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Artificial Intelligence and Robotics Commons™

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2025

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Articles 541 - 570 of 1404

Full-Text Articles in Artificial Intelligence and Robotics

Enhancing Proof-Of-Learning Security Against Spoofing Attacks Using Model Watermarking, Ozgur Ural Jul 2025

Enhancing Proof-Of-Learning Security Against Spoofing Attacks Using Model Watermarking, Ozgur Ural

Doctoral Dissertations and Master's Theses

With the rapid expansion of machine learning (ML) technologies across diverse domains such as healthcare, finance, and autonomous systems, ensuring secure and trustworthy training methodologies has become more critical than ever. Proof-of-Learning (PoL) has recently emerged as a foundational mechanism for verifying the computational effort invested in training ML models, thereby certifying the authenticity and reproducibility of the training process. Yet PoL, when deployed in isolation, remains vulnerable to sophisticated spoofing attacks that manipulate its subset-verification pathways and tolerance parameters. In parallel, model watermarking has become indispensable for safeguarding intellectual property and detecting unauthorized model usage. Motivated by these complementary …

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Future-Proofing Careers: Unlocking The Potential Of Ai-Driven Digital Badges, Harika Rao Jul 2025

Future-Proofing Careers: Unlocking The Potential Of Ai-Driven Digital Badges, Harika Rao

Faculty and Staff Publications & Presentations

This poster explores the transformative role of AI-driven digital badges and microcredentials in addressing the urgent need for adaptable, workforce-ready graduates in an era of rapid technological change. By offering personalized, stackable, and industry-aligned credentials, higher education institutions can empower learners with just-in-time skills that traditional degree programs often fail to provide. This poster highlights how AI can support granular competency assessment, flexible online delivery, and lifelong learning pathways, enabling students to showcase evidence of skill mastery in ways that are both affordable and scalable.

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Ts-Diff: Two-Stage Diffusion Model For Low-Light Raw Image Enhancement, Yi Li, Zhiyuan Zhang, Jiangnan Xia, Jianghan Cheng, Qilong Wu, Junwei Li Jul 2025

Ts-Diff: Two-Stage Diffusion Model For Low-Light Raw Image Enhancement, Yi Li, Zhiyuan Zhang, Jiangnan Xia, Jianghan Cheng, Qilong Wu, Junwei Li

Research Collection School Of Computing and Information Systems

This paper presents a novel Two-Stage Diffusion Model (TS-Diff) for enhancing extremely low-light RAW images. In the pre-training stage, TS-Diff synthesizes noisy images by constructing multiple virtual cameras based on a noise space. Camera Feature Integration (CFI) modules are then designed to enable the model to learn generalizable features across diverse virtual cameras. During the aligning stage, CFIs are averaged to create a target-specific CFIT, which is fine-tuned using a small amount of real RAW data to adapt to the noise characteristics of specific cameras. A structural reparameterization technique further simplifies CFIT for efficient deployment. To address color shifts during …

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O-Mapl: Offline Multi-Agent Preference Learning, The Viet Bui, Tien Mai, Hong Thanh Nguyen Jul 2025

O-Mapl: Offline Multi-Agent Preference Learning, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

Inferring reward functions from demonstrations is a key challenge in reinforcement learning (RL), particularly in multi-agent RL (MARL). The large joint state-action spaces and intricate inter-agent interactions in MARL make inferring the joint reward function especially challenging. While prior studies in single-agent settings have explored ways to recover reward functions and expert policies from human preference feedback, such studies in MARL remain limited. Existing methods typically combine two separate stages, supervised reward learning, and standard MARL algorithms, leading to unstable training processes. In this work, we exploit the inherent connection between reward functions and Q functions in cooperative MARL to …

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Breaking The Reasoning Barrier: A Survey On Llm Complex Reasoning Through The Lens Of Self-Evolution, Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin Jul 2025

Breaking The Reasoning Barrier: A Survey On Llm Complex Reasoning Through The Lens Of Self-Evolution, Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

The release of OpenAI’s O1 and subsequent projects like DeepSeek R1 has significantly advanced research on complex reasoning in LLMs. This paper systematically analyzes existing reasoning studies from the perspective of self-evolution, structured into three components: data evolution, model evolution, and self-evolution. Data evolution explores methods to generate higher-quality reasoning training data. Model evolution focuses on training strategies to boost reasoning capabilities. Self-evolution research autonomous system evolution via iterating cycles of data and model evolution. We further discuss the scaling law of self-evolution and analyze representative O1-like works through this lens. By summarizing advanced methods and outlining future directions, this …

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Simulating Before Planning: Constructing Intrinsic User World Model For User-Tailored Dialogue Policy Planning, Tao He, Lizi Liao, Ming Liu, Bing Qin Jul 2025

Simulating Before Planning: Constructing Intrinsic User World Model For User-Tailored Dialogue Policy Planning, Tao He, Lizi Liao, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Recent advancements in dialogue policy planning have focused on optimizing system agent policies to achieve predefined goals, emphasizing strategy design, trajectory acquisition, and training efficiency. However, these approaches often overlook the critical role of user characteristics, which are essential in real-world scenarios like conversational search and recommendation, where interactions must adapt to individual user traits such as personality, preferences, and goals. To address this gap, we conduct a comprehensive study using task-specific user personas to evaluate dialogue policy planning under diverse user behaviors. Our analysis, based on these user profiles, reveals significant shortcomings in existing approaches, underscoring the necessity for …

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Rustmap: Towards Project-Scale C-To-Rust Migration Via Program Analysis And Llm, Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu, Haitao Wu, Chunmiao Li, Bo Wang, Imam Nur Bani Yusuf, Lingxiao Jiang Jul 2025

Rustmap: Towards Project-Scale C-To-Rust Migration Via Program Analysis And Llm, Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu, Haitao Wu, Chunmiao Li, Bo Wang, Imam Nur Bani Yusuf, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Migrating existing C programs into Rust is increasingly desired, as Rust offers superior memory safety while maintaining C’s high performance. Existing automated translation tools, such as C2Rust, may rely too much on syntactic, template-based translation and generate unsafe Rust code that is hard for human developers to read, maintain, or even compile. More semantic-aware translation that produces safer, idiomatic, and runnable Rust code is much needed. This paper introduces a novel dependency-guided and large language model (LLM)-based C-to-Rust translation approach, RustMap, based on three key ideas: (1) Utilize LLM’s capabilities to produce idiomatic Rust code from given small pieces of …

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Wa-Fdnet: A Unified Weight Adaptation Network For Multimodal Image Fusion And Object Detection, Yanyin Guo, Ying Luo, Junwei Li, Zhiyuan Zhang Jul 2025

Wa-Fdnet: A Unified Weight Adaptation Network For Multimodal Image Fusion And Object Detection, Yanyin Guo, Ying Luo, Junwei Li, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

Multimodal image fusion and object detection are critical tasks in computer vision, particularly in scenarios requiring robust perception under low illumination conditions. Existing approaches that attempt to combine these tasks often rely on cascaded or loosely coupled designs, which can result in suboptimal performance due to gradient conflicts and task imbalance. In this paper, we propose WA-FDNet, a novel Weight Adaptation Fusion Detection Network that unifies multimodal image fusion and object detection into a single end-to-end framework. WA-FDNet adopts a shared encoder–private decoder architecture, enabling efficient feature sharing while preserving task-specific characteristics. The image fusion branch employs a spatial attention-based …

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Fuzzy-Ahp Based Decision Support System For The Selection Of Optimal Maintenance Strategy For Meter Gauge Railway Infrastructure: A Review, Hamisi J. Maulid Jun 2025

Fuzzy-Ahp Based Decision Support System For The Selection Of Optimal Maintenance Strategy For Meter Gauge Railway Infrastructure: A Review, Hamisi J. Maulid

Tanzania Journal of Engineering and Technology (TJET)

There are many uncertainties and complexities associated with maintaining Meter Gauge Railway (MGR) infrastructure, which calls for a methodical approach to decision-making. The development and application of a fuzzy-AHP-based decision support system (DSS) to select the optimal maintenance strategy for the MGR are presented in this study. The review covers research from 2013 to 2023 and focusses on the use of Multi-Criteria Decision Making (MCDM) and Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) techniques in railway infrastructure maintenance. To manage the inherent uncertainties and subjective judgements involved in maintenance decision-making, the Fuzzy-AHP methodology combines fuzzy logic with the Analytic Hierarchy Process (AHP). …

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Energy Optimal Coverage Motion Trajectory Generation Using A Fourth-Order Motion Profile, Mathias Halinga Jun 2025

Energy Optimal Coverage Motion Trajectory Generation Using A Fourth-Order Motion Profile, Mathias Halinga

Tanzania Journal of Engineering and Technology (TJET)

Industrial machines are widely used in manufacturing sector to manufacture several products to meet customer demands. Most of these industries runs all the time throughout a day leading to high operating cost. To cut costs and satisfy customer demand for precise products, industrial machines’ motion generation is important in improving machine motion precision while using less energy. This study presents a coverage motion energy optimization which is generated by linear interpolation of each segment described by the fourth-order motion profile. The phase changes in the profile are attained with continuity of machine kinematic limits jerk, acceleration, and velocity, which are …

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Analysis Of An Improved Reliability Dual-Buck Structured Three-Level Flying Capacitor Inverter, Almachius Kahwa Dr. Jun 2025

Analysis Of An Improved Reliability Dual-Buck Structured Three-Level Flying Capacitor Inverter, Almachius Kahwa Dr.

Tanzania Journal of Engineering and Technology (TJET)

With the increased demand for high-reliability power converters in the electric drive-train and propulsion systems, the efforts to design and analyze converters with high fault tolerance have become apparent. Among the emerging trends to improve the reliability of power converters is the incorporation of dual-buck (DB) structures in traditional converter topologies. Thus, this paper studies a single-phase dual-buck structured three-level flying capacitor (FC) inverter. The dual-buck flying capacitor (DBFC) inverter was constructed in such a way as to suppress the shoot-through problems that may occur because of the switching mismatch and gate driver delay, as exhibited in the traditional FC …

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Towards Metrology 4.0 In Developing Countries’ Manufacturing Industries, Jailos Nzumile Jun 2025

Towards Metrology 4.0 In Developing Countries’ Manufacturing Industries, Jailos Nzumile

Tanzania Journal of Engineering and Technology (TJET)

A systematic literature review was conducted to unveil the status of the digital transformation of metrology in developing countries, as they are lagging in utilising fourth industrial revolution (IR4.0) technologies to transform manufacturing industries. A PRISMA technique was employed using various keywords to identify, screen, and select the relevant literature. Forty publications were selected for the review, mainly discussing IR 4.0 technologies in metrological operations. The results indicate that the digital transformation of metrology has yet to be initiated in developing countries. However, the employment of IR4.0 technologies in advancing metrological operations in manufacturing industries is mostly discussed in the …

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Artificial Intelligence In Higher Education: A Case Study Of Faculty Teaching Methodologies At A Private University, Ellen Ramsey, George Antoniou, Matteo Peroni, Brent Muckridge, Raouf Ghattas, Philip L. Fazio, Wendy Wallberg, Saidi Porta, Gary Solomon, Mary Smith, Kristen Migliano, Karima Lanfranco, Lianfen Qian, David G. Wolf Jun 2025

Artificial Intelligence In Higher Education: A Case Study Of Faculty Teaching Methodologies At A Private University, Ellen Ramsey, George Antoniou, Matteo Peroni, Brent Muckridge, Raouf Ghattas, Philip L. Fazio, Wendy Wallberg, Saidi Porta, Gary Solomon, Mary Smith, Kristen Migliano, Karima Lanfranco, Lianfen Qian, David G. Wolf

Faculty and Staff Publications & Presentations

This qualitative case study investigates faculty perspectives on artificial intelligence (AI) integration within a private university context, examining pedagogical, administrative, and ethical implications. Data collected through semi-structured interviews with faculty across four disciplines revealed ambivalent yet cautiously optimistic attitudes. Participants acknowledged AI’s potential to enhance personalized learning and reduce bureaucratic burdens through automation. However, three critical barriers emerged: (1) insufficient institutional technological infrastructure, (2) lack of systematic faculty training programs, and (3) unresolved ethical dilemmas surrounding data privacy, algorithmic bias, and academic integrity. Notably, while faculty welcomed AI as a supplemental tool, they unanimously emphasized the irreplaceable role of human …

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Exploring The Potential Of Large Language Models (Llms) To Simulate Social Group Dynamics: A Case Study Using The Board Game "Secret Hitler", Kaj Hansteen Izora, Christof Teuscher Jun 2025

Exploring The Potential Of Large Language Models (Llms) To Simulate Social Group Dynamics: A Case Study Using The Board Game "Secret Hitler", Kaj Hansteen Izora, Christof Teuscher

Northeast Journal of Complex Systems (NEJCS)

This study explores the capacity of large language model-powered agents to simulate human-like behavior in multi-agent social systems. Using Secret Hitler — a hidden-role board game centered on trust, deception, and strategic communication — we evaluate how LLM agents navigate dynamic group interactions. Our findings show that agents exhibit human-like behaviors, including strategic temporal adaptation, contextual reasoning, and complex social cognition such as theory of mind and implicit coordination. Notably, 85% of agent decisions factored in at least two other players’ mental states, highlighting their capacity for multi-agent mental state inference. However, they struggled with key aspects of human gameplay, …

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Coarse-Graining Spiking Reservoirs: Reducing Reservoir Size While Preserving Critical Dynamics, Tucker X. Mastin, Christof Teuscher Jun 2025

Coarse-Graining Spiking Reservoirs: Reducing Reservoir Size While Preserving Critical Dynamics, Tucker X. Mastin, Christof Teuscher

Northeast Journal of Complex Systems (NEJCS)

We propose an extension of renormalization into the domain of spiking neural networks, thereby providing a novel framework for coarse-graining neural networks without disrupting their critical properties. The proposed coarse-graining technique merges neurons and synaptic connections based on a graph-theoretic distance derived from synaptic weight strength and is configured to effectively prune the reservoir size while preserving the scale-free spiking dynamics indicative of criticality. Criticality in spiking neural networks may provide information-theoretic advantages by optimizing information processing and sensitivity to input. Using time-series prediction benchmarks, we demonstrate that networks operating at criticality exhibit up to 32% higher prediction accuracy before …

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A Modern Approach To Classifying Medieval Latin Scripts, Robert L. Lane Jr, Rafia Mirza, Robert Slater Jun 2025

A Modern Approach To Classifying Medieval Latin Scripts, Robert L. Lane Jr, Rafia Mirza, Robert Slater

SMU Data Science Review

Paleography, the study of historical handwriting, is essential for preserving societal understanding of cultural, social, and legal frameworks from the past. Medieval manuscripts, often exhibiting refined craftsmanship, present unique challenges to modern readers due to differences in handwriting conventions and the absence of standardized punctuation and spaces. These texts hold valuable insights into the evolution of written communication, literacy, and language development. However, interpreting them requires specialized knowledge and technological solutions. Convolutional Neural Networks (CNNs) can be leveraged to classify scripts, an important step in Historical Document analysis. These models extract and analyze hierarchical features from images, addressing inconsistencies in …

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Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler Jun 2025

Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler

SMU Data Science Review

Large Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating …

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Simulation Study On Optimizing Microgrid Scheduling With Electric Vehicle Participation Under V2g Mode, Zhongan Yu, Hongliang Xiao, Qiangwei Xia, Jiawei Liu Jun 2025

Simulation Study On Optimizing Microgrid Scheduling With Electric Vehicle Participation Under V2g Mode, Zhongan Yu, Hongliang Xiao, Qiangwei Xia, Jiawei Liu

Journal of System Simulation

Abstract: To address the negative impact of source-load uncertainty on the stable operation of the grid, a two-stage optimization scheduling strategy for the microgrid participation of electric vehicles based on the vehicle-to-grid (V2G) mode is proposed. In the first stage, the charging and discharging costs of electric vehicles as well as the load fluctuation target are determined taking into account the battery losses. Through a zero-sum game, we objectively weigh the interests of both vehicle owners and the microgrid, utilizing the mobile energy storage characteristics of electric vehicles to optimize the load curve and integrate renewable energy; in the second …

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Aerial Target Detection Algorithm Fused With Multi-Scale Features, Lu Yang, Junying Pei Jun 2025

Aerial Target Detection Algorithm Fused With Multi-Scale Features, Lu Yang, Junying Pei

Journal of System Simulation

Abstract: In order to solve the problem that UAV aerial images have a large number of small target samples but little extractable feature information, which is not conducive to improving the accuracy of aerial target detection, an improved small target detection algorithm for aerial photography based on YOLOv8s is proposed. The algorithm applies deformable convolution to the feature extraction module of the backbone network to adaptively capture the details of the target at different locations and scales. The feature information at different scales of the backbone network is extracted and enhanced by the feature collection module in the multilevel information …

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Finite-Time Robust Anti-Disturbance Control For Steer-By-Wire System, Jingyi Zhang, Xin Chen, Jingang Ding, Jianguo Luo, Shuo Feng Jun 2025

Finite-Time Robust Anti-Disturbance Control For Steer-By-Wire System, Jingyi Zhang, Xin Chen, Jingang Ding, Jianguo Luo, Shuo Feng

Journal of System Simulation

Abstract: To eliminate the influence of parameter perturbations and external disturbances on the wheel angle tracking control performance of steer-by-wire (SbW) system, a fractional-order integral terminal sliding mode control scheme based on a finite-time disturbance observer is proposed. A sliding modebased second order finite-time disturbance observer (FDO) is designed to precisely estimate the total disturbance of the SbW system, and the estimated total disturbance is compensated into the system control input to reduce the wheel angle tracking error. A fractional-order fast integral terminal sliding mode control (FOFITSMC) scheme is designed to ensure fast convergence of the wheel angle tracking error …

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Research On Obstacle Avoidance Of Substation Robot Based On Spatiotemporal Networks, Chong Cheng, Lixia Wang, Songtao Duan, Xiaoguang Xiong, Xianjun Ge Jun 2025

Research On Obstacle Avoidance Of Substation Robot Based On Spatiotemporal Networks, Chong Cheng, Lixia Wang, Songtao Duan, Xiaoguang Xiong, Xianjun Ge

Journal of System Simulation

Abstract: In order to improve the visual obstacle avoidance ability of substation robots in complex environments, a robot visual obstacle avoidance method based on spatiotemporal networks is proposed. The method utilizes traditional image processing techniques to enhance road information and designs a lightweight deep convolutional neural network structure to extract road features from a spatial domain perspective; based on the spatial characteristics of the road, a long short-term memory network is introduced to mine the changes in the road from a temporal perspective, and a classification regression prediction structure is used to predict the robot's obstacle avoidance direction and angle; …

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Operation System For Simulation Roadheader Based On Visual Motion Capture, Yongling Li, Lingzhi Liu, Baishun Zhou, Jingfa Lei, Miao Zhang, Ruhai Zhao Jun 2025

Operation System For Simulation Roadheader Based On Visual Motion Capture, Yongling Li, Lingzhi Liu, Baishun Zhou, Jingfa Lei, Miao Zhang, Ruhai Zhao

Journal of System Simulation

Abstract: To enhance the natural human-machine interaction in simulation roadheader environment, a vision-based simulation roadheader operation system is proposed. The visual motion capture unit is based on the MediaPipe framework, which captures hand gestures through cameras and creates a correspondence between the physical world and virtual space. An improved Kalman filter algorithm is proposed by setting a weighted centroid to address the issue of unreasonable jumps in hand keypoint data during large-scale movements. The operator's gestures are discerned and the corresponding commands are conveyed. The results show that the improved method has significant advantages over the control group in terms …

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Research On Behavior Control Techniques For Autonomous Vehicles Based On Parallel Behavior Tree Architecture, Jianchao Yuan, Shuo Yang, Qi Zhang, Ge Li Jun 2025

Research On Behavior Control Techniques For Autonomous Vehicles Based On Parallel Behavior Tree Architecture, Jianchao Yuan, Shuo Yang, Qi Zhang, Ge Li

Journal of System Simulation

Abstract: Aiming at the problem of high collision rate and low efficiency of traditional serial behavior tree in autonomous vehicle control, a solution based on improved parallel behavior tree architecture is discussed to achieve safe behavior control. A safety behavior control strategy under dynamic road conditions is proposed, and behavior models for observation, decision-making, and movement are constructed, as well as their temporal constraint relationships; an improved parallel behavior tree control architecture is proposed, which achieves parallel execution and real-time interaction of behaviors through parallel control nodes, improving the real-time performance of decision control. The results show that compared with …

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Simulation Study On Adaptive Signal Control Of Deformed Intersection Based On Lstm-Gnn, Kun Chen, Liang Chen, Jiming Xie, Fengbo Liu, Taixiong Chen, Lukuan Wei Jun 2025

Simulation Study On Adaptive Signal Control Of Deformed Intersection Based On Lstm-Gnn, Kun Chen, Liang Chen, Jiming Xie, Fengbo Liu, Taixiong Chen, Lukuan Wei

Journal of System Simulation

Abstract: Aiming at the traffic congestion at deformed intersections, an improved adaptive traffic signal control scheme based on deep learning is designed, the scheme integrates the adaptive signal control of LSTM and GNN at deformed intersections. LSTM is used to capture the dependence between time series traffic data, while GNN is used to construct a spatial interaction model between lanes. By integrating the information of time and space dimensions, the model can dynamically adjust the phase duration of signal lights according to real-time traffic conditions. The results indicate that the LSTM-GNN adaptive control scheme improves overall traffic throughput efficiency by …

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Modeling And Simulation Of Dual-Podded-Propulsion Ship Motions, Bing Han, Yunhe Lin, Yuhang Chen, Zhouhua Peng Jun 2025

Modeling And Simulation Of Dual-Podded-Propulsion Ship Motions, Bing Han, Yunhe Lin, Yuhang Chen, Zhouhua Peng

Journal of System Simulation

Abstract: Aiming at the autonomous navigation control requirements of the Dalian Maritime University's dual-purpose intelligent research and training ship "Xin Hong Zhuan," the design of the motion model for this dual-podded-propulsion ship is carried out. Utilizing an MMG model structure, it calculates the hull's hydrodynamic viscous forces, single/dual-propeller thrust, and hydrodynamic forces acting on the podded propulsion units. Based on data from sea trials and open-water propeller tests, straight-navigation resistance is derived via data fitting, while a method using simulated turning circle tests and PSO algorithms is proposed to determine some hydrodynamic coefficients, refining existing empirical formulas. The model's maneuvering …

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Research On Scenario-Driven Virtual Simulation Test Method For Autonomous Escort Function Of Habor Tugs, Shijie Li, Jialin Li, Jialun Liu, Chengqi Xu, Zhilin Dong Jun 2025

Research On Scenario-Driven Virtual Simulation Test Method For Autonomous Escort Function Of Habor Tugs, Shijie Li, Jialin Li, Jialun Liu, Chengqi Xu, Zhilin Dong

Journal of System Simulation

Abstract: In order to comprehensively construct the test scenarios and verify the reliability of the tugboat autonomous companionway function, a scenario-driven virtual simulation test method for the tugboat autonomous companionway function is proposed. Based on the relative heading, relative speed and relative position of the target ship and the tugboat, the test cases of the tugboat autonomous companionway scenario are generated, and the complexity of the test cases is evaluated by using the fifthorder Bessel curve. The autonomous companion navigation function of the tug is verified through simulation experiments on the complex typical test scenarios without and with obstacles. The …

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Dynamic Path Planning For Robotic Arms Based On An Improved Ppo Algorithm, Yuhang Wan, Zilu Zhu, Chunfu Zhong, Yongkui Liu, Tingyu Lin, Lin Zhang Jun 2025

Dynamic Path Planning For Robotic Arms Based On An Improved Ppo Algorithm, Yuhang Wan, Zilu Zhu, Chunfu Zhong, Yongkui Liu, Tingyu Lin, Lin Zhang

Journal of System Simulation

Abstract: Aiming at the increased environmental uncertainties and more difficult modeling for robotic arm path planning in unstructured environments, an approach to dynamic path planning of robotic arms based on an improved PPO algorithm is proposed. In order to solve the problem that the input length of the state space is not fixed due to the change of number of obstacles in dynamic environment, an environmental state input processing method based on the LSTM network is proposed, and the network structure of PPO algorithm is also improved; a reward function is designed based on the artificial potential field method, and …

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Self-Supervised Defect Detection Via Discriminative Enhancement-Based Distillation Learning, Zhiyuan Feng, Ying Chen Jun 2025

Self-Supervised Defect Detection Via Discriminative Enhancement-Based Distillation Learning, Zhiyuan Feng, Ying Chen

Journal of System Simulation

Abstract: To address the issues of scarce and unknown types of abnormal defect data and the lack of diversity in anomaly representation in conventional knowledge distillation defect detection methods, a self-supervised distillation learning method based on discriminative enhancement is proposed. An attention-based multi-scale feature fusion module is proposed, which enhances the capability of anomaly representation by amplifying the multi-scale feature differences between the student network and the teacher network. A discriminative network composed of a feature reweighting module and a decoder is designed to generate more accurate anomaly score maps by further emphasizing the anomaly features in the teacher network, …

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Research On Robot Dynamic Obstacle Avoidance Method Based On Improved A* And Dynamic Window Algorithm, Yan Zhang, Binghua Li, Tao Huo, Rong Liu Jun 2025

Research On Robot Dynamic Obstacle Avoidance Method Based On Improved A* And Dynamic Window Algorithm, Yan Zhang, Binghua Li, Tao Huo, Rong Liu

Journal of System Simulation

Abstract: Aiming at the problems that the traditional A* algorithm has too many extension nodes and path turning points, and can't deal with dynamic obstacles in complex environment, a robot obstacle avoidance method combining improved A* algorithm and DWA algorithm is proposed. The A* algorithm improves the neighborhood expansion method and effectively avoids the problem of redundant nodes in the classical four-neighborhood expansion and the path through the obstacle in the eight-neighborhood expansion. A quadrant selection method is proposed, which can effectively reduce the number of extended nodes in the path search process. The redundant point elimination strategy is proposed …

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Modeling And Simulation Of Hybrid Traffic Flow Considering The Inherent Dynamics Of Cacc Vehicular Platoons, Xiujian Yang, Jingjing Huang, Xi Wang Jun 2025

Modeling And Simulation Of Hybrid Traffic Flow Considering The Inherent Dynamics Of Cacc Vehicular Platoons, Xiujian Yang, Jingjing Huang, Xi Wang

Journal of System Simulation

Abstract: To investigate the characteristics of single-lane mixed traffic flow with the presence of cooperative adaptive cruise control (CACC) vehicle platoons, a modeling approach based on cellular automata is proposed. This method distinguishes between the car-following strategies of human-driven vehicles and CACC vehicles, incorporating dynamic inter-vehicle spacing within the platoon and actual control behaviors to construct a mixed traffic flow model with inherent dynamic properties. The model enables an in-depth analysis of the influence of platoon features, such as geometric formation, carfollowing control strategies, and platoon size, on the characteristics of mixed traffic flow. It also allows us to study …

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