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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Lakshadeep Naik Sinan Kalkan Sune L. Sørensen Mikkel B. Kjærgaard Norbert Krüger SDU Robotics Mærsk Mc-Kinney Møller Institute (MMMI) Faculty of Engineering University of Southern Denmark Odense M Denmark Department of Computer Engineering and ROMER Robotics Center Middle East Technical University (METU) Ankara Turkey SDU Software Engineering Mærsk Mc-Kinney Møller Institute (MMMI) Faculty of Engineering University of Southern Denmark Odense M Denmark Danish Institute for Advanced Studies (DIAS) Odense M Denmark

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In many applications, a mobile manipulator robot is required to grasp a set of objects distributed in space. This may not be feasible from a single base pose and the robot must plan the sequence of base poses for grasping all objects, minimizing the total navigation and grasping time. This is a Combinatorial Optimization problem that can be solved using exact methods, which provide optimal solutions but are computationally expensive, or approximate methods, which offer computationally efficient but sub-optimal solutions. Recent studies have shown that learning-based methods can solve Combinatorial Optimization problems, providing near-optimal and computationally efficient *** this work, we present BaSeNet - a learning-based approach to plan the sequence of base poses for the robot to grasp all the objects in the scene. We propose a Reinforcement Learning based solution that learns the base poses for grasping individual objects and the sequence in which the objects should be grasped to minimize the total navigation and grasping costs using Layered Learning. As the problem has a varying number of states and actions, we represent states and actions as a graph and use Graph Neural Networks for learning. We show that the proposed method can produce comparable solutions to exact and approximate methods with significantly less computation time. The code and Reinforcement Learning environments will be made available on the project webpage * .

关键词： Learning systems Navigation Grasping Reinforcement learning Manipulators Graph neural networks Computational efficiency Planning Optimization Intelligent robots

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Heterogeneous Hadoop Cluster-Based Image Processing Workload Distribution Framework between CPU and GPU

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Scientific Programming 2023年第1期2023卷

作者： Naz, Najia Zada, Islam Malik, Abdul Haseeb Nadeem, Muhammad Ali, Sikandar Department of Computer Science Bacha Khan University Charsadda Khyber Pakhtunkhwa Peshawar24420 Pakistan Department of Computer Science and Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Department of Computer Science University of Peshawar Khyber Pakhtunkhwa Peshawar25120 Pakistan Department of Information Technology The University of Haripur Khyber Pakhtunkhwa Haripur22620 Pakistan

Due to the rapid development of image data and the necessity to analyze it to extract meaningful information, heterogeneous systems have gained prominence. One of the most critical aspects of distributed systems is load balancing. When it comes to the distribution of workload in a balanced manner in a cluster, some heterogeneous systems are used for image processing. When workloads are allocated in these systems, the computational power of the processors is not considered. As a result, in these heterogeneous systems for image processing applications, an uneven workload distribution issue is found. A workload distribution programming framework is presented and discussed in this paper. The proposed strategy consists of two parts. As a first step, image data is split into optimal split sizes and distributed across nodes, then the image data is distributed across CPU and GPU in a second step for processing. A heterogeneous environment is created by combining the CPU and GPU. The OpenCL Java bindings are used to set up both the CPU and GPU to run the program. To assess the performance of the suggested technique, certain tests are carried out and compared to current platforms. For image processing applications in heterogeneous clusters, the proposed workload distribution approach distributed image data efficiently. The results of the proposed solution (Hadoop + GPU) show that using an effective workload allocation mechanism in heterogeneous systems reduces average execution time while improving overall application performance. © 2023 Najia Naz et al.

关键词： Graphics processing unit

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Development of an modified Ant Colony Optimization algorithm for solving path planning problems of a robot system

Development of an modified Ant Colony Optimization algorithm...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Sudeep Sharan Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Avda. de la Universidad de Cádiz 10 11519 Puerto Real Cádiz Spain

In the modern world of high-speed technologies, where every operation needs to be performed instantaneously and more efficiently, scientists and engineers have created a Bioinspired algorithm to solve the problems encountered in realworld activities. The Ant Colony optimisation (ACO) algorithm is one such solution that assists in solving the problems of robot path planning. In this work-in-progress article, we propose a new way of using the ACO algorithm which ensures solving the problems encountered in traditional ACO algorithms. This algorithm was tested on two environments to examine output efficiency and computational output time. The results show that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably better results were observed on testing it in the bigger-scale environment. The evaluations prove that the ACO algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test it in any real-world scenarios by increasing the number of ants.

关键词： Ant colony optimization Signal processing algorithms Signal processing Path planning Computational efficiency Robots Optimization

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Discrete RKZD (Reciprocal-Kind Zhang Dynamics) Algorithm Solving Time-Independent Linear Matrix-Vector Equation (TILMVE) Simplified from Time-Dependent Situation

Discrete RKZD (Reciprocal-Kind Zhang Dynamics) Algorithm Sol...

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Chinese Control Conference (CCC)

作者： Yunong Zhang Ji Lu Mingzhi Mao Shuai Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou P. R. China School of Software Engineering Sun Yat-sen University Zhuhai P. R. China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland

We present a continuous reciprocal-kind Zhang dynamics (RKZD) model for solving the time-dependent linear matrixvector equation. On the basis of the model, we deduce its simplified form for solving the time-independent linear matrix-vector equation (TILMVE). Subsequently, for more efficient computation and easier implementation in digital hardware, we utilize Euler forward difference formula (EFDF) to discretize the continuous RKZD model, resulting in a discrete RKZD algorithm. Finally, numerical experimental results attest to the feasibility and high effectiveness of the discrete RKZD algorithm for solving TILMVE. Comparisons with the discrete gradient neural network (or termed discrete gradient dynamics), Jacobi iteration, as well as Gauss-Seidel iteration highlight the superior convergence properties of the discrete RKZD algorithm.

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SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

arXiv

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arXiv 2023年

作者： Wang, Di Zhang, Jing Du, Bo Xu, Minqiang Liu, Lin Tao, Dacheng Zhang, Liangpei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science Faculty of Engineering The University of Sydney Australia National Engineering Research Center of Speech and Language Information Processing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at SAMRS. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

Research Square

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Research Square 2021年

作者： Chen, Bo-Lun Zhu, Guo-Chang Sheng, Yi-Yun Xie, Qian Ji, Min Yu, Yong-Tao Faculty of Computer and software Engineering Huaiyin Institute of Technology Jiangsu Huaian223003 China

Air quality is related to people’s health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. As an important algorithm for signal analysis, empirical mode decomposition can analyze the change trend of air quality well, smooth the complex and changeable air quality data, and get the change trend of air quality under different time scales. According to the change trend under different time scales, the extreme learning machine is used for training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that the signal decomposition fuzzy prediction model has a good learning ability and has good accuracy in predicting the concentration of various pollutants in air quality. © 2021, CC BY.

关键词： Health

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PID Controller Machine Learning Algorithm Applied to the Mathematical Model of Quadrotor Lateral Motion 6

PID Controller Machine Learning Algorithm Applied to the Mat...

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6th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2021

作者： Kucherov, Dmytro Kozub, Andrei Tkachenko, Valerii Rosinska, Galina Poshyvailo, Olexii Faculty of Cybersecurity Computer and Software Engineering National Aviation University Department of Computerized Control Systems Kyiv Ukraine National Space Facilities Control and Test Center State Space Agency of Ukraine Kyiv Ukraine

ISBN: (纸本)9781665438223

The paper discusses the problem of automatic tuning of the PID controller. The auto-tuning algorithm of the PID controller based on one machine learning method, which is equivalent to the steepest descent, is proposed. A feature of the proposed approach is the vector adjustment of the PID controller parameters, where the angular direction of descent is taken into account at first, and the step sizes are made if taking into account the increased accuracy of the approximation near the optimum point. Modeling performed in the Matlab environment confirms the effectiveness of the proposed approach. © 2021 IEEE.

关键词： Three term control systems

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SAMRS: scaling-up remote sensing segmentation dataset with segment anything model 23

SAMRS: scaling-up remote sensing segmentation dataset with s...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Di Wang Jing Zhang Bo Du Minqiang Xu Lin Liu Dacheng Tao Liangpei Zhang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science Faculty of Engineering The University of Sydney Australia National Engineering Research Center of Speech and Language Information Processing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

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Research on Restructuring Path of Children's Digital Publishing Supply and Demand Chain 2

Research on Restructuring Path of Children's Digital Publish...

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2nd International Conference on Information Science and Education, ICISE-IE 2021

作者： Chen, Huan Liu, Ying Shan, Chan Zhang, Hequn School of Journalism and Communication of Minjiang University Fujian Fuzhou350108 China Department of Software Technology Xiamen Institute of Software Technology Fujian Xiamen China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia College of Information Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9781665438292

In the era of rapid development of intelligent technology, the children's digital publishing industry is facing a disruptive change, and this change may be realized through the reconstruction of the supply and demand chain. The value power of source content creation and the ability to gather users have been deconstructed. The increase in the amount of content requires platform institutions to have a more professional content operation system. The convenience of payment technology has greatly increased the digital content payment rate of users of children's digital products. A development model of the supply and demand chain of children's digital publishing with more innovative flexibility and enabling effects. © 2021 IEEE

关键词： Economics

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Dogodo: IoT Based Mobile Application to Provide Essential Health Services to Dogs 3

Dogodo: IoT Based Mobile Application to Provide Essential He...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Thilakarathne, L.V.I.S. Salay, M.S. Wijethilaka, M. G. R Chethana Fernando, T.S. Sriyaratna, Disni Rupasinghe, Shalini Sri Lanka Institute of Information Technology Faculty of Computing Department of Infotmation Tchnology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice of dogs can be heard by people who listen to them. The more you listen, the more you learn about the dogs. This study proposes a platform to identify and observe dogs' behavior and their activities by using the newest technologies. The proposed system will mainly cover the relevant areas that are supposed to be covered to full fill the pet owners' expectations by providing necessary services such as internal health, voice recognition, and emotion translations and external issues such as skin diseases, breeding patterns, and breeding outcomes. Our priority is to emphasize necessary services in the mobile application and provide fluid services with fewer interruptions. Primarily research will offer an IoT device and a mobile application that covers the dog's internal health and external health consecutively. The study revealed current developments and the challenges and possibilities related to the system. © 2021 IEEE.

关键词： Speech recognition

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