Artificial Intelligence and Robotics Commons^™

Research On Infrared And Visible Light Fusion Method Based On Resnet-50 And Laplacian Filtering, Xiao Wang, Xiangyang Li, Feng Liang, Zhili Zhang

Journal of System Simulation

Abstract: In order to solve the problem that existing infrared and visible light image fusion techniques often suffer from artifacts caused by insufficient contrast, spectral distortion, and high computational complexity, a fusion framework based on ResNet-50 and Laplacian filtering was proposed. ResNet-50 was used to extract shallow and deep features, followed by multi-scale feature fusion. Laplacian filtering was applied to optimize feature information, and an automatic discriminator was introduced to further improve the fusion effect. Simulation results show that, compared with comparison algorithms, the proposed method achieves an average increase of 2.71% and 2.16% in information entropy, 5.98% and …

Optimization Of Dynamic Weapon Target Assignment Considering Random Disturbances, Zhenzu Bai, Yizhi Hou, Zhangming He, Juhui Wei, Haiyin Zhou, Jiongqi Wang

Journal of System Simulation

Abstract: The impact of various random disturbances in the actual command and control environment of unmanned systems on problem modeling and solving of weapon target assignment was considered, and three types of uncertainty disturbance constraints were investigated. A multi-objective dynamic sensor weapon target assignment model was established. By considering the issues of model property changes caused by disturbances and insufficient robustness of the traditional single-operator solving algorithm, a multi-operator constrained multi-objective evolutionary framework based on the deep Q-network was proposed. The algorithm described the convergence, diversity, and feasibility of the population in both the objective and decision spaces. It established …

Survey Of Cooperative Multi-Agent Path Finding, Jun Xiong, Wenbo Zhang, Zhi Xiong, Feng Zhou, Bo Yang

Journal of System Simulation

Abstract: Cooperative multi-agent path finding (Co-MAPF) has been widely applied in fields such as UAV formation and multi-agent systems, which enhances the overall system efficiency through task collaboration, path planning, and task execution among multiple agents. This paper introduced three main system architectures, namely centralized, distributed, and hybrid, along with their advantages and disadvantages based on the definition of the Co-MAPF problem, categorized, and reviewed mainstream Co-MAPF algorithms, including those based on sampling, search, intelligent optimization, and learning. Furthermore, this paper analyzed the main current challenges faced by Co-MAPF algorithms on the basis of summarizing existing research and outlined the …

Interoperability Model And Application Of Military Training System For Combination Of Virtuality And Reality, Jianxing Gong, Hai Hu, Haihui Ren, Ruixiang Wu

Journal of System Simulation

Abstract: With the development of AI technology, VR technology, and combat simulation technology, in order to achieve the practical training effect of "how to fight and how to train soldiers", virtual and real training has become a widely popular military training mode. It has become a trend to integrate digital systems, virtual equipment, semi-physical models, physical models, and other heterogeneous systems to carry out training in the same training environment. Therefore, it is necessary to study the interoperability model and application of training systems for the combination of virtuality and reality. This paper proposed the definition of interoperability between virtuality …

Analysis Of Optimal Spectral Bands For Thermal Infrared Hyperspectral Image Reconstruction Driven By Physical Simulation Model, Yonghao Yang, Xiaoyu He

Journal of System Simulation

Abstract: To achieve accurate reconstruction of thermal infrared hyperspectral images under limited spectral bands, this paper proposed a reconstruction method based on physical modeling and simulation. Semi-global decomposition algorithm was adopted to invert the thermophysical properties of the scenario based on the physical model of thermal radiation, simulating and generating full-band hyperspectral data. An optimal spectral band selection strategy driven by a physical model was proposed, which integrated the sensitivity of temperature inversion and the separability of material spectra. Experiments were conducted on both simulated and measured datasets to evaluate the performance of material identification, temperature inversion, and spectral …

Research On Pac-Bayes-Based A2c Algorithm For Multi-Objective Reinforcement Learning, Xiang Liu, Qiankun Jin

Journal of System Simulation

Abstract: To address the theoretical challenges of exploration and exploitation trade-offs and uncertainty modeling in multi-objective reinforcement learning (MORL), this study developed a learning framework, MO-PAC, based on PAC-Bayes theory. By introducing a multi-objective stochastic Critic network and a dynamic preference mechanism, the framework extended the conventional A2C architecture, enabling adaptive and efficient approximation of complex Pareto fronts. Experimental results demonstrate that in multi-objective MuJoCo environments, MO-PAC outperforms baseline algorithms, achieving approximately 20% improvement in hypervolume and 60% increase in expected utility, while exhibiting superior convergence efficiency and robustness. It verifies both theoretical value and practical performance advantages in …

Dual-Channel Supply Chain Network Equilibrium Model Under Retailers’ Risk Aversion, Hongchun Wang, Caifeng Lin, Xinyi He, Haiyue Yin

Journal of System Simulation

Abstract: To study the network equilibrium problem of dual-channel supply chains under the background of retailers' risk aversion, a dual-channel supply chain network equilibrium model including multiple competitive suppliers, manufacturers, retailers, and demand markets was established. The Mean-CVaR method was employed to quantify retailers' risk aversion characteristics, and variational inequalities were utilized to characterize the equilibrium conditions of decision-makers at each tier of the supply chain. The projection contraction algorithm was applied to solve the model and conduct numerical analysis, thereby revealing the impact of retailers' risk aversion behavior on equilibrium outcomes. The simulation results indicate that a higher …

Robust Emergency Dispatch Method Considering Dynamic Frequency Security And N-K Contingency, Tao Huang, Zhi Zhang, Yujie Ding, Yanbo Chen, Jing Wang, Wenqian Zhang

Journal of System Simulation

Abstract: To address the risk of system inertia loss and frequency instability caused by grid integration of high-proportioned new energy and unit failures, an N-k robust emergency dispatch method considering dynamic frequency security constraints was proposed. With the consideration of the frequency response characteristics of variable-speed pumped storage, a dynamic frequency response model incorporating variable-speed pumped storage was constructed, and the nadir frequency constraint was established through second-order cone transformation. Information entropy theory was employed to quantify the uncertainty of unit failures, and an uncertainty set considering N-k unit failures was developed. A twostage robust emergency dispatch model considering N-k …

Vibration Control Of Offshore Wind Turbine Towers Based On Eddy Current Nonlinear Energy Sink, Xiangxing Yu, Yandong Zhao, Baolin Zhang

Journal of System Simulation

Abstract: To address the issue of tower vibrations induced by wind loads, which can damage the structure of wind turbines, a vibration control method for monopile offshore wind turbine towers based on an eddy current-nonlinear energy sink (EC-NES) was proposed. The dynamic model of monopile offshore wind turbines based on EC-NES was constructed according to the Euler-Lagrange equation, and based on the output response of FAST software, the unknown parameters of the model and the wind loads were identified in terms of parameters. The optimal parameters of EC-NES stiffness and damping were obtained using PSO. The eddy current damper …

Fault Diagnosis Method For Photovoltaic Systems Based On Multi-Strategy Fusion, Bin Li, Yuchuo Wang

Journal of System Simulation

Abstract: To address the problem of frequent PV system faults, a multimodal fusion fault diagnosis model based on the optimization of the improved lemming algorithm was proposed. The one-dimensional time series signals of PV currents and voltages were converted into two-dimensional images by Markov transformation field, and the spatial features of the original waveforms were mined by using multiscale CNN (MCCNN); BiGRU was used to extract the temporal dynamic features of the original waveforms, and complementary enhancement of the temporal and spatial features was realized by the feature fusion layer. The improved lemming algorithm was innovatively introduced to adaptively optimize …

Numerical Simulations Of Ship Liquid Tank Sloshing Based On Graph Neural Networks, Wenkang Zhang, Xiaofeng Sun, Yiping Zhong, Yong Yin

Journal of System Simulation

Abstract: To address the high consumption of computational resources in simulating ship liquid tank sloshing using computational fluid dynamics simulation methods, a data-driven numerical simulation model was proposed based on graph neural networks. An encoder-processor-decoder framework was employed in the proposed model. The encoder extracted features of fluid particles from the first five time steps. The processor learnt latent motion patterns of fluid and updated features, and the decoder predicted features of particles at subsequent time steps. The processor incorporated a self-attention mechanism to enable dynamic adjacency weight allocation and emphasize the influence of irregular tank wall regions. Training …

Lightweight Assembly Workpiece Detection Algorithm Based On Improved Yolov8, Shuheng Wu, Yongkui Liu, Lin Zhang, Yingying Xiao, Lihui Wang

Journal of System Simulation

Abstract: To address the issues of low recognition accuracy and slow detection speed with existing deep learning-based object detection algorithms for robotic automatic assembly tasks, a lightweight assembly workpiece object detection algorithm based on YOLOv8 was proposed. The PConv was introduced to improve the C2f module, and a new Faster_C2f module was designed to enhance the detection speed of the model. The SIoU loss function was employed to optimize the location prediction accuracy of the CIoU loss function and improve the localization accuracy of small targets. The high-level screening-feature fusion pyramid networks (HS-FPN) structure was used to improve the Neck …

Real-Time Production Of High-Resolution, Gap-Free, 3-Hourly Aod Over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, And Air Quality Data, Seoyeon Kim, Youjeong Youn, Menas Kafatos, Jaejin Kim, Wonsik Choi, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved …

Application Of Reinforcement Learning To Precision Aerial Delivery System Control In Adverse Wind Conditions, Radman Zarbock

The Journal of Purdue Undergraduate Research

Precision aerial delivery systems (PADS) are a subset of airdropped parachute-leveraging package delivery systems that use autonomous guidance, navigation, and control (GNC) to reach targets with high degrees of accuracy. This technology emerged in the 1990s, and strides have been made since to improve the reliability of traditional physics-based controllers that guide PADS. However, these algorithms still struggle to deliver acceptable performance results when PADS are subjected to austere operating environments, such as those with unpredictable wind. Building on a foundational study in 2022 that used artificial intelligence (AI) and machine learning to improve PADS GNC performance, this study aims …

Amp: Single-Shot Ultra-Wide Fisheye-To-Cubemap Pnp Pose Estimation, Ryan M. Raettig, Richard R. Nyquist, Scott L. Nykl, Clark N. Taylor, Christine M. Schubert Kabban

Faculty Publications

Estimating the position and orientation of a rigid object from an image is critical for situational awareness in robotics and autonomous systems. This study explores relative pose estimation using an ultra-wide fisheye camera for unmanned aircraft inspection vehicles. Ultra-wide fisheye lenses introduce radial distortion and capture features beyond the rectilinear image plane, rendering rectilinear Perspective-n-Point (PnP) algorithms inadequate. Designing a bespoke ultra-wide fisheye localization algorithm requires consideration of both the feature detection method and the pose estimator itself. This study proposes a novel method that combines (1) a fisheye-to-cubemap reprojection, (2) a You Only Look Once (YOLO) convolutional neural network …

Validating Pharmacogenomics Generative Artificial Intelligence Query Prompts Using Retrieval-Augmented Generation (Rag), Ashley Rector, Beth Breeden, Jay Dorris

Student Scholar Symposium

This study evaluated the performance of Sherpa Rx, an artificial intelligence platform leveraging large language models and retrieval-augmented generation (RAG) for pharmacogenomics, by validating its performance across key response metrics. Sherpa Rx integrated Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines with Pharmacogenomics Knowledgebase (PharmGKB) data to generate contextually relevant responses. A dataset (N=260 queries) spanning 26 CPIC guidelines was used to evaluate drug-gene interactions, dosing recommendations, and therapeutic implications. In Phase 1, only CPIC data was embedded; Phase 2 additionally incorporated PharmGKB. Responses were scored on accuracy, relevance, clarity, completeness (5-point Likert scale), and recall. Wilcoxon signed-rank tests compared accuracy between …

Wild Robots: Humans, Wilderness, And Technology In Becky Chambers’ Monk And Robot Series, Melissa Moore

Honors Theses

As technology advances and the environment deteriorates, the way people view the relationships between technology, wilderness, and humans becomes essential for society to move forward. To investigate perceptions about technology’s place in an environmentally conscious society, this project examines manifestations of wilderness/wildness and technology in Becky Chambers’ Monk and Robot series through the lens of ecocriticism. Using Timothy Morton's concept of the ecological thought as a framework for analysis, the circumstances present in the novel suggest that the triangle separating humans, wilderness, and technology has actually collapsed, replaced by an enmeshment of technology, wilderness, and humanity.

Visualizing And Evaluating Binary Classifier Performance With Contingency Space, Colin D. Kehoe, Azim Ahmadzadeh

Undergraduate Research Symposium

Traditional metrics for evaluating binary classifiers, such as Accuracy, F1 Score, and True Skill Statistic (TSS), often obscure the underlying tradeoffs between true positive and true negative performance—particularly in imbalanced or high-stakes domains. This poster introduces the Contingency Space, a two-dimensional representation of classifier behavior defined by true positive rate (TPR) and true negative rate (TNR). Within this space, scalar performance metrics become geometric surfaces, revealing how scores vary across the entire landscape of possible classifier outputs.

We present a Python package that implements this framework, enabling users to map model predictions into the Contingency Space, visualize metric surfaces …

Pixel-Perfect Segmentation Of Solar Filaments, Jamie Harris

Undergraduate Research Symposium

The observation and classification of solar filaments has a drastic impact on the ability to predict solar-magnetic weather phenomena that threatens to put both satellite infrastructure and astronauts at risk. Using the Hɑ filter provided by the Global Oscillations Network Group (GONG), a network of six telescopes around the world dedicated to 24/7 surveillance of the sun, we are able to get images that clearly and prominently display filament activity. With the vast amount of images the GONG takes, it is not possible to manually analyze every image. Using the U-Net model for computer vision, we were able to train …

Face Value: A Computational Approach To Subjective Impressions Of Faces, Kevin Kpankou

Undergraduate Research Symposium

Various computational models of first impressions have been developed to uncover the mechanisms driving these judgments. However, the implicit notion of a singular ``human'' often overlooks meaningful individual differences in beliefs, attitudes, and associations, as well as culturally grounded group-level constructs. In this paper, we extend Cultural Consensus Theory (CCT) to estimate culturally shared beliefs about faces by incorporating latent constructs structured around interpretable facial features extracted via computer vision algorithms. We apply our model to a large-scale dataset of people’s first impressions of faces. Our approach reveals a robust mapping between facial features and culturally constructed impressions, allowing us …