their high computational demands hinder efficient deployment on resource-constrained platforms like UAVs and robotic devices. To address this, we propose OneStar, a template-branch separable vision transformer that balances efficiency and accuracy. Unlike one-stream trackers that process the template in every frame, OneStar performs template feature extraction and fusion only during initialization, reducing redundant computations. A tracking token is designed to guide the use of prior positional and size information from the initial frame, enhancing fusion accuracy. We also provide two model variants optimized for different edge devices, ensuring efficient resource use while maintaining strong tracking performance. Extensive evaluations show that OneStar delivers competitive accuracy with state-of-the-art models while achieving excellent real-time performance on edge platforms. For example, OneStar achieves a 4.9% AO improvement on GOT-10k compared to other real-time trackers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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Adaptive Backstepping Control Design for Nonlinear System

Adaptive Backstepping Control Design for Nonlinear System

引用

international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Mohammad Merei József K. Tar Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Doctoral School of Applied Informatics and Applied Mathematics Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

In this paper, adaptive backstepping control is investigated for coupled nonlinear springs. A dynamic model of coupled special nonlinear springs is established with uncertain parameters. The conventional adaptive approaches for nonlinear systems’ control use Lyapunov’s second method that need full state estimation. To overcome this problem an FPI-based Model Reference Adaptive Backstepping Controller has been invented. The adaptive backstepping algorithm is designed to achieve stability for the nonlinear system. The idea of backstepping design is to coerce the error signal to zero by implementing a virtual control strategy. The consequences of the unknown parameters of the system are compensated by adaptive law. Simulation results are presented to show the effectiveness of the proposed control.

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SLRNet: Shallow Convolutional Neural Network for Efficient Sign Language Recognition

SLRNet: Shallow Convolutional Neural Network for Efficient S...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international Conference on

作者： Lamia Mostafa Faria Jerin Brishti Eishita Sultana Akther MD. Khaliluzzaman MD Jiabul Hoque Department of Computer Science and Engineering International Islamic University Chittagong (IIUC) Chattogram Bangladesh Department of Computer and Communication Engineering International Islamic University Chittagong (IIUC) Chattogram Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

In recent times, recognizing hand gestures from a sign image has become a vital research field in the areas of computer vision and human-computer interaction. Many researchers have taken advantage of various features and deep learning architectures (DLA) to update the performance of existing sign recognition systems. In small datasets such as ASL, shallow DLAs such as convolutional neural networks (CNN) work well. However, the deeper deep learning architectures are overfitted on the small datasets. To overcome this problem, in this work, two shallow CNN models, SLRNet1 and SLRNet2, are proposed. The proposed models use four convolutional layers to extract the features from the sign image and two fully connected layers to recognize the sign. The first and second models gain 98.94% and 99.14% accuracy, respectively. Both models achieve significant improvements with respect to the present state of the ***— Two regularized hand gesture recognition models, SLRNet1 and SLRNet2, were developed to work efficiently on small datasets without overfitting and perform fine-tuning to achieve better accuracy.

关键词： Hands Human computer interaction Sign language Computer vision Image recognition Accuracy computational modeling Assistive technologies Feature extraction Convolutional neural networks

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Integrated Autonomous Model System as Research Media

Integrated Autonomous Model System as Research Media

引用

international symposium on computational intelligence and Informatics

作者： László Horváth Institute of Applied Mathematics and Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

This paper introduces concept and method of Integrated Autonomous Model System (IAMS) which is developed as organized content in dedicated collaborative virtual space (CVS).IAMS is a recent result and at the same time a possible answer to new requests by engineering related research. Development of IAMS in CVS uses capabilities offered by Modeling Sotware Platform (MSP). IAMS is devoted as model system which is capable of system, situation, behavior, and context representation. In its application proposed in this paper, IAMS is a purposeful experimental model serving research and represents results in applied informatics and applied mathematics in model environment suitable for development, validation, and experimental application of results. Research using IAMS is planned in the organization of a Virtual Research Laboratory (VRL). In this paper, first the relevant elements of recently modified concept of VRL are briefed to introduce contributions in this paper. Following this, IAMS is characterized as integrated media of research. Next, definition of global research organization in CVS using cloud organized services by research purpose configured MSP in the VRL is explained. Finally, PhD research program in functional organic shape modeling as means for the representation of living organizations in close connection with modeling of engineering shapes is introduced as example for key areas in future research at VRL.

关键词： Mechatronics Shape computational modeling Organizations Media Mathematical models Software

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Optimizing Mobile Charger Scheduling in Wireless Rechargeable Sensor Networks Using Min-Heap Algorithm

Optimizing Mobile Charger Scheduling in Wireless Rechargeabl...

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international Conference on computational intelligence and Communication Networks (CICN)

作者： Md. Kamaruzzaman Abhijit Chandra Department of Robotics and Automation The Neotia University Kolkata India Dept. of Instrumentation and Electronics Engineering Jadavpur University Kolkata India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

In Wireless Rechargeable Sensor Networks (WRSNs), efficient energy management is crucial for prolonging network lifetime. Mobile Charging Vehicles (MCVs) play a vital role in replenishing the energy of sensor nodes (SNs); but optimizing their scheduling to minimize energy depletion and maximize network coverage remains a significant challenge till date. This paper proposes a novel scheduling approach based on the Min Heap algorithm to optimize the deployment of MCVs in WRSNs. By prioritizing SNs with the lowest remaining energy, the Min-heap algorithm dynamically adjusts the charging sequence, reducing the risk of node failures and improving overall network performance. To show the effectiveness of the proposed scheme, extensive simulations have been done by using MATLAB. Simulation results demonstrate that the proposed approach significantly improves the performance in terms of working time, total energy received, ratio of successful energy replacement and moving distance as compared to traditional scheduling approaches such as EDF and ETLBO.

关键词： Wireless communication Wireless sensor networks Processor scheduling Heuristic algorithms Simulation Scheduling Communication networks MATLAB Energy management computational intelligence

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Analysis of time series data for anomaly detection

Analysis of time series data for anomaly detection

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international symposium on computational intelligence and Informatics

作者： Katalin Ferencz József Domokos Levente Kovács Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Faculty of Technical and Human Sciences Târgu Mure. Romania Sapientia Hungarian University of Transylvania Faculty of Technical and Human Sciences Târgu Mure Sapientia Hungarian University of Transylvania Târgu Mure. Romania John Von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

The integration of sensors in our everyday lives and in industry presents a serious challenge to data analysis professionals. Since the use of smart devices has exponentially increased the amount of data collected in all areas, we must not only store these data, but also extract valuable information from those using some data analysis method. In many cases, the increased amount of data also causes problems for data analysis algorithms, so we need to be continuously updated and specialized for fundamental purposes. In the article, we will present some data analysis techniques, starting from the simplest statistical methods to more complex techniques using machine learning and presenting one of their possible applications. In our study, we will use the KMeans clustering algorithm and examine its effectiveness in time series sensor data analysis.

关键词： Support vector machines Analytical models Data analysis Machine learning algorithms computational modeling Time series analysis Clustering algorithms

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Optimization Algorithm of Multi-Attribute Group Decision-Making Based on Information Entropy Theory

Optimization Algorithm of Multi-Attribute Group Decision-Mak...

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Industrial automation, robotics and Control Engineering (IARCE), international Conference on

作者： Xuan Feng Youbao Cai Institute of Computer Science of Sichuan Province Chengdu China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

The problems of complex mathematical models, high computational complexity, and repeated parameter adjustments in the subjective and objective weighting method is studied. Such problems are generally defined as multi-attribute group decision-making models. Multi-attribute group decision-making models have been widely applied in fields such as achievement evaluation, scientific research performance assessment, and project review, and are a popular research direction in the field of artificial intelligence. How to balance the subjectivity among experts and the objectivity of attribute weights is a challenging problem in the research of multi-attribute group decision-making models. The entropy weighting method is firstly used to obtain the objective weights of attributes. Then, the grey correlation degree method is applied to adjust the subjective weight. Finally, based on the minimum Kullback-Leibler divergence method criterion reflecting the distance between different weights, an optimization model is established to solve the integrated weight. It is provided a feasible weight integration method based on information entropy theory for multi-attribute group decision-making. A numerical experiment shows the feasibility and practicability of the method. It is provided a new solution to the problems of prioritizing quantity over quality in the performance evaluation of scientific research in universities and research institutions, which can effectively promote the enthusiasm of scientific and technological workers for scientific research innovation.

关键词： Performance evaluation Technological innovation Service robots Reviews computational modeling Optimization models Decision making Information entropy computational complexity Optimization

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Multi-output Infinite Horizon Gaussian Processes

Multi-output Infinite Horizon Gaussian Processes

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IEEE international Conference on robotics and automation (ICRA)

作者： Lim, Jaehyun Park, Jehyun Nah, Sungjae Choi, Jongeun Yonsei Univ Sch Mech Engn Seoul South Korea Yonsei Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (纸本)9781728190778

Learning the uncertain dynamical environments for online learning and prediction from noisy sensory measurement streams is essential for various tasks in robotics. Recently, Gaussian process (GP) online learning such as an infinitehorizon Gaussian process (IHGP) has shown effectiveness to cope with non-stationary dynamical random processes in learning hyperparameters online by reducing the computational cost. However, the IHGP was originally proposed to deal with only a single-output. Therefore, to tackle complex real-world problems, we propose a multi-output infinite-horizon Gaussian process (MOIHGP) that generalizes the single-output IHGP to deal with multiple outputs for better prediction. Our approach allows us to consider correlations between multiple outputs for better prediction, even with occlusions in a Bayesian way. Finally, we successfully demonstrate the effectiveness of our approach by benchmark and experimental results. For simulated benchmark experiments with high noise levels, our approach reduced 16.6% of the averaged RMSE value achieved by the single-output IHGP.

关键词： Correlation Measurement uncertainty Gaussian processes Benchmark testing Robot sensing systems Bayes methods Random processes

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