Artificial Intelligence and Robotics Commons^™

Obstacle Avoidance And Simulation Of Carrier-Based Aircraft On The Deck Of Aircraft Carrier, Junxiao Xue, Xiangyan Kong, Bowei Dong, Hao Tao, Haiyang Guan, Lei Shi, Mingliang Xu

Journal of System Simulation

Abstract: A predictive depth deterministic policy gradient (PDDPG) algorithm is proposed by combining the least squares method with deep deterministic policy gradient(DDPG) for the problems of strong randomness, poor real-time performance, and slow planning speed by obstacle avoidance on aircraft carrier deck. The short-term trajectory of dynamic obstacles on the deck is predicted by the least square method. DDPG is used to provide agents with the ability to learn and make decisions in continuous space by the short-term trajectory of dynamic obstacles. The reward function is set based on the artificial potential field to improve the convergence speed and accuracy …

Floor Evacuation Simulation Based On Bim And Mr, Zhijie Li, Shuangyu Ma, Changhua Li, Xiao Liang, Jie Zhang

Journal of System Simulation

Abstract: Facing with the problem that the floor evacuation simulation only annotates the floor plan route, which is relatively single and not intuitive, a 3D building evacuation simulation method integrating mixed reality and building information model is proposed. The BIM components are reasonably planned and segmented, and reasonable annotation is performed. The BIM information is routed through the surface area heuristic optimization algorithm based on the bounding volume hierarchy. The evacuation simulation process is imported into the Microsoft Hololens2 hardware platform using the Unity3D development engine. The experimental results show that, compared with the previous evacuation simulation expressed only …

Multiagent Following Multileader Algorithm Based On K-Means Clustering, Guodong Yuan, Ming He, Ziyu Ma, Weishi Zhang, Xueda Liu, Wei Li

Journal of System Simulation

Abstract: Three K-means clustering algorithms are proposed to prevent chaos in the formation of a multi-agent system (MAS) with multiple leaders. The algorithm divides the cluster into communities with the same number of leaders, and the agents within the community will follow the same leader. Among the three proposed algorithms, algorithm #1 is suitable for scenarios with widely distributed agents wherein rapid consensus can be achieved in the shortest time; algorithm #2 is suitable for scenarios with a sparse agent distribution and effectively prevented agent collisions; and algorithm #3 exhibits rapid convergence and considerably reduces the MAS control cost, …

Costume Pattern Sketch Colorization And Style Transfer Based On Neural Network, Xingquan Cai, Zhijun Li, Mengyao Xi, Haiyan Sun

Journal of System Simulation

Abstract: Aiming at the problems of color overflow in pattern sketch colorization and lack of fabric texture features in style transfer, this paper proposes a method of costume pattern sketch colorization and style transfer based on neural network. This paper initializes the data set, collects the costume pattern image, extracts the costume pattern sketch, synthesizes the costume pattern sketch with color features and constructs the style data set. The research builds the conditional generative adversarial nets and achieves the costume pattern sketch with color features colorization based on the generator. The study constructs a convolutional neural network model, uses the …

Hyper-Heuristic Three Dimensional Eda For Solving Green Two-Sided Assembly Line Balancing Problem, Rong Hu, Shuai Ding, Bin Qian, Changsheng Zhang

Journal of System Simulation

Abstract: This paper establishes a model for green robotic two-sided assembly line balancing problem of type-I, and a hyper-heuristic three dimensional estimation of distribution algorithm (HH3DEDA) is proposed for solving this problem. In HH3DEDA, a combinatorial encoding rule based on process selectors is designed via considering the characteristics of the problem. Then, HH3DEDA with a high and low layered structure is proposed. In the upper layer, the three-dimensional probability matrix is utilized to learn high-quality high individual block structure and its distribution information, and then the matrix is sampled to generate new high level individuals. Each high individual is …

Research On Modeling And Solution Method Of Operational Tasks Optimization, Yue Ma, Lin Wu, Yun Liu, Guangzhao Ding

Journal of System Simulation

Abstract: Aiming at the problem of tasks optimization in operation task planning, this paper defines an operational tasks graph based on property graph and influence network to describe tasks, effects and their relationship. The model of operational tasks optimization is constructed based on the operational tasks graph, and the effect network transmission algorithm and resource constraint judgment algorithm are proposed. The problem is solved by the improved differential evolution algorithm. The experimental result shows that the operational tasks graph can vividly describe the relationship between operational tasks and effects, and the model and solution method are feasible and effective.

Multi-Objective Optimization Algorithm Based On Multi-Index Elite Individual Game Mechanism, Xu Wang, Weidong Ji, Guohui Zhou, Jiahui Yang

Journal of System Simulation

Abstract: In order to improve the convergence of multi-objective optimization algorithm and the diversity of optimization solution set, and alleviate the flown down of population in target space, a multi-objective optimization algorithm based on multi-attribute elite individual game mechanism is proposed. This paper uses Pareto dominance relationship and multi-index to comprehensively screen elite individuals. The elite individual game mechanism with K-means clustering is integrated with cross and mutation strategy, which effectively improves the convergence and diversity of the algorithm. A detailed convergence analysis of the algorithm is performed to prove the convergence of the algorithm. Eight representative comparison algorithms are …

Research On Decision Behavior Modeling Method Of Key Figures, Xiao Zheng, Xiaodong Peng, Minyu Lu, Tiejun Liu

Journal of System Simulation

Abstract: The decision-making of key figures is an important factor affecting the evolution of concerned events. The research on their decision-making behavior is of great significance for the prediction of important events. For the problem of decision behavior modeling and decision propensity prediction of key figures, the character attribute and measurement methods required for character modeling are analyzed, the character decision-making process and decision-making related influencing factors are analyzed, the exploration research of decision propensity prediction method is carried out, and the prediction model of decision propensity based on comprehensive interest characteristics and psychological characteristics is constructed. Through the research …

Flexible Job-Shop Scheduling Problem Based On Improved Wolf Pack Algorithm, Chaoyang Zhang, Liping Xu, Jian Li, Yihao Zhao, Kui He

Journal of System Simulation

Abstract: An improved wolf pack algorithm is proposed for solving multi-objective scheduling optimization for flexible job shop problems. A multi-objective flexible job shop scheduling model is developed with the maximum completion time of the workpiece and the energy consumption of the machine as the optimization goals. An improved wolf pack algorithm is proposed for solving the shortcomings that traditional wolf pack algorithm is easy to fall into the local optimization. Through improving the intelligent behavior of the wolf pack algorithm, individual codes are designed from the two levels of job's process and machine, and POX(precedence operation crossover) cross operation is …

Calculation Of Optimal Vocs Emission Reduction Based On Improved Seirs Model In Cloud Environment, Guangqiu Huang, Xixuan Zhao, Qiuqin Lu

Journal of System Simulation

Abstract: Volatile organic compounds (VOCs) emissions in different regions are correlated and influenced. In order to minimize the impact of VOCs on the atmospheric environment and achieve synergistic governance of VOCs regions, an optimal emission reduction model is established with the maximum VOCs emission reduction as the primary goal. An improved SEIRS infectious disease dynamics optimization algorithm considering environmental pollution(SEIRS-CE) is proposed and the model is solved in cloud environment. Taking Xi'an city as an example, the SEIRS-CE algorithm is used in Ali cloud server to calculate the emission reduction of VOCs associated with 13 meteorological monitoring stations in Xi …

Learning-Based High-Performance Algorithm For Long-Term Motion Prediction Of Fluid Flows, Jingyuan Zhu, Huimin Ma, Jian Yuan

Journal of System Simulation

Abstract: Simulating the dynamics of fluid flows accurately and efficiently remains a challenging task nowadays, and traditional fluid simulation methods consume large computational resources to obtain accurate results. Deep learning methods have developed rapidly, which makes data-based fluid simulation and generation possible. In this paper, a motion prediction algorithm for long-term fluid simulation is proposed, which is based on a density field with a single frame and a previous velocity field of a sequence. The model focuses on matching the velocity and density fields predicted by the neural network with the simulated data based on the Navier-Stokes equation …

Simulation On Cooperative Control Of Connected And Automated Vehicles At Interchange Based On Petri Net, Mingbao Pang, Zhen Liu

Journal of System Simulation

Abstract: To improve the traffic efficiency of interchange, a simulation model of complete process is built by timed Petri net (TdPN) considering multiple separation and merging behaviors in the process of connected and automated vehicles (CAVs) passing through the interchange. In the light of vehicle priority, a speed guidance strategy is proposed and a CAVs cooperative control model is established, so as to form a complete interchange TdPN model. This method is verified by simulation and compared with the cooperative control method of interchange exit and its connecting area, cooperative control method of multi-merging areas within the interchange. The results …

Multi-Strategy Hybrid Abc For Microarray High-Dimensional Feature Selection, Chuandong Qin, Baosheng Li, Baole Han

Journal of System Simulation

Abstract: Traditional feature selection approaches have major limitations for high-dimensional microarrays, and it is difficult to accurately and efficiently propose the best feature subset. To address this problem, a multi-strategy hybrid artificial bee colony (ABC) algorithm based on wrapper is proposed, which mixes chaotic opposition-based learning strategy, elite guidance strategy, and Mantegna Lévy distribution strategy, and proposes two new search strategies in the employed and onlooker bee phases respectively. A new objective function is proposed for the microarray high-dimensional feature selection problem, which balances the optimal performance of the model with the minimization of the feature subset …

Dynamic Performance Evaluation Method For Transfer In Rail Transit Station Based On Station Simulation And Lstm, Bisheng He, Hongxiang Zhang, Yongjun Zhu, Gongyuan Lu

Journal of System Simulation

Abstract: Given the boom increasing of rail transit passenger volume, the dynamic performance evaluation method for transfer in rail transit stations based on machine learning are proposed to effectively evaluate the performance of the transfer station in different scenarios. Based on the proposed dynamic performance evaluation indexes of effective transfer number, transfer time and congestion, the influence factors of station dynamic performance are analyzed. The simulation model integrated train operation and pedestrian movement is built to provide the time-series data for the machine learning method. The long short-term memory (LSTM) is implemented to forecast the evaluation indicators, and the …

Application Of Digital Twin Model In Grinding Of Bearing Rings, Hongbin Liu, Zhiqiang Shen, Yize Wang, Ming Qiu, Wenrong Lin

Journal of System Simulation

Abstract: The digital twin model can effectively promote the virtual-real interaction between the actual product and the product model. Aiming at the grinding force generated in the grinding process of spherical roller bearings, this paper constructs the bearing ring raceway grinding process by performing dynamics, contact algorithm modeling and rigid-flexible coupling treatment on the components of the grinding work area. The digital twin model completes the virtual mapping of the grinding work area of the bearing ring in the digital space. The model is used to analyze and test the process parameters such as the grinding wheel linear speed and …

Multi-Media Energy Planning Optimization Of Steel Based On Improved Moea/D, Hongcai Ouyang, Dinghui Wu, Junyan Fan, Jing Wang

Journal of System Simulation

Abstract: To address the problems of multi-media iron and steel energy planning model with more variables, complex constraints and high difficulty in model solving, an improved MOEA/D (decomposition-based multi-objective evolutionary algorithm) based on adaptive neighborhood is proposed to realize multi-media energy planning optimization. Considering the characteristics of TOU price and the buffer effect of gas holder, the objective function to minimize operation cost and total energy consumption is constructed. And the model constraints are designed such as energy supply and demand balance. The decoding method based on energy production and consumption rules is designed to determine the target value. The …

A New Electromagnetic Positioning Model With Single Coil Receiver For Virtual Interventional Surgery, Jianhui Zhao, Peijun Zhong, Zhiyong Yuan, Wenyuan Zhao, Tingbao Zhang

Journal of System Simulation

Abstract: To meet the needs of 3D positioning of guide wire catheter in interventional surgery and the requirements of smaller size sensor for narrow cerebral vessels, a new electromagnetic positioning model is proposed with single coil receiver. Based on the electromagnetic theory and geometry principle, the electromagnetic field transmitter with groups of three orthogonal coils and the single coil receiver with smaller size than existing sensors are designed. Based on Biot-Savart Law, the distance between receiving end and geometric center of orthogonal coils is calculated, and spatial coordinate of receiving end is computed based on the spherical intersection formula. To …

Integrated Soft Sensor Modeling Of Fermentation Process Based On Transfer Component Analysis, Yuesheng Zhou, Weili Xiong

Journal of System Simulation

Abstract: The Penicillin fermentation process is an uncertain and multi-stage process. There are different working conditions among different batch fermentation processes, and the distribution of process data is not necessarily the same, which degrades the performance of the traditional soft sensing model. Combined with the transfer learning strategy and Gaussian mixture model, a multi-model ensemble soft sensor modeling method based on transfer component analysis is proposed. In this method, the transfer component analysis is used to get the shared feature mapping matrix between samples, and adapt the edge probability distribution of labeled dataset and unlabeled dataset; the modeling data are …

Lightweight Webvr Real-Time Simulation Of Large-Scale Fire Scenario In Metro, Yang Li, Huijuan Zhang, Chenchen Ge, Kang Xie, Zhuang Li, Jinyuan Jia

Journal of System Simulation

Abstract: Large-scale fire simulation requires a huge amount of calculation and excellent rendering capabilities, which poses a challenge to the realization of a real-time online fire simulation system on the Web. A lightweight Web-based real-time simulation technology framework for subway station fire is proposed. Based on the simplification of calculation formulas in the field of fire safety and the analysis of the impact of smoke prevention facilities in subway stations, a two-stage smoke diffusion model based on smoke bay is proposed to achieve the smoke diffusion trend calculation; a Web-side multi-granularity particle emitter framework at the smoke bay level is …

Impacts Of Cutting-Edge Artificial Intelligence On Economic Research Paradigm, Yongmiao Hong, Shouyang Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

No abstract provided.

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Conversations With Chatgpt About C Programming: An Ongoing Study, James C. Davis, Yung-Hsiang Lu, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

AI (Artificial Intelligence) Generative Models have attracted great attention in recent years. Generative models can be used to create new articles, visual arts, music composition, even computer programs from English specifications. Among all generative models, ChatGPT is becoming one of the most well-known since its public announcement in November 2022. GPT means {\it Generative Pre-trained Transformer}. ChatGPT is an online program that can interact with human users in text formats and is able to answer questions in many topics, including computer programming. Many computer programmers, including students and professionals, are considering the use of ChatGPT as an aid. The quality …

Applications Of Generative Adversarial Networks In Single Image Datasets, Dylan E. Cramer

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

One of the main difficulties faced in most generative machine learning models is how much data is required to train it, especially when collecting a large dataset is not feasible. Recently there have been breakthroughs in tackling this issue in SinGAN, with its researchers being able to train a Generative Adversarial Network (GAN) on just a single image with a model that can perform many novel tasks, such as image harmonization. ConSinGAN is a model that builds upon this work by concurrently training several stages in a sequential multi-stage manner while retaining the ability to perform those novel tasks.

Deep Reinforcement Learning Based Optimization Techniques For Energy And Socioeconomic Systems, Salman Sadiq Shuvo

USF Tampa Graduate Theses and Dissertations

Optimization, which refers to making the best or most out of a system, is critical for an organization's strategic planning. Optimization theories and techniques aim to find the optimal solution that maximizes/minimizes the values of an objective function within a set of constraints. Deep Reinforcement Learning (DRL) is a popular Machine Learning technique for optimization and resource allocation tasks. Unlike the supervised ML that trains on labeled data, DRL techniques require a simulated environment to capture the stochasticity of real-world complex systems. This uncertainty in future transitions makes the planning authorities doubt real-world implementation success. Furthermore, the DRL methods have …

Reference Frames In Human Sensory, Motor, And Cognitive Processing, Dongcheng He

Electronic Theses and Dissertations

Reference-frames, or coordinate systems, are used to express properties and relationships of objects in the environment. While the use of reference-frames is well understood in physical sciences, how the brain uses reference-frames remains a fundamental question. The goal of this dissertation is to reach a better understanding of reference-frames in human perceptual, motor, and cognitive processing. In the first project, we study reference-frames in perception and develop a model to explain the transition from egocentric (based on the observer) to exocentric (based outside the observer) reference-frames to account for the perception of relative motion. In a second project, we focus …

Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha

Electronic Theses and Dissertations

The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This …

Solving Fjssp With A Genetic Algorithm, Michael John Srouji

Computer Science and Software Engineering

The Flexible Job Shop Scheduling Problem is an NP-Hard combinatorial problem. This paper aims to find a solution to this problem using genetic algorithms, and discuss the effectiveness of this. Initially, I did exploratory work on whether neural networks would be effective or not, and found a lot of trade offs between using neural networks and chromosome sequencing. In the end, I decided to use chromosome sequencing over neural networks, due to the scope of my problem being on a small scale rather than on a large scale.

Therefore, the genetic algorithm was implemented using chromosome sequencing. My chromosomes were …

Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill

Theses and Dissertations

Federated learning (FL) is a budding machine learning (ML) technique that seeks to keep sensitive data private, while overcoming the difficulties of Big Data. Specifically, FL trains machine learning models over a distributed network of devices, while keeping the data local to each device. We apply FL to a Parkinson’s Disease (PD) telemonitoring dataset where physiological data is gathered from various modalities to determine the PD severity level in patients. We seek to optimally combine the information across multiple modalities to assess the accuracy of our FL approach, and compare to traditional ”centralized” statistical and deep learning models.

Predicting Success Of Pilot Training Candidates Using Interpretable Machine Learning, Alexandra S. King

Theses and Dissertations

The United States Air Force (USAF) has struggled with a sustained pilot shortage over the past several years; senior military and government leaders have been working towards a solution to the problem, with no noticeable improvements. Both attrition of more experienced pilots as well as wash out rates within pilot training contribute to this issue. This research focuses on pilot training attrition. Improving the process for selecting pilot candidates can reduce the number of candidates who fail. This research uses historical specialized undergraduate pilot training (SUPT) data and leverages select machine learning techniques to determine which factors are associated with …

A Reinforcement Learning Approach To A Beyond Visual Range Air Combat Maneuvering Problem, Caleb A. Taylor

Theses and Dissertations

A one-versus-one air combat maneuvering problem is considered wherein a friendly autonomous aircraft must engage and defeat an adversary autonomous aircraft in a beyond visual range environment. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) is leveraged to model the complex and interdependent operations of aircraft, sensors, and weapons utilized in beyond visual range air combat. We formulate a Markov decision process to obtain high-quality decision policies wherein our autonomous aircraft makes maneuvering and missile firing decisions. We utilize a reinforcement learning solution procedure that implements a linear value function approximation to represent state-decision pairs due to the high …