Artificial Intelligence and Robotics Commons^™

Research On Latent Space-Based Anime Face Style Transfer And Editing Techniques, Haixin Deng, Fengquan Zhang, Nan Wang, Wancai Zhang, Jierui Lei

Journal of System Simulation

Abstract: To address issues such as image distortion and style uniformity in existing anime style transfer networks within the field of image simulation, we propose the TGFE-TrebleStyleGAN (textguided facial editing with TrebleStyleGAN) for anime facial style transfer and editing. This framework leverages vector guidance within the latent space to generate facial imagery and incorporates a detail control module and a feature control module to constrain the aesthetic attributes of the generated images. The images generated by the transfer network serve as style control signals and constraints for fine-grained segmentation. Text-to-image generation technology captures correlations between styletransferred images and semantic information. …

Research On Digital Twin Simulation Method Of Industrial Robot Integrated With Reinforcement Learning, Tianyue Miao, Lu Wang, Jiaxiao He, Nenggang Xie

Journal of System Simulation

Abstract: In response to the lack of comprehensive functionality and limited application scenarios in the current field of industrial robot digital twin systems, which results in low versatility, a method for constructing a digital twin system for industrial robots with high versatility is proposed. A four-dimensional system architecture for the digital twin is designed, and the components and functions of the four-dimensional system are analyzed, based on the system level planning of the four-dimensional system, the concept of integrating reinforcement learning into the virtual replacement of real concept is defined. By constructing a multi-attribute virtual model and using TCP communication …

Research On Multi-Objective Gait Planning Of Biped Robot Based On Virtual Prototype, Yankai Zhang, Xuesong Wang, Yubin Jin, Dongsheng Zhang

Journal of System Simulation

Abstract: A multi-objective gait optimization method based on virtual prototype is proposed to address the difficulty of balancing personalisation and performance in gait planning for bipedal robots. A scale prototype of a planar underactuated biped robot is created according to the body structure of Chinese people, and an identification approach is used to determine the robot's exact inertial parameters. A virtual prototype of the robot is created, and three optimization goals—speed, energy use, and stability are developed. Using the enhanced NSGA-II algorithm, the Pareto optimal solution set for the robot multi-objective gait planning issue is produced. Numerous gaits that conform …

Intersection Braking Guidance For Trams Based On Lineside Signs, Wencong Tong, Jing Teng, Junxian Li, Xing Yao, Zhongjie Zhang

Journal of System Simulation

Abstract: Trams need to brake frequently due to signal control and safety speed limits at intersections. Due to the inaccuracy of distance judgment, tram drivers tend to reserve extra braking distance at intersections, resulting in lower braking coefficients and a decrease in speed. An intersection braking guidance method using lineside signs is proposed for trams to reduce braking distance redundancy and improve running speed. Drivers are guided to brake with the shortest possible distance by marking the initial braking position and speed of trams with fixed lineside signs. A tram driving simulation system was developed based on a vehicle dynamics …

A Clustering-Based Location Allocation Method For Delivery Sites Under Epidemic Situations, Yaqiong Zhou, Junqi Chen, Weishi Li, Sihang Qiu, Rusheng Ju

Journal of System Simulation

Abstract: To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness, efficiency, and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines. After establishing the optimization objectives and constraints, we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions. Building on the characteristics of these Pareto front solutions, we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City. The results demonstrate that the proposed algorithm outperforms …

Research On Verification Method Of Motor Startups In Nuclear Power Plants Based On Topology Recognition, Baozhu Li, Weijie Dong, Chao Chen

Journal of System Simulation

Abstract: There are many motors in operation or on standby in nuclear power plants, and the startup of group motors will have a great impact on the voltage of the emergency bus. At present, there is no special or inexpensive software to solve this problem, and the experience of engineers is not accurate enough. Therefore, this paper developed a method and system for the startup calculation of group motors in nuclear power plants and proposed an automatic generation method of circuit topology in nuclear power plants. Each component in the topology was given its unique number, and the component class …

Modeling And Integration Method Of Sysml Model For Complex Business Scenarios, Bing Yu, Baoran An, Shicao Zhao

Journal of System Simulation

Abstract: The development process of complex equipment involves multi-stage business processes, multi-level product architecture, and multi-disciplinary physical processes. The relationship between its system model and various disciplinary models is extremely complicated. In the modeling and integration process, extensive customized development is needed to realize model integration and interoperability in different business scenarios. Meanwhile, the differences in modeling and interaction between different modeling tools make it difficult to support the consistent representation of models in complex scenarios. To improve the efficiency of system modeling and integration in complex business scenarios, a system modeling and integration method was proposed. This method took …

Algorithm And Semi-Physical System Simulation For Command Intent Recognition Of Uav In Low-Resource Environment, Hongfu Liu, Yajing Fu, Wanpeng Zhang, Hu Zhang

Journal of System Simulation

Abstract: When a communication network is partially disabled or disrupted, an UAV is plunged into a "low-resource environment" and must rely on local hardware resources. This situation imposes constraints on computing power, storage capacity, and energy availability. To address the need for command intent recognition in such environments, a semi-physical simulation system for UAV in emergency rescue operations has been designed and implemented. Based on the low resource airborne hardware in the loop, the system simulates UAV command intention recognition and mission planning through GIS+BIM 3D environment modeling task scenarios. A new lightweight algorithm for intent recognition has been proposed, …

Uav Swarm Obstacle Avoidance Based On Visual Filed And Adaptive Radius, Gaohang Ai, Chuntao Li

Journal of System Simulation

Abstract: Aiming at the obstacle avoidance problem of large-scale UAV swarm tracking flight route, a swarm obstacle avoidance algorithm based on distributed model predictive control combined with visual field and adaptive obstacle avoidance radius is proposed. In the process of swarm flight, the UAV obtains the reference route information of the current moment according to its own position, and obtains the predicted trajectory of its neighbors through local information interaction. When encountering obstacles, the adaptive obstacle avoidance radius and field of view topology method are combined to effectively solve the problem that the internal safety distance cannot be maintained and …

A Novel Research Pattern For The Simulation Of Complex Systems Sigd, Bin Chen, Runkang Guo, Zhengqiu Zhu, Yong Zhao, Yatai Ji, Aiguo Chen, Guangquan Cheng

Journal of System Simulation

Abstract: The complexity of the system is mainly reflected in the numerous components and extremely complex interactions. Combined with the current trend of artificial intelligence development, this paper analyzes and considers the changes in thinking mode brought by simulation discipline research, and forms an understanding of the connotation and research scope of simulation intelligence. A new pattern for complex system simulation research is proposed: "simulation intelligence based generating decisions (SIGD)". In the SIGD pattern, the similar principles, modeling methods, and decision-guiding modes in simulation disciplines are different from those in traditional simulation. Under the guidance of this concept, a connection-oriented …

A Confidence-Based Knowledge Integration Framework For Cross-Domain Table Question Answering, Yuankai Fan, Tonghui Ren, Can Huang, Beini Zheng, Yinan Jing, Zhenying He, Jinbao Li, Jianxin Li

Research outputs 2022 to 2026

Recent advancements in TableQA leverage sequence-to-sequence (Seq2seq) deep learning models to accurately respond to natural language queries. These models achieve this by converting the queries into SQL queries, using information drawn from one or more tables. However, Seq2seq models often produce uncertain (low-confidence) predictions when distributing probability mass across multiple outputs during a decoding step, frequently yielding translation errors. To tackle this problem, we present CKIF, a confidence-based knowledge integration framework that uses a two-stage deep-learning-based ranking technique to mitigate the low-confidence problem commonly associated with Seq2seq models for TableQA. The core idea of CKIF is to introduce a flexible …

Optimal Operation Scheduling Of Integrated Energy System Considering Energy Priority, Dongli Jia, Keyan Liu, Zhaoying Ren, Zezhou Wang, Dongsheng Tang

Journal of System Simulation

Abstract: Integrated with the actual situation of power grid and the growth of new energy, a multiobjective model for optimal scheduling of the integrated energy system(IES) is established based on the analysis of the energy-flow relationship of the IES and taking into account the priority of energy utilization and the load demand response in terms of the mismatch between the distributed energy sources and the loads, the net benefit of the unit cost of the IES, and the load response degree. Combined with the equipment and the environmental benefits system, a priority constraint for energy utilization has been established for …

Research On Scheduling Strategies Simulation For Building Air-Conditioning Systems Based On Transfer Imitation Learning, Qiaochu Wang, Yan Ding, Chuanzhi Liang, Haozheng Zhang, Chen Huang

Journal of System Simulation

Abstract: To solve the problem of unstable performance and inefficient training process of low-quality data conditions at the initial stage of online deployment of air conditioner scheduling, we propose a migration-imitation learning-based air conditioning scheduling strategy simulation method. Reinforcement learning methods are used to generate building operation strategies. A standard building simulation model serves as the source domain, upon which migration learning is applied. An imitation learning loss function is incorporated into the intelligent loss function to enhance algorithm performance. The results indicate that, compared with the non-use of migration learning, the proposed method can improve the operational efficiency by …

Behavioral Modeling Of Manned-Unmanned Cooperative Air Combat Based On Improved Abc Algorithm, Peng Wang, Haoyu Liu, Ni Li, Zexi Yu, Shangjie Jia

Journal of System Simulation

Abstract: To solve the problem of difficulty in establishing collaborative behavior models and weak adversarial capabilities in typical MAV/UAV air combat scenarios, a mixed decision based MAV/UAV behavior modeling framework is proposed. Using collaborative rule sets, rule subsets, tactical action sets, and other tools, a hierarchical decision collaborative behavior model supporting five types of collaborative tactics, including grinding tactics and unilateral flanking tactics, is constructed in this framework. a behavior model parameter optimization method based on an improved artificial bee colony (ABC) algorithm is proposed. By using the Mason rotation method to initialize the population, a better initial honey source …

Harmonic Impedance Modeling And Oscillation Analysis Of Modular Multilevel Converter, Yuhong Wang, Wensheng Chen, Shilin Gao, Jianquan Liao, Yangfan Cheng

Journal of System Simulation

Abstract: To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current (MMC-HVDC) systems and streamline the simulation process for determining MMC impedance characteristics, a simplified mathematical simulation model for MMC closed-loop impedance is developed using the harmonic state space method. This model considers various control strategies and includes both AC-side and DC-side impedance models. By applying a Nyquist criterion-based impedance analysis method, the stability mechanisms on the AC and DC sides of the MMC are examined. In addition, a data-driven oscillation stability analysis method is also proposed, leveraging a global sensitivity algorithm based …

Path Planning Of Desert Robot Based On Deep Reinforcement Learning, Ming Li, Wangzhong Ye, Jiehua Yan

Journal of System Simulation

Abstract: Due to the complexity and variability of the desert environment, the key to the high-efficient of mobile robot is how to avoid obstacles and plan its path. To solve the problems of poor search efficiency and slow convergence of deep reinforcement learning algorithm in complex environment, an improved deep reinforcement learning path planning algorithm is proposed. The exploration factor is improved and dynamically adjusted according to the convergence degree of the algorithm, so that the exploration factor dynamically decreases with the increase of the understanding degree of the agent to the environment, thus speeding up the convergence speed of …

Feasibility Of Large Language Models For Ceus Li-Rads Categorization Of Small Liver Nodules In Patients At Risk For Hepatocellular Carcinoma, Jiayan Huang, Rui Yang, Xiaotong Huang, Keyu Zeng, Yan Liu, Jun Luo, Andrej Lyshchik, Qiang Lu

Department of Radiology Faculty Papers

BACKGROUND: Large language models (LLMs) offer opportunities to enhance radiological applications, but their performance in handling complex tasks remains insufficiently investigated.

PURPOSE: To evaluate the performance of LLMs integrated with Contrast-enhanced Ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) in diagnosing small (≤20mm) hepatocellular carcinoma (sHCC) in high-risk patients.

MATERIALS AND METHODS: From November 2014 to December 2023, high-risk HCC patients with untreated small (≤20mm) focal liver lesions (sFLLs), were included in this retrospective study. ChatGPT-4.0, ChatGPT-4o, ChatGPT-4o mini, and Google Gemini were integrated with imaging features from structured CEUS LI-RADS reports to assess their diagnostic performance for sHCC. …

Safety And Optimality Monitors For Learning-Enabled Systems Using Conformal Prediction, Jackson Cox

McKelvey School of Engineering Graduate Student Theses & Dissertations

The use of machine learning to create data-driven plant models and controllers has led to an increased need for safety and optimality monitors for model-based systems. System plant models are subject to uncertainty due to learning constraints such as unseen data and overfitting or physical constraints such as unknown dynamics and noise. This uncertainty is detrimental to safety-critical systems and must be properly regulated. To curb this uncertainty, we create prediction sets using the guarantees provided by Conformal Prediction. With a user-specified high probability, these prediction sets contain the true plant system states for an entire prediction horizon, which we …

The Chinese Room And Creating Consciousness: How Recent Strides In Ai Technology Revitalize A Classic Debate, Thomas Held

Departmental Honors & Graduate Capstone Projects

Since 1950, when Alan Turing first posed the question of whether a machine could think, the possibility of artificial consciousness has sparked intense and ongoing debate, and strong positions have been staked out on each side of the argument. On the one hand, the historically popular functionalist school of thought claims that any system capable of producing suitably “conscious” behavior in a given environment should be considered conscious. On the other hand, John Searle’s famous “Chinese Room” argument insists that this cannot be the case, and that consciousness is in all likelihood not artificially reproducible. However, both positions have issues—the …

Artificial Intelligence-Based Methodologies For Early Diagnostic Precision And Personalized Therapeutic Strategies In Neuro-Ophthalmic And Neurodegenerative Pathologies, Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Jeremy Saintyl, Jahnavi Yelamanchi, Robert Nahouraii, Ram Jagadeesan, Alireza Tavakkoli

SKMC Student Presentations and Publications

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and …

Implication Of Generative Ai On Education And Research, Riddhi Gupta

The Journal of Purdue Undergraduate Research

No abstract provided.

Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian

The Journal of Purdue Undergraduate Research

Predictive maintenance in aviation and aerospace applications is among the most explored problems in machine learning (ML) and artificial intelligence (AI), and datasets such as NASA’s C-MAPPS turbofan engine degradation simulation data have proven invaluable, helping researchers explore numerous questions on engine performance, maintenance, and failure. The purpose of this study was to extend the current research on predicting the remaining useful life (RUL) of engines and their risk classification. Starting with simple yet under-investigated nonlinear survival and random forest models, the analysis implemented eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) from TensorFlow’s Keras library. For both regression …

Utilizing Large Language Models To Synthesize Product Desirability Datasets, John D. Hastings, Sherri Weitl-Harms, Joseph Doty, Zachary L. Myers, Warren Thompson

Research & Publications

This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor …

La Creatividad En Peligro: Como La Inteligencia Artificial Es Un Reto Para Los Artistas., Nathaly Cisneros

Capstones

Los artistas digitales han creado obras maestras que nos han dejado sin aliento con sus pinceles digitales, lápices y pinturas. Desde retratos que parecen saltar de la pantalla hasta paisajes que nos transportan a mundos desconocidos, su arte ha sido una fuente constante de inspiración.

Pero en los últimos años, una nueva fuerza ha comenzado a cambiar el juego. La inteligencia artificial ha estado avanzando a pasos agigantados y ahora se perfila como una amenaza para el futuro de los artistas digitales. ¿Qué significa esto para el arte y la creatividad?

Link: https://docs.google.com/document/d/1xe8UxDMekX_SwiIppyt_JppK8M-lB-YWNWGyeyShlJM/edit?usp=sharing

The Implementation Of Artificial Intelligence In University Classrooms: Perspective And Applications, Erika Grodzki, Gary Carlin, Stefanie Powers, Hung Chum Kao

Faculty and Staff Publications & Presentations

This study examined the integration of Artificial Intelligence (AI) in university classrooms, focusing on its benefits, challenges, and the diverse perspectives of academic faculty. While AI was widely embraced in disciplines like animation and design for enhancing creativity and efficiency, traditional fields remained cautious due to concerns about academic integrity and its impact on critical thinking. By analyzing literature and case studies, the presentation highlighted AI’s transformative potential in higher education, fostering dialogue on its strategic adoption to balance innovation with ethical and pedagogical considerations.

Closed Domain Question Answering With Language Models: Application Of Retrieval-Augmented Generation And Parameter Efficient Fine-Tuning In Healthcare, Aaron Cummings

Master's Theses

Dementia care presents significant challenges for informal caregivers, particularly in managing behavioral symptoms that affect over 90% of individuals with Alzheimer’s Disease and Related Dementias (ADRD) during the moderate-to-severe stages. These symptoms, including agitation, wandering, and repetitive activities, impose emotional and physical burdens on caregivers, often exacerbated by a lack of reliable, accessible, and personalized resources. Non-pharmacological interventions, while evidence-based, are underutilized due to knowledge gaps and the inefficiency of traditional training and information retrieval methods.

This research explores the adaptation of large language models (LLMs) to address these challenges by developing a framework for closed-domain Question Answering (QA) systems, …

Real-Time Feedback-Driven Framework For Automated Cybersickness Mitigation, Md Jahirul Islam

Master's Theses

As technologies are becoming more advanced day by day, the embracement of virtual reality (VR) technology among users is also increasing in daily activities for various purposes, and subsequently, the barrier between the real and virtual world is fading. Despite the versatile uses, cybersickness (CS) is a major problem which is induced among users due to the immersive VR experience. There is a plethora of research findings and methods to measure the users’ CS such as virtual reality sickness questionnaire (VRSQ), simulator sickness questionnaire (SSQ), fast motion scale questionnaire (FMS), and others. Recently, machine learning approaches have also been adopted …

How Does Augmentation Affect Feature Space: A Study Using Various Augmentation Methods In Distributed Learning, Nikil Sharan Prabahar Balasubramanian

Computer Science Theses

This thesis examines the impact of data augmentation techniques on model performance within a distributed learning framework, focusing on enhancing feature diversity and improving representation for under-represented classes. Data augmentation, commonly used to address data imbalance, significantly influences the feature space learned by deep learning models, with varied effects in distributed settings where data is split across nodes. Our study reveals that inconsistencies in feature learning across nodes reduce the benefits of local augmentation in capturing complex patterns, leading to suboptimal model performance. To address this, we propose a coherent augmentation approach that embeds consistent transformations in the central server, …

Predicting Chaotic Systems With Quantum Echo-State Networks, Erik Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan

Publications

Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively …

On The Benefits Of Directness In Virtual Characters For Motivational Interviews, Michael O'Mahony, Cathy Ennis, Robert Ross

Conference papers

Understanding the factors influencing successful engagement with Embodied Conversational Agents (ECAs) remains a significant challenge. This understanding could be used to personalise agents to users to improve interactions. Some studies have shown that simulating personalities in healthcare agents can improve effectiveness and engagement. However, it is not yet well understood how variations of agent personality can be leveraged to improve user engagement with Motivational Interviewing (MI) ECAs. Specifically how the balance between agent warmth and directness can be controlled in an MI agent to improve likeability and engagement. We conducted an online Wizard-of-Oz (WoZ) mediated study of two variants of …