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7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020

作者： Zhu, Yongjie Zuo, Yi Li, Tieshan Navigation College Dalian Maritime University Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728162461

With the rapid development of marine trade and transportation, a large number of ships sail at sea. The ship energy conservation and environment protection are becoming public interested problems. Precise prediction of ship fuel consumption is vital for controlling emissions from ships. Some scholars used regression analysis to interpret the relations between fuel consumption and its influence factors. However, considering that there are a large amount of influence factors affecting ship fuel consumption, such as pitch, ship speed, wind and wave, some machine learning models are employed. While, ship fuel consumption data is time-related. A time series problem arises. The regression analysis and machine learning models cannot capture the time series characteristics. Therefore, LSTM neural network is applied in this paper. Experimentally, we compared LSTM with three traditional machine learning models, namely linear regression (LR), support vector regression (SVR), and artificial neural network (ANN). It found that the prediction accuracy can be improved with 11.8% compared with artificial neural network. The LSTM model can catch the time series characteristics of fuel consumption data and get a higher accuracy. © 2020 IEEE.

关键词： Forecasting

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Radar Sensing via OTFS Signaling: A Delay Doppler Signal Processing Perspective

arXiv

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arXiv 2023年

作者： Zhang, Kecheng Yuan, Weijie Li, Shuangyang Liu, Fan Gao, Feifei Fan, Pingzhi Cai, Yunlong Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electrical Electronic and Computer Engineering University of Western Australia PerthWA6009 Australia Department of Automation Tsinghua University Beijing China Southwest Jiaotong University Sichuan Chengdu611756 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain. Since the range and velocity, which can be derived from the delay and Doppler shifts, are the parameters of interest for radar sensing, it is natural to consider implementing DD signal processing for radar sensing. In this paper, we investigate the potential connections between the OTFS and DD domain radar signal processing. Our analysis shows that the range-Doppler matrix computing process in radar sensing is exactly the demodulation of OTFS with a rectangular pulse shaping filter. Furthermore, we propose a two-dimensional (2D) correlation-based algorithm to estimate the fractional delay and Doppler parameters for radar sensing. Simulation results show that the proposed algorithm can efficiently obtain the delay and Doppler shifts associated with multiple targets. © 2023, CC BY-NC-SA.

关键词： Doppler effect

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Rapid and Label-Free Detection of Human Epididymal Protein 4 by Piezoelectric Immunosensor

Rapid and Label-Free Detection of Human Epididymal Protein 4...

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SPAWDA Symposium on Piezoelectricity, Acoustic Waves, and Device Applications

作者： Yan Chen Huashan Shi Bo Mu Feng Tan School of Information Engineering Southwest University of Science and Technology Mianyang Sichuan P.R. China Department of biological therapy West China Hospital Sichuan University Chengdu Sichuan P.R. China School of basic medical sciences North Sichuan Medical College Nanchong Sichuan P.R. China School of automation engineering research University of Electronic Science and Technology of China Chengdu Sichuan P.R. China

In recent years, human epididymal protein 4 (HE4), which is a new tumor marker, has been discovered with high specificity in the diagnosis of ovarian cancer. Current laboratory methods for detecting HE4, such as enzyme-linked immunosorbent assay (ELISA) and chemiluminescence immunoassay (CLIA), have the disadvantages of being time-consuming and requiring labeling steps. In this study, piezoelectric immunosensors were used to measure the concentration of HE4 antigen in PBS solution which containing various concentrations of the antigen. The performance of this immunoassay such as detection range, test time and accuracy were investigated, respectively. The results revealed that the concentration range of HE4 detected was 200–8000 pmol/ml, the analysis time was no more than 30 min and the relative error was less than 5%. Therefore, the proposed rapid and label-free piezoelectric sensing method in HE4 detection has the great potential in tumor markers detection from clinical serum for early ovarian cancer.

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First Indication of Solar B8 Neutrinos through Coherent Elastic Neutrino-Nucleus Scattering in PandaX-4T

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Physical Review Letters 2024年第19期133卷 191001页

作者： Zihao Bo Wei Chen Xun Chen Yunhua Chen Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Zhixing Gao Lisheng Geng Karl Giboni Xunan Guo Xuyuan Guo Zichao Guo Chencheng Han Ke Han Changda He Jinrong He Di Huang Houqi Huang Junting Huang Ruquan Hou Yu Hou Xiangdong Ji Xiangpan Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shuaijie Li Tao Li Zhiyuan Li Qing Lin Jianglai Liu Congcong Lu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Yue Meng Xuyang Ning Binyu Pang Ningchun Qi Zhicheng Qian Xiangxiang Ren Dong Shan Xiaofeng Shang Xiyuan Shao Guofang Shen Manbin Shen Wenliang Sun Yi Tao Anqing Wang Guanbo Wang Hao Wang Jiamin Wang Lei Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Xu Wang Zhou Wang Yuehuan Wei Weihao Wu Yuan Wu Mengjiao Xiao Xiang Xiao Kaizhi Xiong Yifan Xu Shunyu Yao Binbin Yan Xiyu Yan Yong Yang Peihua Ye Chunxu Yu Ying Yuan Zhe Yuan Youhui Yun Xinning Zeng Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Wei Zhang Yang Zhang Yingxin Zhang Yuanyuan Zhang Li Zhao Jifang Zhou Jiaxu Zhou Jiayi Zhou Ning Zhou Xiaopeng Zhou Yubo Zhou Zhizhen Zhou School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Beihang University Beijing 102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China International Research Center for Nuclei and Particles in the Cosmos & Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA School of Physics Nankai University Tianjin 300071 China School of Physics Sun Yat-Sen University Guangzhou 510275 China State Key Laboratory of Particle Detection and Electronics Department of Modern Physics Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Qingdao 266237 China Key Laboratory of Particle Physics and Particle

The PandaX-4T liquid xenon detector at the China Jinping Underground Laboratory is used to measure the solar B8 neutrino flux by detecting neutrinos through coherent scattering with xenon nuclei. Data samples requiring the coincidence of scintillation and ionization signals (paired), as well as unpaired ionization-only signals (US2), are selected with energy threshold of approximately 1.1 keV (0.33 keV) nuclear recoil energy. Combining the commissioning run and the first science run of PandaX-4T, a total exposure of 1.20 and 1.04 tonne·year are collected for the paired and US2, respectively. After unblinding, 3 and 332 events are observed with an expectation of 2.8±0.5 and 251±32 background events, for the paired and US2 data, respectively. A combined analysis yields a best-fit B8 neutrino signal of 3.5 (75) events from the paired (US2) data sample, with ∼37% uncertainty, and the background-only hypothesis is disfavored at 2.64σ significance. This gives a solar B8 neutrino flux of (8.4±3.1)×106 cm−2 s−1, consistent with the standard solar model prediction. It is also the first indication of solar B8 neutrino “fog” in a dark matter direct detection experiment.

关键词： Nucleus-neutrino interactions Particle dark matter Solar neutrinos Neutrino detection Time-projection chambers

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Investigations on effect of primary mass flow change rate on flow instability among parallel U-tubes of steam generator

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nuclear engineering and technology 2025年第11期57卷

作者： Jianli Hao Wenzhen Chen Houqi Lv Junjie Ma Yanping Huang Lei Yu Nuclear Power Institute of China Chengdu 610041 China College of Nuclear Science and Technology Naval University of Engineering Wuhan 430033 China

Current investigations reveal that reverse flow phenomena in parallel steam generator U-tubes under natural circulation significantly reduce circulation capacity, compromising operational safety of marine nuclear power plant. During transitional phases from forced to natural circulation, the steam generator's primary mass flow rate experiences an abrupt decline, inducing complex reverse flow phenomena. To solve the above problems, this paper used experimental methods to measure the effect of the steam generator primary mass flow change rate on the reverse flow based on the steam generator experimental loop. The results demonstrated that with an increase in the primary mass flow change rate, the critical mass flow rate of parallel U-tubes decreased obviously, while the critical pressure drop remained nearly unchanged. In addition, the mechanism was analyzed using the RELAP5 code. The results can facilitate the technical supportnecessary for the operation safety analysis of marine nuclear power plant.

关键词： Natural circulation Steam generator Parallel inverted U-Tubes Reverse flow

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Prediction of ship fuel consumption based on Elastic network regression model

Prediction of ship fuel consumption based on Elastic network...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Shanshan Li Xinyu Li Yi Zuo Tieshan Li Dalian Maritime University Dalian China College of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665402460

Predicting the fuel consumption of ships sailing under different navigation conditions and improving the operation efficiency of shipping industry has become an important topic. There are many characteristic variables affecting ship fuel consumption during navigation, such as trim, draft, wind speed, wind direction and so on. And some variables are highly correlated, which is easy to produce multicollinearity problems. It makes the fuel consumption prediction complex. The study established an Elastic network regression model by combining the least absolute contraction and selection operator (LASSO) and Ridge regression algorithm. The model reduces the complexity and improves the interpretability and accuracy by selecting the characteristic variables affecting ship fuel consumption. The study is verified by the navigation data of a ferry within two months. The results show that compared with long short term memory (LSTM) and back-propagation neural network (BPNN), the Elastic network regression model can not only explain the relationship between fuel consumption and variables, but also predict fuel consumption more accurately and effectively.

关键词： Industries Navigation Wind speed Neural networks Predictive models Prediction algorithms Data models

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Search for Solar Boosted Dark Matter Particles at the PandaX-4T Experiment

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Physical Review Letters 2025年第16期134卷 161003-161003页

作者： Guofang Shen Zihao Bo Wei Chen Xun Chen Yunhua Chen Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Zhixing Gao Lisheng Geng Karl Giboni Xunan Guo Xuyuan Guo Zichao Guo Chencheng Han Ke Han Changda He Jinrong He Di Huang Houqi Huang Junting Huang Ruquan Hou Yu Hou Xiangdong Ji Xiangpan Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shuaijie Li Tao Li Zhiyuan Li Qing Lin Jianglai Liu Congcong Lu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Yue Meng Xuyang Ning Binyu Pang Ningchun Qi Zhicheng Qian Xiangxiang Ren Dong Shan Xiaofeng Shang Xiyuan Shao Manbin Shen Wenliang Sun Yi Tao Anqing Wang Guanbo Wang Hao Wang Jiamin Wang Lei Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Xu Wang Zhou Wang Yuehuan Wei Weihao Wu Yuan Wu Mengjiao Xiao Xiang Xiao Kaizhi Xiong Yifan Xu Shunyu Yao Binbin Yan Xiyu Yan Yong Yang Peihua Ye Chunxu Yu Ying Yuan Zhe Yuan Youhui Yun Xinning Zeng Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Wei Zhang Yang Zhang Yingxin Zhang Yuanyuan Zhang Li Zhao Jifang Zhou Jiaxu Zhou Jiayi Zhou Ning Zhou Xiaopeng Zhou Yubo Zhou Zhizhen Zhou Haipeng An Haoming Nie School of Physics Beihang University Beijing 102206 China School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics and Ion-beam Application (MoE) Institute of Modern Physics Fudan University Shanghai 200433 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA School of Physics Nankai University Tianjin 300071 China School of Physics Sun Yat-Sen University Guangzhou 510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China Research Center for Particle Science and Technology

We present a novel constraint on light dark matter utilizing 1.54 metric ton/year of data acquired from the PandaX-4T dual-phase xenon time projection chamber. This constraint is derived through detecting electronic recoil signals resulting from the interaction with solar-enhanced dark matter flux. Low-mass dark matter particles, lighter than a few MeV/c2, can scatter with the thermal electrons in the Sun. Consequently, with higher kinetic energy, the boosted dark matter component becomes detectable via contact scattering with xenon electrons, resulting in a few keV energy deposition that exceeds the threshold of PandaX-4T. We calculate the expected recoil energy in PandaX-4T considering the Sun’s acceleration with heavy mediators and the detection capabilities of the xenon detector. The first experimental search results using the xenon detector yield the most stringent upper limits cross section of 3.51×10−39 cm2 at 0.08 MeV/c2 for a solar boosted dark matter mass ranging from 0.02 to 10 MeV/c2, achieving a 23-fold improvement compared with earlier experimental studies.

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Broad Learning System-Based Adaptive Optimal Course-Keeping Control of Marine Surface Vessel 15

Broad Learning System-Based Adaptive Optimal Course-Keeping ...

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15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

作者： Ma, Yuzhuo Li, Tieshan Philip Chen, C.L. Yuan, Liang'en Dalian Maritime University Navigation College Dalian China University of Electronic Science and Technology of China School of Automation Engineering Chengdu China University of Macau Faculty of Science and Technology China

ISBN: (纸本)9781728151694

In this paper, a novel optimal control method by broad learning system (BLS)-based adaptive dynamic programming is proposed. This method is used to course-keeping control of ship under the conditions of unknown system dynamics, energy saving and reduced equipment waste. First, it is necessary to identify the unknown nonlinear dynamics in the ship's course keeping system, so a model network is established by BLS. Then, a BLS based optimal control scheme is proposed, the data used for the BLSs training is composed of current data and recorded data. The connection weights of the approximator is obtained by real system without need of iteration. Therefore, compared with the traditional adaptive dynamic programming (ADP) algorithm that requires multiple iterations, BLS-based ADP prove the effectiveness and high performance of the proposed optimal control law for course-keeping in ship autonomous driving. © 2020 IEEE.

关键词： Learning systems

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Segmented Prediction Model of Lithium-ion Battery Remaining Useful Life Based on LSTM and SVR

Segmented Prediction Model of Lithium-ion Battery Remaining ...

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IEEE International Conference of Intelligent Robotic and Control engineering (IRCE)

作者： Yingzhou Wang Chengyuan Liu Xiuyu Zhang Yifan Deng Lei Chen Hui Liu Bingzheng Liu Yanhui Zhang College of Automation Engineering Northeast Electric Power University Jilin China Department of Computer Engineering Chongqing Aerospace Polytechnic Chongqing China Shenzhen Branch China Nuclear Industry Maintenance Co. Ltd Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

As an important part of battery health management, accurate prediction on remaining useful life of lithium-ion battery can maintain and replace the batteries in advance to guarantee the safety and stability of the energy storage system. Most extant models use a single model throughout the entire stage for prediction, which may lead to the decrease of the accuracy for subsequent prediction due to insufficient learning, as well as disability to predict battery remaining useful life with data from other batteries. To solve these issues, a segmented prediction model of lithium-ion battery remaining useful life is proposed. In this method, the knee point of the capacity decline curve is detected and then long short-term memory and support vector regression models are used to predict the remaining useful life before and after the knee point, respectively. The experimental prediction results represent that the proposed method achieves higher prediction accuracy compared with long short-term memory method.

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