Artificial Intelligence and Robotics Commons^™

Personalized Driving Using Inverse Reinforcement Learning, Rodrigo J. Gonzalez Salinas

Theses and Dissertations

This thesis introduces an autonomous driving controller designed to replicate individual driving behaviors based on a provided demonstration. The controller employs Inverse Reinforcement Learning (IRL) to formulate the reward function associated with the provided demonstration. IRL is implemented through a dual-feedback loop system. The inner loop utilizes Q-learning, a model-free reinforcement learning technique, to optimize the Hamilton-Jacobi-Bellman (HJB) equation and derive an appropriate control solution. The outer loop leverages this derived control solution to generate parameters for the reward function, which are subsequently integrated into the HJB equation. The ultimate control policy is deduced from the final reward function obtained …

Transforming Libraries Through Engagement: Lessons From A Library Ai Interest Group, Lily Dubach, Rachel Vacek

Faculty Scholarship and Creative Works

Learn how one academic library is engaging with its employees to explore the latest trends, tools, and topics in AI through an interest group (IG). Through stimulating discussions, webinars, guest speakers, demos, and sharing of experiences with AI, the IG empowers its library community to explore and become more comfortable with AI. This session caters to varying levels of AI expertise. Challenges, successes, and valuable insights for deeper engagement will be shared so you can learn how to establish a similar initiative in your library.

Predicting Choroidal Nevus Transformation To Melanoma Using Machine Learning, Prashant D. Tailor, Piotr K. Kopinski, Haley S. D'Souza, David A. Leske, Timothy W. Olsen, Carol L. Shields, Jerry A. Shields, Lauren A. Dalvin

Wills Eye Hospital Papers

PURPOSE: To develop and validate machine learning (ML) models to predict choroidal nevus transformation to melanoma based on multimodal imaging at initial presentation.

DESIGN: Retrospective multicenter study.

PARTICIPANTS: Patients diagnosed with choroidal nevus on the Ocular Oncology Service at Wills Eye Hospital (2007-2017) or Mayo Clinic Rochester (2015-2023).

METHODS: Multimodal imaging was obtained, including fundus photography, fundus autofluorescence, spectral domain OCT, and B-scan ultrasonography. Machine learning models were created (XGBoost, LGBM, Random Forest, Extra Tree) and optimized for area under receiver operating characteristic curve (AUROC). The Wills Eye Hospital cohort was used for training and testing (80% training-20% testing) with …

Empowering Traditional Industries With Digital Intelligence For Transformation And Upgrading, Jiyuan Zang, Huanyong Ji, Qingxue Huang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Traditional industries are the basic support and driving force for the high-quality development of the economy. Digital intelligence is an important lever for traditional industries to achieve the transformation of old and new kinetic energy and the leapfrogging of the global value chain. This study firstly deconstructs the rich connotations of digital intelligence from the perspectives of digitalization, networkization, and intelligentization. Secondly, it analyzes the transformation mechanism and various pathways through which digital intelligence impacts traditional industries. Thirdly, it analyzes the barriers faced by traditional industries in the process of adopting digital intelligence technologies. Finally, this study puts forward four …

Strategic Study On Leading Construction Of Modern Agricultural System By Technology Innovation, Yan Yan, Xurong Mei, Xiudong Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Technology innovation is the fundamental solution to build a modern agricultural system, and it is also an important support for building the large base, large enterprise, and large industry of modern agriculture and achieving the modernization of material equipment, technology, management, agricultural informatization, and sustainable resource utilization of agriculture. This paper explains the scientific concepts and logical relationships of the agricultural production system, industrial system and management system, which are the main components of a modern agricultural system. It emphasizes that ensuring food security, promoting industrial integration and upgrading, and cultivating leading enterprises are current key tasks. Nevertheless, from the …

A Qualitative Study On The Integration Of Artificial Intelligence In Cultural Heritage Conservation, Kholoud Ghaith, James Hutson

Faculty Scholarship

The widespread adoption of generative artificial intelligence (GAI) technologies heralds an era of expanding possibilities in the domain of cultural heritage conservation. This paradigm shift is marked by a confluence of innovative methodologies, including digital twin mapping, digital archiving, and enhanced preservation strategies, aimed at safeguarding the vestiges of our shared past. The application of AI within this field represents a frontier where technology and tradition intersect, offering new vistas for the preservation of historical structures and artifacts that are at risk of deterioration or oblivion. This article endeavors to elucidate the perspectives of professionals within the conservation domain on …

Riesz Particle Markov Chain Monte Carlo Methods, Xiongming Dai

LSU Doctoral Dissertations

Markov chain Monte Carlo (MCMC) methods are simulations that explore complex statistical distributions, while bypassing the cumbersome requirement of a specific analytical expression for the target. This stochastic exploration of an uncertain parameter space comes at the expense of a large number of ``burn-in'' samples, and the computational complexity leads to the curse of dimensionality. Although at the exploration level, some methods have been proposed to accelerate the convergence of the algorithm, such as tempering, Hamiltonian Monte Carlo, Rao-redwellization, and scalable methods for better performance, they cannot avoid the stochastic nature of this exploration. We develop algorithms for the energy …

Effective Position Intelligent Decision Method Based On Model Fusion And Generative Network, Liqiang Guo, Liang Ma, Hui Zhang, Jing Yang, Lianfeng Li, Yaqi Zhai

Journal of System Simulation

Abstract: Military intelligence technology is currently the most dynamic frontier and the inevitable trend for the development of unmanned equipment in the future. Aiming at the dual requirements of reliability and real-time performance of unmanned platform autonomous decision-making in complex environments and the shortcomings of existing combat simulation technology based on rule reasoning in terms of dynamics and flexibility, a research method of principle analysis and experimental verification is adopted. Based on the shooting experiment dataset of an unmanned platform, the effective position recognition link of attack decision-making is transformed into a binary classification problem with imbalanced categories in the …

Digital Twin Modeling And Control Of Robots For Intelligent Manufacturing Scenarios, Ying Li, Lan Gao, Zhisong Zhu

Journal of System Simulation

Abstract: The introduction of Industry 4.0 and the Made in China 2025 development policy has accelerated the transformation of the manufacturing industry from automation to intelligence. Industrial robots, as the representative equipment of intelligent manufacturing, will also become more intelligent. Based on digital twin technology, digital modeling, and simulation debugging are conducted for such problems as interference and collision, tedious operation, and low efficiency of industrial robot spot welding debugging in production. Process Simulate from TECNOMATIX software is utilized to digitally model the robot spot welding station and define its motion, and TIA Portal and S7-PLCSIM Advanced are applied to …

Research On Learnable Wargame Agent Driven By Battle Scheme, Yifeng Sun, Zhi Li, Jiang Wu, Yubin Wang

Journal of System Simulation

Abstract: To enable the agent to cope with complex battle scenarios and objectives in wargame, a learnable wargame agent architecture driven by a battle scheme is proposed. By analyzing the "attachment characteristics" and "loose coupling characteristics" of the agent to wargame system, the learnable requirements of the agent are obtained. In the design of the agent framework, battle schemes are used to reduce the learning range of the agent. The finite state machine corresponds to the knowledge of the operational phase in the battle scheme, and the decision-making space of the agent is determined according to the framework of the …

A Deep Fuzzy Classifier Based On Feature Transform And Reconstruction, Rui Yin, Wei Lu, Jianhua Yang

Journal of System Simulation

Abstract: To obtain a classifier with good classification accuracy and interpretability, a deep fuzzy classifier based on feature transform and reconstruction (FR-DFC) is proposed. In FR-DFC, several fuzzy systems (FT_FS) for feature transform and a multi-prototype fuzzy classification system (MPRFD_FS) are stacked together to realize the classification process of the model, based on the hierarchically stacked thought originated from deep learning. Specifically, the stacked FT_FSs explore the hidden features in the data by transferring data from the original data space to the high-level feature space. MPRFD_FS, on the other hand, implements classification based on multiple prototypes that characterize the distribution …

Adaptive Particle Swarm Optimization Algorithm Based On Trap Label And Lazy Ant, Wei Zhang, Yuefeng Jiang

Journal of System Simulation

Abstract: Many existing strategies for improving particle swarm optimization (PSO) fall short in assisting particles trapped in local optima and experiencing premature convergence to recover optimization performance. In response, an adaptive particle swarm optimization algorithm based on trap label and lazy ant (TLLA-APSO) is proposed. Firstly, the trap label strategy dynamically adjusts particle velocities, enabling the particle swarm to escape from local optima. Secondly, the lazy ant optimization strategy is employed to diversify particle velocity and enhance population diversity. Finally, the inertia cognition strategy introduces historical position into velocity updates, promoting path diversity and particle exploration while effectively mitigating the …

Simulation Optimization Of Airport Baggage Import System Based On Multi-Objective Wolf Pack Algorithm, Yifei Tao, Xiaopeng Ding, Junbin Luo, Xiao Fu, Jiaxing Wu, Yirong Li

Journal of System Simulation

Abstract: Aiming at the problems of long waiting time for passenger baggage import and high system energy consumption during the operation of the baggage import system in civil aviation airports, a simulation optimization framework for solving this problem is proposed by comprehensively considering the influence of key control parameters on the operation efficiency of the baggage import system in airports, including the virtual window control mode, the operation speed of the collection belt conveyor, the length of the virtual window and the number of check-in counters opened at the same time. By analyzing the actual operating conditions of the airport …

Task Analysis Methods Based On Deep Reinforcement Learning, Xue Gong, Pengfei Peng, Li Rong, Yalian Zheng, Jun Jiang

Journal of System Simulation

Abstract: In response to the high coupling of task interaction and many influencing factors in task analysis, a task analysis method based on sequence decoupling and deep reinforcement learning (DRL) is proposed, which can achieve task decomposition and task sequence reconstruction under complex constraints. The method designs an environment for deep reinforcement learning based on task information interaction, while improving the SumTree algorithm based on the difference between the loss functions of the target network and the evaluation network, achieving the priority evaluation among tasks. The activation function operation mechanism is introduced into the deep reinforcement learning network, followed by …

Modeling And Verification Of Cooperative Vehicle Infrastructure System At Unsignalized Intersection Based On Time Automata, Wei Liu, Qirui Xiao, Xinhai Chen, Chang Rao, Yu Zhang, Bosi Wang

Journal of System Simulation

Abstract: Cooperative vehicle infrastructure system (CVIS) is one of the advanced solutions to enhance intersection vehicle passage safety. Due to the lack of clear specifications and standards regarding the dynamic timing and transition processes of system object state interaction in existing CVIS technologies, ensuring the safety of passage control logic is challenging. This study utilizes formal language to describe the functional logic of CVIS in unsignalized intersections, verifying the safety of system object state interaction and control logic to improve vehicle passage safety at unsignalized intersections. Simulations are conducted for scenarios including single-vehicle non-conflict, dual-vehicle conflict, and multi-vehicle conflict to …

Real-Time Non-Photorealistic Rendering Method For Black And White Comic Style In Games And Animation, Yan Hu, Lizhe Chen, Hanna Xie, Yuyao Ge, Shun Zhou, Xingquan Cai

Journal of System Simulation

Abstract: To address the issues of high resource consumption and lengthy workflow in general nonphotorealistic, this paper proposes a real-time non-photorealistic rendering method for black and white comic style in games and animation. A specialized lighting model is designed to highlight the main environmental light and the grayscale grading of diffuse reflection based on the analysis of the lighting model effect. The pre-processing of the scene is achieved by merging the various components of the lighting model. A screen space three-phase edge detection method is proposed to sequentially perform depth edge detection, normal edge detection, and color edge detection on …

Simulation System For Carrier-Based Aircraft Ammunition Support Scheduling, Zhe Liu, Jiafeng Chen, Junfei Ma, Songhua Ma

Journal of System Simulation

Abstract: For inefficient scheduling of carrier-based aircraft ammunition support on the flight deck of aircraft carriers, a Unity3D-based 3D virtual demonstration method for carrier-based aircraft ammunition scheduling is proposed, and a 3D virtual simulation system for carrier-based aircraft ammunition scheduling on the flight deck of aircraft carriers is constructed. A problem model targeted at carrierbased aircraft ammunition transportation and loading scheduling process as well as processing rules for path determination, sequence constraint, and loading position compensation are established based on the defining system. An optimal solution for the scheduling process is realized by using the improved discrete grey wolf optimizer …

Uav Path Planning Based On Improved Harris Hawk Algorithm And B-Spline Curve, Zhifeng Huang, Yuanhua Liu

Journal of System Simulation

Abstract: Aiming at the global path planning problem of unmanned aerial vehicles (UAVs) in dynamic environments, this paper proposes an improved Harris Hawk optimization algorithm. To address the problem of insufficient search performance in the later stage of the algorithm, an adaptive chaos and core population dynamic partitioning strategy is proposed to improve the searchability of the algorithm in the later stage. The Harris Hawk update formula is modified, and the golden sine strategy is introduced to improve the search efficiency of the algorithm. Then, an adaptive dynamic cloud optimal solution perturbation strategy is integrated to improve the ability of …

Optimal Scheduling Of Vehicle-Network Interaction Based On Interval Stackelberg Game Of Virtual Power Plant, Weiliang Liu, Qianwen Yan, Qiliang Zhang, Shuai Liu, Changliang Liu, Jiayao Kang, Xin Wang

Journal of System Simulation

Abstract: To better exploit the regulation potential of electric vehicles (EVs), resolve the conflicts of interest among the stakeholders in vehicle-to-grid (V2G) interactions, and overcome the uncertainty of distributed energy sources and load, this paper proposes a two-level optimization scheduling model for V2G interactions based on the interval Stackelberg game of a virtual power plant (VPP). The VPP aggregator is considered as the upper level, and the EV users as the lower level. The upper level model uses interval numbers to describe the uncertainty of sources and loads, with the aim of minimizing the operating cost of the VPP aggregator, …

Maglev Ball Control Algorithm Based On Levant Differentiator, Zhenli Zhang, Yongzhuan Wang, Yao Qin, Jie Yang

Journal of System Simulation

Abstract: To solve the problem of unsatisfactory control effect of permanent magnet electromagnetic hybrid suspension system caused by signal mutation and noise interference, the control method ILevant- PID, the combination of an improved Levant differentiator and PID, is proposed. The proposed method combines the strong adaptability of PID control and the robust characteristic of Levant differentiator on input noise to solve the chattering problem of the system output. The simulated anneal-particle swarm optimization is utilized to solve the constraints of the ILevant-PID controller, such as multiple parameters and strong correlation. The simulation results show that compared with the traditional PID …