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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Qu, Zhihai Li, Xiuxian Li, Li Hong, Yiguang College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology Tongji University Department of Control Science and Engineering Shanghai China

ISBN: (纸本)9798350337020

Within the realm of distributed optimization, each node in the network possesses computational capabilities. Nodes perform local calculations on their own data, and by communicating local information (e.g., local gradients) with neighboring nodes, agents collectively achieve a globally optimal solution. In recent years, distributed optimization has garnered interest across diverse disciplines, particularly in situations where communication abilities are limited or data are private. In this paper, a distributed second-order algorithm, based on an augmented Lagrangian function, is proposed with an enhanced diffusion communication strategy. An R-linear convergence rate is established under a relaxed locally restricted strong convexity assumption, along with a widely employed L-smoothness assumption. Finally, the superiority of the algorithm is showcased by a distributed logistic regression example utilizing a synthetic dataset with various parameter settings. © 2023 IEEE.

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Development of a Method and System for Recycling Polystyrene Production Waste 25

Development of a Method and System for Recycling Polystyrene...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Farahov, Rustam R. Burnashev, Rustam A. Enikeeva, Adelya I. Bolsunovskaya, Marina V. Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan Russia Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia

ISBN: (纸本)9798350389234

Currently, the consumption of natural resources is important for humanity. Therefore, reducing the production of production waste not only saves natural resources, but also increases the profit of the manufacturer. When producing polystyrene powder, the waste produced is a coarse powder with a wide range of particle sizes and irregular geometry. It is possible to obtain low-grade products from waste, such as disposable tableware, industrial containers, Petri dishes, etc. Manufacturers are interested in developing new processing methods to use waste materials to produce high-quality products. The purpose of the work is to study the possibility of using polystyrene production waste in selective laser sintering processes. The method we are developing for processing polystyrene-based polymer powder allows us to avoid the use of additional chemical exposure, and to select the optimal operating mode for a selective laser sintering installation with the resulting processed material. It is planned to introduce this method into the field of materials processing for additive manufacturing. © 2024 IEEE.

关键词： Laser heating

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Behavioral Decision-Making of Mobile Robots Simulating the Functions of Cerebellum, Basal ganglia and Hippocampus

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1639-1650页

作者： Wang, Dongshu Liu, Qi Duan, Yihai School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou450001 China Innovation Center of Intelligent Systems Longmen Laboratory Henan Luoyang471000 China State Key Laboratory of Intelligent Agricultural Power Equipment Henan Luoyang471004 China China Information Technology Design and Consulting Institute Co. Ltd. Henan Zhengzhou450007 China

In unknown environments, behavioral decision-making of mobile robots is a crucial research topic in the field of robotics applications. To address the low learning ability and the difficulty of learning from the unknown environments for mobile robots, this work proposes a new learning model which integrates the supervised learning of the cerebellum, reinforcement learning of the basal ganglia, and memory consolidation of the hippocampus. Firstly, to reduce the impact of noise on inputs and enhance the network's efficiency, a multi-neuron winning strategy and the refinement of the top-k competition mechanism have been adopted. Secondly, to increase the network's learning speed, a negative learning mechanism has been designed, which allows the robot to avoid obstacles more quickly through weakening the synaptic connections between error neurons. Thirdly, to enhance the decision ability of cerebellar supervised learning, simulating the hippocampal memory consolidation mechanism, memory replay during the agent's offline state enables autonomous learning in the absence of real-time interactions. Finally, to better adjust the roles of cerebellar supervised learning and basal ganglia reinforcement learning in robot behavioral decision-making, a new similarity indicator has been designed. Simulation experiments and real-world experiments validate the effectiveness of the proposed model in this work. © 2020 IEEE.

关键词： Mobile robots

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Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection 46

Reconstruction of 3D lumbar spine models from incomplete seg...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Blomenkamp, Lara Kramer, Ivanna Bauer, Sabine Weirauch, Kevin Paulus, Dietrich University of Koblenz Active Vision Group Institute for Computational Visualistics Germany University of Koblenz Institute for Medical Technology and Information Processing Germany University of Koblenz Intelligent Autonomous Systems Group Institute for Computational Visualistics Germany

ISBN: (纸本)9798350371499

Patient-specific 3D spine models serve as a foundation for spinal treatment and surgery planning as well as analysis of loading conditions in biomechanical and biomedical research. Despite advancements in imaging technologies, the reconstruction of complete 3D spine models often faces challenges due to limitations in imaging modalities such as planar X-Ray and missing certain spinal structures, such as the spinal or transverse processes, in volumetric medical images and resulting segmentations. In this study, we present a novel accurate and time-efficient method to reconstruct complete 3D lumbar spine models from incomplete 3D vertebral bodies obtained from segmented magnetic resonance images (MRI). In our method, we use an affine transformation to align artificial vertebra models with patient-specific incomplete vertebrae. The transformation matrix is derived from vertebra landmarks, which are automatically detected on the vertebra endplates. The results of our evaluation demonstrate the high accuracy of the performed registration, achieving an average point-to-model distance of 1.95 mm. Additionally, in assessing the morphological properties of the vertebrae and intervertebral characteristics, our method demonstrated a mean absolute error (MAE) of 3.4° in the angles of functional spine units (FSUs), emphasizing its effectiveness in maintaining important spinal features throughout the transformation process of individual vertebrae. Our method achieves the registration of the entire lumbar spine, spanning segments L1 to L5, in just 0.14 seconds, showcasing its time-efficiency. Clinical relevance: the fast and accurate reconstruction of spinal models from incomplete input data such as segmentations provides a foundation for many applications in spine diagnostics, treatment planning, and the development of spinal healthcare solutions. © 2024 IEEE.

关键词： Affine transforms

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Spinal ligaments detection on vertebrae meshes using registration and 3D edge detection 46

Spinal ligaments detection on vertebrae meshes using registr...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Kramer, Ivanna Blomenkamp, Lara Weirauch, Kevin Bauer, Sabine Paulus, Dietrich University of Koblenz Active Vision Group Institute for Computational Visualistics Germany University of Koblenz Intelligent Autonomous Systems Group Institute for Computational Visualistics Germany University of Koblenz Institute for Medical Technology and Information Processing Germany

ISBN: (纸本)9798350371499

Spinal ligaments are crucial elements in the complex biomechanical simulation models as they transfer forces on the bony structure, guide and limit movements and stabilize the spine. The spinal ligaments encompass seven major groups being responsible for maintaining functional interrelationships among the other spinal components. Determination of the ligament origin and insertion points on the 3D vertebrae models is an essential step in building accurate and complex spine biomechanical models. In our paper, we propose a pipeline that is able to detect 66 spinal ligament attachment points by using a step-wise approach. Our method incorporates a fast vertebra registration that strategically extracts only 15 3D points to compute the transformation, and edge detection for a precise projection of the registered ligaments onto any given patient-specific vertebra model. Our method shows high accuracy, particularly in identifying landmarks on the anterior part of the vertebra with an average distance of 2.24 mm for anterior longitudinal ligament and 1.26 mm for posterior longitudinal ligament landmarks. The landmark detection requires approximately 3.0 seconds per vertebra, providing a substantial improvement over existing methods. Clinical relevance: using the proposed method, the required landmarks that represent origin and insertion points for forces in the biomechanical spine models can be localized automatically in an accurate and time-efficient manner. © 2024 IEEE.

关键词： Digital elevation model

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Yolov5 Outdoor Dynamic Object Detection Based on Multi-scale Feature Fusion 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Yu, Chengfeng Chen, Ken Jiang, Lin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Outdoor object target detection is a very popular research task. 7 categories of data that fit the outdoor scenario were selected from the VOC2012 dataset as the current dataset for this study. In order to improve the detection accuracy of outdoor objects while keeping the network in good real-time, we propose a new yolov5 outdoor object detection network structure based on multi-scale feature fusion, which fuses shallow and deep features at different scales so that the fused feature layer has rich semantic information while enhancing the positioning information. The MAP of its proposed network on the VOC2012 dataset improved by 1.5% compared to Yolov5, and the FPS reached 61.51, which is better than the original Yolov5 method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A PREA and OODA Computing Framework of Unmanned systems to Perform Complex Tasks

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-18页

作者： Yang, Dongsheng Yu, Lei Zhang, Yi Zhu, Fenghua Li, Qiang Cui, Huachao Ma, Qiongmin Fan, Lili Tian, Yonglin Jinan University Guangzhou China Intelligent Science & Technology Academy Limited of CASIC Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China 28th Research Institute of China Electronics Technology Group Corporation Nanjing China National Key Laboratory for Complex Systems Simulation National University of Defense Technology Changsha China School of Information and Electronics Beijing Institute of Technology Beijing China

A C2 computing framework for unmanned systems to perform complex tasks is proposed using methods of system of systems engineering, which is formed by combining the macro-scale command and control (C2) process mechanism model (PREA loop) and micro-scale C2 process mechanism model (OODA loop). Guided by PREA loop and OODA loop, the computing framework is divided into four steps and three kinds of transformations. The four steps are design, construction, operation monitoring, and assessment. The three transformations are tactical feedback, campaign feedback, and strategic feedback. A C2 organization model of joint landing combat force is established against the background of a typical complex task of joint landing operation, and the C2 computing framework based on the PREA &OODA is used to implement the C2 activities of the joint landing combat force. The influence of the computing framework on the performance of unmanned systems under different task conditions is verified, including task coordination load, task efficiency and sensitivity of unmanned systems response. Authors

关键词： Feedback

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A Low Cost Triple-Node-Upset Self-Recoverable Latch Design for Nanoscale CMOS

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IEEE Transactions on Device and Materials Reliability 2025年第2期25卷 296-307页

作者： Bai, Na Zhang, Pengshuai Xu, Yaohua Guo, Yunhui Li, Hang Wang, Yi Wen, Xiaoqing Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Hefei230601 China National University of Defense Technology College of Computers Hunan Changsha410073 China Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka8208502 Japan

Due to the constant reduction of semiconductor feature sizes, the charge sharing effect is getting worse. The occurrence probability of triple-node upset (TNU), which has a significant impact on the circuit’s reliability, is rising in nanoscale digital circuits. This paper proposes a low overhead and TNU self-recovery latch (TNU-SR) based on the 28 nm CMOS technology to efficiently tolerate TNU and reduce the power dissipation and latency of the latch. The latch design proposed in this article adopts stacked transistors to reduce sensitive nodes and area overhead. The TNU-SR latch uses clock gating and high-speed transfer path technology, resulting in low power dissipation and low delay. For simulation results, compared with existing latches, it can be known that the TNU-SR latch performs well in terms of delay, power consumption, and area, so it is a good choice for radiation-hardened design for space applications. In addition, compared with the latest TNU self-recovery latch (HLTNURL), the TNU-SR latch proposed in this paper reduces the Power-Area-Delay Product (PADP) metric by 111.13%. © 2001-2011 IEEE.

关键词： CMOS integrated circuits

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SPARROW: Smart Precision Agriculture Robot for Ridding of Weeds 5

SPARROW: Smart Precision Agriculture Robot for Ridding of We...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Arachchige, Gayantha Godakanda Bandara, Anuththara Wellalage, Sasini Pandithage, Dinithi Hansika, Mahaadikara M.D.J.T. de Silva, Rajitha Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka Melbourne Australia Lincoln Agri-Robotics University of Lincoln United Kingdom

ISBN: (纸本)9798350361155

The advancements in precision agriculture are vital to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemical usage for pest and weed control in agriculture. A novel spot spraying algorithm that autonomously detects weeds and performs trajectory planning for the sprayer nozzle has been proposed. Furthermore, this research introduces a vision-based autonomous navigation system that operates through the detected crop row, effectively synchronizing with an autonomous spraying algorithm. This proposed system is characterized by its cost effectiveness that enable the autonomous spraying of herbicides onto detected weeds. © 2024 IEEE.

关键词： Computer vision

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Efficient multi-target classification for bug priority and resolution time prediction

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Multimedia Tools and Applications 2024年 1-30页

作者： Madaraboina, Satya Narayana Sharma, Sahil Singh, Simranjit Kumar, Vijay Liverpool John Moores University Liverpool United Kingdom School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom Department of Information Technology Dr. B R Ambedkar National Institute of Technology Punjab Jalandhar India

Bug triage teams allocate substantial resources to determine the priority and resolution time of reported defects, frequently hampered by a lack of specialized knowledge in the relevant subject. This work presents sophisticated multi-target classification models for the simultaneous prediction of problem priority and resolution time. We investigate transformation strategies, such as Classifier Chains, Binary Relevance, Label Power Sets, and Random k-Label Sets (RAKEL), over various machine learning and deep learning models utilising the Functional API. The models are trained using a newly launched dataset, the Spring JIRA Bug Dataset, available at IEEE ***/documents/spring-jira-bug-dataset, which includes bug summaries from the Jira bug tracking software. Model performance evaluation involves using metrics such as Hamming Loss, Accuracy, Weighted Precision, Weighted Recall, Weighted F1-Score, and MCC. The experimental findings indicate substantial enhancements in prediction accuracy and computing efficiency, showcasing the potential of these models to significantly improve the bug triage process by automating estimations of priority and resolution time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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