Computer Engineering Commons^™

Dynamic Obstacle Avoidance Control Of Three-Order Multi-Robot Cooperative Formation, Yuchao Zhang, Yuan Jiang, Jiyang Dai

Journal of System Simulation

Abstract: Aiming at the obstacle avoidance and consensus control of underwater vehicles formation in a 3D complex environment, a cooperative formation dynamic obstacle avoidance control algorithm is proposed. An adaptive repulsion gain term based on the speed of dynamic obstacles is established and it is introduced into the repulsion potential field function to enable the robot to safely avoid static and dynamic obstacles. The potential field function based on the gain term of the potential field and the communication weight between the AUVs are defined to solve the robots of easy self collision and leaving the formation. The total acceleration …

Research On Time-Dependent Vehicle Routing Problem With Multiple Time Windows, Nan Li, Rong Hu, Bin Qian, Huaiping Jin, Naikang Yu

Journal of System Simulation

Abstract: Aiming at the time-dependent vehicle routing problem with multiple time windows (TD_VRPMTW) that considers urban traffic congestion, a hybrid discrete gray wolf optimizer (HDGWO) is proposed. In the HDGWO, a new grey wolf individual updating formula is designed, and the integer coding method based on customer permutation is adopted, so that the algorithm can directly perform the global search based on GWO individual updating mechanism in the discrete problem solution space.A population initialization strategy based on the nature of the problem is designed to generate the initial population with high quality and diversity.The information exchange formula of …

Rigid-Liquid Coupling Simulation Of Liquid-Filled Spacecraft Based On Openfoam, Zhijun Song, Zongyu Chen, Lü Jing, Tianshu Wang

Journal of System Simulation

Abstract: Aiming at the design of the spacecraft attitude control system, a simulation software for the rigid-liquid coupling calculation of the liquid-filled spacecraft is built on the basis of the open source CFD software OpenFOAM. In the moving boundary problem, the N-S equation in the non-inertial frame is derived to improve the calculation efficiency, which avoids a large number of grid conversion calculations of the moving grid method in the traditional CFD software. PIMPLE algorithm is used to build the sloshing dynamics solution module, and the variable step length Runge-Kutta method is used to build the attitude …

Effectiveness Evaluation Method Of Space-Based Information Systems Based On Sem, Chi Han, Wei Xiong, Wenwen Liu, Xiaolan Yu, Ping Jian

Journal of System Simulation

Abstract: Aiming at the nonlinear emergence of effectiveness of the coupling and interaction of space-based information system (SIS), an operational effectiveness evaluation (OEE) model based on structural equation modeling (SEM) is proposed. Through the analysis of capacity requirements and system composition, the internal connections of sub-systems are obtained, and the effectiveness evaluation index system is constructed. By considering the synergistic interaction within SIS, the linear and nonlinear SEMs are constructed respectively to evaluate the effectiveness, and the analytical model of OEE is established on the basis of the corresponding parameter equations. With the background of integrated joint operations under …

Bioinformation Heuristic Genetic Algorithm For Solving Tsp, Jia Xu, Fengqing Han, Qixin Liu, Xiaoxia Xue

Journal of System Simulation

Abstract: Genetic algorithm (GA) is one of the universal path optimization algorithms for traveling salesman problem (TSP). Aiming at the slow convergence and unstable solution of the traditional GA, a bioinformation heuristic genetic algorithm (BHGA) is proposed. By optimizing the fitness function and initial population, the gene sequence comparison technique in bioinformatics is introduced to carry out the cross recombination sorting. The gene reversal operation is used to implement mutation, to accelerate the convergence speed and get a better path solution. The numerical examples in TSPLIB database are solved by BHGA and the experimental simulation results show that the …

Design And Implementation Of Uav Swarm Self-Organizing Search Model, Kan Li, Yunpeng Li, Jiangbo Zhao

Journal of System Simulation

Abstract: The UAV swarm self-organizing search for moving target under the urban threat is an important implement of UAV swarm. Though Agent-based complex system modeling and simulation tools, the framework of UAV swarm search simulation model is constructed, and the self-organizing search model of UAV swarm is designed. Under the possible threats to the operational use of UAVs, the concept of self-organizing search for UAV swarm is preliminarily realized and demonstrated, and the solution of autonomous decision making for UAV swarm based on the probability-based finite state machine model is explored, which is analyzed and verified by a case. …

Servicing Method Of Lvc Experiment Resources Based On Object Metamodel, Nan Du, Yaxin Tan, Bin Feng

Journal of System Simulation

Abstract: In the process of live-virtual-constructive (LVC) experiment there are a large number of heterogeneous simulation resource objects. Aiming at the traditional object model not meeting the rapid response experiment requirements of equipment systems in informationized war, the object metamodel based on LVC experiment resource servitization method research is studied. The object metamodel based on resource description method is given, based on three basic resource servitization forms of object interaction, message passing, remote method invocation, virtualization infrastructure object(VIO), VIO-virtualization object model (VIO-VOM) components of publish/subscribe, VIO-VOM components of aggregation, composition, inheritance, callback mechanism, Localclass-VOM, Message-VOM components are proposed. The …

Development Of Vehicle Dynamics Virtual Simulation System Based On Carsim, Jianlei Liu, Xuejian Jiao, Huaiqian Wang

Journal of System Simulation

Abstract: Aiming at the "high cost, high consumption and high risk" of real vehicle test, a virtual simulation system of vehicle dynamics based on CarSim is developed. The real-time vehicle model is created by CarSim. A virtual scene is built in Unity3D, and an active stereoscopic display technology is used to realize the 3D visual effects. The driver's operation information is collected by simulating the steering wheel of Fanatec racing car and LabView, and the vehicle dynamics model is solved in NI-Pxie8840 controller to ensure the real-time operation. The calculated data is fed back to the driver through the six-degree-of-freedom …

Realization Of Domestic Ship Hydrodynamic Numerical Software On Industrial Cloud Platform, Yingyan Zhao, Qunsheng Cao, Zhengnan Cao, Jianchun Wang

Journal of System Simulation

Abstract: Developing the user-friendly cloud platform for high-performance numerical software deployment has great engineering significance. Based on the high performance computing resource of Web industry cloud platform, the large-scale high performance test on the domestic ship hydrodynamics numerical software is carried out. Selecting a typical Knock Nevis KCS model with a bulbous bow, through the parallel solver of the software, the wave-making problem of a real ship is simulated, in which the wave shape near the actual ship hull is basically consistent with that of the real ship. The successful test of a typical application scenario of the domestic …

An Efficient Tracker Via Multi-Feature Adaptive Correlation Filter, Sixian Zhang, Yi Yang, Meng Zhang, Pengbo Mi

Journal of System Simulation

Abstract: Aiming at the low tracking effect of the correlation filters tracker based on manual features in challenging scenes of rapid deformation and background clutter, a new correlation filter tracker based on Staple tracker is proposed. An appearance model based on HOG features and color-naming features is built to enhance the robustness to the challenging scenes of rapid deformation and background clutter. A self-adjust evaluation function is designed to merge the two kinds of feature information and a more discriminative feature is obtained. The novel online update strategies to reduce the training over-fitting and model drift for different features are …

Day Ahead Thermal-Photovoltaic Economic Dispatch Considering Uncertainty Of Photovoltaic Power Generation, Xinghua Liu, Chen Geng, Shenghan Xie, Jiaqiang Tian, Hui Cao

Journal of System Simulation

Abstract: Aiming at the uncertainty and randomness of photovoltaic power generation affected by weather factors, a mathematical model of day ahead thermal-photovoltaic economic dispatch considering seasonal weather factors is established. The mathematical model takes the operation cost of thermal power units, the cost of photovoltaic power generation, the cost of spinning reserve and the forecast error cost of photovoltaic power generation affected by weather factors as the economic objective function, and the sulfur dioxide emission of thermal power units as the environmental objective function. In order to improve the accuracy of photovoltaic output prediction, the long short term memory neural …

Scheduling Optimization And Comparative Analysis Of Twin 40 Feet Yard Crane Based On Saga, Meng Yu, Zhenli Xu, Tianjiao Tan

Journal of System Simulation

Abstract: Based on the operating characteristics of twin 40 ft yard crane, the scheduling models of the single-container yard crane, twin 40 ft yard crane with single-lift structure and twin 40 ft yard crane with twin-lift structure in the mixed container area are established respectively to minimize the operating time. simulate anneal genetic algorithm(SAGA) hybrid algorithm is used in the yard crane scheduling model to optimize the yard crane equipment configuration and scheduling strategy, shorten the average loading and unloading time, and improve the operation efficiency of the automatic terminal. By comparing the operation efficiency of three kinds of cranes …

Identification Of Switching Operation Based On Lstm And Moe, Xiaoqing Zhang, Wanfang Xiao, Yingjie Guo, Bowen Liu, Xuesen Han, Jingwei Ma, Gao Gao, He Huang, Shihong Xia

Journal of System Simulation

Abstract: Aiming at the individual differences of different personnel in the same operation and differences of the same person in the same operation at different times, a switching operation recognition model(MoE-LSTM) based on Mixture of experts model (MOE) and long short-term memory network(LSTM) is proposed. Based on MoE, LSTM is integrated to learn the feature distribution of different sources data. The acceleration data is collected to build the switching operation dataset and the action sequence is segmented and aligned based on sliding window. The action sequence is input to MoE-LSTM, and the temporal dependencies of different actions are independently learned …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

Automated Identification Of Astronauts On Board The International Space Station: A Case Study In Space Archaeology, Rao Hamza Ali, Amir Kanan Kashefi, Alice C. Gorman, Justin St. P. Walsh, Erik J. Linstead

Art Faculty Articles and Research

We develop and apply a deep learning-based computer vision pipeline to automatically identify crew members in archival photographic imagery taken on-board the International Space Station. Our approach is able to quickly tag thousands of images from public and private photo repositories without human supervision with high degrees of accuracy, including photographs where crew faces are partially obscured. Using the results of our pipeline, we carry out a large-scale network analysis of the crew, using the imagery data to provide novel insights into the social interactions among crew during their missions.

Feed Forward Neural Networks With Asymmetric Training, Archit Srivastava

School of Computing: Dissertations, Theses, and Student Research

Our work presents a new perspective on training feed-forward neural networks(FFNN). We introduce and formally define the notion of symmetry and asymmetry in the context of training of FFNN. We provide a mathematical definition to generalize the idea of sparsification and demonstrate how sparsification can induce asymmetric training in FFNN.

In FFNN, training consists of two phases, forward pass and backward pass. We define symmetric training in FFNN as follows-- If a neural network uses the same parameters for both forward pass and backward pass, then the training is said to be symmetric.

The definition of asymmetric training in artificial …

Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram …

Cyber Deception For Critical Infrastructure Resiliency, Md Ali Reza Al Amin

Computational Modeling & Simulation Engineering Theses & Dissertations

The high connectivity of modern cyber networks and devices has brought many improvements to the functionality and efficiency of networked systems. Unfortunately, these benefits have come with many new entry points for attackers, making systems much more vulnerable to intrusions. Thus, it is critically important to protect cyber infrastructure against cyber attacks. The static nature of cyber infrastructure leads to adversaries performing reconnaissance activities and identifying potential threats. Threats related to software vulnerabilities can be mitigated upon discovering a vulnerability and-, developing and releasing a patch to remove the vulnerability. Unfortunately, the period between discovering a vulnerability and applying a …

Evaluation Of Generative Models For Predicting Microstructure Geometries In Laser Powder Bed Fusion Additive Manufacturing, Andy Ramlatchan

Computer Science Theses & Dissertations

In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex …

Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque

Electrical & Computer Engineering Theses & Dissertations

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …

Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras

Electrical and Computer Engineering Faculty Publications

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed ob-servations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an …

Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar

LSU New Orleans Theses and Dissertations

Parallel computing plays a crucial role in processing large-scale graph data. Complex network analysis is an exciting area of research for many applications in different scientific domains e.g., sociology, biology, online media, recommendation systems and many more. Graph mining is an area of interest with diverse problems from different domains of our daily life. Due to the advancement of data and computing technologies, graph data is growing at an enormous rate, for example, the number of links in social networks is growing every millisecond. Machine/Deep learning plays a significant role for technological accomplishments to work with big data in modern …

Ocean Wave Prediction And Characterization For Intelligent Maritime Transportation, Pujan Pokhrel

LSU New Orleans Theses and Dissertations

The national Earth System Prediction (ESPC) initiative aims to develop the predictions
for the next generation predictions of atmosphere, ocean, and sea-ice interactions in the scale of days to decades. This dissertation seeks to demonstrate the methods we can use to improve the ESPC models, especially the ocean prediction model. In the application side of the weather forecasts, this dissertation explores imitation learning with constraints to solve combinatorial optimization problems, focusing on the weather routing of surface vessels. Prediction of ocean waves is essential for various purposes, including vessel routing, ocean energy harvesting, agriculture, etc. Since the machine learning approaches …

Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu

Open Access Theses & Dissertations

Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …

Feature Analysis Of Indus Valley And Dravidian Language Scripts With Similarity Matrices, Sarat Sasank Barla, Sai Surya Sanjay Alamuru, Peter Revesz

School of Computing: Conference and Workshop Papers

This paper investigates the similarity between the Indus Valley script and the Kannada, Malayalam, Tamil, and Telugu scripts that are used to write Dravidian languages. The closeness of these scripts is determined by applying a feature analysis of each sign of these scripts and creating similarity matrices that describe the similarity of any pair of signs from two different scripts. The feature list that we use for the analysis of these Dravidian language-related scripts includes six new features beyond the thirteen features that were used for the study of Minoan Linear A and related scripts by Revesz. These new features …

Structured Encryption For Knowledge Graphs, Yujie Xue, Lanxiang Chen, Yu Mi, Lingfang Zeng, Fatemeh Rezaeibagha, Robert H. Deng

Research Collection School Of Computing and Information Systems

We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents-symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

Adaptive Risk Network Dependency Analysis Of Complex Hierarchical Systems, Katherine L. Smith

Computational Modeling & Simulation Engineering Theses & Dissertations

Recently the number, variety, and complexity of interconnected systems have been increasing while the resources available to increase resilience of those systems have been decreasing. Therefore, it has become increasingly important to quantify the effects of risks and the resulting disruptions over time as they ripple through networks of systems. This dissertation presents a novel modeling and simulation methodology which quantifies resilience, as impact on performance over time, and risk, as the impact of probabilistic disruptions. This work includes four major contributions over the state-of-the-art which are: (1) cyclic dependencies are captured by separation of performance variables into layers which …

Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers …