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Tsinghua Science and Technology 2020年第4期25卷 458-470页

作者： Li Chen Nan Ma Patrick Wang Jiahong Li Pengfei Wang Guilin Pang Xiaojun Shi beijing Key Laboratory of information Service Engineering College of RoboticsBeijing Union UniversityBeijing 100101China Northeastern university BostonMA 02115USA the Communication and information center of Ministry of Emergency Management of the People’s Republic of China Beijing 100013China the college of Robotics Beijing Union UniversityBeijing 100101China

The development of autonomous driving has brought with it requirements for intelligence,safety,and stability.One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic,complex,and uncertain environments.Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies.Specifically,vehicles need to detect pedestrians,recognize their limb movements,and understand the meaning of their actions before making appropriate decisions in response.In this survey,we present a detailed description of the architecture for pedestrian action recognition in autonomous driving,and compare the existing mainstream pedestrian action recognition techniques.We also introduce several commonly used datasets used in pedestrian motion recognition.Finally,we present several suggestions for future research directions.

关键词： autonomous driving pedestrian action recognition action datasets two-stream network

Design of a networked Tracking Control System With a Data-based Approach

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1261-1267页

作者： Shiwen Tong Dianwei Qian Xiaoyu Yan Jianjun Fang Wei Liu the college of Robotics Beijing Union UniversityBeijing 100101and also with Beijing Key Laboratory of Information Service EngineeringBeijing Union UniversityBeijing 100101China the School of Computer and Control Engineering North China Electric Power UniversityBeijing 102206China the Graduate School Beijing Union UniversityBeijing 100101China the college of Urban Rail Transit and Logistics Beijing Union UniversityBeijing 100101China

Tracking control is a very challenging problem in the networked control system(NCS), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-based design approach for the networked tracking control system(NTCS). The method utilizes the input-output data of the controlled process to establish a predictive model with the help of fuzzy cluster modelling(FCM)technology. Then, the deduced error and error change in the future are treated as inputs of a fuzzy sliding mode controller(FSMC) to obtain a string of future control actions. These candidate control actions in the controller side are delivered to the plant side. Thus, the network induced time delays are compensated by selecting appropriate control action. Simulation outputs prove the validity of the proposed method.

关键词： Delay compensation fuzzy cluster modeling (FCM) fuzzy sliding mode control (FSMC) networked tracking control(NTC)

A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

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Machine Intelligence Research 2024年第2期21卷 400-410页

作者： Dezheng Wang Yinglong Wang Fan Yang Liyang Xu Yinong Zhang Yiran Chen Ning Liao School of Automation Southeast UniversityNanjing 210096China Software and Artificial Intelligence college Chongqing Institute of EngineeringChongqing 400056China beijing National Research center for information Science and Technology(BNRist) Department of AutomationTsinghua UniversityBeijing 100084China Liangjiang International college Chongqing University of TechnologyChongqing 401135China Smart City college Beijing Union UniversityBeijing 100101China

In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.

关键词： Multi-scale feature extractor deep neural network(DNN) multirate sampled industrial processes prediction

Systematic screening reveals synergistic interactions that overcome MAPK inhibitor resistance in cancer cells

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Cancer Biology & Medicine 2022年第2期19卷 229-252页

作者： Yu Yu Minzhen Tao Libin Xu Lei Cao Baoyu Le Na An Jilin Dong Yajie Xu Baoxing Yang Wei Li Bing Liu Qiong Wu Yinying Lu Zhen Xie Xiaohua Lian Department of Cell Biology Basic Medical CollegeArmy Medical University(Third Military Medical University)Chongqing 400038China MOE Key Laboratory of Bioinformatics and Bioinformatics Division Center for Synthetic and System BiologyDepartment of AutomationBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China National Cancer center/National Clinical Research center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China Department of Thoracic Surgery Peking Union Medical College HospitalChinese Academy of Medical SciencesBeijing 100730China beijing Syngentech Co. LtdBeijing 102206China School of Life Sciences Tsinghua UniversityBeijing 100084China The Comprehensive Liver Cancer center The 5th Medical Center of PLA General HospitalBeijing 100039China

Objective:Effective adjuvant therapeutic strategies are urgently needed to overcome MAPK inhibitor(MAPKi)resistance,which is one of the most common forms of resistance that has emerged in many types of cancers.Here,we aimed to systematically identify the genetic interactions underlying MAPKi resistance,and to further investigate the mechanisms that produce the genetic interactions that generate synergistic MAPKi resistance.Methods:We conducted a comprehensive pair-wise sgRNA-based high-throughput screening assay to identify synergistic interactions that sensitized cancer cells to MAPKi,and validated 3 genetic combinations through competitive growth,cell viability,and spheroid formation assays.We next conducted Kaplan-Meier survival analysis based on The Cancer Genome Atlas database and conducted immunohistochemistry to determine the clinical relevance of these synergistic combinations.We also investigated the MAPKi resistance mechanisms of these validated synergistic combinations by using co-immunoprecipitation,Western blot,qRTPCR,and immunofluorescence assays.Results:We constructed a systematic interaction network of MAPKi resistance and identified 3 novel synergistic combinations that effectively targeted MAPKi resistance(ITGB3+IGF1R,ITGB3+JNK,and HDGF+LGR5).We next analyzed their clinical relevance and the mechanisms by which they sensitized cancer cells to MAPKi exposure.Specifically,we discovered a novel protein complex,HDGF-LGR5,that adaptively responded to MAPKi to enhance cancer cell stemness,which was up-or downregulated by the inhibitors of ITGB3+JNK or ITGB3+IGF1R.Conclusions:Pair-wise sgRNA library screening provided systematic insights into elucidating MAPKi resistance in cancer cells.ITGB3-+IGF1R-targeting drugs(cilengitide+linsitinib)could be used as an effective therapy for suppressing the adaptive formation of the HDGF-LGR5 protein complex,which enhanced cancer stemness during MAPKi stress.

关键词： Pair-wise sgRNA library genetic interactions MAPKi resistance combinatorial therapy cancer stemness

Time Series Forecasting Fusion network Model Based on Prophet and Improved LSTM

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Computers, Materials & Continua 2023年第2期74卷 3199-3219页

作者： Weifeng Liu Xin Yu Qinyang Zhao Guang Cheng Xiaobing Hou Shengqi He college of Urban Rall Translt and Logistics Beijing Union UniversityBeijing100101China beijing Key Laboratory of information Service Engineering Beijing Union UniversityBeijing100101China Faculty of business Economicsand LawThe University of QueenslandBrisbane4072Australia Department of Oncology Wangjing HospitalChina Academy of Chinese Medical SciencesBeijing100102China

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

关键词： Time series data prediction regression analysis long short-term memory network prophet

CHDbase:A Comprehensive Knowledgebase for Congenital Heart Disease-related Genes and Clinical Manifestations

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Genomics, Proteomics & Bioinformatics 2023年第1期21卷 216-227页

作者： Wei-Zhen Zhou Wenke Li Huayan Shen Ruby W.Wang Wen Chen Yujing Zhang Qingyi Zeng Hao Wang Meng Yuan Ziyi Zeng Jinhui Cui Chuan-Yun Li Fred Y.Ye Zhou Zhou State Key Laboratory of Cardiovascular Disease Beijing Key Laboratory for Molecular Diagnostics of Cardiovascular DiseasesCenter of Laboratory MedicineFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100037China International Joint Informatics Laboratory&Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China Institute of Molecular Medicine Peking UniversityBeijing 100871China

Congenital heart disease(CHD)is one of the most common causes of major birth defects,with a prevalence of 1%.Although an increasing number of studies have reported the etiology of CHD,the findings scattered throughout the literature are difficult to retrieve and utilize in research and clinical practice.We therefore developed CHDbase,an evidence-based knowledgebase of CHD-related genes and clinical manifestations manually curated from 1114 publications,linking 1124 susceptibility genes and 3591 variations to more than 300 CHD types and related syndromes.Metadata such as the information of each publication and the selected population and samples,the strategy of studies,and the major findings of studies were integrated with each item of the research record.We also integrated functional annotations through parsing50 databases/tools to facilitate the interpretation of these genes and variations in disease pathogenicity.We further prioritized the significance of these CHD-related genes with a gene interaction network approach and extracted a core CHD sub-network with 163 genes.The clear genetic landscape of CHD enables the phenotype classification based on the shared genetic origin.Overall,CHDbase provides a comprehensive and freely available resource to study CHD susceptibilities,supporting a wide range of users in the scientific and medical communities.CHDbase is accessible at http://***.

关键词： Congenital heart disease Congenital heart defect Database Genetics Classification

kLDM:Inferring Multiple Metagenomic Association networks Based on the Variation of Environmental Factors

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Genomics, Proteomics & Bioinformatics 2021年第5期19卷 834-847页

作者： Yuqing Yang Xin Wang Kaikun Xie Congmin Zhu Ning Chen Ting Chen Department of Computer Science and Technology and Institute of Artificial Intelligence Tsinghua UniversityBeijing 100084China Sogou Inc. Beijing 100084China Department of Bioinformatics Key Laboratory of Ministry of Education for Gastrointestinal CancerSchool of Basic Medical SciencesFujian Medical UniversityFuzhou 350122China Peking union Medical college Chinese Academy of Medical ScienceBeijing 100005China Department of Ultrasound Peking Union Medical College HospitalBeijing 100005China beijing National Research center for information Science and Technology Beijing 100084China State Key Laboratory of networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

Identification of significant biological relationships or patterns is central to many metagenomic studies.Methods that estimate association networks have been proposed for this purpose;however,they assume that associations are static,neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors(EFs),which can result in inaccurate estimations.Therefore,in this study,we propose a computational model,called the k-Lognormal-Dirichlet-Multinomial(kLDM)model,which estimates multiple association networks that correspond to specific environmental conditions,and simultaneously infers microbe-microbe and EF-microbe associations for each network.The effectiveness of the kLDM model was demonstrated on synthetic data,a colorectal cancer(CRC)dataset,the Tara Oceans dataset,and the American Gut Project dataset.The results revealed that the widely-used Spearman’s rank correlation coefficient method performed much worse than the other methods,indicating the importance of separating samples by environmental conditions.Cancer fecal samples were then compared with cancer-free samples,and the estimation achieved by kLDM exhibited fewer associations among microbes but stronger associations between specific bacteria,especially five CRC-associated operational taxonomic units,indicating gut microbe translocation in cancer patients.Some EF-dependent associations were then found within a marine eukaryotic community.Finally,the gut microbial heterogeneity of inflammatory bowel disease patients was detected.These results demonstrate that kLDM can elucidate the complex associations within microbial ecosystems.The kLDM program,R,and Python scripts,together with all experimental datasets,are accessible at https://***/tinglab/kLDM.git.

关键词： Metagenomics Association inference Environmental condition Bayesian model Clustering

YOLO-Banana:An Effective Grading Method for Banana Appearance Quality

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Journal of beijing Institute of Technology 2023年第3期32卷 363-373页

作者： Dianhui Mao Xuesen Wang Yiming Liu Denghui Zhang Jianwei Wu Junhua Chen School of Computer Beijing Technology and Business UniversityBeijing 100048China Research center of information Technology Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China beijing PAIDE Science and Technology Development Co.Ltd. Beijing 100097China Institute of Standardization Theory and Strategy China National Institute of StandardizationBeijing 100088China

The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.

关键词： YOLOv5 banana appearance grading clustering algorithm weighted non-maximum suppression(weighted NMS) progressive aggregated network(PANet)

network Invulnerability Enhancement Algorithm Based on WSN Closeness Centrality

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Computers, Materials & Continua 2022年第11期73卷 3021-3038页

作者： Qian Sun Fengbo Yang Xiaoyi Wang Jing Li Jiping Xu Huiyan Zhang Li Wang Jiabin Yu Xiao Peng Ruichao Wang School of Artificial Intelligence Beijing Technology and Business UniversityBeijing100048China beijing Laboratory for Intelligent Environmental Protection Beijing100048China beijing Institute of Fashion Technology Beijing100029China Smart City college Beijing Union UniversityBeijing100101China university college Dublin Dublin4Ireland

Wireless Sensor network(WSN)is an important part of the Internet of Things(IoT),which are used for information exchange and communication between smart objects.In practical applications,WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption,etc.In order to overcome these problems,a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network.Also,a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics.Simulation results show that the life cycle and data transmission volume of the network can be improved with a lower network construction cost,and the invulnerability of the network is effectively enhanced.

关键词： Wireless sensor networks invulnerability small world characteristics heterogeneous nodes node centrality