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Chinese automation Congress (CAC)

作者： Ruijie Chang Wenzhuo Li Na Zhao Shanglai Li Ang Li Xin Ji Beijing Key Laboratory of Research and System Evaluation of Dispatching Automation Technology China Electric Power Research Institute Beijing China China Electric Power Research Institute Co. Ltd Beijing China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665426480

Power dispatching automation software system is a software-intensive system. The safe and stable operation of the software system is related to the security and stability of the whole power system. The degree and scope of the damage caused by malicious attacks are also constantly deepening and expanding, so the evaluation and prediction of the reliability of the software is particularly important. In this paper, the dual-process model is used to evaluate and predict the reliability of the software of power grid dispatching automation system, and the two models are weighted and mixed to improve the accuracy and robustness of the software reliability model.

关键词： automation Predictive models Power system stability Software systems dispatching Robustness Power grids

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Geoscience knowledge graph in the big data era

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Science China Earth Sciences 2021年第7期64卷 1105-1114页

作者： Chenghu ZHOU Hua WANG Chengshan WANG Zengqian HOU Zhiming ZHENG Shuzhong SHEN Qiuming CHENG Zhiqiang FENG Xinbing WANG Hairong LV Junxuan FAN Xiumian HU Mingcai HOU Yunqiang ZHU State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China School of Earth Sciences and Resources China University of Geosciences(Beijing)Beijing 100083China Key Laboratory of Deep-Earth Dynamics of Ministry of Natural Resources Institute of GeologyChinese Academy of Geological SciencesBeijing 100037China Institute of Artificial Intelligence Beihang UniversityBeijing 100191China State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing UniversityNanjing 210023China Sinopec Petroleum Exploration&Production Research Institute Beijing 100083China School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China Institute of Sedimentary Geology Chengdu University of TechnologyChengdu 610059China

Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the *** is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge *** on adopting the graph pattern of general knowledge representation,the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge,and integrates geoscience knowledge elements,such as map,text,and number,to establish an all-domain geoscience knowledge representation model.A federated,crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here,which realizes the construction of high-quality professional knowledge graph in collaboration with global *** also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis,which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph.A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis,but also advance the construction of the high-precision geological time scale driven by big data,the compilation of intelligent maps driven by rules and data,and the geoscience knowledge evolution and reasoning analysis,among *** will further expand the new directions of geoscience research driven by both data and knowledge,break new ground where geoscience,information science,and data science converge,realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.

关键词： Geoscience knowledge graph All-domain geoscience knowledge representation model Federated crowd intelligence collaboration High-precision geological time scale

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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 ***,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 ***: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 *** next conducted Kaplan-Meier survival analysis based on The Cancer Genome Atlas database and conducted immunohistochemistry to determine the clinical relevance of these synergistic *** also investigated the MAPKi resistance mechanisms of these validated synergistic combinations by using co-immunoprecipitation,Western blot,qRTPCR,and immunofluorescence ***: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 ***,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+***:Pair-wise sgRNA library screening provided systematic insights into elucidating MAPKi resistance in cancer ***3-+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

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions

arXiv

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arXiv 2024年

作者： Zhu, Xiang Ruan, Guangchun Geng, Hua Liu, Honghai Bai, Mingfei Peng, Chao The Department of Automation Tsinghua University Beijing10084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing10084 China MIT BostonMA02139 United States The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical & Electronic Engineering North China Electric Power University Beijing102206 China The School of Automation Engineering University of Electronics and Technology of China Chengdu611731 China

Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOSO) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simultaneously maximize the economic benefits and minimize carbon emissions, and the degradation of the battery energy storage system (BESS) is modeled as a nonlinear function of power throughput. A self-adaptive multi-objective genetic algorithm (SAMOGA) is proposed to solve the SMOSO model, and this algorithm is enhanced by pre-grouped hierarchical selection and self-adaptive probabilities of crossover and mutation. Several case studies are conducted to determine the microgrid size by analyzing Pareto frontiers, and the simulation results validate that the proposed method has superior performance over other algorithms on the solution quality of optimum and diversity. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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An Integration evaluation Method for Substation Protection and Monitoring system Based on G1 Method and Expert Investigation

An Integration Evaluation Method for Substation Protection a...

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New Energy and Power Engineering (ICNEPE), International Conference on

作者： Xiongyao Deng Yuxiang Yan Chang Lin Hao Xia Ancheng Xue Zhihuai Shu Yu Liu Naichao Chang Chen Fan Zhongqing Li Jiangwen Meng Xuewei Dou Shenqiang Wang Long Zhang Fangying Wu State Key Laboratory of Alternate Electric Power System with Renewable Energy Source (North China Electric Power University) Beijing China National Electric Power Dispatching and Control Center of State Grid Corporation of China Beijing China Power Automation Department China Electric Power Research Institute Nanjing China China Electric Power Research Institute Beijing China Beijing Sifang Engineering Co. Ltd Beijing China

Substation is the core hub of power system. With the expansion of the scale of the power system and the improvement of its intelligence, the problems of the redundancy of architecture and equipment in the protection and monitoring system of substation can no longer be ignored. This paper proposes an evaluation method for integration in the protection and monitoring system based on G1 method. Firstly, according to the requirements of integration in the protection and monitoring system, the corresponding indicators are proposed to form an index system for evaluating integration. And then, an evaluation method is proposed for integration of the protection and monitoring system, based on G1 method and expert investigation. Finally, the feasibility of the evaluation method is verified by comparing and analyzing the evaluation results of a typical substation and its structural optimization substation.

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A Compatibility evaluation Method for Substation Protection and Monitoring system Based on G1 Method and Expert Investigation

A Compatibility Evaluation Method for Substation Protection ...

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New Energy and Power Engineering (ICNEPE), International Conference on

作者： Hao Xia Xiongyao Deng Shizhong Ma Yuxiang Yan Ancheng Xue Yu Liu Zhihuai Shu Naichao Chang Zhongqing Li Jiangwen Meng Xuewei Dou Shenqiang Wang Fangying Wu Chen Fan State Key Laboratory of Alternate Electrical Power System with Renewable Energy Source (North China Electric Power University) Beijing China National Electric Power Dispatching and Control Center of State Grid Corporation of China Beijing China China Electric Power Research Institute Beijing China Beijing Sifang Engineering Co. Ltd Beijing China Power Automation Department China Electric Power Research Institute Nanjing China

In existing protection and monitoring system of substation(S-PMS), there are compatibility problems between hardware and software or between software-hardware combinations and systems, including differences in communication protocols between different manufacturers and differences in system adaptation. Compatibility issues have become a growing focus area for the power industry. This paper proposes a method to evaluates the compatibility of the protection and monitoring system of substation based on the G1 and expert investigation, for the existing or unbuilt substations. In detail, Firstly, according to the compatibility requirements of the substation protection and monitoring system, corresponding indicators are proposed and a comprehensive compatibility evaluation model is constructed. Secondly, based on the comprehensive evaluation model, the evaluation results of typical stations without domestic chips and optimized stations using domestic chips are compared to verify the feasibility of the proposed method.

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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IEEE International Conference on Robotics and Biomimetics

作者： Muyang Hou Zhenya Lei Haidong Hu Zhong Wang Yong Wang Le Xie Hesheng Wang Department of Automation Shanghai Jiao Tong University Shanghai China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Beijing Institute of Control Engineering Beijing China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant; on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A* which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion.

关键词： Hands Trajectory tracking Trajectory planning Service robots Stability analysis Safety Mobile robots Resource management Splines (mathematics) Trajectory optimization

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Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models

arXiv

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arXiv 2024年

作者： Lan, Zhengxing Li, Hongbo Liu, Lingshan Fan, Bo Lv, Yisheng Ren, Yilong Cui, Zhiyong The School of Transportation Science and Engineering Beihang University Beijing100191 China The State Key Lab of Intelligent Transportation System Beijing100191 China The Research Institute for Frontier Science Beihang University Beijing100191 China The Beijing Key Laboratory of Traffic Engineering College of Metropolitan Transportation Beijing University of Technology Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Zhongguancun Laboratory Beijing100194 China The Beihang Hangzhou Innovation Institute Yuhang Hangzhou310023 China

Predicting the future trajectories of dynamic traffic actors is a cornerstone task in autonomous driving. Though existing notable efforts have resulted in impressive performance improvements, a gap persists in scene cognitive and understanding of the complex traffic semantics. This paper proposes Traj-LLM, the first to investigate the potential of using Large Language Models (LLMs) without explicit prompt engineering to generate future motion from agents’ past/observed trajectories and scene semantics. Traj-LLM starts with sparse context joint coding to dissect the agent and scene features into a form that LLMs understand. On this basis, we innovatively explore LLMs’ powerful comprehension abilities to capture a spectrum of high-level scene knowledge and interactive information. Emulating the human-like lane focus cognitive function and enhancing Traj-LLM’s scene comprehension, we introduce lane-aware probabilistic learning powered by the pioneering Mamba module. Finally, a multi-modal Laplace decoder is designed to achieve scene-compliant multi-modal predictions. Extensive experiments manifest that Traj-LLM, fortified by LLMs’ strong prior knowledge and understanding prowess, together with lane-aware probability learning, outstrips state-of-the-art methods across evaluation metrics. Moreover, the few-shot analysis further substantiates Traj-LLM’s performance, wherein with just 50% of the dataset, it outperforms the majority of benchmarks relying on complete data utilization. This study explores equipping the trajectory prediction task with advanced capabilities inherent in LLMs, furnishing a more universal and adaptable solution for forecasting agent motion in a new way. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

Procedure Recognition by Knowledge-Driven Segmentation in Ro...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Zhen Li Yawen Deng Qiang Ye Weihong Yu Haoxiang Qi Yaliang Liu Zhangguo Yu Gui-Bin Bian School of Electronic and Information Engineering Tongji University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China School of Mechatronic Engineering Beijing Institute of Technology Beijing China Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS),’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99.27% for instruments and macular holes and an average accuracy of 98.97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

关键词： Image segmentation Visualization Accuracy Medical robotics Motion segmentation Surgery Lighting

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