Energy Management Strategy For Hybrid Electric Buses Considering Vehicle Mass Variation,
2026
School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
Energy Management Strategy For Hybrid Electric Buses Considering Vehicle Mass Variation, Jinjun Tang, Shuaijie Zhang
Journal of System Simulation
The vehicle mass variation during the operation of buses affects power demand of the vehicle, which can result in poor performance of energy management strategies. To this end, a hybrid electric bus energy management strategy based on proximal policy optimization-adaptive simulated annealing (PPOASA) is proposed. ASA is introduced into PPO to perturb policy parameters according to policy entropy before the policy update, and the perturbed policies are adaptively accepted or rejected by employing the Metropolis criterion, thus improving the exploration capability of the policy and convergence stability. Experimental results show that the proposed method outperforms the charge depleting-charge sustaining (CD-CS) …
Improved Nsga-Ii For Dual-Resource Flexible Job Shop Scheduling Considering Worker Load,
2026
School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
Improved Nsga-Ii For Dual-Resource Flexible Job Shop Scheduling Considering Worker Load, Guohui Zhang, Yuan Ren, Changjun Wu, Xiaofei Kou
Journal of System Simulation
For the dual-resource-constrained flexible job shop scheduling problem considering worker load, an evolutionary algorithm integrating reinforcement learning was proposed. A three-stage encoding conforming to the problem characteristics was designed, and three initialization methods were combined to improve the population quality; a left-insertion decoding method based on worker load was designed to ensure that the completion time of the operation is less than the maximum processable time of the worker on the current day; two neighborhood structures based on the critical path were constructed to enhance the local exploration ability of the population; reinforcement learning was integrated to enable the …
Impact Angle-Constrained Dive Maneuver Guidance Method For Hypersonic Vehicles,
2026
School of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China
Impact Angle-Constrained Dive Maneuver Guidance Method For Hypersonic Vehicles, He Wang, Gang Lei, Shaopeng Li
Journal of System Simulation
To address the impact angle control and maneuvering flight problem of hypersonic vehicles in the dive phase, this paper proposed a tracking guidance method integrating optimal Bézier curves and super-twisting sliding mode control. A three-dimensional Bézier curve trajectory satisfying the impact angle constraint was designed, and the maneuvering flight in dive phase was achieved by adding dynamic control points; to optimize impact velocity, a rapid calculation method for the impact velocity of the vehicle flying along the curve was derived, and the optimal reference trajectory was obtained by optimizing the control point parameters through sequential quadratic programming; to ensure …
Application Of Improved Multi-Objective Differential Algorithm In Robotic Arm Multi-Objective Trajectory Planning,
2026
School of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730000, China
Application Of Improved Multi-Objective Differential Algorithm In Robotic Arm Multi-Objective Trajectory Planning, Manqiang Liu, Ziqiang Shang
Journal of System Simulation
It is difficult for single-objective trajectory planning methods to meet the requirements of precision, diversity and complexity of robotic arms. A trajectory planning model based on an improved multi-objective differential evolution algorithm (guided multi-objective differential evolution, GMODE) algorithm is proposed. Cubic polynomial interpolation and B-spline curves are employed to construct multi-objective functions, while GMODE is adopted to overcome the limitations of traditional algorithms, such as insufficient population diversity, the tendency to fall into local optima, and slow convergence. A grouping strategy, parameter generation mechanism, and elite mutation based on fuzzy Cmeans clustering are introduced to optimize B-spline control nodes. …
Two-Stage Calibration And Optimization Method For Microscopic Traffic Simulation Model Parameters Based On Neural Network Surrogate Models,
2026
School of Civil Engineering and Transportation, Guangzhou University, Guangzhou 510700, China
Two-Stage Calibration And Optimization Method For Microscopic Traffic Simulation Model Parameters Based On Neural Network Surrogate Models, Yijia Liu, Chenjing Zhou, Dong Pan, Jian Rong, Yang Xiao
Journal of System Simulation
A two-stage calibration and optimization method is proposed to address the problem that parameter calibration methods for microscopic traffic simulation models are time-consuming. In the first stage, a surrogate model based on neural networks is trained to establish the mapping relationship between model parameters and evaluation indicators, and a genetic algorithm (GA) is combined to screen candidate parameters. In the second stage, after obtaining the approximate optimal parameters, by employing this set of parameters as initial values, a genetic algorithm is re-executed by combining the real simulation model for optimization to further improve calibration accuracy. Experimental results show that the …
Pattern Identification And Mechanism Analysis Of Nonlinear Oscillations In Grid-Connected Direct-Drive Wind Turbines,
2026
Electric Power Research Institute, Guangxi Power Grid Co. , Ltd. , Nanning 530023, China; Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, Nanning 530023, China
Pattern Identification And Mechanism Analysis Of Nonlinear Oscillations In Grid-Connected Direct-Drive Wind Turbines, Libin Wen, Shaopu Tang, Xianfa Hu, Jinji Xi, Tongtong Zhang, Hong Hu, Weijie Zhang
Journal of System Simulation
Taking a grid-connected direct-drive wind turbine system as an example, a comprehensive model is developed that incorporates nonlinear elements such as prime mover control, machine-side and grid-side converter control, multiple limiters, and control switching. A nonlinear oscillation pattern identification method based on density clustering and manual identification is proposed. The results show that the proposed method can efficiently identify various typical patterns, including quasi-constant amplitude oscillations, period-doubling oscillations, and chaotic oscillations. Oscillations dominated by nonlinear factors such as control switching, limiter collision, and limiter saturation are essentially caused by the transition of the associated components from passive responses to …
Research On Optimization Modeling Method For Eye Tracking In Solfeggio Cognitive Simulation,
2026
School of Electrical and Automation, Nantong University, Nantong 226019, China
Research On Optimization Modeling Method For Eye Tracking In Solfeggio Cognitive Simulation, Kun Zhang, Jiajie Qian, Shuhong Ma, Zengxu Zhao, Yuchen Pan, Yaoqi Tang
Journal of System Simulation
To address the fixation offset problem caused by head movement in music solfeggio teaching simulation and the lack of system-level simulation validation in existing methods, this paper proposed a fixation accuracy optimization method integrating image semantic understanding, temporal trajectory modeling, and solfeggio cognitive simulation. With Vision Transformer as the core, after preprocessing via Mahalanobis distance, sliding window, and region of interest, position offset perception, offset residual regression, and dual-pathway fusion were introduced to achieve offset modeling and correction under unlabeled conditions. Simulation results indicate that the error of this method decreases by 43.9% compared with the original value error; removing …
Where Evidence-Based Medicine Meets Ai: Promise, Pitfalls, And Practice,
2026
Thomas Jefferson University
Where Evidence-Based Medicine Meets Ai: Promise, Pitfalls, And Practice, Sangil Lee, Joshua Davis, Ken Milne, Christina Shenvi, Lars K. Beattie, Martin Wegman, Laura Melville, Richard D. Shih, Bryan Kane
Department of Emergency Medicine Faculty Papers
No abstract provided.
Artificial Intelligence–Driven Paradigm Transformation In Biopharmaceutical R&D: Applications And Emerging Scenarios,
2026
Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
Artificial Intelligence–Driven Paradigm Transformation In Biopharmaceutical R&D: Applications And Emerging Scenarios, Lili Liu, Mingyue Zheng, Ye Yuan, Xutong Li, Rong Fan, Wei Wei, Jinxin Zhao, Guobin Qi, Hua Yue, Likun Gong, Songping Zhang, Jiachen Li, Yuchen Sun, Xiaoyan Chen, Yao Chen, Xin Liu, Xiao Zhang, Yuehong Gao, Jianfeng Li, Kaixian Chen, Guanghui Ma, Jianmin Yue
Bulletin of Chinese Academy of Sciences (Chinese Version)
The biopharmaceutical industry is a critical domain underpinning national scientific and technological innovation development and public health. With the rapid advancement of artificial intelligence (AI) and its deep integration with the life sciences, biomedicine research is undergoing a paradigm shift from traditional experience-driven trial-and-error approaches to data-driven and predictive validation-based models. This study systematically examines the pathways for reshaping research in biomedicine paradigms under the convergence of data-driven, mechanism-driven, and intelligence-driven approaches. It focuses on recent advances in the application of AI across key stages, including drug discovery and design, druggability evaluation, delivery system design and optimization, nonclinical and clinical …
Artificial Intelligence Empowering Remote Sensing: Challenges, Paradigms And Strategic Layout,
2026
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; National Key Laboratory of Target Cognition and Application Technology, Beijing 100190, China
Artificial Intelligence Empowering Remote Sensing: Challenges, Paradigms And Strategic Layout, Jiayuan Shen, Peirui Cheng, Zhirui Wang, Wei Liang, Xian Sun, Yirong Wu
Bulletin of Chinese Academy of Sciences (Chinese Version)
Remote sensing science and technology, as a key discipline for Earth observation and global change research, faces systemic challenges in processing massive multi-source data, accurately extracting complex information, and delivering high-timeliness application services. The rapid advances in artificial intelligence (AI) provide a new opportunity to address the deep-seated dilemma in remote sensing of being “data-rich but insufficient in effective information mining”. Guided by a problem-oriented approach, this study first systematically analyzes the core challenges facing the development of remote sensing across four dimensions: data understanding, technical methods, scientific mechanisms, and application ecosystems. It then reviews the technical evolution of AI-empowered …
Artificial Intelligence For Science: Connotations, Characteristics, And System,
2026
School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for National Frontier S&T Integration and Innovation, University of Chinese Academy of Sciences, Beijing 100049, China
Artificial Intelligence For Science: Connotations, Characteristics, And System, Kaihua Chen, Heyang Li, Hongxin Liu, Binbin Zhao, Shuo Yang
Bulletin of Chinese Academy of Sciences (Chinese Version)
Artificial intelligence is profoundly transforming the fundamental nature of scientific research, reshaping its modes of knowledge production and organizational operation, driving the emergence of a new artificial intelligence for science (AI4S) research paradigm, and accelerating full-chain innovation paradigm transformation. This study defines the basic connotations of AI4S across three dimensions, namely, enabling applications, tools and methods, and epistemic knowledge, and systematically identifies five core characteristics: human-machine symbiosis, autonomous evolution, interdisciplinary integration, resource intensity, and open ecosystems. It further constructs a supporting system and operational architecture encompassing layers of infrastructure, data resources, model tools, task execution, and application scenarios. Building on …
Preface Of National Think Tank In Science And Technology: Ai Empowers Scientific Research,
2026
Chinese Academy of Sciences
Preface Of National Think Tank In Science And Technology: Ai Empowers Scientific Research
Bulletin of Chinese Academy of Sciences (Chinese Version)
No abstract provided.
Disciplinary System Of Artificial Intelligence: Connotation, Architecture, And Development Suggestions,
2026
Chinese Academy of Sciences, Beijing 100864, China
Disciplinary System Of Artificial Intelligence: Connotation, Architecture, And Development Suggestions, Academic Divisions Of The Chinese Academy Of Sciences Discipline Group Of Advisory Project On Ai-Empowered Scientific Research
Bulletin of Chinese Academy of Sciences (Chinese Version)
The discipline of artificial intelligence studies the theories, methods, systems, applications, enabling functions, ethics, and governance of artificial intelligence, and is a typical interdisciplinary field. With the rapid development of artificial intelligence in recent years, its disciplinary connotations and system architecture urgently require renewed examination. Based on the analysis of development trends of artificial intelligence, this paper elucidates the connotations of the AI discipline from four perspectives: theoretical methods, forms of intelligence, disciplinary integration, and application empowerment. It further proposes a disciplinary system framework for artificial intelligence comprising foundational supporting disciplines, core body of knowledge, major forms of intelligence, and …
Artificial Intelligence Empowers Particle Physics And Nuclear Physics: From Fundamental Research To Major Applications,
2026
Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
Artificial Intelligence Empowers Particle Physics And Nuclear Physics: From Fundamental Research To Major Applications, Yifang Wang, Yuan He, Yao Huang, Wanbing He, Yi Jiao, Congqiao Li, Ke Li, Beijiang Liu, Yingqi Ma, Yugang Ma, Longgang Pang, Fazhi Qi, Sichao Tan, Chunpeng Wang, Meng Wang, Xiaoheng Xu, Xing Xu, Zhentang Zhao, Yingxun Zhang, Zhengde Zhang, Hongwei Zhao, Lina Zhao
Bulletin of Chinese Academy of Sciences (Chinese Version)
Particle physics and nuclear physics are core foundational disciplines for exploring the fundamental structure of matter and the origin of the universe. The deep integration of artificial intelligence (AI) technology is providing entirely new pathways to address systemic challenges such as the processing of massive amounts of multimodal data, the realization of extreme experimental conditions, bottlenecks in theoretical calculations, and the intelligent control of large-scale scientific facilities. The article systematically elaborates on how AI deeply empowers particle physics and nuclear physics, particularly in major application scenarios such as research on the fundamental structure and origin of mass of matter, the …
Consolidating Chemical Substance Creation Capability Through Ai For Science-Enabled Innovation Equity,
2026
National Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
Consolidating Chemical Substance Creation Capability Through Ai For Science-Enabled Innovation Equity, Mengchu Jin, Wandong Wang, Jun Zhang, Yi Luo, Zaiku Xie, Jinlong Yang, Jun Jiang
Bulletin of Chinese Academy of Sciences (Chinese Version)
AI for Science (hereinafter referred to as AI4S) is driving profound changes in the paradigm of scientific research. Chemistry, as a central discipline for creating new substances and supporting major national strategic needs such as energy, health, dual carbon goals, advanced manufacturing, and ecological governance, is an important application scenario of AI4S. At present, innovation in the discipline of chemistry by young researchers still faces knowledge silos, capability silos, and resource silos: the accumulation of professional knowledge requires years of effort, frontier knowledge is highly differentiated, experimental capabilities are difficult to reuse, and high-end resources are difficult to coordinate, which …
Artificial Intelligence-Enabled Materials Innovation: Implementation Levels And Strategic Layout,
2026
Dongguan Institute of Materials Science and Technology, Chinese Academy of Sciences, Dongguan 523830, China; Songshan Lake Materials Laboratory, Dongguan 523830, China
Artificial Intelligence-Enabled Materials Innovation: Implementation Levels And Strategic Layout, Ziwei Zhao, Fengxiang Zhou, Yanglili Zhou, Can Wang, Pei Zhang, Weihua Wang
Bulletin of Chinese Academy of Sciences (Chinese Version)
Materials innovation has long been constrained by vast design spaces, complex processing routes, lengthy validation cycles, and difficulties in engineering translation. Traditional research and development models, which mainly rely on accumulated experience, theoretical deduction, and experimental trial and error, have become increasingly insufficient to meet the demand for rapid breakthroughs in critical materials. In recent years, artificial intelligence has been increasingly integrated into materials design, synthesis, and processing, characterization, evaluation, optimization, and application feedback, promoting the transformation of materials innovation from experience-driven exploration to data-driven development and from discrete trial and error to closed-loop optimization. Based on an analysis of …
Artificial Intelligence Empowered Biological Research: Paradigm Shifts, Application Scenarios, And Strategic Layout,
2026
Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China
Artificial Intelligence Empowered Biological Research: Paradigm Shifts, Application Scenarios, And Strategic Layout, Xinguang Zhu, Yiming Bao, Zhenong Jin, Xin Li, Sijia Wang, Yueming Wang, Yungui Yang, Cao Xu, Yan Xiong, Bin Han
Bulletin of Chinese Academy of Sciences (Chinese Version)
Life related processes are characterized by high dimensionality and multi-scale properties. Understanding mechanisms underpinning life processes helps promote national healthcare, agricultural development, sustainable ecological civilization, and national security. Current life science research is confronted with an enormous challenge of dimensionality stemming from data explosion and data fragmentation, for which the recent rapid advancement of artificial intelligence (AI) provides novel solutions. AI will catalyze a paradigm shift in life science research from the current experiment based empirical induction to a new closed-loop knowledge acquisition including large scale data collection, model building, model prediction, experimental validation, and iterative of these procedures. Life …
Artificial Intelligence For Cybersecurity: Opportunities, Challenges, And Approaches,
2026
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100085, China
Artificial Intelligence For Cybersecurity: Opportunities, Challenges, And Approaches, Kai Chen, Ding Li, Guozhu Meng, Shouling Ji, Changjiang Li, Yi Yang, Dengguo Feng
Bulletin of Chinese Academy of Sciences (Chinese Version)
Cybersecurity research, institutional structures, and governance policies are undergoing profound transformations. Currently, increasingly covert and rapidly evolving intelligent attacks, coupled with the national urgent expectations for high-level security, are driving significant shifts in the roles and interactions of governments, research institutions, and enterprises. Consequently, this study, based on analyzing the challenges and opportunities of the AI era, explores core application scenarios such as critical information infrastructure protection, national data security, and the maintenance of cyberspace sovereignty. It provides an analysis of artificial intelligence in dimensions such as correlation and causality, and proposes a governance framework, aiming to provide insights for …
Artificial Intelligence For Astronomy: Strategic Opportunities, Policy Challenges And Development Strategies,
2026
University of Science and Technology of China, Hefei 230026, China
Artificial Intelligence For Astronomy: Strategic Opportunities, Policy Challenges And Development Strategies, Jin Chang, Yihan Song, Bing Du, Kefei Wu, Ali Luo, Jifeng Liu
Bulletin of Chinese Academy of Sciences (Chinese Version)
To alleviate the bottlenecks hindering the integrated development of artificial intelligence and astronomy in China and to reinforce the country’s strategic edge in science and technology, this study uses systematic analysis and path-comparison approaches to examine the policy requirements for their deep integration. The findings indicate that this topic is closely tied to global competition in science and technology, strategic security, and industrial upgrading. At present, the world has entered a new “astronomy + AI” paradigm, with the United States and the European Union already having taken the lead in establishing corresponding strategic frameworks. Leveraging major scientific infrastructures such as …
Artificial Intelligence Empowering Space Science—Case Study Of Space Weather,
2026
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Artificial Intelligence Empowering Space Science—Case Study Of Space Weather, Chi Wang, Hui Li, Bingxian Luo, Fang Shen, Jingjing Wang, Lingqian Zhang, Yi Yang, Dong Zhao
Bulletin of Chinese Academy of Sciences (Chinese Version)
Space science is currently confronted with a triple challenge: the explosive growth of observational data, the strongly coupled cross-scale nature of physical processes, and the increasingly urgent national strategic demands. The limitations of traditional research paradigms in analytical efficiency, forecast accuracy, and autonomous capability hinder their effectiveness in meeting critical requirements such as safeguarding on-orbit satellites and ensuring the successful execution of major space missions. This study proposes a three-layer “perception–cognition–decision-making” architecture for intelligent space science. Taking space weather—a domain with strong operational relevance—as a representative case, the four-dimensional paradigm transformation driven by artificial intelligence is systematically examined across key …
