Computer Sciences Commons^™

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 …

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; …

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 …

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 …

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 …

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 …

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 …

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 …

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, …

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 …

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 …

Construction Method Of Digital Twin System For High-Low Temperature Test Chamber, Qinghua Chen, Zuoyou Liang, Weijuan Guan, Jiadong Ji, Ping Liu

Journal of System Simulation

Abstract: In view of the construction requirements of the digital twin system of the high-low temperature test chamber, the EMQX server with MQTT as the communication protocol is used for data transmission. Driven by real-time data, real-time dynamic interactive mapping between the physical entity and the virtual model is realized. The neural network model and genetic algorithm are used to evaluate and predict the running state of the equipment and provide the system adjustment strategy, so as to realize the whole climate, life and working condition of the staff to understand the running state of the equipment, and effectively ensure …

Cooperative Guidance For Multigroup Flight Vehicles Against Multiple Targets With Separated Impact Time, Guofei Li, Shituo Li, Yilun Huangfu, Yueyang Hua, Yunjie Wu, Zongyu Zuo

Journal of System Simulation

Abstract: To cope with cooperative guidance against multiple targets, a distributed cooperative guidance for multigroup flight vehicles to strike multiple targets with separated impact time is proposed. The collaborative variables for multigroup flight vehicles with separated impact time are given, and the guidance law in the line of sight (LOS) is proposed. The guidance laws on the normal and lateral directions of the LOS are proposed to make the LOS deflection angle rate and LOS the inclination angle rate converge rapidly, which ensures that each vehicle is able to strike the target. The finite-time convergence of the proposed guidance laws …

Automatic Multi-Objective Optimization Based On Dynamic Storage Location Allocation Strategy, Juan Chen, Wang Zheng, Qianqian Liu, Bin Lu

Journal of System Simulation

Abstract: Based on the dynamic storage allocation strategy, the two-stage optimization model is constructed with the whole warehouse as the main optimization body, in order to meet the safety and rationality of the storage allocation goals, and to meet the dispatching goals of the shortest operation time and the lowest energy consumption of each stacke. The upper and lower levels of the model are typical multi-objective optimization problems, and the ideal solution of the upper level model will be the initial condition of the lower level model. The multi-objective genetic algorithm is used to solve the ideal solution of the …

Soft Sensor Modeling Based On Adaptive Sparse Broad Learning System⋅, Kangping Du, Lin Sui, Weili Xiong

Journal of System Simulation

Abstract: To address the challenges posed by nonlinearity and the coupling of multiple features in complex industrial processes, resulting in increased model complexity and decreased performance, a soft sensor modeling method based on adaptive sparse broad learning system is proposed. Building upon the lateral enhancement transmission of features, the trace least absolute shrinkage and selection operator (LASSO) is further used to optimize the feature weights of the network, adaptively adjusting the penalty intensity based on the correlation between different variables to enhance the feature extraction capabilities of the model. The Dropout mechanism is introduced in the enhanced part, and the …

Enhanced Artificial Gorilla Algorithm For Mobile Robot Path Planning, Chen Ye, Peng Shao, Shaoping Zhang, Wenting Li, Tengming Zhou

Journal of System Simulation

Abstract: To address the issues of susceptibility to local optima and slow convergence in mobile robot path planning within complex terrain scenarios, an enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies (QGGTO) is proposed. The algorithm integrates quadratic interpolation and elite individual genetic strategies to promote information exchange among candidate solutions, thereby accelerating convergence, while maintaining population diversity to avoid local optima. For complex terrains containing both regular and irregular obstacles, a cost function that comprehensively considers walking distance, safety, and turning angles is constructed to uniformly evaluate the path planning performance of …

Multi-Model Based Iterative Method For System-Of-Systems Architecture Design, Xuemeng Zhao, Tianzhu Ren, Zhemei Fang

Journal of System Simulation

Abstract: In order to solve the problems of difficulties in expressing dynamic characteristics and lack of decision analysis support in developing models of the department of defense architecture framework (DoDAF), an integrated iterative method for combat SoS architecture design is proposed. The DoDAF architecture model integrates and expresses combat-related information from multiple perspectives; the ExtendSim executable model simulates the emergence behavior and dynamic characteristics of combat SoS architecture in multiple scenarios; and the decision model quantitatively analyzes and selects architecture schemes by multi-objective decision rules. Ultimately, a SoS architecture integrated iterative design method of "view-simulate-decide-iterate" is formed. The design process …

A Drl⁃Based Approach For Distributed Equipment Nodes Selection, Ziyi Wang, Kai Zhang, Dianwei Qian, Yuzhen Liu

Journal of System Simulation

Abstract: Aiming at the problem of insufficient solution speed and poor generalization of traditional algorithms in large-scale scenarios, this paper intelligently solves the large-scale distributed equipment system preference problem based on deep reinforcement learning. According to the characteristics of distributed equipment system combat, using the complex network to its graph form modeling, and based on the attention mechanism to the equipment between the connecting edge relationship for the characterization, in order to build a distributed equipment system digital simulation environment. Simulation results show that compared with the genetic evolutionary algorithm, the obtained model has obvious advantages in terms of solution …

Research On Improving Design Efficiency Of Coaxial Magnetic Gear Based On Linear Model, Shuguang Zhao, Ce Chen, Xiaochang Xie, Fuping Li, Jin Han

Journal of System Simulation

Abstract: To address the issues of large model computation load and cumbersome magnetization direction setting during the simulation design of coaxial magnetic field modulation type magnetic gears, a simplified design method is proposed, which uses a linear model to replace the original conventional circular ring model. Based on the periodicity of the structure and magnetic field of each part of the magnetic gear, the modeling work is simplified and the computational load of the simulation analysis is reduced. The results show that compared with the circular ring structure, the number of magnetization coordinate system settings for the linear structure is …

Evaluating The Evaluation Matrices: Integrating Spatial Assessment In Geospatial Ai Model Training And Evaluation, Fangzheng Lyu

I-GUIDE Forum

This paper examines the limitations of current evaluation metrics in GeoAI. Through two case studies on deep learning models—a building detection classification problem and a remote sensing image fusion regression problem—this paper demonstrates how traditional statistical evaluation matrices alone can be misleading in geospatial problems. The findings indicate that traditional metrics (e.g., RMSE, MAE) used in current GeoAI models can have difficulty capturing the spatial dimensions inherent to geospatial problems. This paper suggests that the model evaluation process in GeoAI should move beyond traditional evaluation matrices by integrating spatial thinking throughout the modeling pipeline—not only incorporating spatial accuracy in model …

Topic Shift Detection And Triggering In Natural Dialogue Systems: A Lightweight Approach, Rohith Perumandla

College of Computing and Digital Media Dissertations

This research address a key challenge in dialogue system: enabling the proactive, human-like shifting using lightweight approaching using MobileBERT (~25M) model was proposed and fine-tuned for topic shift detection, augmented with liguistic featuers for for topic trigger detection. Despite its smaller size (~25M parameters), the MobileBERT-based system achieved competitive results (F1 = 74.16%,) compared to the much larger XLNet model (~110M parameters, F1 = 79.95%), while offering greater efficiency. The topic trigger module, combining MobileBERT with linguistic features, further demonstrated effective performance (F1 = 71.61%).

Revitalization Of Endangered Languages With Ai, Ivory Yang

Dartmouth College Master’s Theses

The preservation and revitalization of endangered languages, particularly those with minimal digital presence, presents significant challenges for computational linguistics. This thesis addresses these challenges by proposing novel methods for language identification and data generation, focusing on underrepresented Indigenous languages, specifically Nüshu, Native American and Native Alaskan languages.

In the first study, a COLING 2025 paper, we present NüshuRescue, an AI-driven framework designed to facilitate the preservation of Nüshu, an endangered script used exclusively by Yao women in China. Using minimal seed data, we demonstrate how GPT-4-Turbo can generate new translations, expanding a publicly available Nüshu-Chinese corpus, achieving 48.69% accuracy in …

Deep Neural Networks For Particle Identification In Simulated Proton-Proton Collisions At Lhc And Rhic, Omar Mazhar Khalaf

Theses and Dissertations

Particle identification is an essential part of experimental high-energy physics, which allows the study of the most fundamental constituents of matter. This thesis explores the use of deep neural networks for identifying particles in simulated proton-proton collisions at the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC). The deep neural networks were trained on LHC datasets which have various momentum ranges including regions of high transverse momentum above 3 GeV/c. The key findings of thesis include achieving an accuracy of 99.99%, 98.3%, and 90.14% for 3-5 pt, 5-7 pt and above 7 pt regions respectively for the …

Enhancing Water Scarcity Resilience In Egypt Through Machine Learning-Driven Phenological Crop Mapping And Water Use Efficiency Analysis, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Aqil Tariq, Rejoice Thomas, Cyril Rakovski, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Agriculture forms the backbone of Egypt’s economy, with the Nile Valley and Delta serving as key production zones for crops like wheat, rice, and clover. However, the sector faces mounting pressure from water scarcity, as it depends almost entirely on the Nile for irrigation, making it necessary to map major crops for assessing Water Use Efficiency (WUE) and informing agricultural planning. In this study, we used machine learning (ML) techniques—specifically Support Vector Machine (SVM) to time-series phenological data and optical indices (Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and …

Multi-Label Classification Of Acoustic And Electronic Drum Sounds Using Machine Learning, Sean Perman

Electronic Theses and Dissertations

This paper presents a system for multi-class classification of drum sounds using audio signal processing and machine learning techniques. The project utilizes a diverse dataset of both acoustic and electronic drum samples and extracts ten distinct audio features to capture the timbral and temporal characteristics of each sound. The methodology includes signal preprocessing, feature extraction, and the application of supervised classification algorithms to distinguish between multiple drum classes. Experimental evaluations demonstrate that the selected features significantly enhance classification accuracy across a varied dataset. These findings underscore the effectiveness of combining traditional audio processing with modern machine learning, offering promising applications …

Collaborative Federated Learning For Robots In Heterogeneous Environments, Karlan Schneider

Electronic Theses and Dissertations

This research investigates the performance of Federated Averaging (FedAvg) in simulated Federated Learning (FL) scenarios with varying degrees of environmental heterogeneity among robotic agents. The study explores the impact of data heterogeneity on both the convergence of FedAvg and the fairness of learning, with regard to consistency of performance across agents. Experiments were conducted with simulated robots trained to perform a target collection task, where a subset of agents encountered an unfamiliar environment. The results demonstrate that while FedAvg exhibits resilience to the introduction of new environmental data, it struggles to ensure both convergence and fairness in heterogeneous settings. Specifically, …

Object-Based Image Analysis And Artificial Intelligence Identification Of Anthropogenic Disturbance On Lesser Prairie Chicken Habitat In Cheyenne County, Colorado, Tara Hoelzer

Geography and the Environment: Graduate Student Capstones

Renewable energy projects often require extensive landcover for their operations. When one of these projects encroaches into territory of threatened species, such as Lesser Prairie Chickens, an analysis of habitat suitability and human disturbance is required to proceed. Traditionally, this involved manually reviewing aerial imagery within a 6-mile radius, digitizing features, and interpreting them using a human technician—an approach that was time-consuming and prone to human error. By using pretrained AI models within Model Builder™, the identification of roads and structures was automated, making the process faster and more consistent than manual visual analysis. As AI and technology continue to …

Early Diagnosis And Detection Of Skin Cancer Using Deep Neural Network Models And Feature Extraction From Pre-Trained Cnns, Sahil Khanna

Harrisburg University Dissertations and Theses

Skin cancer is one of the most common and lethal cancer types. While accurate diagnosis at an early stage is essential for skin cancer treatment it remains difficult to achieve in many regions due to lack of sufficient dermatologists and proper diagnostic equipment. Prior studies show Convolutional Neural Network (CNN) models excel at skin lesion classification and consistently achieve better results than standard diagnostic practices. However, the focus of many studies remains confined to image-based learning while neglecting useful patient metadata that could improve prediction accuracy. This research project created a specialized CNN model to classify skin lesions and evaluated …

A Comparative Study Of Neural Networks And Xgboost Models For Flight Time Prediction, Ioannis Paraschos, Taryn E. Trimble, Eshna Bhargava, Jake Klingler, Benjamin R. Nicolai

Beyond: Undergraduate Research Journal

Flight time prediction plays a crucial role in modern air travel, benefiting airlines and passengers alike. Accurate predictions enable airlines to optimize schedules, allocate resources effectively, and ensure passenger safety and satisfaction. In recent years, machine learning models, such as neural networks and XGBoost, have gained popularity for predicting flight times. This study aims to compare the performance of neural network and XGBoost models in predicting flight times, considering factors such as weather conditions, air traffic control, and aircraft performance. The results indicate that both models are effective, with XGBoost achieving slightly higher accuracy. However, neural networks offer advantages in …

Teamwork And Artificial Intelligence (Ai) : Examining The Effects Of Teammate Identity, Deception, And Ai Literacy On Team Dynamics And Performance In Human-Human Vs. Human-Ai Teams, Jenna Korentsides

Doctoral Dissertations and Master's Theses

As artificial intelligence (AI) continues to be integrated into collaborative work environments, understanding how humans interact with AI teammates is increasingly important. This study examined how people’s beliefs about who they are working with (whether a teammate is human or AI) can influence teamwork outcomes. Specifically, we explored how perceived teammate identity affects task performance and team experience, with a focus on trust and communication as potential mediators, and AI literacy (familiarity and comfort with AI) as a moderator. Participants completed a series of timed, collaborative problem-solving tasks using a bomb defusal simulation. Each participant worked with both a human …