Artificial Intelligence and Robotics Commons^™

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. …

Robot Friction Force Compensation Algorithm Integrating Temperature And Speed Factors, Jinwang Lü, Ankai Ying, Ming Li, Tao Song, Jie Zhang, Fanghui Qiu, Changcheng Shi, Guokun Zuo, Jialin Xu

Journal of System Simulation

Insufficient friction force compensation accuracy degrades motion smoothness, stability, and assistive compliance of elbow joint rehabilitation robots. To address this issue, an improved Stribeck friction force model integrating temperature and speed factors was proposed. The model employed an exponentially decaying friction factor to describe the characteristic that the increase rate of friction force slowed down with the rise of the robot's operating speed and designed a viscous function considering temperature effects to suppress friction force fluctuations caused by temperature changes. Experimental results indicate that the model achieves stable friction force compensation under different operating states of the robot and has …

Current Status And Prospects Of Complex Scene Reconstruction Based On Gaussian Splatting, Hong'an Li, Jiale Yang, Qingfang Liu, Yu Shi

Journal of System Simulation

Three-dimensional Gaussian splatting (3DGS) provides an alternative approach for novel view synthesis from the perspective of explicit representation. By reconstructing scenes using 3D Gaussian primitives and replacing traditional ray integration with a point-based rasterization process, it not only improves training and rendering efficiency but also offers new insights for complex scene reconstruction. This paper divided 3DGS-based complex scene reconstruction methods into three major categories and elaborated on them around large-scale scenes, sparse views, and dynamic scenes. It reviewed the current development status of this field and pointed out possible future research directions.

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 …

Experimental And Simulation Study On Energy Release Of Extended Sources Influenced By Atmospheric Pressure Variation, Kai Guo, Feiyu Zhao, Hao Zhang, Liang Wang, Kai Zhang

Journal of System Simulation

To investigate the energy release characteristics of extended sources in low-pressure environments, a combined experiment and simulation approach was adopted. Four typical altitudecorresponding pressures were selected as experimental conditions. An infrared thermal imager was employed to monitor parameters such as combustion temperature, radiance, and combustion area during the combustion process of the extended source. When the pressure decreases from 101 kPa to 30 kPa, the ignition time of the extended source doubles; the total energy release attenuates by 44.78%, and the combustion area reduces by 45.95%, but the fluctuations of peak temperature and average temperature are less than 3%, …

Object Detection Networks And Their Interpretability In Rain, Fog, And Snow Scenarios, Yanji Jiang, Jiayu Cui, Hao Dong, Daqian Liu, Bowen Fei, Miao Yu, Jinshan Huang

Journal of System Simulation

To address the severe degradation of object detection performance under extreme weather conditions, a detection framework based on the Kolmogorov-Arnold theorem, termed KADet, is proposed. A dynamic Kolmogorov-Arnold Transformer is designed, which leverages learnable nonlinear activation functions to enhance the modeling capability for complex distortions introduced by weather degradation. A Kolmogorov-Arnold spatial-channel network is developed by integrating KAT convolution with spatial-channel convolution to strengthen feature learning of relationships between targets and backgrounds in degraded scenes. An improved loss function is introduced to guide the optimization of the activation functions, and interpretability is analyzed through visualization of their curves. …

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.

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, 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, 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, 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, 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, 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–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 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, 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, 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 …

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 Deep Earth Science: Key Challenges, Major Application Scenarios And Development Pathways, Qingyun Di, Liang Zhao, Yikang Zheng, Zhi Geng, Zhichao Yu, Xiaocai Shan, Chao Li, Zhiyao Xu, Pengfei Lv

Bulletin of Chinese Academy of Sciences (Chinese Version)

Deep Earth science is central to understanding Earth’s internal architecture and the coupled evolution of its major spheres, while also underpinning energy security, the supply of critical mineral resources, and resilience to major geohazards. Nevertheless, the advancement of deep Earth science is currently hindered by insufficient in situ observations under extreme conditions, the difficulty of integrating multi-source heterogeneous data, and the limited capability to model complex multiphysics coupling processes. Recent advances in artificial intelligence offer a potential route beyond these limitations. By integrating data-driven learning with physical and geological understanding, AI is reshaping deep Earth science from empirical interpretation to …

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 …

Get Ready To Lead: Human-Centered Leadership In An Ai-Driven World, Ellen Ramsey

Faculty and Staff Publications & Presentations

As AI becomes increasingly integrated into our daily lives, online students continue to seek instructors who consistently appear, genuinely care about them as individuals, and provide guidance, challenge, and support. AI tools can help with speed and structure, but human-centered leadership keeps connection and meaning at the forefront of the learning experience. 

This interactive workshop invites online instructors and faculty leaders to explore how human-centered leadership can support their teaching in the middle of rapid technological change.

Grounded in a six-pillar leadership model that encompasses conscious self-awareness, relational intelligence, ethical influence, adaptive growth, transparent communication, and empowered action, this session …

Load Profile Analysis And Forecasting For Rural Mini Grids In Uganda, Prossy Mutesi, Santos L. Kihwele, Emmanuel S. Matee

Tanzania Journal of Science

Accurate load forecasting is essential for the reliable and cost-effective operation of rural mini grids, where constrained generation capacity and high penetration of renewable energy resources require well-informed operational decisions. This study examines electricity demand characteristics and forecasting performance for the Buzaami and Ssenyondo mini grids in Uganda, with particular focus on diurnal load profiles, peak demand behavior, and seasonal variability. 2022 operational data show extended peak demand from early morning to late evening, driven by socio-economic activities that strain resource scheduling and reliability management. To address these challenges, the study evaluates and compares Long Short-Term Memory (LSTM) networks, fuzzy …

A System For The Prediction Of Election Results Using Vader And Hybridized Machine Learning Model, Abraham E. Evwiekpaefe, Khadijah Kabir, Georgina N. Obunadike

Tanzania Journal of Science

Integrating different classifiers along with sentiment lexicons like Vader, can enhance the performance of sentiment analysis systems. However, such a hybrid model remains underexplored, particularly in the context of regional elections in developing countries like Nigeria. The aim of this research is to develop a hybrid model that combines three machine learning classifiers and Vader lexicon to possibly achieve a higher accuracy. A case study of the 2023 governorship election in Kogi, Bayelsa and Imo State, Nigeria was examined. Twitter API library was utilized to extracted public and personal tweets using hashtags and keywords related to the target data from …

Robust Real-Time Uav Target Tracking With Onboard Vision-Based Yaw Control, Rylan Malarchick, Jose Castelblanco, Enrique Amaya, Carmen Dimario, Graysen Brinkman, Chirag Kumar, Kiwon Yoon, Sajid Berhane

Beyond: Undergraduate Research Journal

Autonomous tracking of agile unmanned aerial vehicles (UAVs) presents significant challenges for real-time perception and control systems. This work presents AIRHOUND (Autonomous Intelligent Rotorcraft for Hostile Object Unified Navigation and Detection), a UAV platform implementing vision-based yaw tracking through a modular ROS2 software architecture. The system employs YOLOv8 object detection optimized with NVIDIA TensorRT for embedded deployment on an NVIDIA Jetson Orin companion computer. Detected targets are processed through a geometric tracking module that converts pixel coordinates to angular yaw errors using pinhole camera intrinsics, with a proportional controller generating rate-limited yaw commands. These commands are streamed to a PX4 …

Synthetic-Chicken-Fillets, Chirantan Sen Mukherjee, Seung-Chul Yoon, William J. Beksi

Agriculture

This Synthetic-Chicken-Fillets dataset contains 1,000 synthetic 3D meshes designed to capture the natural variance and size diversity of real broiler fillets. The collection was developed to test automated woody breast detection algorithms within a physics-based simulation environment. We utilized a seed dataset of 2D depth maps derived from 40 real-world RGBD point cloud scans. These real depth maps were fed into a few-shot transfer learning pipeline using a generative adversarial network architecture. The resulting generated depth maps were reconstructed back into 3D meshes. The length and thickness of each mesh were randomly scaled based on physical measurements of real broiler …

Beyond The Best Prompt: A Coverage View Of Multilingual Reasoning, Harshiv Mistry

University Honors Theses

Multilingual LLMs reason more accurately in English than in other languages, and recent work links part of this gap to reasoning behavior: native-language traces contain fewer cognitive behaviors (verification, backtracking, subgoal setting, backward chaining) that support effective problem solving. We test whether prompting for these behaviors at inference time narrows the gap, across seven conditions varying chain-of-thought, instruction and reasoning language, and cognitive-behavior descriptions, on two models, three languages. We find that English-scaffolded reasoning is the strongest single strategy on both models, closing the Hindi gap on Qwen, though the explicit scaffold's value over plain chain-of-thought is model-dependent. Beyond aggregate …

Digital Bodily Autonomy: Consent Issues, Labor Displacement, And Legal Understandings Of Ai Generated Deepfake Pornography, Mariah Barrett

Undergraduate Theses, Capstones, and Recitals

In the United States, nonconsensual pornographic deepfakes are becoming an increasingly prevalent problem as AI deepfake creation software improves and becomes widely available. Despite this, patchwork legislation across the country is inconsistent and conflicting regarding this issue. In this paper, I explore the background of pornography and obscenity laws and demonstrate how these frameworks are not properly constructed to apply to the digital sphere. Then, I address major themes within deepfake literature such as consent issues, bodily autonomy, labor displacement, and verifiable identity as a commodity through the case study of OnlyFans. I explore current and proposed legislation within the …

A Novel, Embedding-Based Approach To Longitudinal Survey Data Imputation, Julia Rezvani

University Honors Theses

Longitudinal surveys are ubiquitous in the social sciences as a means of tracking changes in behavior and opinions with time and identifying potential causal mechanisms. These surveys are frequently plagued by missing data and semantic drift, both of which limit their effectiveness and scientific utility. Imputation algorithms allow researchers to fill gaps in collected survey datasets, imperfectly reconstructing lost data. Although deep learning algorithms have been used in imputation to great success, approaches which simultaneously leverage the semantic and temporal structure of longitudinal surveys have not yet been developed. We propose a novel imputation architecture which is capable of leveraging …

Data Governance Maturity, Ai Integration, And Equity In Colorado K-12 Public Schools, John R. Curtin

Electronic Theses and Dissertations

Colorado's 179 K-12 public school districts operate as autonomous governance units, each responsible for securing and managing student data assets that span health, financial, residential, and academic records. The accelerating integration of artificial intelligence (AI) and machine learning (ML) tools into administrative workflows, productivity software, and instructional platforms has fundamentally altered the risk landscape for student data, yet governance frameworks at the state, district, and school levels have not kept pace. This dissertation investigates whether Colorado's decentralized educational governance structure is institutionally capable of producing equitable, secure, and sustainable data governance outcomes in the AI era.

Drawing on Institutional Theory …

Evaluation And Distillation Of Source Code Generation Tasks By Large Language Models, Danny Brahman

Electronic Theses and Dissertations

Large Language Models (LLMs) are predominantly assessed based on their common sense reasoning, language comprehension, and logical reasoning abilities. While models trained in specialized domains like mathematics or coding have demonstrated remarkable advancements in logical reasoning, there remains a significant gap in evaluating their code generation capabilities. Existing benchmark datasets fall short in pinpointing specific strengths and weaknesses, impeding targeted enhancements in models’ reasoning abilities to synthesize code.

To bridge this gap, this thesis introduces two novel contributions: CodeEval and CodeQual. CodeEval is an innovative, pedagogical benchmarking method that mirrors the evaluation processes encountered in academic programming courses. It comprises …