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A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1681-1694页

作者： Qiuyu Zhang Lipeng Wang Hao Meng Wen Zhang Genghua Huang the College of Intelligent Systems Science and Engineering Harbin Engineering UniversityHarbin 150001China Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://***/zqy411470859/ship_dataset.

关键词： 3D point clouds dataset dynamic tail wave fog simulation rainy simulation simulated data

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Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays:Optimized Algorithm Design and Boundedness Analysis

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Journal of Systems science & Complexity 2023年第4期36卷 1423-1442页

作者： HU Jun HU Zhibin DONG Hongli LIU Hongjian School of Automation Harbin University of Science and TechnologyHarbin 150080China Department of Mathematics Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318China School of Mathematics-Physics and Finance Anhui Polytechnic UniversityWuhu 241000China

This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical *** addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering *** aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample *** the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion ***,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is ***,a numerical example is provided,which can show the usefulness of the developed DRFF approach.

关键词： Distributed resilient fusion filtering matrix-weighted fusion nonlinear time-varying systems random sensor delays

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Reinforcement Learning of space Robotic Manipulation with Multiple Safety Constraints 41

Reinforcement Learning of Space Robotic Manipulation with Mu...

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第41届中国控制会议

作者： Linfeng Li Yongchun Xie Yong Wang Ao Chen Beijing Institute of Control Engineering Science and Technology on Space Intelligent Control Laboratory

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

To execute numerous space robotic manipulation tasks(for example on-orbit capture,refueling,assembly,etc.) reliably in presence of high environment uncertainty and multi-source obstacles,it is critical to incorporate safety design into trajectory ***-based planning approaches are commxonly used in the literature,yet suffer insufficient robustness to model *** this paper,we study the sample-based,model-free learning of safe space robotic *** consider numerous practical on-orbit working conditions and propose a novel algorithm called Safe policy optimization with multiple constraints(SPOMC).It incorporates two constraints(namely energy constraint and geometric constraint) into the constrained policy optimization framework,maximizing expected total reward while maintaining a bounded expected total *** simulation on rigid robotic arm manipulation at randomized base positions shows the effectiveness of SPOMC.

关键词： Safety space robotic manipulation Sample-based trajectory optimization Reinforcement learning Constrained Markov decision processes

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Trajectory tracking control of autonomous heavy-duty mining dump trucks with uncertain dynamic characteristics

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science China(Information sciences) 2023年第10期66卷 246-262页

作者： Liang CHEN Zhaobo QIN Manjiang HU Hongbo GAO Yougang BIAN Biao XU Xiaoyan PENG State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Wuxi Intelligent Control Research Institute of Hunan University Department of Automation University of Science and Technology of China

The proportion of mines using autonomous mining trucks is still very low at present. To promote the development of intelligent mining, it is urgent to proceed with the drive-by-wire modification to common mining trucks and design a motion control algorithm considering uncertain dynamic *** paper proposes a trajectory tracking control method for autonomous heavy-duty mining dump trucks(AHMDTs) with uncertain dynamic characteristics. In this method, a driving/braking force compensation algorithm based on an inverse dynamic model is designed to guarantee accurate longitudinal control with less control gain tuning. Using road curvatures, a modified rear-wheel position feedback control method is proposed to deal with reverse-path tracking, which can simultaneously reduce the lateral error and yaw angle error. A modified Stanley controller considering the collaborative preview based on speed and curvature is constructed to achieve accurate path tracking in the forward gear. Moreover, the proposed method focuses on the practice of trajectory tracking control in an open-pit mine condition with the adverse effects caused by uncertain vehicle dynamics, huge variable load, and large actuator lag. Real vehicle tests show that the proposed methodology can control AHMDTs with a low tracking error.

关键词： vehicle control trajectory tracking control driving/braking force compensation rear-wheel position feedback

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High-precision end-to-end adaptive optics aided orbital angular momentum wireless communication with XFTC-Net

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Optics Letters 2025年第10期50卷 3265-3268页

作者： Xu, Panpan Chang, Huan Wang, Fei Gao, Ran Guo, Dong Zhu, Lei Li, Zhipei Zhou, Sitong Xu, Qi Chen, Jing Xin, Xiangjun Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Beijing100876 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology 250353 China

In this Letter, we demonstrate high-precision end-to-end adaptive optics (AO) technique based on the X-Shape Fusion Transformer-convolutional neural network (XFTC-Net) without an additional probe path to compensate for distortions caused by atmospheric turbulence (AT) in orbital angular momentum (OAM) optical wireless communication links. The experimental results show that the XFTC-Net effectively extracts the intensity distribution features of distorted OAM beams, predicting AT phase screens and identifying OAM modes. After XFTC-Net-aided AO compensation, the bit error rate of the OAM optical communication system remains below 3.8 × 10−3, staying within Cn2 = 1 × 10−15–1 × 10−13 m−2/3. Moreover, the optical power with l= 3 increases from 5.08 dBm to 8.23 dBm at a transmission distance of 800 m. These experimental results indicate that the proposed XFTC-Net AO technique effectively compensates for OAM beam distortion in both single- and multiplexed-channel OAM links, which effectively balances the system complexity and transmission performance of OAM-based free space optical (FSO) communication systems, opening up new avenues for enhancing the performances of high-capacity optical communication. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Fiber to the x

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Multi-View Joint Target Selection for Relativistic-Perturbed Starlight Navigation

Multi-View Joint Target Selection for Relativistic-Perturbed...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Zhao, Yaru Ge, Dantong Xiong, Kai School of Aerospace Engineering Beijing Institute of Technology Beijing China Key Laboratory of Autonomous Navigation Control for Deep Space Exploration Beijing Institute of Technology Beijing China National Key Laboratory of Space Intelligent Control Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798350384185

Autonomous navigation is one of the key technologies for spacecraft to realize long-term space operation and carry out science missions. In order to meet the requirement of high precision autonomous navigation, the effects of the gravitational deflection of light, stellar aberration and measurement bias of sensor are considered to establish a relativistic-perturbed starlight navigation scheme. To obtain high quality measurements, a multi-probe star sensor composed of multiple field of view probes is adopted for observation. A polygonal star pair selection method is proposed to conduct multi-view joint target selection. By constructing irregular polygons to select high quality star pair observations, the accuracy of navigation estimation is expected to be improved. The performance of relativistic-perturbed starlight navigation using the multi-view joint target selection method is then verified through a numerical simulation. © 2024 IEEE.

关键词： Probes

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A Novel Grades Prediction Method for Undergraduate Students by Learning Explicit Conditional Distribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4837-4848页

作者： Zhang, Na Liu, Ming Wang, Lin Liu, Shuangrong Sun, Runyuan Yang, Bo Zhu, Shenghui Li, Chengdong Yang, Cheng Cheng, Yuhu China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Quan Cheng Laboratory Jinan250100 China Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China Shandong Jianzhu University School of Information and Electrical Engineering Jinan250101 China Ministry of Education Engineering Research Center of Intelligent Control for Underground Space Xuzhou221116 China

Educational data mining (EDM) offers an effective solution to predict students' course grades in the next term. Conventional grade prediction methods can be viewed as regressing an expectation of the probability distribution of the student's grade, typically called single-value grade prediction. The reliable prediction outcomes of these methods depend on the complete input information related to students. However, next-term grade prediction often encounters the challenge of incomplete input information due to the inaccessibility of future data and the privacy of data. In this scenario, single-value grade prediction struggles to assess students' academic status, as it may not be represented and assessed by relying on a singular expectation value. This limitation increases the risk of misjudgment, and may lead to errors in educational decision-making. Considering the challenge of collecting complete input information, we shift from traditional single-value predictions to forecasting the explicit probability distribution of the course grade. The probability distribution of the grade can assess the students' academic status by providing probabilities corresponding to all possible grade values rather than relying solely on an expectation value, which offers the foundation to support the educators' decision-making. In this article, the course grade distribution prediction (CGDP) model is proposed, aiming to estimate an explicit conditional probability distribution of course grades in the next term. This model can identify at-risk students, offering comprehensive decision-making information for educators and students. To ensure precise distribution predictions, a calibration method is also employed to improve the alignment between predicted and actual probabilities. Experimental results verify the effectiveness of the proposed model in early grade warning for undergraduates, based on real university data. © 2020 IEEE.

关键词： Probability distributions

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TransCGAN-based Parameter Extraction Framework for SAR Image Simulation 2

TransCGAN-based Parameter Extraction Framework for SAR Image...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Deng, Sidan He, Jingfei Mao, Yongfei Zhao, Liangbo Chen, Liang Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Chongqing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images. © 2024 IEEE.

关键词： Generative adversarial networks

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Detector-grade perovskite single-crystal wafers via stress-free gel-confined solution growth targeting high-resolution ionizing radiation

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Light(science & Applications) 2023年第5期12卷 754-765页

作者： Yilong Song Lixiang Wang Yongqiang Shi Weihui Bi Jianwu Chen Mingwei Hao Anran Wang Xueying Yang Yuan Sun Fan Yu Liansheng Li Yanjun Fang Deren Yang Qingfeng Dong State Key Laboratory of Supramolecular Structure and Materials College of ChemistryJilin UniversityChangchun 130012China State Key Laboratory of Silicon Materials and School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027China Beijing Institute of Control Engineering Beijing 100190China Science and Technology on Space Intelligent Control Laboratory Beijing 100190China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China

Solution-processed organic‒inorganic halide perovskite(OIHP)single crystals(SCs)have demonstrated great potential in ionizing radiation detection due to their outstanding charge transport properties and low-cost ***,the energy resolution(ER)and stability of OIHP detectors still lag far behind those of melt-grown inorganic perovskite and commercial CdZnTe counterparts due to the absence of detector-grade high-quality OIHP ***,we reveal that the crystallinity and uniformity of OIHP SCs are drastically improved by relieving interfacial stress with a facial gel-confined solution growth strategy,thus enabling the direct preparation of large-area detector-grade SC wafers up to 4 cm with drastically suppressed electronic and ionic *** resultant radiation detectors show both a small dark current below 1 nA and excellent baseline stability of 4.0×10^(-8) nA cm^(-1) s^(-1) V^(-1),which are rarely realized in OIHP ***,a record high ER of 4.9% at 59.5 keV is achieved under a standard 241Am gamma-ray source with an ultralow operating bias of 5 V,representing the best gamma-ray spectroscopy performance among all solution-processed semiconductor radiation detectors ever reported.

关键词： resolution confined stress

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