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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Wang, Lijun Wu, Chaohang Chen, Xianzhong Li, Qing Yao, Qijia School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Science and Technology Beijing Beijing China

ISBN: (纸本)9789819617760

Based on the personalized customization model in bearing steel enterprises, it is studied in this paper how historical data can be fully utilized to predict orders in the face of multi-variety and small-batch order models.. In order to overcome the bottleneck of information exchange that cannot be obtained from pure historical data based on diverse small batch customer needs. A demand fusion mechanism based on local rules is proposed, taking fuzzy customer demand preferences as "individuals" and introducing historical data such as order area and external environmental factors. First, based on the Hegselmann-Krause (HK) model, a personalized customized customer demand preference prediction model is proposed to simulate and predict the evolution process of large-scale customer demand preferences. Secondly, according to the fuzzy rules, the customer's demand preference in the historical data is used as the center point to introduce the fuzzy individual, and the standard deviation is determined based on the order area. Finally, the effectiveness of the proposed method was verified through simulation results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Detector array with digital data acquisition system for charged-particle decay studies

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Nuclear Science and Techniques 2025年第4期36卷 140-150页

作者： Hao Jian Xin-Xing Xu Kai-Long Wang Jia-Jian Liu Chao-Yi Fu Peng-Jie Li Yan-Yun Yang Guang-Xin Zhang Kang Wang Fang-Fang Duan Long-Hui Ru Guang-Shun Li Bing Ding Yun-Hua Qiang Cen-Xi Yuan Jun-Bing Ma Shi-Wei Xu Yu-Feng Gao Rui Fan Fan-Chao Dai Si-Xian Zha Hao-Fan Zhu Jin-Hai Li Shu-Lian Qin Zhi-Fang Chang Cheng Kong He-Xuan Yan Hao-Wei Xu Jia-Long Ning Bo-Ren Liu Jie Zhou Yu-Dong Chen Bo-Shuai Cai Yu-Ting Wang Hong-Yi Wu Zhi-Xuan Wang Dong-Sheng Hou Hu-Shan Xu Xiao-Hong Zhou Yu-Hu Zhang Meng Wang Zheng-Guo Hu Jenny Lee State Key Laboratory of Heavy Ion Science and Technology Institute of Modern PhysicsChinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516003China Department of Physics The University of Hong KongHong KongChina Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China Institute of Particle and Nuclear Physics Henan Normal UniversityXinxiang 453007China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China School of Physics and State Key Laboratory of Nuclear Physics and Technology Peking UniversityBeijing 100871China

A state-of-the-art detector array with a digital data acquisition system has been developed for charged-particle decay studies,includingβ-delayed protons,αdecay,and direct proton emissions from exotic proton-rich *** digital data acquisition system enables precise synchronization and processing of complex signals from various detectors,such as plastic scintillators,silicon detectors,and germaniumγ*** system's performance was evaluated using theβdecay of^(32)Ar and its neighboring nuclei,produced via projectile fragmentation at the first Radioactive Ion Beam Line in Lanzhou(RIBLL1).key measurements,including the half-life,charged-particle spectrum,andγ-ray spectrum,were obtained and compared with previous results for *** the implantation–decay method,the isotopes of interest were implanted into two doublesided silicon strip detectors,where their subsequent decays were measured and correlated with preceding implantations using both position and time *** detection system has potential for further applications,including the study ofβ-delayed charged-particle decay and direct proton emissions from even more exotic proton-rich nuclei.

关键词： β-delayed proton decay Double-sided silicon strip detector High-purity germanium detector Digital data acquisition system Implantation–decay correlation

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Design of PMCW Millimeter Wave Radar Algorithms on an Embedded DSP 16

Design of PMCW Millimeter Wave Radar Algorithms on an Embedd...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhao, Jinghao Liu, Qiuming Tan, Bin School of Software engineering Jiangxi University of Science and Technology Nanchang China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang China Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics China School of Electronics and Information Engineering Jinggangshan University Ji’an China

ISBN: (纸本)9781510689251

The rapid advancements in autonomous driving technology necessitate the extensive deployment of automotive radars operating within the 77-81 GHz millimeter-wave band in the forthcoming years. In contrast to earlier Frequency Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars exhibit notable improvements in processing speed and flexibility, offering superior range and velocity resolution. These enhancements are critical for the precise detection and interpretation of dynamic and complex traffic scenarios. This paper initially presents the simulation and testing of algorithms for range and speed measurement using single-input single-output (SISO) PMCW millimeter-wave radar. Building upon these results, further simulations incorporating multi-input multi-output (MIMO) systems utilizing Hadamard codes are conducted to augment PMCW radar performance. The final phase of this study involves implementing the relevant signal processing algorithms on a custom-developed digital signal processor (DSP) named SWIFT. Experimental findings demonstrate that PMCW radar significantly mitigates multipath effects and clutter, maintaining high performance in complex environments. Furthermore, the algorithms executed on the DSP meet the anticipated performance standards. The proposed methodology not only validates the theoretical framework but also establishes a foundation for future hardware implementation. © 2025 SPIE.

关键词： Automotive radar

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Reinforcement Learning Based data-Driven Control Method for Dynamic Systems and Its Application to FLUIDMechatronix Platform 16th

Reinforcement Learning Based Data-Driven Control Method for...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Zou, Zonghan Gao, Jingjing Yang, Xu Huang, Jian Zhang, Tao Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789819617760

With the increasing complexity of industrial process, accurate models of automatic control systems are difficult to be established, which makes model-based control methods become less effective. On this premise, a reinforcement learning (RL) based data-driven control method for dynamic systems is proposed in this paper. For our purposes, a model-free RL controller with an Actor-Critic structure is developed by using neural networks (NN), combining the merits of both value-based and policy-based RL algorithms. Policy of the actor is assessed by the critic, updating parameters of RL controller in real-time for online optimization. The effectiveness of the RL controller is demonstrated by an experimental study on FLUIDMechatronix platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Legal judgment prediction via legal knowledge extraction and fusion

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Journal of King Saud university - Computer and Information Sciences 2025年第3期37卷 1-16页

作者： Zhao, Qihui School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University at Qinhuangdao Qinhuangdao 066004 China

Legal judgment prediction (LJP) task aims to predict relevant law articles, charges and terms of penalty through the textual fact description of legal cases. However, existing methods face the core challenge is external legal knowledge under-exploitation, which hinders the performance of LJP. To overcome this limitation, a legal knowledge enhanced prompt learning based approach (LKEPL) is proposed. Our method first design the legal knowledge extraction which utilizes the model trained on our labeled legal dataset to identify legal entity relation knowledge and obtains law articles knowledge by the attention mechanism. Afterwards, our method employs legal knowledge projector to translate legal knowledge vectors into the transformer space. Then our method incorporates two kinds of knowledge by concatenating those vectors before the key and value matrix of each multi-head attention of BERT. Finally, our method designs the LJP task-specific prompt learning method. Experimental results on the CAIL2018 dataset demonstrate that our method outperforms existing strong baseline models. Furthermore, our method offers interpretability and shows effectiveness under few-shot scenarios. © The Author(s) 2025.

关键词： Legal intelligence Legal judgment prediction Legal knowledge fusion Prompt learning

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YOLOv9-AAG: Distinguishing Birds and Drones in Infrared and Visible Light Scenarios

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IEEE Access 2025年 13卷 76609-76619页

作者： Suo, Peichun Zhu, Jincan Zhou, Qingyang Kou, Weili Wang, Xiuyan Suo, Wen Yunnan Medical Health Vocational College Information Center Kunming650033 China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming650224 China Key Laboratory of State Forestry and Grassland Administration on Forestry Ecological Big Data Kunming650224 China Yunnan College of Business Management Kunming650106 China

The ability to distinguish between birds and drones is essential for applications in wildlife preservation, aviation security, and defense operations. Reliable identification not only reduces the risk of bird strikes and monitors potential drone threats but also fosters the development of a secure and intelligent ecological environment. However, achieving high precision in recognition under complex and dynamic conditions remains a significant hurdle. To overcome this, we present an advanced detection framework, YOLOv9-AKConv-AFF-GSConv(YOLOv9-AAG), which substantially improves the accuracy of distinguishing birds from drones, even in varied poses, and enhances generalization capabilities in both infrared and visible light scenarios. The framework incorporates several key advancements. First, the GSConv is embedded into the head of the RepNSCPELAN4-AKConv module, enabling efficient extraction of edge and contour information without introducing additional computational cost. Second, the inclusion of the Attentional Feature Fusion (AFF) mechanism within AKConv amplifies the representation of shape features, particularly under challenging environments and multimodal scenarios. Lastly, the optimized AKConv module is effectively integrated with the original RepNSCPELAN4 model, facilitating more accurate and robust feature extraction for small targets, including birds and drones. Experimental evaluations underscore the effectiveness of the proposed framework. On an optical dataset containing multiple bird and drone targets, YOLOv9-AAG achieved recognition accuracy rates of 78.2%, while on an infrared dataset, it reached 86.1%. These results reflect improvements of 7.4% and 6.0%, respectively, over the baseline YOLOv9 model. Additionally, the F1 scores increased by 5.9% and 2.8%. On the publicly available CUB-200-2011 dataset and a combined dataset of visible and infrared images, the model attained mean Average Precision (mAP) scores of 81.9% and 81.6%, respectively, outper

关键词： Birds

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Fair Personalized Learner Modeling Without Sensitive Attributes 34

Fair Personalized Learner Modeling Without Sensitive Attribu...

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34th ACM Web Conference, WWW 2025

作者： Xu, Hefei Hou, Min Wu, Le Liu, Fei Yang, Yonghui Bai, Haoyue Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China Institute of Dataspace Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400712746

Personalized learner modeling uses learners’ historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD). This is essential for web-based learning services such as learning resource recommendation and adaptive testing. However, prior studies have shown that CD models may unfairly correlate learners’ abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes. While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical. Furthermore, the presence of multiple sensitive attributes complicates fairness improvements. In this paper, we explore how to achieve fair personalized learner modeling without relying on any sensitive attribute input. We introduce a novel fairness objective tailored for personalized learner modeling and design a max-min strategy to facilitate both sensitive information inference and fair CD modeling. In the max step, we infer pseudo-labels by maximizing the fairness objective, while in the min step, we retrain the CD model by minimizing it. Additionally, we provide a theoretical guarantee that our framework reduces the upper bound of fairness generalization error. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods. © 2025 Copyright held by the owner/author(s).

关键词： Cognitive Diagnosis Debiasing Fairness Out-of-distribution User Modeling

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RIS-Assisted ISAC Systems for Robust Secure Transmission With Imperfect Sense Estimation

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3979-3992页

作者： Jiang, Chengjun Zhang, Chensi Huang, Chongwen Ge, Jianhua Niyato, Dusit Yuen, Chau Xidian University State Key Laboratory of Integrated Service Networks Xi’an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Nanyang Technological University College of Computing and Data Science Nanyang Avenue 639798 Singapore Nanyang Technological University School of Electrical and Electronics Engineering Nanyang Avenue 639798 Singapore

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems for robust physical layer security (PLS) schemes. Traditionally, eavesdroppers (Eves) typically avoid interacting with the base station, making it challenging to obtain their relevant information, which limits the implementation of PLS. Fortunately, the sensing information obtained by ISAC can contribute to the design of PLS. Therefore, leveraging imperfect sensing estimation and employing dedicated radar signals as artificial noise, we formulate an RIS-assisted joint active and passive beamforming design problem to maximize the sum secrecy rate while satisfying the user’s quality of service constraints, the transmission power constraints, and the sensing signal strength requirements. To make the problem tractable, we first derive the bound for Eve’s channel state information uncertainty region based on security approximations. Subsequently, we employ the S-procedure and the symbolic-deterministic methods to transform the infinite number of inequalities. We then utilize the first-order Taylor expansion, the second-order cone methods, and the successive convex approximation to address the nonconvexity problem, leading to an efficient suboptimal solution obtained by an iterative algorithm. Finally, the simulation results demonstrate the significant potential of the sensing function in enhancing security and the effectiveness of the proposed robust scheme in flexibly balancing communication and sensing quality. © 2002-2012 IEEE.

关键词： Iterative methods

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An Anti-Clutter Distance Measurement Method for IoT Linear Frequency Modulation Radar in Heavy Rainfall Environment

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IEEE Internet of Things Journal 2025年第11期12卷 17906-17919页

作者： Zhu, Lingzhi Li, Yi Xia, Weijie Chen, Kuiyu Ma, Qun Zhang, Qi Nanjing University of Aeronautics and Astronautics Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing211106 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Nanjing210023 China City University of Macau Faculty of Data Science 999078 China

Linear frequency modulation (LFM) radar which can measure distance of target is the key sensor in autonomous driving system in the context of Internet of Things (IoT). However, huge amount of raindrops in rainfall environment would reflect signal and result in negative influence. In order to improve the detection ability of LFM radar, echo signal model of LFM radar target in rainfall environment is established. Working principle of LFM radar, characteristics of raindrops, influence of rain clutter and comparison with measured data in time-domain and frequency-domain distribution are given in sequence. Secondly, three features are defined and extracted from frequency-domain spectrum and singular-domain spectrum to recognize target echo signal, rain clutter and mixed signal with high precision. When the rainfall rate is 3.33 mm/h, the overall recognition accuracy can still be up to 95.15%. Thirdly, an adaptive ensemble empirical mode decomposition (EEMD) algorithm is proposed to suppress rain clutter in mixed signal. Simulation results prove the effectiveness of EEMD in improving ranging accuracy in rainfall environment. At last, outfield experiments in normal environment and rainfall environment are conducted respectively. Measured distance obtained by proposed method in rainfall environment is still more accurate than that obtained by traditional fast Fourier transform (FFT) method in normal environment, illustrating that the proposed method can ensure the high performance of LFM radar in rainfall environment. Research in this paper has positive influence on improving reliability of LFM radar in inclement environment and can contribute to more stable target information for the whole in-vehicle IoT. © 2014 IEEE.

关键词： Frequency domain analysis

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A multi-channel voltage driving circuit with adjustable and independent RF amplitude

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Quantum Information Processing 2025年第5期24卷 1-11页

作者： Wang, Qikun Wang, Zhao Hu, Tianyang Mai, Jun Ma, Wei Wang, Xu College of Big Data and Information Engineering Guizhou University Guiyang550025 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science Shenzhen518055 China International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

A helical resonator with a certain resonant frequency and a high-quality factor (Q-factor) is critical for an ion trap system, which results in a larger trap depth, longer trap time, and lower radiofrequency (RF) noise. Here, we propose a new method for driving amplitude-adjustable multiple RF ion trap electrodes. By dividing the output of the helical resonator into multiple circuits and simultaneously driving the ion trap multi-RF electrodes, each circuit is composed of capacitors connected in series, and the desired amplitude is applied to the ion trap RF electrodes using capacitive voltage dividing. In contrast, the capacitance of the multi-RF capacitors connected in series is determined by three constraints. Different voltages for each circuit are realized while keeping the phase consistent. Both the circuit simulation and analog simulations have been adopted to demonstrate the validity of proposed method, which has significant potential to be applied in the ion trap structure of multiplexed RF electrodes and contributing to the realization of ion trap-based quantum information processing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Differentiating circuits

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