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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Huanfeng Zhao Meibao Yao Xueming Xiao Bo Zheng Tao Cao Tao Hu Intelligent Robotics Lab (IRL) School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China CVIR Lab Changchun University of Science and Technology Changchun China Key Lab of Opto-Electronic Measurement and Optical Information Transmission Technology Shanghai Aerospace Control Technology Institute Shanghai China

In LiDAR SLAM, data association of sparse LiDAR scan and high computational complexity of point-to-model metrics limit real-time applications of joint optimization module like Bundle Adjustment in Visual SLAM, which leads to cumulative errors deteriorating performance. To address this drawback, in this paper, we propose LPL-SLAM with plane and line optimization for the structural environment. Since plane and line primitives are ubiquitous in man-made environments, we treat the primitives as landmarks to process scans. We introduce line and plane factors relying on line-to-line and plane-to-plane metrics to optimize keyframe poses, planes and lines. With the line and plane factors, our system avoids a large-scale optimization problem and yields accurate and consistent tracking poses. Experimental results demonstrate that LPL-SLAM outperforms the state-of-the-art algorithms and achieves real-time performance.

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TransHD: Spatial Transformer Features Extraction for HDC Synergetic Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hassan, Eman Bettayeb, Meriem Halawani, Yasmin Genssler, Paul R. Tesfai, Huruy Zweiri, Yahya Amrouch, Hussam Hadjileontiadis, Leontios J. Saleh, Hani Mohammad, Baker Khalifa University System on Chip Lab Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates University of Dubai College of Engineering and IT Dubai United Arab Emirates Munich Institute of Robotics and Machine Intelligence Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Germany Abu Dhabi United Arab Emirates Stuttgart Germany Khalifa University of Science and Technology Department of Biomedical Engineering Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

Artificial intelligence (AI) relies on pattern recognition and classification algorithms to achieve accurate and efficient decision-making. Convolutional neural networks (CNNs) are the state-of-the-art for processing 2D image data, but their high computational and data requirements limit their use in resource-constrained environments. Hyperdimensional computing (HDC) offers a lightweight alternative, excelling in 1D tasks, but struggles to achieve competitive accuracy for 2D image classification. Hybrid frameworks combining HDC with CNNs have been proposed to improve accuracy but inherit the computational demands of deep learning, making them unsuitable for edge and IoT devices. To address this, we propose TransHD, a framework integrating lightweight spatial transformer networks (STNs) with HDC to enhance image classification performance while maintaining efficiency. TransHD achieves up to 9% higher accuracy than base HDC models on MNIST and Fashion-MNIST using only 30% of the training data, and reduces computational complexity by 2.5x through optimized STN feature map usage. On resource-constrained platforms like the Raspberry Pi 4, TransHD accelerates inference times by 3.4x and improves energy efficiency by 3.2x, with an accuracy trade-off of approximately 3% compared to CNNs. This study demonstrates the potential of combining HDC with STNs to develop efficient AI solutions for IoT and edge computing, where low energy consumption and computational efficiency are critical. © 2020 IEEE.

关键词： Convolutional neural networks

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Efficient Random Subspace Decision Forests with A Simple Probability Dimensionality Setting Scheme

SSRN

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SSRN 2022年

作者： Wang, Quan Wang, Fei Li, Zhongheng Jiang, Peilin Ren, Fuji Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

The random subspace concept is widely used in decision forests. However, there is not a reasonable approach to specify the appropriate number of randomly selected features. Previous random subspace decision forests simply specify the random subspace dimensionality d s to a preset function value such as [log 2 d ] or [√ d ] where d is the number of the total feature dimensions. Actually, this preset value may be a relatively poor choice for high dimensional data sets. In this paper, a novel framework named Efficient Random Subspace decision forest (ERS) is proposed which pays attention to how to set the random subspace dimensionality for each decision tree of decision forests. Specifically, a simple discrete uniform distribution is employed to set with probability the number of randomly selected features for each tree in random subspace decision forests. The proposed Half Range Discrete Uniform distribution-based Varied Dimensionality setting (HRDUVD) method removes the hesitation of how to preset an appropriate d s for different data sets but also results in good enough classification performance along with relatively short running time. Plenty of experiments on public benchmark data sets have demonstrated the effectiveness and the efficiency of the proposed ERS. © 2022, The Authors. All rights reserved.

关键词： Decision trees

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives

Integrated Bi-Manual Motion Generation and Control shaped fo...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jonathan Vorndamme João Carvalho Riddhiman Laha Dorothea Koert Luis Figueredo Jan Peters Sami Haddadin Department of Computer Engineering Technische Universität München (TUM) TUM School of Computation Information and Technology (CIT) Munich Institute of Robotics and Machine Intelligence (MIRMI) Germany Intelligent Autonomous Systems group Technische Universitat Darmstadt (TUD) Germany Center for Cognitive Science TUD Germany

ISBN: (纸本)9798350309805

This work introduces a novel cooperative control framework that allows for real-time reactiveness and adaptation whilst satisfying implicit constraints stemming from proba-bilistic/stochastic trajectories. Stemming from task-oriented sampling and/or task-oriented demonstrations, e.g., learning based on motion primitives, such trajectories carry additional information often neglected during real-time control deployment. In particular, methods such as probabilistic movement primitives offer the advantage to capture the inherent stochasticity in human demonstrations - which in turn reflects human's understanding about task-variability and adaption possibilities. This information, however, is often poorly exploited and, mostly, used during offline trajectory planning stage. Our work instead introduces a novel real-time motion-generation strategy that explicitly exploits such information to improve trajectories according to changes in the environmental condition and robot task-space topology. The proposed solution is particularly well-suited for bi-manual and coordinated systems where the increased kinematic complexity, tightly-coupled constraints and reduced workspace have detrimental effects on the manipula-bility, joint-limits, and are even capable of causing unstable behavior and task-failure. Our methodology addresses these challenges, and improves performance and task-execution by taking the confidence range region explicitly into account whilst maneuvering towards better configurations. Furthermore, it can directly cope with different closed-chain kinematics and task-space topologies, resulting for instance from different grasps. Experimental evaluations on a bi-manual Franka panda robot show that the method can run in the inner control loop of the robot and enables successful execution of highly constrained tasks.

关键词： Trajectory planning Robot kinematics Humanoid robots Kinematics Probabilistic logic Real-time systems Trajectory

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Superior Solar Water Purification Achieved by the Excellent Photothermal Conversion and Thermal Management of Porous Plasmonic Tin Nanoparticles

SSRN

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SSRN 2023年

作者： Gao, Yaping Zhou, Xiaojie Fu, Nuo Su, Sihua Ma, Baopeng Ruan, Qifeng Wu, Di Zhang, Nan Deng, Ziwei Jiang, Ruibin Key Laboratory of Applied Surface and Colloid Chemistry National Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science and Engineering Shaanxi Normal University Xi’an710119 China Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen518055 China

Solar photothermal steam generation promises decentralized water purification, but the lack of materials that have strong sunlight absorption in wide spectral range, superior thermal management, large specific surface area as well as good water wettability limits the overall solar water purification efficiency. Herein, porous TiN nanospheres are prepared for solar water purification. The monodispersed porous TiN nanospheres are prepared through the wet-chemistry synthesis of TiO2 and followed nitridation. The prepared porous TiN nanospheres have relatively large specific surface area and very good hydrophilicity. In comparison with solid TiN nanospheres, the porous counterparts have stronger light absorption and better thermal management. The optimum solar evaporation film prepared with the porous TiN nanospheres and filter membrane (TiN/FM) exhibit a reflectance smaller than ~19% in the entire solar spectrum. All these merits of the porous TiN nanospheres give rise to a water evaporation rate of 1.68 kg m-2 h-1 and a solar-steam conversion efficiency of 93.4% under AM 1.5 irradiation. Both seawater and polluted water can be purified to potable water with TiN/FM. Our work not only provides a preparation approach for monodispersed porous TiN nanospheres but also demonstrates the great potential of porous plasmonic nanoparticles for the future development of solar water purification. © 2023, The Authors. All rights reserved.

关键词： Nanospheres

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A Learning Approach to Multi-robot Task Allocation with Priority Constraints and Uncertainty

A Learning Approach to Multi-robot Task Allocation with Prio...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Fuqin Deng Huanzhao Huang Lanhui Fu Hongwei Yue Jianmin Zhang Zexiao Wu Tin Lun Lam School of Intelligent Manufacturing Wuyi University Jiangmen Guangdong China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Guangdong China The 3irobotix Co. Ltd Shenzhen Guangdong China School of physics and information engineering Guangdong University of Education Guangzhou Guangdong China School of Science and Engineering the Chinese University of Hong Kong Shenzhen Guangdong China

ISBN: (纸本)9781728119496

Multi-robot task allocation has an important impact on the efficiency of multi-robot collaboration. For single-shot allocation without complicated constraints, some exact algorithms and heuristic algorithms can find the optimal solution efficiently. However, considering the priority constraints and uncertain execution time of robots for multiple times of allocation in an approximate dynamic programming environment, traditional methods such as heuristic algorithms have limited performance. To obtain better performance, we propose a method based on deep reinforcement learning. Specifically, we first use the directed acyclic graph to describe the priority relationship between tasks. Then we propose a graph neural network with a hierarchical attention mechanism to extract the characteristics of the task groups. Finally, we design the policy network to solve the approximate dynamic programming problem of multi-robot task allocation. Through training on the dataset of a given environment, the policy network can gradually refine the decision-making process by reinforcement learning. Experiment results show that the proposed modeling and solving method can find better solutions than existing heuristic algorithms. Furthermore, the learned strategy can be directly applied in other untrained environments with superior performance.

关键词： Training Uncertainty Heuristic algorithms Reinforcement learning Approximation algorithms Graph neural networks Dynamic programming

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Optimization of Simulation Parameters of Input Shaper Based on Genetic Algorithm

Optimization of Simulation Parameters of Input Shaper Based ...

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Advanced Technologies in intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： Tao Yi Quanshen Pei Deying Li Shugong Wei Danping Jia Hualiang Zhang Hunan University of Information Changsha China School of Information Science and Engineering Shenyang University of Technology Shenyang China College of Physics and Electronic Engineering Guangxi Normal University for Nationalities Chongzuo China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665493970

In the motion control system, input shaper is often used to suppress the residual oscillation of high-precision positioning system, but the selection of input shaper parameters is difficult. In view of the difficulty of selecting input shaper parameters, this paper proposes to use genetic algorithm to select the parameters of input shaper. Firstly, the input shaper is designed in the light of the three-loop mathematical model of motor; Secondly, because the genetic algorithm directly optimizes the output of the motor and the algorithm can reduce the influence of the deviation of generator parameters on the parameter selection of the input shaper to a certain extent, the parameters of the input shaper are selected by the genetic algorithm to optimize the output of the motor; In the finish, a comparative experiment between a system with input shaper and a system with simple PID control is carried out. The results show that the optimized system can obtain higher robustness and smaller tracking deviation.

关键词： Vibrations Tracking Mathematical models Robustness Decoding Rigidity Motion control

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ruijie Xu Shichao Chen Wenqiao Sun Yisheng Lv Jialiang Luo Ying Tang The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems The Center of National Railway Intelligent Transportation System Engineering and Technology Institute of Automation Chinese Academy of Sciences China Academy of Railway Sciences Corporation Limited Beijing China China Academy of Railway Sciences Corporation Limited Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing Beijing China Department of Electrical and Computer Engineering Rowan University Glassboro USA

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation system (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

引用

2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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