Digital Commons Network^™

Research On Performance Evaluation Of P300 Brain-Computer Interface Under Environment Modeling And Simulation, Xiaofei Ge, Jinling Lian, Jin Han, Xin'an Fan, Danmei Luo, Yuxiang Hua, Hao Liu, Lijian Zhang

Journal of System Simulation

Abstract: In the process of brain-computer interface(BCI) technology stepping from laboratory to practical application scenarios, it is difficult to make an accurate prediction and evaluation of the effect of real environment factors on the system performance. Therefore, a research method based on a multifactor simulation experiment was proposed. A controllable simulation experiment environment was constructed, and the parametric modeling of two key physical environmental factors, namely noise and light, was carried out. By taking the number of electroencephalogram channels as the system parameter, this paper systematically studied the influence mechanism of the aforementioned factors on the decoding performance of P300-BCI. …

Neural Radiance Fields Based On Explicit Feature Matching And Scaled Dot-Product Attention, Mingwei Cao, Fengna Wang, Zilong Wang, Haifeng Zhao

Journal of System Simulation

Abstract: To address the problems that neural radiance fields(NeRF) are prone to artifacts and texture blurring in novel view synthesis under sparse view input and complex scenes, this paper proposed neural radiance fields based on explicit feature matching and scaled dot-product attention(EMD-NeRF). A multiscale feature extraction network was used to extract multi-scale feature information from the input sparse views. A fusion dot-product module was utilized to calculate view interaction information as a shared branch. Cosine similarity was adopted as a matching clue for similarity embedding volume rendering. A regularization loss function was used to enhance the quality of the scene …

Integrating Geometric Priors And Importance Sampling For High-Fidelity Indoor Scene Reconstruction, Tao Yang, Min Shi, Xigang Zhao, Suqin Wang, Qi Wang, Dengming Zhu

Journal of System Simulation

Abstract: Gaussian splatting suffers from geometric distortion during scene reconstruction, particularly in weakly textured indoor scenes. To address this issue, this paper proposes a high-precision indoor scene reconstruction method that integrates geometric priors and importance sampling. The proposed method fully considers the effect of the initialization process on reconstruction quality. An advanced feed-forward model is employed to generate high-quality geometric initialization, thus improving overall reconstruction stability and accuracy. An importance sampling strategy is introduced to mitigate the adverse effects of blurry images. Furthermore, a supervision mechanism based on a geometric prior model is designed to constrain the scene structure, further …

Analytical Reentry Guidance Method Based On Lift-To-Drag Ratio-Velocity Profile, Weibo Sun, Ping Ma, Yuxuan Wang, Songyan Wang, Tao Chao

Journal of System Simulation

Abstract: An analytical reentry guidance method based on the lift-to-drag ratio and velocity profile was proposed to address the challenges of precise flight time control and prohibited area avoidance for the hypersonic glide flight vehicle during the reentry phase. A reduced-order reentry motion model was established; the Chebyshev series was used to approximate its nonlinear integral terms and differential equations; analytical expressions for the glide trajectory were yielded. The analytical relationships among flight time, remaining range, and the lift-to-drag ratio and velocity profile were derived. The optimization problem of lift-to-drag ratio and velocity profile was converted into a segmented parameter …

Research And Analysis Of Algorithm For Detecting Surface Defects On Automotive Wheel Hubs Based On Ccl-Yolov8, Yanjun Chen, Min Zhou, Meng Zha, Meizhou Zhang

Journal of System Simulation

Abstract: To address the challenges such as low detection efficiency, difficulties in identifying small defects, and poor accuracy in detecting surface defects on automotive wheel hubs, a lightweight neural network called CCL-YOLOv8 was proposed based on an improved YOLOv8n architecture. A synergistic improvement in both detection accuracy and efficiency was achieved through a three-stage model optimization strategy. A convolutional attention fusion module was introduced, which integrated convolution operations with self-attention mechanisms, thereby enhancing the model's ability to capture local features of small defects while perceiving global context under low signal-to-noise ratio conditions.A C2f-Star module was constructed to reduce computational overhead …

Research On Inter-Satellite Topology Design And Simulation Of Giant Leo Constellation Network With Consistent Pattern, Zhicheng Li, Shuaijun Liu, Lixiang Liu

Journal of System Simulation

Abstract: The giant low earth orbit (LEO) constellation network uses inter-satellite links to form an intersatellite topology, realizing the transmission of data between satellites. In order to adapt to the nature of uniform and symmetrical distribution of satellites in the constellation, this paper used a consistent connection pattern between satellites to construct an inter-satellite topology, and by analyzing the arrangement of non-mirror links in the constellation, it was found that the connection method of each link of the satellite itself could be independent of each other, which reduced the simulation complexity and the solution space of the inter-satellite topology. …

A Precise Damage Assessment Method For Lethal Blast Warheads Against Quadruped Robots, Xueqian Wang, Jianbing Men, Xin Zhou, Shuyou Wang, Mei Li

Journal of System Simulation

Abstract: To accurately evaluate the damage efficiency of a lethal blast warhead on quadruped robots, a typical quadruped robot replication model and vulnerability damage tree were constructed through Autodesk Inventor. The power field calculation model of a lethal blast warhead was introduced. Based on the high-precision collision detection and graphic rendering technology of UE, this paper carried out the intersection detection of destructive elements and targets and realistic scene visualization. A visualization system for damage assessment of quadruped robots by a lethal blast warhead was developed, featuring capabilities such as parametric modeling of the lethal blast warhead, power field evolution …

Simulation And Multi-Perspective Recognition Algorithm For Typical Trajectory Shapes, Xuejian Feng, Han Ding, Yiqi Tong, Chaoying Huo, Yanjin Zhang

Journal of System Simulation

Abstract: Current trajectory simulation methods inadequately address geometric shape features and kinematic properties of the target trajectory. To bridge this gap, a target trajectory shape simulation algorithm based on kinematic laws was proposed. The polar coordinate equations and curvature equations of multiple trajectories were integrated. The aircraft state parameters were solved by combining kinematic equations. Angular Gaussian noise was introduced to enhance trajectory diversity and authenticity. Additionally, a multi-perspective trajectory shape recognition algorithm was designed, which could effectively integrate image and sequential multi-modal features by adopting a multilayer perceptron, enabling precise trajectory shape recognition. Experimental results demonstrate that the proposed …

Virtual Train Operation Platform Based On Digital Twin, Ziying Wang, Congjun Sun, Guihu Li, Tianhao Zhang

Journal of System Simulation

Abstract: In response to the limitations of traditional train operation simulation modeling, such as simplification, lack of adaptive adjustment capability for parameters, and proneness to error accumulation, a virtual train operation platform based on digital twin technology was proposed. A train model under specific railway lines was constructed. By combining with the intelligent operation and maintenance platform of the railway line, real-time train operation data was obtained and preprocessed. The adaptive chaos optimization algorithm was used to optimize the key parameters of train operation simulation online and establish a digital twin model of the railway line. This model adopted a …

Large-Scale Scene Registration Technology Based On 3d Gaussian Splatting Fusing Gps Prior Information, Fei Wan, Yong Yin

Journal of System Simulation

Abstract: To address the challenges of low computational efficiency, slow convergence, and limited accuracy in large-scale 3D scene registration, a 3D Gaussian splatting (3DGS) registration method integrating GPS prior information was proposed. Spatial position priors provided by GPS were utilized to establish initial alignment through coordinate system transformations, narrowing the registration search space. Dense point cloud models were efficiently reconstructed by combining 3DGS technology. Highprecision alignment was achieved through a two-stage optimization of GPS coarse registration and fine registration. Experiments demonstrate that the GPS-assisted method reduces translation errors by 25%~50% and increases success rates to 98% in vegetation-covered and …

Task Planning Method For Cross-Domain Cooperative Combat Operations Of Unmanned Systems Under Complex Constraints, Haojie Fang, Ziyang Zhen, Huajun Gong, Xu Xie, Wei Luo

Journal of System Simulation

Abstract: In pre-combat task planning for cross-domain cooperative combat operations, to solve the problems of diverse and complex constraints and difficulties in solving planning models caused by performance differences of unmanned systems and increased requirements for cooperative combat operations, a multi-strategy enhanced grey wolf optimization (MSEGWO) algorithm was proposed. By considering various complex constraints such as performance of each type of unmanned systems, munition usage, task timing, task time window, and flight path, a task planning mathematical model with minimizing the comprehensive cost as the objective was established. Improvement strategies such as nonlinear adjustment of convergence factor, alternative solution space …

Design And Application Of Bds Visual Simulation Teaching Platform Based On Cesium, Liguo Lu, Yulin Cai, Tangting Wu, Canhui Lin

Journal of System Simulation

Abstract: The deep development of the BeiDou navigation satellite system(BDS) cannot be separated from the support of corresponding talent education, especially the development of new digital resources and tools. A visual simulation teaching platform for BDS was designed based on open source Cesium engine library to address issues such as the large and complex size of the BDS project, the unreachable space environment, and the difficult understanding of principles and concepts. The platform adopted a B/S architecture to achieve visual simulation of course content such as orbit cognition, orbit calculation, constellation simulation, service performance, and satellite links. Teaching applications have …

Research On Uav Path Planning Method Based On Collision Free Trajectory, Jun Xie, Qi Zhang, Yanyun Peng, Haonan Shi, Dongyang Li, Xi Liu

Journal of System Simulation

Abstract: In view of the problems of poor quality, long time consumption, and low efficiency of the autonomous path planning method for unmanned aerial vehicles, a path planning method for unmanned aerial vehicles based on a collision-free trajectory was proposed. Under the premise of uncertainty, the time-related virtual points and collision threshold were set; the obstacle was modeled as a rectangle; the interest points around the rectangle were defined. The uncertainty optimization model between the unmanned aerial vehicles and the obstacle was established, so as to obtain the allowable edge of the collision-free trajectory of the unmanned aerial vehicles. The …

Construction Approach Of Llm-Empowered Tactical Wargame Decision-Making Agents, Dayong Liu, Zhiming Dong, Qisheng Guo, Ang Gao, Xuehuan Qiu

Journal of System Simulation

Abstract: Decision-making agents are critical enablers for implementing human-machine, machinemachine, and hybrid human-machine adversarial interaction in tactical wargaming, where the intelligence level of the agent is crucial. To address the limitations of traditional decision agents such as insufficient adaptability, simplistic strategies, and high construction costs, a fusion decision framework driven by the large and small models was proposed. It specifically investigated the fusion approach of large language models with conventional decision-making agent construction approaches, including behavior trees, finite state machines, heuristic search, and deep reinforcement learning. New ideas and technical pathways are provided for the construction of tactical wargame …

Research On Visual Place Recognition Algorithms For Complex Urban Environments, Peijin Liu, Minxin Zhang, Lin He, Yige Sun, Tingqi Su

Journal of System Simulation

Abstract: Dynamic factors such as traffic flow and crowd density in complex urban environments reduce the accuracy of visual place recognition (VPR) algorithms. To solve these problems, a semantic-guided visual place recognition (SG-VPR) algorithm was proposed. A semantic-guided feature suppression module was designed. A semantic-guided module and feature suppression layer were constructed to reduce the dynamic object interference and more accurately extract the key static features. An adaptive triplet margin loss function (ATML) was proposed by improving the traditional triplet margin loss. The margins were adaptively adjusted according to the sample distribution, solving the problem of suboptimal solution convergence …

Key Problems Of Intent Recognition Research: A Survey On Activity, Plan And Goal Recognition, Yi Zhang, Kai Xu, Shuilin Li, Dejun Chen, Yunxiu Zeng, Yong Peng

Journal of System Simulation

Abstract: With the development of artificial intelligence technology, realizing intent recognition in human-computer interaction has become one of the key challenges. In this paper, the current research status of three fields was systematically sorted out, namely activity recognition, plan recognition, and goal recognition, and the progress from the problem proposal to the current development was analyzed. The main research approaches in each field were reviewed, and a survey of research on activity recognition, a development overview of plan recognition, and a retrospective analysis of hotspots in goal recognition were conducted. This general view of the problem helped to clarify …

Research On Gaussian Splatting Modeling Of Power Equipment In 3d Scenes, Haiying Li, Haonan Xu, Junfang Hao

Journal of System Simulation

Abstract: To address the issues of missing camera poses in captured images and poor reconstruction quality in 3D modeling of power equipment, a 3D Gaussian splatting 3D modeling method for power equipment based on video sequences was proposed. Theffmpeg was adopted to extract video frames at a reduced rate, and the Scharr operator was employed to quantify the sharpness of video frames to screen high-quality images for forming an input dataset, ensuring the completeness of equipment poses and the quality of modeling data. Through multi-view feature point extraction and matching, combined with an incremental structure-from-motion algorithm to optimize and …

Robot Path Planning By Reinforcement Learning Based On Sac3q-Hdm, Dequan Li, Wan Xiong

Journal of System Simulation

Abstract: To address the issues of overestimated and underestimated biases, low sample utilization rate, and the inability to balance exploration and exploitation in reinforcement learning for path planning, an improved SAC method was proposed. The size balance of entropy was explored and utilized through adaptive temperature coefficient adjustment; on the basis of the SAC framework, a triple Critic architecture was introduced to dynamically weight and fuse the minimum and average values through Qvalue uncertainty, balancing overestimated and underestimated biases. A mixed dynamic sampling experience replay buffer was designed; experience data was partitioned based on reward thresholds; sampling ratios were dynamically …

Nerf Optimization Method And Simulation Research Based On Pre-Training And Differentiable Fuzzy Modeling, Yunjng Zhang, Minghui Yang, Hao Wang

Journal of System Simulation

Abstract: To address the challenges of significant geometric modeling errors, severe detail loss, and low training efficiency in neural radiance field(NeRF) reconstruction under defocused blurred input scenarios, this paper proposes two optimization strategies. One strategy is introducing Triplane features generated by the pre-trained LRM as prior knowledge, and combining a lightweight decoder and directional LoRA module to replace large MLP, thereby reducing parameters and shortening convergence time. The second strategy is integrating a differentiable blurring model into the volumetric rendering step. By jointly optimizing the radiation field and spatially variable blurring kernels, reconstruction accuracy under defocused blurred scenarios is enhanced …

Generalizing Networks To Address Data Imbalance For Facial Action Unit Recognition, Andrew W. Sumsion

Theses and Dissertations

Facial action unit (AU) recognition is used in a variety of downstream applications, including sign language translation, dementia detection, pain detection, and other high-impact tasks that require understanding facial motion. Although AU recognition is a well-established field, a number of unresolved issues remain. An initial problem is the need to improve overall performance on the benchmark datasets, as measured by the average F1 score. Also, the performance across all individual AUs, not just the average F1 score, needs to be increased. Recently, the field has shifted its attention towards ensuring that AU recognition models generalize beyond the distributions represented in …

Bicuspid Valve Closure And Backflow Prevention: Role Of Leaflet Geometry, Badr Kauoi, A Bou Orm, P Navet, James W. Baish, Lance Munn

Faculty Journal Articles

Bicuspid valves with crescent-shaped leaflets are found in lymphatic vessels and veins, where their primary function is to prevent reflux and ensure unidirectional flow toward the heart. These valves are passive, and their functionality emerges spontaneously from a complex interplay between the properties of the valve leaflets and the flow patterns developing within the vessel sinus region surrounding the valve. The main function of the valves is to limit retrograde flow, or reflux, but the optimal valve structure has not been well characterized. Here we investigate numerically how the length of the leaflets affects the valve efficiency in preventing reflux. …

Tusqh: Topological Control Of Volume-Fraction Meshes Near Small Features And Dirty Geometry, Brian Reese Shawcroft

Theses and Dissertations

This work develops a framework to create meshes with user-specified homology from potentially dirty geometry by coupling background grids, persistent homology, and a generalization of volume fractions. For a mesh with fixed grid size, the topology of the output mesh changes predictably and monotonically as its volume-fraction threshold decreases. Topological anti-aliasing methods are introduced to resolve pinch points and disconnected regions that are artifacts of user choice of grid size and orientation, making the output meshes suitable for downstream processes including analysis. The methodology is demonstrated on geographical, mechanical, and graphics models in two-dimensions (2D) and three-dimensions (3D) using a …

Latex: Overleaf, Anette Moreno-Lozano Phd, John Connolly Phd

Day Family Research Lab Workshop Series

This workshop introduces participants to LaTeX for scientific writing. Attendees will learn to create and compile documents in Overleaf, insert and format equations, figures, tables, and references, use collaboration and version control tools, and find discipline-specific resources and templates.

Understanding And Defending Large-Scale Networked Systems: From Wi-Fi Localization To Provenance- Based Intrusion Detection, Xiao Han

USF Tampa Graduate Theses and Dissertations

Modern networked systems increasingly depend on wireless sensing and large-scale telemetry to provide critical services and maintain security. However, many of these systems function as black boxes or encounter significant scalability limitations, which leave them vulnerable to sophisticated adversaries. In this dissertation, I present my research on enhancing the security and efficiency of networked systems through measurement-driven vulnerability analysis and the design of scalable defense mechanisms.

First, I present my research on the security and privacy risks of Wi-Fi–based localization services. Although location spoofing attacks against Wi-Fi positioning systems have been studied for over a decade, constructing practical attacks in …

Engineering 50 Years From Today: A Purdue Engineering Community Perspective, Arvind Raman

Purdue University Press Books

Since opening its doors in 1874, Purdue University’s College of Engineering and its graduates have been at the forefront of technological breakthroughs and are now poised to lead the next generation of engineers in the United States and around the globe. Featuring more than twenty-five essays from former Boilermakers, Engineering 50 Years From Today both celebrates the program’s 150th anniversary and looks ahead, exploring what the field of engineering might look like half a century from now. Leading luminaries from Purdue’s engineering community, including prominent university professors as well as innovators working in the private sector, reveal how technologies related …

Scenarioxp: A Complete Scenario–Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Student Research Symposium (SRS)

Today is an age of exiting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

Enhanced Antenna Selection Techniques For Energy-Efficient Code Index Modulation Aided Spatial Modulated Wireless Communication Systems, Fati̇h Çögen, Burak Ahmet Özden, Erdoğan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an integrated multiple-input multiple-output (MIMO) transceiver framework, termed CIM-HQAM-SM, which combines code index modulation (CIM) and spatial modulation (SM) with energy-efficient hexagonal quadrature amplitude modulation (HQAM). In the proposed bit mapping, the information bits jointly select (i) the active transmit-antenna index, (ii) the Walsh–Hadamard spreading-code indices for the in-phase and quadrature branches, and (iii) an HQAM symbol. Hence, the payload is conveyed through the constellation symbol as well as through antenna and code indices. For the considered Rayleigh-fading scenarios and matched spectral-efficiency settings, the proposed framework offers BER improvements over conventional SM and quadrature SM (QSM), while …

An Ensembled Two-Phase Deep Learning Approach For A Psychiatric Disorder Detection, Prajna Paramita Debata, Midhun Chakkaravarthy, Brojo Kishore Mishra

Turkish Journal of Electrical Engineering and Computer Sciences

Computational Psychiatry represents a burgeoning realm within scientific inquiry, delving into the intricate interplay of neurobiology within the brain. The escalating prevalence of mental illness underscores the urgency to confront this challenge. Among the prevalent disorders, Schizophrenia and Bipolar Disorder loom large, affecting a significant portion of the population at some point in their lives. However, pinpointing psychiatric disorders poses a formidable challenge. Genetic predispositions significantly influence the development of mental illnesses, with intriguing overlaps observed among certain disorders. This convergence complicates accurate diagnosis. Here, a deep learning approach is considered for significant gene biomarker identification and classification of Schizophrenia …

Sentisec: Combining Keyword Heuristics And Sentiment Modeling For Ai-Powered Threat Detection, Ridho Surya Kusuma, Erum Ashraf, Selvakumar Manickam, Shankar Karuppayah

Turkish Journal of Electrical Engineering and Computer Sciences

This work presents SENTISEC, a hybrid LLM-based threat detection framework designed to classify security logs by integrating keyword heuristics, domain-adapted sentiment scoring, and Retrieval-Augmented Generation (RAG). The system achieves an overall accuracy of 93.67%, with 91.46% macro recall, 89.07% macro F1, and 95.15% threat recall, while maintaining a low false-positive rate of 1.68%. Its methodology incorporates strict keyword and IOC matching, a domain-tuned DistilBERT sentiment module, hybrid BM25–MiniLM retrieval enhanced with BGE reranking, adaptive quantile-based threshold calibration, and SHAP-based explainability. Comparative evaluations against keyword-only, sentiment-only, classical machine-learning models, and DistilBERT-only baselines show that SENTISEC consistently improves both true-positive and true-negative …

Optimal Network Reconfiguration Based On Discrete Metaheuristic Techniques For Reduction Of Power Loss And Carbon Emission In Distribution Networks, Asad Ali, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Hussain Shareef, Hasmaini Mohamad, Munir Azam Muhammad

Turkish Journal of Electrical Engineering and Computer Sciences

Power distribution systems play a crucial role in transmitting electrical power from generation sources to end users. During transmission, significant power losses occur in the form of heat as the current flowing along the lines/cables has resistance. To minimize power losses, distribution network reconfiguration (DNR) has been widely adopted. This paper proposes optimal DNR based on metaheuristic techniques with discrete mutation feature targeting active power loss reduction, which subsequently lowers carbon emissions and operational costs. Through the discrete mutation feature, computational time to find optimal solution has been reduced significantly with fewer iterations compared to conventional mutation techniques. The proposed …