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A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

Computer Modeling in Engineering & Sciences 2024年第7期140卷 845-862页

作者： Bo Liu Li Feng Yufeng Chen School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Computer Science and Artificial Intelligence Chaohu UniversityChaohu238000China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive TechnologyShiyan442002China

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro

关键词： 3D lidar point cloud data augmentation RandomFusion semantic segmentation

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Learning-based tracking control of AUV:Mixed policy improvement and game-based disturbance rejection

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CAAI Transactions on Intelligence technology 2025年第2期10卷 510-528页

作者： Jun Ye Hongbo Gao Manjiang Hu Yougang Bian Qingjia Cui Xiaohui Qin Rongjun Ding State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina School of Information Science and Technology and Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefeiChina Nanyang Technological University SingaporeSingapore Wuxi Intelligent Control Research Institute of Hunan University Hunan UniversityWuxiChina

A mixed adaptive dynamic programming(ADP)scheme based on zero-sum game theory is developed to address optimal control problems of autonomous underwater vehicle(AUV)systems subject to disturbances and safe *** combining prior dynamic knowledge and actual sampled data,the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP ***,the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling ***,the control object interacts with the real environment and continuously gathers adequate sampled data in the *** comprehensively leverage the advantages of model-based and model-free methods during training,an adaptive tuning factor is introduced based on the dataset that possesses model-referenced information and conforms to the distribution of the real-world environment,which balances the influence of model-based control law and data-driven policy gradient on the direction of policy *** a result,the proposed approach accelerates the learning speed compared to data-driven methods,concurrently also enhancing the tracking performance in comparison to model-based control ***,the optimal control problem under disturbances is formulated as a zero-sum game,and the actor-critic-disturbance framework is introduced to approximate the optimal control input,cost function,and disturbance policy,***,the convergence property of the proposed algorithm based on the value iteration method is ***,an example of AUV path following based on the improved line-of-sight guidance is presented to demonstrate the effectiveness of the proposed method.

关键词： adaptive dynamic programming autonomous underwater vehicle game theory optimal control reinforcement learning

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Fault diagnosis method of link control system for gravitational wave detection

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Journal of Systems Engineering and Electronics 2024年第4期35卷 922-931页

作者： GAO Ai XU Shengnan ZHAO Zichen SHANG Haibin XU Rui School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Ministry of Industry and Information TechnologyBeijing 100081China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of EducationBeijing 100081China

To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is *** from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling *** constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault *** simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.

关键词： large scale multi-satellite formation gravitational wave detection laser link monitoring fault diagnosis deep learning

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MMLN: Multi-directional and Multi-constraint Learning network for Remote Sensing Imagery Semantic Segmentation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 1-16页

作者： Sun, Hang Xie, Yongchang Ren, Dong Wen, Fangqing Tong, Lyuyang Chang, Laibin Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive Technology Shiyan China School of Computer Science Wuhan University Wuhan China

Recent advances in satellite remote sensing technology and computer technology have significantly impacted practical applications in remote sensing image segmentation. However, the prevalent hybrid segmentation models that combine Convolutional Neural networks (CNNs) and Transformers, often overlook the critical exploration of local and global feature correlations across various scales. This exploration is essential for learning more representative features and strengthening context modeling capabilities. Additionally, the decoding layers of these models do not effectively exploit the pixel-level semantic relationships within cross-layer feature maps, thereby limiting the models' ability to discern small object features. To address these challenges, this paper introduces a Multi-directional and Multi-constraint Learning network (MMLN) designed for semantic segmentation of remote sensing imagery. This network features a Multi-directional Dynamic Complement Decoder (MDCD), which enhances the interaction between local and global features in the feature space, and subsequently improves the feature discrimination within the segmentation network. Moreover, a Multi-constraint Saliency Boundary-adaptive Module (MSBM) is implemented to reinforce the spatial constraints on saliency at the edge regions and ensure semantic consistency along the mask boundaries. This, in turn, augments the segmentation model's capability to detect small objects. The evaluation on four datasets reveals that the MMLN outperforms the existing state-of-the-art methods in remote sensing imagery segmentation. The code is available at https://***/zhongyas/MMLN. Authors

关键词： Semantics

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Optimal crater landmark selection based on optical navigation performance factors for planetary landing

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Chinese Journal of Aeronautics 2023年第3期36卷 254-270页

作者： Yi XIU Shengying ZHU Rui XU Maodeng LI School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Ministry of Industry and Information TechnologyBeijing 100081China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of EducationBeijing 100081China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijing 100091China

Planetary craters are natural navigation landmarks that widely exist and are easily *** navigation based on crater landmarks has become an important autonomous navigation method for planetary *** to the increase in observed crater landmarks and the limitation of onboard computation,the selection of good crater landmarks has gradually become a research hotspot in the field of landmark-based optical *** paper designs a fast crater landmark selection method,which not only considers the configuration observability of crater subsets but also focuses on the influence on navigation performance arising from the measurement uncertainty and the matching confidence of craters,which is different from other landmark selection *** factor of measurement uncertainty,which is anisotropic,correlated and nonidentically distributed,is quantified and integrated into selection based on crater pairing detection and localization error *** addition,the concept of the crater matching confidence factor is introduced,which reflects the possibility of 2D projection measurements corresponding to 3D *** with the configuration observability factor,the crater landmark selection indicator is ***,the effectiveness of the proposed method is verified by Monte Carlo simulations.

关键词： Crater-based optical navigation Crater landmark selection Crater matching Measurement uncertainty Planetary landing

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On the relationship between the activity at point of interests and road traffic

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Communications in Transportation Research 2023年第1期3卷 150-160页

作者： MátéKolat Tamás Tettamanti TamásBécsi Domokos Esztergár-Kiss Department of Control for Transportation and Vehicle Systems Faculty of Transportation Eng.and Vehicle Eng.Budapest University of Technology and EconomicsBudapest1111PestHungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Lorand Research NetworkBudapest1053PestHungary Department of Transport Technology and Economics Faculty of Transportation Eng.and Vehicle Eng.Budapest University of Technology and EconomicsBudapest1111PestHungary

The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth,safe,and green ***,effective traffic monitoring is an essential topic alongside the planning of sustainable transportation systems and the development of new traffic management *** contrast to classical traffic detection solutions,this study investigates the correlation between travelers'social activities and road *** s's primary goal is to investigate the presence of the relationship between social activity and road traffic,which might allow an infrastructure-independent traffic monitoring technique as ***'s general activities at Point of Interest(POI)locations(measured as occupancy parameter)are correlated with traffic data so that,finally,proper proxys can be defined for link-level average traffic speed *** method is tested and evaluated using real-world traffic and POI occupancy data from Budapest(District XI.).The results of the correlation investigation justify an indirect relationship between activity at POIs and road traffic,which holds promise for future practical applicability.

关键词： Road traffic estimation Google popular times(GPT) Point of interest(POI) POI occupancy Genetic algorithm(GA)

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Lightweight UAV target detection algorithm 6

Lightweight UAV target detection algorithm

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2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhang, Xiaozheng Bao, Dayang Zhao, Haili Wang, Qi Department of Information and Control Technology China North Vehicle Research Institute Beijing100072 China School of Electronic Information Engineering Changchun University of Science and Technology Changchun130022 China

Aiming at the public area UAV detection task, the target is easy to occlude, the scale variation is large, and the existing detection algorithm model parameters are large. In this paper, the lightweight network model YOLOv5s-UD is designed for the low-altitude UAV target detection algorithm. In this detection model, the proposed approach employs the streamlined Shufflenetv2 architecture to enhance the core network. To optimize the UAV’s relevant feature data, the CBAM attention module is integrated during the feature amalgamation phase. Furthermore, the GSConv is implemented to upgrade conventional convolution within the neck network, ensuring a reduction in model parameters without compromising detection precision. Experimental outcomes reveal a 36.2% computational reduction compared to the YOLOv5s algorithm, with an average detection accuracy of 84.1%. This demonstrates the algorithm’s capability to efficiently detect UAV targets while minimizing computational loads, thereby exhibiting significant practical significance. © 2024 institute of Physics Publishing. All rights reserved.

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THE TORQUE PREDICTION OF HEAVY-DUTY vehicle DRIVESHAFT BASED ON MSGWO-LSTM 10

THE TORQUE PREDICTION OF HEAVY-DUTY VEHICLE DRIVESHAFT BASED...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Wu, Jianpeng Zhao, Peng Song, Yaochen Wang, Liyong Zhang, Jinle Huang, Xiaozan Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China China North Vehicle Research Institute Beijing China

ISBN: (纸本)9781837242672

The driveshaft is the core component in the driveline system of Heavy-Duty vehicles (HDV). Its torque transfer performance is related to the operational capability. The torque prediction is of great significance for studying the dynamics of HDV. Therefore, this paper proposes a driveshaft torque prediction model using a Multi-Strategy Grey Wolf Optimizer (MSGWO) to optimize the Long Short-Term Memory (LSTM) network. First, a torque calculation method based on real vehicle sensing data is proposed to provide samples for neural network training. Second, the MSGWO is used to optimize the hyperparameters of LSTM to construct the prediction model based on MSGWO-LSTM. Finally, the model is validated using a dataset composed of real vehicle sensing data and calculated torque. Compared to GWO-LSTM, LSTM, and Backpropagation (BP) models, the MSGWO-LSTM model proposed in this paper reduces the RMSE of real-time shifting torque prediction values by 41.59%, 66.14%, and 86.78%, respectively. The RMSE of short mileage prediction value decreases by 43.63%, 62.22%, and 68.92%, respectively. The results show that the model has high prediction accuracy and can effectively predict the torque of the HDV driveshaft. © The Institution of Engineering & technology 2024.

关键词： Prediction models

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HEALTH EVALUATION OF TRANSMISSION MECHANISM IN POWERTRAIN BASED ON COMPOSITE WEIGHT CLOUD MODEL 10

HEALTH EVALUATION OF TRANSMISSION MECHANISM IN POWERTRAIN BA...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Wang, Liyong Huang, Xiaozan Wu, Jianpeng Yang, Jian Zhang, Jinle Li, Pengpeng Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China China North Vehicle Research Institute Beijing China

ISBN: (纸本)9781837242672

The performance degradation of the powertrain in service significantly affects the stability and reliability of tracked vehicles. To assess the powertrain health state of tracked vehicles, this study proposes a health state evaluation method for the transmission mechanism of the powertrain based on a composite weight cloud model. Firstly, the model establishes a health evaluation indexes system for the transmission mechanism, calculates the subjective index weights based on the AHP method and utilizes the EWM-CRITIC method to compute the objective index weights. However, the subjective and objective weights have some limitations. Therefore, the weights of subjective and objective indexes are fused through the game theory to obtain the composite weights of indexes. Secondly, this research categorizes the health status levels of the transmission mechanism to establish an evaluation standard cloud model and combines index weights and data to generate a comprehensive cloud model. Then, the cloud memberships are calculated for different mileage comprehensive clouds to quantitatively analyse the cloud model. Finally, the feasibility of the composite weight cloud model health evaluation method is verified by comparing it with other health evaluation methods, which provides a reference for the health state evaluation of the powertrain. © The Institution of Engineering & technology 2024.

关键词： CLOUD MODEL HEALTH EVALUATION SUBJECTIVE AND OBJECTIVE WEIGHTS TRANSMISSION MECHANISM

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A SCIENTIFIC METHOD FOR COMPILING THE LOAD SPECTRUM OF TRANSMISSION SHAFT FOR HYBRID SPECIAL vehicle BASED ON PARAMETER EXTRAPOLATION 10

A SCIENTIFIC METHOD FOR COMPILING THE LOAD SPECTRUM OF TRANS...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Chen, Boyu Li, Le Wang, Qian Zheng, Cunjin Zhang, Ximing Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China China North Vehicle Research Institute Beijing China

ISBN: (纸本)9781837242672

To compile the load spectrum for the transmission shaft in hybrid special vehicles with high reliability, this paper employs the parameter extrapolation method for load spectrum compilation. By collecting experimental real vehicle torque load data and related driving records, the operation process of the special vehicle would be divided into 5 typical operating conditions. Throughout the pre-processed load data, the rainflow counting method is applied to develop statistically analysis and distribution-fitting. Based on the probability statistical distribution results, it could indicate that: the amplitude values for each operating condition follow a lognormal distribution;the means value for each operating condition follow a 3-parameter lognormal distribution, and they are mutually independent. Accordingly, the load extremes and frequencies are extrapolated for the target life cycle, and the rationality of the extrapolation is verified. Based on the extrapolation results, an 8-level two-dimensional load spectrum and a one-dimensional program spectrum are compiled. In general, the research results provide a foundation for the reliability testing of hybrid special vehicles. © The Institution of Engineering & technology 2024.

关键词： Extrapolation

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