Artificial Intelligence and Robotics Commons^™

Design Of A Simulation Dataset For Fine-Grained Detection Of Space Target Sequence Images, Shiyan Wang, Danpu Zhao, Haikun Hong

Journal of System Simulation

Abstract:

With the increasing number of space objects such as satellites, there is a growing need for fine-grained detection and recognition of distant space targets. However, obtaining real observational data remains difficult due to high costs and insufficient annotation detail. To overcome these limitations, asynthetic dataset generation system based on multi-module collaboration is developed. Using joint simulation with STK and Blender, the proposed system simulates optical imaging conditions of space targets and automatically produces sequential images along with corresponding fine-grained labels. A large-scale simulation dataset is constructed for fine-grained detection in space target image sequences. It contains …

Robotic Digital Twin Modeling Method Integrating Mbse And Modelica, Zaiwu Mei, Jiangman Zhou, Cong Zhang, Yiping Gao, Zerun Zhu

Journal of System Simulation

Abstract: To address the issues of disconnection between system architecture design and dynamic simulation, as well as the limited reusability and scalability of models in robotic digital twin modeling, a modular modeling approach integrating MBSE (model-based systems engineering) and Modelica is proposed. The method employs MBSE for system architecture design and requirement traceability, and combines Modelica for multi-domain dynamic simulation, forming a full-chain digital twin model from system design to physical behavior simulation. Standardized interfaces and a generic component model library are developed to decouple and integrate mechanical, electrical, and control subsystems, enabling rapid model assembly and functional expansion. A …

Modeling And Simulation Of Multi-Hop Routing In 3d Tunnel Wireless Sensor Networks, Fang Cao, Jiguang Yang, Jiuyuan Huo

Journal of System Simulation

Abstract: To address the unbalanced energy consumption and short network lifetime caused by the complex topology and strong energy coupling of three-dimensional tunnel wireless sensor networks, this study constructs a system-level operating model integrating spatial topology, energy evolution, and routing decision-making, and proposes an energy management for balanced and efficient routing (EMBER) algorithm based on the competition of tribes and cooperation of members (CTCM) algorithm. The optimal number of cluster heads is derived through tunnel spatial modeling. Clustering is performed using a Gaussian mixture model optimized by CTCM, and efficient cluster-head selection is achieved by combining data envelopment analysis. An …

Rrt*-Based Robotic Manipulator Path Planning With Artificial Potential Field Guidance And Accumulator-Based Adaptive Adjustment, Fangzheng Gao, Xu Guo, Quan Sun, Jiacai Huang, Zhongcai Zhang

Journal of System Simulation

Abstract: To address the problems of blind sampling, slow convergence, and path redundancy in robotic manipulator path planning in cluttered and unstructured three-dimensional environments, An RRT* algorithm integrating artificial potential field guidance and accumulator-based adaptive adjustment, namely APGA-RRT* (artificial potential field guidance and accumulator adaptive adjustment-RRT*). The proposed algorithm improves the overall planning performance from three aspects: sampling, expansion, and path optimization. In the sampling stage, a potential-field-guided sampling strategy based on target-distance weighting and virtual-target switching is constructed, obstacle suppression and safe-distance gating mechanisms are incorporated to dynamically adjust the guidance intensity, thereby enhancing target directivity and local escape …

Online Fine-Tuning Of Binocular Camera Extrinsic Parameters Based On Position-Iteration Dual Perception, Qian Long, Tuerhong Dilixiati, Qi Zheng, Lijun Shen

Journal of System Simulation

Abstract: In long-term operation of binocular systems, small drifts in extrinsic parameters may occur due to temperature changes, vibration, and mechanical shocks. As these drifts accumulate, the ranging error increases with distance and seriously degrades long-range measurement performance. Because online compensation is difficult, such small drifts are often neglected. To address this problem, an online fine-tuning method based on position-iteration dual perception is proposed. A multiplicative coupling weighting model is established by integrating spatial reliability with an iteration-stage modulation function. A three-stage progressive optimization algorithm is designed to balance convergence speed and robustness. A simulation framework for offline calibration and …

Review Of Evaluation Metrics For The Credibility Of Autonomous Driving Test Scenarios, Jiwei Li, Runmin Wang, Yu Zhu, Jingjun Cheng, Xiangmo Zhao

Journal of System Simulation

Abstract: Currently, scenario-based simulation testing has become a core technical approach for verifying autonomous driving system functionality. However, due to the absence of unified evaluation standards for simulation scenarios, significant differences exist among the metrics constructed and adopted by different studies, each with its own limitations. This hinders mutual recognition of research outcomes and prevents scientific measurement of scenario quality. This study systematically reviews relevant research from the past five years, categorizing autonomous driving test scenarios into four types: parameter-descriptive, trajectory-driven, environment-simulation, and perception-data-driven. It clarifies the core characteristics, application objectives, and generation mechanisms of each scenario type. Subsequently, focusing …

Hybrid Attention For Low-Overlap Point Cloud Registration, Huaijun Wang, Zonghua Yu, Chenxin Xu, Junhuai Li, Chenzhi Li, Junjie Yin

Journal of System Simulation

Abstract: To address the significant registration errors caused by large initial pose deviations in low overlap ratio point cloud, this paper presents a two-stage optimization method that integrates selfattention and cross-attention mechanisms. Orientation feature representation is enhanced using equivariant self-attention, semantic alignment is achieved through invariant cross-attention, and rotational equivariance is modeled with an octahedral group for coarse registration. Various attention variants are employed to aggregate features along the anchor dimension, and a local-to-global module is utilized for fine registration. On the 3DLoMatch dataset, the proposed method achieves a registration recall of 77.0% and an inlier ratio of 44.3%. Even …

Numerical Simulation Study On Flow-Induced Vibration Energy Harvesting Characteristics Of Piezoelectric Energy Harvester With Elliptical Cylinder Bluff-Body, Jiechao Lei, Long Li, Ji Yao

Journal of System Simulation

Abstract: To improve the self-powering efficiency of miniature internet of things devices, this paper establishes a fluid-structure-electric multi-field coupled computational fluid dynamics (CFD) model based on the ANSYS Fluent platform. It conducts a numerical simulation study on the flow-induced vibration energy harvesting characteristics of a piezoelectric cantilever beam energy harvester with an elliptical cylinder bluff-body. A systematic analysis is performed on the influence mechanism of flow velocity within the range of 0.1~0.5 m/s on the output power. The results indicate that the output power of the harvester exhibits phased growth with increasing flow velocity. In the range below 0.15 m/s, …

Generative Artificial Intelligence, Academic Integrity And Authentic Assessment Within An Irish University, Louise Nagle, Brigid Crowley, Laura Rafferty, Susan Horgan, Colin O'Brien

Publications

Academics need both an overarching policy on Generative Artificial Intelligence (Gen AI) use in teaching and learning, yet agency in its application across various disciplines. Clarity on the use of the technology for both students and staff is therefore a challenge and characterised by uncertainty given how its application is still unfamiliar. This research examines the organisational context in which Gen AI is being embraced and was conducted by the digital teaching support functions within an Irish university. Students and staff were surveyed (n=1,746) on various aspects of digital use within their education and workplace, including Gen AI. …

Neural Network-Based Analysis Of Heroin Epidemic Models With Modified Fractional Operators, M. A. El-Shorbagy, Sedat Pak, Mati Ur Rahman, Hossam A. Nabwey

Mathematical Modelling and Numerical Simulation with Applications

Heroin and synthetic narcotic abuse have become a major global concern, posing challenges to individuals, families, and communities. Their widespread availability and low cost have intensified the crisis. This study investigates a heroin transmission model using the modified Atangana--Baleanu--Caputo (mABC) fractional operator, with emphasis on non-zero solutions. Series solutions are derived by combining the Laplace transform with the Adomian decomposition method to address nonlinear components. Qualitative analysis is conducted through fixed-point theory, while stability is assessed using the T-Picard method. Numerical simulations explore the effects of different fractional orders and transmission parameters on the system. The study incorporates a deep …

Jpnet: A Multi-Layered Fusion Deep Learning Architecture For The Detection And Classification Of Pests In Jute Crops, Mejbah Ahammad, Md. Ashraful Babu, Muhammad Sajjad Hossain, Md. Fayz-Al-Asad, Nadim Ahmed, Md. Khaled Hossain, Md. Mortuza Ahmmed, M. Mostafizur Rahman, Mufti Mahmud

Mathematical Modelling and Numerical Simulation with Applications

Detecting and classifying insect pests is a critical challenge in agricultural pest management, as infestations can reduce crop yield and quality. This study introduces JPNet, a convolutional neural network (CNN) architecture that uses multi-layer feature fusion to detect and classify insect pests affecting jute crops. The architecture integrates complementary feature representations extracted at different network depths, preserving fine-grained visual characteristics alongside high-level semantic information. JPNet is evaluated on the JutePest dataset, which comprises approximately 6,460 RGB images spanning 17 pest classes. Preprocessing and data augmentation—including resizing, normalization, rotation, shifting, zooming, and flipping—improve the consistency and diversity of the training data. …

Ai In Higher Education: Some Notes From The Front, Debra Rienstra

University Faculty Publications and Creative Works

We’re four weeks into the semester now. This past summer, our university unveiled a campus-wide AI policy that exhorts students and faculty to use discernment and tries to lay out some rules about security. Meanwhile, we have purchased a campus-wide subscription to an AI aggregator tool now available to all students, faculty, and staff (in part, from what I understand, as an attempt to effect some boundaries). And then, in the news, all this hand-wringing over the end of humanity, etc., on the one hand and techno-utopian promises on the other.

Closing The Interpretability Gap: Explainable Ml-Based Malware Detection For Defensive Cyberspace Operations, Tashi Stirewalt, Sean Hodgson, Puumaaya Tahiru, Assefaw Gebremedhin

Military Cyber Affairs

This paper presents an end-to-end, explainable malware triage pipeline designed for defense-oriented cyber operations. It combines high-performance static detection methods with analyst-centered interpretability. Utilizing the EMBER 2024 Windows PE subset, we train and evaluate four classifiers and select LightGBM as the production model based on its predictive performance, inference efficiency, and compatibility with exact tree-based attribution. The deployed system consists of four sequential components: PE feature extraction, malware probability scoring, dual explainability (using SHAP and LIME), and large language model (LLM) report generation, all integrated within a Flask web interface. On a temporal test set of 1,080,000 samples, LightGBM achieves …

Construction And Application Of Clinical Evidence Framework For Brain-Computer Interface, Wenxiu Qi, Cheng Zhou

Bulletin of Chinese Academy of Sciences (Chinese Version)

Brain-computer interface systems are emerging neurotechnologies that are gradually moving from laboratory research toward clinical application. However, their inherent features, including small sample sizes, heterogeneous technical pathways, and rapid product iteration, make it difficult to integrate safety and efficacy data across studies or to conduct meaningful cross-study comparisons. These challenges not only hinder the cumulative development of evidence in evidence-based medicine, but also complicate the assessment of clinical access and regulatory review. In addition, subjective evidence, such as patient experience, remains insufficiently captured in existing evaluation frameworks. It is therefore necessary to examine the structure of evidence for brain-computer interface …

On-Device Computing Systems For Embodied Ai: Current Research Status And Strategic Directions, Shaoshan Liu, Zhenhua Zhu, Yiming Gan, Yu Wang, Yuan Xie

Bulletin of Chinese Academy of Sciences (Chinese Version)

Embodied AI is emerging as a key paradigm empowering general-purpose autonomy, but it requires on-device computing systems that simultaneously support high-throughput “cognition–planning” tasks and millisecond-level real-time “perception–control” loops. Converging solutions now coalesce around three pillars: (1) dataflow- and chiplet-based architectures, (2) memory-centric heterogeneous dies, and (3) RISC-V customizable cores with open tool-chains. This study distills the latest technical progress, pinpoints the remaining core technical bottlenecks, and charts an actionable course for academia, industry, and policymakers. The study calls for unified benchmarking and standardization, open-source software–hardware ecosystems, memory-centric dataflow architectures, and efficient on-device deployment of embodied foundation models. Finally, it outlines …

Application Of Machine Learning To Discover Dynamical Structures In 4d Poincaré Maps In The Circular Restricted Three-Body Problem, Kevin M. Trigg, Daniel J. Broyles, Robert A. Bettinger, Tyler J. Kapolka

Faculty Publications

The spatial Circular Restricted Three-Body Problem (CR3BP) produces complex orbital dynamics that are difficult to classify at scale using traditional, manual Poincaré map analysis. A Poincaré map simplifies continuous orbital motion by recording the points where a trajectory intersects a chosen surface in phase space, converting a continuous path into a discrete set of crossings that reveal underlying topological structure. These crossings make it easier to characterize trajectory behavior, but interpreting them visually becomes impractical for large datasets and as Poincaré maps evolve from two into four dimensions. To address this challenge, an unsupervised clustering pipeline for discovering dynamical structures …

Kanmultisign: Multi-Scale Sequence-Based Pose Animation From Sign Language Notation With Kolmogorov-Arnold Networks, Guanyi Du, Lintao Wang, Kun Hu, Ziyang Wang

Research outputs 2022 to 2026

Sign language production from symbolic notation offers a scalable route to accessible sign animation. We present KANMultiSign, a multi-scale sequence generator that translates HamNoSys notation into two-dimensional human pose sequences. Our framework makes two complementary contributions. First, we introduce a coarse-to-fine generation strategy with multi-scale supervision: the model is first guided by an intermediate body–hand–face scaffold to encourage global structural coherence, and then refines fine-grained hand articulation to improve finger-level detail. Second, we investigate integrating Kolmogorov–Arnold Network modules into a Transformer backbone, using learnable univariate function primitives to model the highly non-linear mapping from discrete phonological symbols to continuous body …

When Does Global Search Pay For Itself? A Measured Exploration Cost And Energy Break-Even Analysis Of A Metaheuristic Search Controller In Solar Energy Systems, Mohamed Ali Muammar Ezgour

Communications of the IIMA

Autonomous energy systems increasingly delegate the choice of operating point to embedded search algorithms, trading a fast local optimizer that can settle on a wrong point against a slower global search that guarantees the right one at a measurable cost. This paper reframes maximum power point tracking under partial shading as that decision and measures its economics on a fixed photovoltaic plant in MATLAB/Simulink. A Hippopotamus Optimization global search handed over to incremental conductance is compared with incremental conductance alone across seventeen initial duty cycles and thirty random seeds. The hybrid reached the global peak in all thirty seeds, whereas …

From Data To Victory: The Race For Analytic Superiority In Warfare, Robert Grossman, Emily Goldman

Joint Force Quarterly

Artificial intelligence technologies have reached a tipping point after decades of development. They are diffusing widely across defense and national security applications. Twenty-first century warfighters rely on analytic models in all systems, at all echelons, and in all domains. As more powerful models built on ever larger data sets become ubiquitous, militaries are in a new competition to deploy artificial intelligence. Operational art must embrace “analytic superiority.” This is the operational advantage from collecting and ingesting data, building robust models and computing infrastructure, deploying the models into operational systems, and denying adversaries' ability to do the same

This article explains …

From Data To Decision-Making: The Role Of Local Digital Twins In Cross-Domain Management Within Municipalities – A Research-In-Progress Study In Veenendaal, Diana M.E. Boekman, Koen Smit, Guido Ongena, Rob Peters

Communications of the IIMA

Municipalities are facing increasingly complex, interconnected challenges in areas like housing, climate adaptation, mobility, and social policy. Local Digital Twins (LDTs) are seen as a promising tool to make this complexity more understandable and support decision-making. At the same time, both literature and practice show that few initiatives get past the pilot phase, even though getting through that phase is essential for successful long-term adoption.

This paper presents a research-in-progress study on the development and application of an implementation method for LDT technology within the municipality of Veenendaal, based on human values rather than driven by technological possibilities. Based on …