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Automated, Modular, Agentless Adversarial Emulation In Cloud Environments For Higher Education And Student Training, Doc Harley Apr 2026

Automated, Modular, Agentless Adversarial Emulation In Cloud Environments For Higher Education And Student Training, Doc Harley

Senior Honors Theses

Currently, the leading technologies in the market of adversarial emulation are MITRE Caldera, Atomic Red Team by IBM, and multiple proprietary products that come with support packages for different vendors like AttackIQ, Cymulate, SafeBreach, and many more. While it is clear that much work has been done in the broad category of adversarial emulation, when it comes to open source solutions, there are no agentless options with built in automation and modularity that have good support for cloud environments. Agentless adversarial emulation provides a unique advantage in that it can be both simpler and a better representation of the true …

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Drug Risk Knowledge Discovery For Western Medicines Based On Knowledge Graph Link Prediction, Jianxiang Wei, Ma Hengyuan Ma, Yuehong Sun, Wenwen Du, Letian Hu Apr 2026

Drug Risk Knowledge Discovery For Western Medicines Based On Knowledge Graph Link Prediction, Jianxiang Wei, Ma Hengyuan Ma, Yuehong Sun, Wenwen Du, Letian Hu

Journal of Scientific Information Research

[Purpose/significance] The risk information contained in drug instructions is usually incomplete, and some new adverse reactions can only be discovered in actual clinical use. This paper proposes an information organization and knowledge discovery method for pharmacovigilance, in order to timely and accurately identify missing risk knowledge in drug instructions. [Method/process] Drug instructions of 8 152 Western medicines are collected as the research data; On the basis of ontology construction, data annotation, and model training, the UIE model is used to jointly extract entity and relationship triplets from the research data; A new knowledge graph link prediction method CompGCN-RotatE, is proposed, …

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Research On Temporal Knowledge Graph Completion Method For Emergent Events Based On Bigru And Graph Contrastive Learning, Peng Wu, Zhenyu Lu, Xuechen Zhang Apr 2026

Research On Temporal Knowledge Graph Completion Method For Emergent Events Based On Bigru And Graph Contrastive Learning, Peng Wu, Zhenyu Lu, Xuechen Zhang

Journal of Scientific Information Research

[Purpose/significance] During emergencies, social media short texts contain critical information but are heavily interfered with by noise. Traditional static knowledge graph completion techniques struggle to effectively address their dynamic evolution and data sparsity issues, making it imperative to introduce temporal modeling methods. [Method/process] This study proposes a dynamic completion framework that combines the temporal feature capture capability of Bidirectional Gated Recurrent Units (BiGRU) with the noise-resistant representation learning advantages of Graph Contrastive Learning (GCL). At the completion level, the ConBiTE method is introduced, which captures temporal dependencies through self-attention mechanisms and BiGRU, while leveraging GCL to enhance the completion of …

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On The Need For Intelligent Link Management In Wifi 7 Mlo, Logan Lux, André Gomes Apr 2026

On The Need For Intelligent Link Management In Wifi 7 Mlo, Logan Lux, André Gomes

STEM Student Research Symposium Posters

Wi-Fi 7 EMLSR allows devices to use multiple wireless links to improve throughput and reduce latency, but its real-world performance depends heavily on network conditions. This project investigates the need for intelligent link management in Wi-Fi 7 EMLSR by building a real testbed to measure performance.

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Isolation, Identification And Antibiotic-Resistance Profiling Of Bacteria Isolated From Mobile Phone Surfaces In Karbala., Kawkab Abdullah Alsaadi, Zahraa Raheem Murshidy, Dhuha Ali Hussein, Sura Abd Ali Kadhim, Kawakib Aboudi Hanoon Apr 2026

Isolation, Identification And Antibiotic-Resistance Profiling Of Bacteria Isolated From Mobile Phone Surfaces In Karbala., Kawkab Abdullah Alsaadi, Zahraa Raheem Murshidy, Dhuha Ali Hussein, Sura Abd Ali Kadhim, Kawakib Aboudi Hanoon

Karbala International Journal of Modern Science

Due to the diverse environments that mobile phones are exposed to via human handling they become reservoirs of microorganisms. The objective of the current study was to determine the level and character of bacterial contamination on mobile phones by identifying bacterial strains, and the degree of antimicrobial susceptibility. A cross-sectional study was conducted from April to July 2025 where phones belonging to 115 individuals were randomly chosen from various departments and units at Kerbala University and swabbed. The VITEK 2 automated system was used to identify bacterial taxa and test for antimicrobial susceptibility, and 50 of the swabs exhibited bacterial …

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Developing A Framework To Improve Phishing Response After Detection, Kevin Umba Apr 2026

Developing A Framework To Improve Phishing Response After Detection, Kevin Umba

Master's Theses (2009 -)

Phishing belongs to the most prevalent attack vectors in the cybersecurity industry. Academia, industry, and government resources offer a wealth of information on the subject. However, insights on phishing techniques and remediations are scattered across those sources. Most governmental and academic literature focuses on user awareness, policy guidelines, or phishing training. This leaves the security practitioners underserved. Furthermore, actionable information for security operations is often unstructured or vendor-specific. The goal of the thesis is to provide a preliminary framework to guide security professionals through indicators of compromise and remediation, using a vendor-neutral approach.

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Worldview-Bench: A Benchmark For Evaluating Global Cultural Perspectives In Large Language Models, Abdullah Mushtaq, Imran Taj, Rafay Naeem, Ibrahim Ghaznavi, Junaid Qadir Apr 2026

Worldview-Bench: A Benchmark For Evaluating Global Cultural Perspectives In Large Language Models, Abdullah Mushtaq, Imran Taj, Rafay Naeem, Ibrahim Ghaznavi, Junaid Qadir

All Works

Background: Large Language Models (LLMs) are predominantly trained and aligned in ways that reinforce Westerncentric epistemologies and socio-cultural norms, leading to cultural homogenization and limiting their ability to reflect global civilizational plurality. Existing benchmarking frameworks fail to adequately capture this bias, as they rely on rigid, closed-form assessments that overlook the complexity of cultural inclusivity. Objectives: To address this cultural bias problem, we introduce WorldView-Bench, a benchmark designed to evaluate Global Cultural Inclusivity (GCI) in LLMs by analyzing their ability to accommodate diverse worldviews. Methods: Our approach is grounded in the Multiplex Worldview proposed by Senturk et al., which distinguishes …

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The Role Of Human-Centered Artificial Intelligence In Crewed Mars Missions, Jordan Neuman Apr 2026

The Role Of Human-Centered Artificial Intelligence In Crewed Mars Missions, Jordan Neuman

Student Publications and Presentations

The suggestion of implementing Artificial Intelligence (AI) in crewed missions to the Martian surface can revolutionize mission operations, psychological support, and ethical decision‑making under challenging conditions. This study is a structured, qualitative literature review of current AI research for the future of human space exploration. It uses publicly available peer‑reviewed journal articles and NASA/ESA technical reports on AI for Mars missions. Using Perplexity, the researcher first prompted the AI tool to identify recurring topics across these sources and generate themes. The researcher independently reviewed all sources to confirm and refine those themes. Three themes—mission autonomy, local resource utilization and life‑support, …

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High Performance Ae-Cnn Model Enhanced Via Gaussian Augmentation For Rssi-Dependent Indoor Positioning In Wireless Sensor Networks, Kahlaa K. Al-Nassrawy, Ghaidaa A Al-Sultany Apr 2026

High Performance Ae-Cnn Model Enhanced Via Gaussian Augmentation For Rssi-Dependent Indoor Positioning In Wireless Sensor Networks, Kahlaa K. Al-Nassrawy, Ghaidaa A Al-Sultany

Karbala International Journal of Modern Science

Precise indoor positioning system remains a significant challenge within Wireless Sensor Networks (WSNs) due to the instability and noise sensitivity of Received Signal Strength Indicator (RSSI) data. This paper proposes an advanced hybrid deep learning framework designed for 2D indoor localization, utilizing RSSI measurements to predict human or object position. Furthermore, to enhance the robustness of the model against signal variance and promote its generalization capability under this uncertainty, a data augmentation strategy is applied using three methods: Gaussian Noise Injection (GNI), Gaussian Mixture Model (GMM), and Bayesian Gaussian Mixture Model (BGMM). The purpose of data augmentation is to simulate …

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Ur-166-204 Multi-User Stem Learning Experience Powered By Llm Conversational Agents, Devon Haynes, Chance Boecker, Oliver Haggard Apr 2026

Ur-166-204 Multi-User Stem Learning Experience Powered By Llm Conversational Agents, Devon Haynes, Chance Boecker, Oliver Haggard

C-Day Computing Showcase

While Extended Reality (XR) provides experiential and interactive foundations for STEM education, current storytelling and narrative-driven applications often lack responsive nonplayer characters (NPCs), limiting interactive potential through pre-scripted stories. Additionally, despite the growth of Large Language Model (LLM) integration in XR, limited research explores the combined use of multi-user XR systems and conversational Artificial Intelligence (AI) to facilitate real-time, adaptive instruction. This project seeks to address these gaps by 1) Developing a narrative-driven STEM learning XR prototype that incorporates synchronous multi-user interaction and an embedded LLM-driven conversational agent and 2) Exploring the effectiveness of combining these technologies to improve learning …

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Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak Apr 2026

Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak

C-Day Computing Showcase

This project addresses the urgent need for transparent chatbot safety evaluations amid rising concerns about AI-facilitated self-harm. Using public social media datasets, we simulate two tasks: (1) detecting suicidal ideation via emotion-based risk scoring, and (2) stress-testing a support-style chatbot against 888 high-risk prompts, including euphemisms and “for a story” framing. A multi-label classifier trained on GoEmotions feeds emotion profiles into a logistic regression model to generate suicidality risk scores. These scores guide a local chatbot built with Ollama’s llama3, which analyzes user messages and steers responses toward safe, empathetic behavior. Evaluation shows ~90% of replies were safe or supportive. …

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Gc-161-210 Hybrid Path Planning Using Genetic Algorithm, Caitlin Tigani, Ramisa Fariha Joyee, Wasif Mohammad Apr 2026

Gc-161-210 Hybrid Path Planning Using Genetic Algorithm, Caitlin Tigani, Ramisa Fariha Joyee, Wasif Mohammad

C-Day Computing Showcase

This research investigates whether uninformed search (BFS) or informed search (A*) is more effective when combined with a Genetic Algorithm for maze path planning. We design and implement four algorithms: baseline BFS and A*, hybrid GA+A*, and hybrid GA(BFS+A*). Our findings show that while A* alone performs optimally, integrating it with GA can produce alternative quality solutions, though with computational trade-offs. The study demonstrates that GA+A* provides the best balance between solution quality and runtime efficiency.

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Grp-01-196 Topological Drift Predicts Epidemic Instability, Charles Fanning Apr 2026

Grp-01-196 Topological Drift Predicts Epidemic Instability, Charles Fanning

C-Day Computing Showcase

We study whether changes in contact-network topology predict transitions into high-risk epidemic periods across several classical empirical proximity network datasets. We use temporal graph learning with persistent homology-based topological signals and evaluate large-outbreak risk using SIR simulations to test whether topological drift serves as an early warning signal for epidemic instability.

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Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg Apr 2026

Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg

C-Day Computing Showcase

This project evaluates automated SMS spam classification by comparing traditional machine learning against modern transformer architectures. We built a Bidirectional LSTM (BiLSTM) baseline using TF-IDF feature extraction and NearMiss-1 undersampling to handle severe class imbalances. We then compared this against a fine-tuned Hugging Face Sentence-BERT model. Preliminary results show Sentence-BERT significantly outperformed the BiLSTM baseline (99.01% vs. 95.65% accuracy). These findings demonstrate that transformer-based embeddings offer a highly accurate, scalable solution for spam mitigation without relying on aggressive data undersampling.

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Grp-093-174 Transforming Everyday Smartwatch Data Into Clinical Early Warnings, Nursat Jahan Apr 2026

Grp-093-174 Transforming Everyday Smartwatch Data Into Clinical Early Warnings, Nursat Jahan

C-Day Computing Showcase

Cardiovascular Disease (CVD) related most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of derived CVD risk profile groups. Our study introduces a non-invasive continuous health monitoring framework, demonstrating that passively collected daily …

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Grp-09-169 Using Logic To Explain And Formally Verify The Behavior Of Relu Neural Networks, Nguyen Thi Binh Nguyen Apr 2026

Grp-09-169 Using Logic To Explain And Formally Verify The Behavior Of Relu Neural Networks, Nguyen Thi Binh Nguyen

C-Day Computing Showcase

Homeschooling in the United States has expanded rapidly, reaching about 3.4 million students (6.26% of K-12 school-age population) in 2024-2025, with accelerated growth following COVID-19. Understanding the reasons behind these decisions is important for informing education policy, resource allocation, and the design of schooling systems that better meet families’ needs. Most of the research on homeschooling relies on statistical methods such as logistic regression or probit models to identify significant factors associated with homeschooling decisions. In this work, we approach this research question from a different perspective by leveraging Explainable AI techniques to provide deeper insights into the homeschooling decisions.

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Inc3vits Model: A Hybrid Architecture To Accelerate And Reduce Complexity For The Deepvariant Model For Variant Calling, Mustafa Al-Saffar, Sura Z. Al Rashid Apr 2026

Inc3vits Model: A Hybrid Architecture To Accelerate And Reduce Complexity For The Deepvariant Model For Variant Calling, Mustafa Al-Saffar, Sura Z. Al Rashid

Karbala International Journal of Modern Science

Deep learning has revolutionized genomic variant calling, yet the computational cost of current systems continues to limit scalability. We present a controlled efficiency study of DeepVariant-style pileup architectures under identical training and inference conditions, comparing architectural downsizing with a hybrid CNN–local attention design. Inc3ViTs pairs a streamlined InceptionV3 stem with a lightweight local attention head based on patch tokenization and windowed self-attention, enabling a direct comparison with a CNN-only reduced Inception baseline. Across whole-genome and whole-exome short-read datasets, Inc3ViTs reduces training time by ~40–50% and reduces inference runtime relative to the original DeepVariant. The CNN-only baseline indicates that most speedups …

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Method For Testing And Evaluating Intelligence Level Of Virtual Forces Based On Operational Experiments, Dayong Liu, Zhiming Dong, Weidong Zhang, Wenjun Zhang, Jiancheng Gao Apr 2026

Method For Testing And Evaluating Intelligence Level Of Virtual Forces Based On Operational Experiments, Dayong Liu, Zhiming Dong, Weidong Zhang, Wenjun Zhang, Jiancheng Gao

Journal of System Simulation

Abstract: The intelligence level of virtual forces is a key factor affecting the credibility and effectiveness of tactical confrontation simulations. To address the current lack of a testing and evaluation system, a method for testing and evaluating the intelligence level of virtual forces based on operational experiments is proposed. Guided by operational experiment theory, the method stimulates the intelligent behavior of virtual forces by constructing dynamic confrontation environments, and collects, calculates, analyzes, and evaluates their intelligence performance data according to a systematic process. The overall architecture, logical functional modules, and basic evaluation process of the method are designed. A "4M" …

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Research On Control Of Coaxial Dual-Rotor Unmanned Aerial Vehicle Based On Improved Reaching Law, Xinhang Chen, Xiaodong Ling, Chengchang Lang, Shijun Zheng, Yiqi Tang Apr 2026

Research On Control Of Coaxial Dual-Rotor Unmanned Aerial Vehicle Based On Improved Reaching Law, Xinhang Chen, Xiaodong Ling, Chengchang Lang, Shijun Zheng, Yiqi Tang

Journal of System Simulation

Abstract: To address the issues of significant chattering, slow convergence speed, and large overshoot in the control system of a coaxial dual-rotor unmanned aerial vehicle, a control method based on an improved double-power and hyperbolic function integral sliding mode reaching law was proposed. A novel reaching law was designed to achieve fast convergence when the system state is far from the sliding surface and smooth transition when approaching the sliding surface, thereby enhancing the overall convergence speed of the system and ensuring that the system reaches the sliding surface within a finite time. The saturation characteristic of the hyperbolic function …

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Large Language Model For X Language Simulation: Architecture, Key Technologies, And Typical Applications, Laichunyang Peng, Fei Ye, Xiaoming Guo, Jinglin Zhou Apr 2026

Large Language Model For X Language Simulation: Architecture, Key Technologies, And Typical Applications, Laichunyang Peng, Fei Ye, Xiaoming Guo, Jinglin Zhou

Journal of System Simulation

Abstract: General-purpose large language models lack training on X language-specific corpora, and traditional fine-tuning methods lack targeted adaptation to the interdisciplinary integration and multimodule coupling of X language, resulting in problems such as non-standard syntax and semantic deviation in generated code. To address these issues, this paper systematically proposed the definition and integrated architecture of a large language model for X language simulation. Modeling subclasses were defined according to the disciplines and classes of X language, and dedicated adapters were constructed for each subclass. By merging their weights during the inference phase, the incremental integration of multi-domain modeling skills was …

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Vehicle Routing Optimization For Underground Mines Considering Fuzzy Demand And Time Tolerance, Guorong Wang, Haishun Deng, Ziming Kou, Xuanxuan Yan, Zhixiang Huang Apr 2026

Vehicle Routing Optimization For Underground Mines Considering Fuzzy Demand And Time Tolerance, Guorong Wang, Haishun Deng, Ziming Kou, Xuanxuan Yan, Zhixiang Huang

Journal of System Simulation

Abstract: To solve the problem of material demand fluctuation and different time windows in auxiliary transportation of underground mines, a routing optimization method for material distribution considering fuzzy demand and time tolerance was proposed. Based on the fuzzy credibility theory, uncertain demand was constrained by fuzzy chance constraints, and a multi-objective routing optimization model for underground mine auxiliary transportation was constructed with the objectives of minimizing the total operating cost of vehicles and maximizing time tolerance. A multi-objective genetic algorithm with mixed dominance strength was designed to solve the model. By calculating the deviation difference of Pareto frontier solutions, …

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Auv Path Planning Integrating Local-Global Strategies In Unknown Environments, Wenlong Meng, Yanbo Pu, Ya Gong Apr 2026

Auv Path Planning Integrating Local-Global Strategies In Unknown Environments, Wenlong Meng, Yanbo Pu, Ya Gong

Journal of System Simulation

Abstract: Existing path planning algorithms often struggle to efficiently explore and generate high-quality trajectories. To address this issue, this paper proposes a path planning algorithm that integrates local-global strategies. By employing the rolling window technique, the global one-time path planning problem is transformed into an iterative process of multiple local planning stages. During the global exploration phase, the rolling window is used to determine high-level path branches and to identify branch waypoints, thereby refining the calculation of local paths. In the local exploration phase, an improved RRT-Connect algorithm is proposed, which combines adaptive circular sampling with dynamic step length to …

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Modelling Method Of Unmanned Vehicle Dynamics Based On Neural Network, Jun Wang, Min Liu, Xiaochuan Zhang, Yishan Ding, Juhui Feng, Ye Zhuang Apr 2026

Modelling Method Of Unmanned Vehicle Dynamics Based On Neural Network, Jun Wang, Min Liu, Xiaochuan Zhang, Yishan Ding, Juhui Feng, Ye Zhuang

Journal of System Simulation

Abstract: To address the challenges of high data acquisition costs of test data on dynamic characteristics between tires and soft terrain and low speed of numerical calculation for unmanned vehicles in complex terrestrial environments, a modeling method of unmanned vehicle dynamics based on a neural network was proposed. Tire-terrain contact dynamics models were built by using discrete element method (DEM) simulations for tire-terrain contact and experimental data, thereby creating a dataset of tire contact forces for various tire materials in terrestrial environments. The neural network was applied to regressively learn the dataset, and a nonlinear neural network tire model was …

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Large-Scale Multi-Objective Evolutionary Algorithm Based On Multi-Region Dynamic Grouping, Binhao Liang, Jingxuan Wei, Fengqin Liang Apr 2026

Large-Scale Multi-Objective Evolutionary Algorithm Based On Multi-Region Dynamic Grouping, Binhao Liang, Jingxuan Wei, Fengqin Liang

Journal of System Simulation

Abstract: The decision variable dimension of large-scale multi-objective optimization problems can reach hundreds or even thousands. For existing large-scale multi-objective evolutionary algorithms based on decision variable analysis, which usually consume a large amount of computational resources for grouping and fail to consider the interactions between convergence-related variables and diversity-related variables, a large-scale multi-objective evolutionary algorithm based on multi-region adaptive dynamic grouping was proposed. The algorithm employed a Gaussian mixture model to partition the decision space into multiple regions; within each region, feature vectors were constructed for each decision variable, and spectral clustering was utilized to perform grouping. To validate …

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Intelligent Competition Platform And Mode Driven By Cloud-Native Simulation, Long Qin, Hesong Huang, Lujia Yin, Chuan Ai, Qi Zhang, Xinmeng Li Apr 2026

Intelligent Competition Platform And Mode Driven By Cloud-Native Simulation, Long Qin, Hesong Huang, Lujia Yin, Chuan Ai, Qi Zhang, Xinmeng Li

Journal of System Simulation

Abstract: To solve the problems faced by the adversarial competition mode of agents, including difficult development and deployment, low resource utilization, poor reusability, and difficulty in accessing reinforcement learning algorithms, a new agent simulation training platform was designed. The software components of the competition platform were decoupled based on cloud-native technology; a high-performance simulation engine for the competition environment was proposed; a new method of an embedded reinforcement learning model for an intelligent control terminal was designed, with multiple online and offline policy-based reinforcement learning algorithms set. The experiment demonstrates that the development and deployment of the system is efficient, …

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Intersection Positioning Algorithm With Spatial Translation Based On Sar Scene Matching, Cheng'en Pu, Yumin Lai, Rui Shi, Yan Liao, Kezi Meng, Lifeng Qu Apr 2026

Intersection Positioning Algorithm With Spatial Translation Based On Sar Scene Matching, Cheng'en Pu, Yumin Lai, Rui Shi, Yan Liao, Kezi Meng, Lifeng Qu

Journal of System Simulation

Abstract: To address the problem that the positioning precision of the inertial navigation system cannot meet the requirements of autonomous positioning when the aircraft flies, an autonomous positioning method with spatial translation based on circular scanning scene matching of SAR was proposed. The spatial translation positioning model of the aircraft was established according to the position of the ground matching points obtained by single point and single circular scanning scene matching of SAR, the oblique distance between the matching points and the aircraft, and the inertial measurement information of the aircraft. The characteristic information of the matching point sequence was …

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State Monitoring Of Nuclear Power Connection Sleeve Quality Inspection Equipment Driven By Digital Twin, Yandong Nan, Jinda Zhu, Xinbin Lu, Zhiying Qin, Dandan Qi, Zhiheng Ding Apr 2026

State Monitoring Of Nuclear Power Connection Sleeve Quality Inspection Equipment Driven By Digital Twin, Yandong Nan, Jinda Zhu, Xinbin Lu, Zhiying Qin, Dandan Qi, Zhiheng Ding

Journal of System Simulation

Abstract: To address the problems of delayed state perception, single monitoring dimension, and insufficient visualization in the quality inspection equipment for nuclear power connection sleeves, a state monitoring method driven by digital twin was proposed. A digital twin-based collaborative state monitoring framework for the inspection equipment was constructed. Based on the OPC UA technology, a multi-source information interconnection model was established. A finite state machine model was employed to discretize and logically drive the inspection process, and a hierarchical verification strategy was proposed to establish a multi-dimensional motion state monitoring mechanism. A surrogate model coupling the radial basis interpolation function …

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Modeling And Simulation Of Target Characteristic Architecture Of Target Missile Based On Dodaf, Tuo Zhao, Yuqiao Liu, Yuan Ma, Yun Cheng, Shen Li Apr 2026

Modeling And Simulation Of Target Characteristic Architecture Of Target Missile Based On Dodaf, Tuo Zhao, Yuqiao Liu, Yuan Ma, Yun Cheng, Shen Li

Journal of System Simulation

Abstract: In view of the problems of incomplete elements and missing architecture in the research of target characteristics of target missiles, this paper introduced the panoramic view, operational view, and capability view models in the DoDAF from the perspective of system engineering. According to the view development sequence of "panoramic description, operational decomposition, and capability matching", a target characteristic architecture of the target missile was constructed, which included target elements such as maneuvering characteristics and infrared radiation characteristics. The mapping relationship among "operational layer, capability layer, and target characteristic layer" of the target missile was obtained, and the architecture was …

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Dynamic Model-Driven Verification Framework For Modular Aerial Bomb Systems, Wenlong Li, Shuhan Sang, Yusheng Liu, Haiyan He, Zan Liang, Wenqiang Yuan, Biao Niu, Weifeng Luo Apr 2026

Dynamic Model-Driven Verification Framework For Modular Aerial Bomb Systems, Wenlong Li, Shuhan Sang, Yusheng Liu, Haiyan He, Zan Liang, Wenqiang Yuan, Biao Niu, Weifeng Luo

Journal of System Simulation

Abstract: To address the problems of high verification costs, difficulty in covering dynamic behaviors, and lack of quantitative closed loops in the design stage of modular complex equipment, a dynamic model-driven modular system verification framework was proposed. Based on model-based systems engineering (MBSE) modeling, a structural coupling quantification model was constructed using the number of interfaces, signal interaction frequency, and dependency intensity. Dynamic tests were conducted in high-fidelity virtual simulation to collect data; performance rating for indicators such as accuracy, response, and stability, as well as system's comprehensive rating, were obtained, and the rating feedback was used for iterative optimization. …

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Model-Based System Verification: Theoretical Framework, Key Technologies, And Future Prospects, Bo Sun, Yi Ren, Silin Wang, Qi Liu, Zhidong Li Apr 2026

Model-Based System Verification: Theoretical Framework, Key Technologies, And Future Prospects, Bo Sun, Yi Ren, Silin Wang, Qi Liu, Zhidong Li

Journal of System Simulation

Abstract: Traditional system verification methods face significant challenges in terms of efficiency, coverage, and traceability. To address these issues, this paper introduced model-based system verification (MBSV), which deeply integrated verification activities within the model-based systems engineering model system and evolution process. It presented the foundational logic of MBSV and proposed a multiview unified verification modeling strategy based on system modeling language (SysML), integrating requirements, structure, behavior, and constraints. The paper discussed the algorithms for selecting representative paths and reducing equivalent classes to enhance verification efficiency, the principles of test path search, as well as the intelligent path search mechanism based …

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