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UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker

CAAI Transactions on Intelligence Technology 2024年第4期9卷 1031-1041页

作者： Meng Liu Jianwei Niu Yu Liu Collective Intelligence&Collaboration Laboratory China North Artificial Intelligence and Innovation Research InstituteBeijingChina China North Vehicle Research Institute BeijingChina State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina

Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.

关键词： autonomous vehicle transportation

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Computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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Diving into Continual Ultra-fine-grained Visual Categorization

Diving into Continual Ultra-fine-grained Visual Categorizati...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhang, Pengcheng Yu, Xiaohan Bai, Xiao Zheng, Jin Wu, Xiaoyu Gao, Yongsheng Beihang University School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beijing China Griffith University Institute for Integrated and Intelligent Systems Nathan4111 Australia

ISBN: (纸本)9798350382204

Recent advance in ultra-fine-grained visual categorization (ultra-FGVC) has significantly boosted the capability of deep neural networks for ultra-FGVC tasks. However, building models for continually learning to recognize increasing ultra-fine-grained categories is still under-explored. This limits the application of ultra-FGVC techniques in real-world production. To this, we take the first attempt for continual ultra-FGVC. By evaluating existing continual learning methods on the constructed continual ultra-FGVC benchmark, we observe that the main bottleneck lies in the limited model plasticity for incrementally adapting to new tasks. This can be caused by excessive anti-forgetting constraints as the difficult ultra-FGVC task requires substantial update of parameters, and over-fitting on early tasks given that the ultra-fine-grained categories are with very few training samples. To tackle these problems, we propose a joint self-supervised learning and prompting model. The prompt-based continual learning framework offers proper anti-forgetting operation by fixed pretrained vision transformer and adaptive prompt selection. By jointly optimizing the learnable prompts with an adversarial self-supervised loss, the over-fitting on each continual learning task is mitigated. Extensive experiments demonstrate that the proposed method outperforms existing continual learning methods on the challenging continual ultra-FGVC problem. © 2023 IEEE.

关键词： Deep neural networks

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research on Energy-Saving Optimization of Different Formation Configurations Based on Four Catamarans 30th

Research on Energy-Saving Optimization of Different Formatio...

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30th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2024

作者： Jiang, Bo Liang, Xu Wang, Longkan Zhang, Zhifan Wang, Yuemei State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Naval Architecture Engineering Dalian University of Technology Dalian116024 China CSSC Systems Engineering Research Institute Beijing100080 China China Ship Research and Development Academy Beijing100192 China

ISBN: (纸本)9783031687747

Nowadays, sailing in formation has been the major trend with the rise of unmanned ships. In the past, research about unmanned catamaran formation were more focused on algorithmic control, but the changes in hydrodynamic performance and energy are often overlooked. In nature, various collective behaviors indicate that followers would obtain significant energy consumption from beneficial interaction in certain formation configurations. Based on the previous research, the high-speed catamaran delft-372 was chosen as the basic numerical model in this paper. Four ship’s formation configurations (tandem, "trimaran", and rectangle formations) are proposed to analyze the influence on the hydrodynamic performance of each ship, based on the Reynolds Averaged Navier-Stokes (RANS) method in combination with the Shear Stress Transport (SST) k-ω turbulence model. Through comparative analysis, it can be seen that the resistance and motion of the following ships have been optimized under specific formation configurations, but the impact of various formation configurations is quite different. In terms of single ship’s energy preservation, the last following ship of "trimaran" formation has the best performance. For the whole, tandem formation is the optimal fleet configuration. In conclusion, the hydrodynamic performance of four catamarans in different formation configurations has been studied, which can provide some reference significance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drag reduction

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning 37

CLGT: A Graph Transformer for Student Performance Prediction...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Tightly-Coupled Lidar-Based Lifelong Mapping using Wheels and a MEMS Gyroscope for Mobile Robots in Dynamic Environment 24

Tightly-Coupled Lidar-Based Lifelong Mapping using Wheels an...

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3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering, RAIIE 2024

作者： Hu, Lizhi Chen, Huilin Li, Xiang Lu, Wei Zhao, Yifan Hu, Songyu Navigation and Localization Algorithm Department Zhejiang HuaRay Technology Co. Lt. Hangzhou China Software Research and Development Center Zhejiang HuaRay Technology Co. Lt. Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798400718311

Localization and mapping technology of mobile robots is important in production and warehousing. The changing warehousing environment always result in poor accuracy and robustness of the mobile robot's localization. It is necessary to detect the degree of environmental changes and update the map. This paper proposes a novel lifelong mapping method fusing measurements from the lidar, wheels and a MEMS gyroscope, which can create a reliable map corresponding to the current working environment. Firstly, we use the wheels and gyroscopes measures for pre-integration to provide relative motion constraints, while integrating wheels and gyroscopes to provide initial pose for lidar matching. We also establish lidar odometry by submaps to provide another type of relative motion constraints. The absolute constraints are obtained by matching the lidar measurements with prior map. These three types of constraints are optimized in a factor graph. At the same time, during the process of updating the map, detect newly added artificial markers such as reflective columns or strips, and update them into the map timely. Then, in order to delete redundant lidar data keyframes for building map, we propose a determine redundancy method based on submaps. Finally, we propose a method for calculating the degree of map changes, which can update the map timely. Experimental results prove that our method can establish higher quality maps, optimize lidar external parameters timely during the mapping process, delete redundant lidar keyframes more efficiently, and update the current environmental map more accurately and timely. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Gyroscopes

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Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 307-315页

作者： Wang, Wei Chen, Jian Hu, Yuzhu Liu, Han Chen, Junxin Gadekallu, Thippa Reddy Garg, Lalit Guizani, Mohsen Hu, Xiping Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518000 China School of Software Dalian University of Technology Dalian116621 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India Faculty of Information and Communication Technology University of Malta Msida Malta United Arab Emirates

The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently. © 2024 The Authors

关键词： Internet of things

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Computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Tailin Liang Zhaolin Yuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Industrial and Systems Engineering Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

关键词： Meters Deep learning Accuracy Ultrasonic variables measurement Transportation Manuals Feature extraction Surface texture Convolutional neural networks Surface treatment

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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

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29th European systems, software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Diving into Continual Ultra-fine-grained Visual Categorization

Diving into Continual Ultra-fine-grained Visual Categorizati...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Pengcheng Zhang Xiaohan Yu Xiao Bai Jin Zheng Xiaoyu Wu Yongsheng Gao School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute for Integrated and Intelligent Systems Griffith University Nathan Australia

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