Computer Sciences Commons^™

Research On Six Degrees Of Freedom Platform Control In Special Vehicle Simulated Driving Training, Yihao Li, Zhili Zhang, Xiangyang Li, Long Yong

Journal of System Simulation

Abstract: In order to simulate various postures of driving the special vehicles in a limited space, a set of six-degree-of-freedom motion platform for the simulation driving training system of special vehicles is developed. The mechanical structure of the six-degree-of-freedom motion platform is designed to meet the motion posture simulation requirement. The control of each degree of freedom in the motion platform is realized through the design of the embedded control system. The displacement of each electric cylinder is obtained by inverse solution algorithm, and the somatosensory simulation of acceleration and angular displacement is realized by the wash-out algorithm. It has …

Optizimation Of Vaccination Supply Chain Based On Scg In Nanshan District, Zhenning Dong, Shunzhou Huang, Jiajun Chen, Huiqiong Zheng

Journal of System Simulation

Abstract: To optimize the vaccination network, inventory strategy and human resource allocation in Nanshan District, Supply Chain Guru's (SCG) network optimization method is used to select 50 alternative stations to decrease the fixed operating cost. SCG's inventory optimization method is used to set inventory strategy for each station, and simulation method is designed to compare total cost of all schemes. To optimize the opening days of vaccination stations, an medical personnel allocation rule is designed, which reduces some stations' opening days to 2 or 3 days and increases some stations' medical personnel. An simulation method is designed to compare the …

Single-Frame Image Motion Parallax Key Point Estimation Combined With Self-Supervised Learning, Zhihao Huo, Weidong Jin, Tang Peng

Journal of System Simulation

Abstract: The motion parallax key point FOE (Focus of Expansion) is an important parameter of railway catenary video inspection. The current method of calculating FOE requires multi-frame image matching estimation, which has high time complexity. Aiming at the single-frame image FOE estimation, a single-frame image FOE estimation algorithm fused with self-supervised learning is proposed. A full convolutional network F-VGG(Fully-Visual Geometry Group) is built as the FOE predictor, and the training label of the sample data is automatically generated through the fusion agent task, which realizes the end-to-end single-frame image FOE estimation. The experimental results show that the method has an …

Research On Intelligent Gait Recognition Method Based On Plantar Pressure Perception, Xueqin Liu, Liu Ning, Su Zhong, Jingxiao Wang, Chaojie Yuan

Journal of System Simulation

Abstract: In view of the complexity and low accuracy of gait recognition in the past, an intelligent gait recognition method based on plantar pressure perception is proposed. The pressure data of the gait of plantar periodic motion is collected and the obtained gait data is classified by the vector machines,the intelligent gait recognition of plantar pressure perception is realized, and the accuracy of gait feature analysis is improved. Through experiment verification, the overall classification accuracy of the classifier is more than 90%, which verifies the rationality of the feature extraction. By evaluating the real state and the results of …

A Natural Computing Method Based On Spatial Division Search Strategy, Xiaoqing Sun, Cheng Hao, Luyao Zhang, Weidong Ji, Wang Xu

Journal of System Simulation

Abstract: A natural computing method based on spatial division search strategy is proposed. The strategy can map the high-dimensional space to the three-dimensional Cartesian coordinate system by grouping the dimensional space into a group of three dimensions. The individual after spatial segmentation is numbered into subindividual, to increases the particle number while reducing the dimension, thus the individual is distributed over wider search space to effectively increases the diversity of the population. The algorithm iterates to a certain extent and can synthesize the individual into the original individual through the numbered index. By calculating the fitness value, some poor …

Short-Term Wind Power Prediction Method Based On Random Forest, Liu Xing, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: In order to effectively predict the power and value fluctuation range of the short-term wind, a wind power prediction method based on clustering and kernel principal component analysis combined with random forest algorithm is proposed. The clustering analysis data processing method is used to preprocess the meteorological wind power generation data to improve the data quality, and the kernel principal component analysis method is used to reduce the dimensionality of the eight groups of characteristic data to remove the correlation of the wind power data, the random forest algorithm is used to forecast the wind power, to obtain …

Adaptive Center Node Selection Method For Unmanned Cluster, Hua Xiang, Chenglong Shi, Baohua Li, Jietao Zhang, Jiaxian Zuo

Journal of System Simulation

Abstract: In the unmanned cluster task execution, following the change of relative position of unmanned system, network changes in real time leads to the change of node importance of each unmanned system, and the corresponding change of data transmission and communication flow. For the better network management, the central node for controlling data communication needs to be selected. An adaptive selection method for the center node of unmanned cluster is proposed, and the mapping and feature of unmanned cluster network is expressed as graph theory. Laplacian centrality is introduced to evaluate the importance of nodes themselves. Weakening factors are …

Intelligent Evaluation Of Rescuing Persons From Water In Navigation Simulator, Haichao Wang, Yin Yong

Journal of System Simulation

Abstract: Aiming at the arbitrariness and inconsistent standards in the subjective assessment of the personnel overboard rescue training evaluation in the navigation simulator, the maneuvering process of Williamson turn rescue overboard personnel is analyzed. The evaluation index system is obtained by using the expert investigation method. The sample data of the personnel overboard rescue operation is obtained by the navigation simulator. Combining the expert investigation method, the subjective score of each sample is obtained. By using the BP neural network to train and test the samples, the intelligent evaluation model of personnel overboard rescue is obtained, and the intelligent evaluation …

Access Control Mechanism Of Uav Cluster Based On Blockchain Smart Contract, Ting Duan, Weiping Wang, Yifan Zhu, Wang Tao, Meigen Huang

Journal of System Simulation

Abstract: A Unmanned Aerial Vehicle (UAV) cluster access control mechanism based on Ethereum blockchain smart contract is proposed to solve the problems of the strategic stability and low security of UAV cluster access control mechanism. The role-based access control mechanism model is improved, and the formal definition of the access control model for UAV cluster is given. The access control architecture of UAV cluster based on blockchain technology is proposed, and the corresponding basic framework and execution process is proposed, which can effectively reduce the cost of UAV cluster operation management resources, solve the problem of incomplete state …

Modeling And Simulation Of Radiation Measurement System Based On Monte Carlo Method, Jinghai Cheng, Hongzhi Wang, Luoyuan Xu, Xia Tian

Journal of System Simulation

Abstract: A method is applied to build a virtual simulation radiometric measurement system. The mathematical and physical models of gamma ray interaction with matter, radiation sources, measurement electronics system and protective materials are constructed by using Monte Carlo method. Through numerical calculation and scene simulation of the radiation measurement system, virtual simulation acquisition and energy spectrum processing of radiation measurement data are realized. It, the system, can simulate single channel measurement and computer multi-channel measurement experiments. It can realize energy measurement, activity measurement and energy spectrum measurement of mixed, unknown or custom radiation sources in different size crystals. It can …

System Performance Evaluation Method Based On Multi-Source Prior Data, Haozhe Liu, Li Wei, Ma Ping, Yang Ming

Journal of System Simulation

Abstract: When using the Bayes method to evaluate the performance of the system with multi-source prior data, the multi-source prior data is fused, the posterior distribution is calculated by synthesizing the fused prior distribution and test data. The parameters of posterior distribution are estimated to obtain the performance evaluation results. A weighted fusion method of multi-source prior data based on Kullback-Leibler divergence is proposed, which can effectively integrate the multi-source prior data. The commonly used Markov Chain Monte Carlo method is used to estimate the parameters of Bayes posterior distribution. The influence of different proposal distributions on the sampling results …

Normalization Of Simulation System Credibility Index Based On Vague Set, Yuhang Ren, Li Wei, Ma Ping, Yang Ming

Journal of System Simulation

Abstract: Focus on various types of simulation system credibility indexes and the difficulty to convert the index results to credibility, a normalization method of credibility indexes based on Vague sets is proposed, which includes qualitative and quantitative conversion methods; Aiming at the problem of credibility Vague value index synthesis, the weighted arithmetic mean operator and the weighted geometric mean operator based on Vague set are given, and the applications are explained; According to the similarity principle of Vague sets, a method of transforming the credibility Vague value to the credibility single value is proposed, which improves the …

Trajectory Tracking Control Of Planetary Entry Phase Based On Neural Network And Fractional Sliding Mode, Cunli Fan, Dai Juan, Haitao Liu, Su Zhong, Zhu Cui, Wenting Xu

Journal of System Simulation

Abstract: A fractional order sliding mode control method based on Radial Basis Function (RBF) neural network is proposed to solve the landing accuracy being affected by the interference during the landing process of planetary probe. Based on sliding mode control, a trajectory tracking control method for the entry phase of the probe is designed. Fractional calculus is introduced to alleviate the chattering caused by sliding mode control. RBF neural network is used to estimate and compensate the atmospheric density uncertainty. The method is applied to Mars landing scene simulation. The simulation results show that the proposed control method can accurately …

An Electromechanical-Electromagnetic Transient Stability Simulation System For Ac/Dc Hybrid Power System, Weijie Dong, Huang Min, Guoqing He, Bao Wei, Yilong Wang, Liu Quan

Journal of System Simulation

Abstract: In order to improve the hybrid simulation speed of AC/DC power grid, it is necessary to improve the simulation method of sub grid parallel. An electromechanical transient stability simulation system is presented for AC/DC hybrid power grid. The AC/DC sub network module is used to divide the large AC/DC power grid into small AC/DC sub networks, so that each sub network can be simulated in parallel. The numerical calculation method is improved for the efficiency and accuracy of simulation calculation. Taking IEEE 10-39 bus as an example, the effectiveness of the method is verified in PSCAD (Power Systems Computer …

Supplier Selection Based On Supplier Portrait And Markov Monte Carlo Method, Bingli Sun, Song Xiao, Guanghong Gong

Journal of System Simulation

Abstract: The supplier selection problem is a complex multi-objective decision-making problem and the key is how to establish the supplier's portrait. For the supplier selection of aerospace equipment, enterprise qualification management, business risks, and product quality are comprehensively considered. Based on Bayesian theory, the multi-parameter joint distribution derivation of portrait sample data is realized. Combined with the mathematical model derived, a Markov Monte Carlo simulation method is proposed. And combined with Gibbs sampler, the supplier ranking and selection are achieved when data is difficult to obtain or missing, which provides a new idea for supplier selection in the aerospace …

Automated Classification Model With Otsu And Cnn Method For Premature Ventricular Contraction Detection, Liang-Hung Wang, Lin-Juan Ding, Chao-Xin Xie, Su-Ya Jiang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Pao-Cheng Huang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm …

Provenance: An Intermediary-Free Solution For Digital Content Verification, Bilal Yousuf, M. Atif Qureshi, Brendan Spillane, Gary Munnelly, Oisin Carroll, Matthew Runswick, Kirsty Park, Eileen Culloty, Owen Conlan, Jane Suiter

Articles

The threat posed by misinformation and disinformation is one of the defining challenges of the 21st century. Provenance is designed to help combat this threat by warning users when the content they are looking at may be misinformation or disinformation. It is also designed to improve media literacy among its users and ultimately reduce susceptibility to the threat among vulnerable groups within society. The Provenance browser plugin checks the content that users see on the Internet and social media and provides warnings in their browser or social media feed. Unlike similar plugins, which require human experts to provide evaluations and …

Reducing Kidney Discard With Artificial Intelligence Decision Support: The Need For A Transdisciplinary Systems Approach, Richard Threlkeld, Lirim Ashiku, Casey I. Canfield, Daniel Burton Shank, Mark A. Schnitzler, Krista L. Lentine, David A. Axelrod, Anil Choudary Reddy Battineni, Henry Randall, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Purpose of Review: A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard.

Recent Findings: Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and …

Cognizant Composites: Seamless Integration Of Circuitry And Sensors Into Structural Composites, Reuben Fresquez

Computer Science ETDs

This thesis describes a set of novel techniques for embedding sensors, circuitry, and electronics into structural composites. I leverage recent developments in human computer interaction to create sensors and circuitry that are seamlessly incorporated into structural composites. I fabricate bend and compression sensors, along with circuitry, from textiles, which enables me to add electronic capabilities without impacting the composite’s structural integrity. I describe the construction of these “cognizant composites” and demonstrate their functionality. I also explore techniques for embedding standard electronic components, including microcontrollers, into structural composites. Potential applications of this technology include buildings that can warn occupants if load-bearing …

Online Optimization Of File Transfers In High-Speed Networks, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

File transfers in high-speed networks require network and I/O parallelism to reach high speeds, however, creating arbitrarily large numbers of I/O and network threads overwhelms system resources and causes fairness issues. In this paper, we introduce Falcon that combines a novel utility function with state-of-the-art online optimization algorithms to discover the degree of I/O and network parallelism for file transfer that can maximize the throughput while keeping system overhead low and ensuring fairness among competing transfers. Our extensive evaluations in several dedicated and production high-speed networks show that Falcon can find near optimal solution in as little as 20 seconds …

Learning Transfers Via Transfer Learning, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Detecting performance anomalies is key to efficiently utilize network resources and improve the quality of service. Researchers proposed various approaches to identify the presence of anomalies by analyzing performance statistics using heuristic (e.g., change point detection) and Machine Learning (ML) models. Although these models yield high accuracy in the networks that they are trained for, their performance degrade severely when transferred to different network settings. This is because of the fact that existing models detect anomalies by capturing the changes in transfer throughput and observed RTT values, which are dependent to network settings. In this paper, we propose a novel …

Understanding The Dynamics Of Human Reliance And Trust On Automation, Carlos E. Bustamante Orellana, Lucero Rodriguez Rodriguez, Jordy Cevallos Chavez, Yun Kang

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Warmonger: Inflicting Denial-Of-Service Via Serverless Functions In The Cloud, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

We debut the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a result, a malicious user on this platform can purposefully misbehave and cause these egress IPs to be blocked by the content server, resulting in a platform-wide denial of service. To validate the Warmonger attack, we ran months-long experiments, collected and analyzed the …

Reconstructing Mathematical Models With Chaotic Attractors Via Genetic Algorithms, Luis A. Ramirez Islas, Paul A. Valle

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Facilitating Heuristic Evaluation For Novice Evaluators, Anas Abulfaraj

College of Computing and Digital Media Dissertations

Heuristic evaluation (HE) is one of the most widely used usability evaluation methods. The reason for its popularity is that it is a discount method, meaning that it does not require substantial time or resources, and it is simple, as evaluators can evaluate a system guided by a set of usability heuristics. Despite its simplicity, a major problem with HE is that there is a significant gap in the quality of results produced by expert and novice evaluators. This gap has made some scholars question the usefulness of the method as they claim that the evaluation results are a product …

Experimental Analysis Of Gbm To Expand The Time Horizon Of Irish Electricity Price Forecasts, Conor Lynch, Christian O'Leary, Preetham Goving Kolar Sundareshan, Yavuz Akin

NIMBUS Articles

In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electricity market and achieve favorable results, relative to the current state-of-the-art. Presently, electricity prices are published 10 h in advance of the trade day of interest. Using the forecasting methodology outlined in this paper, an estimation of these prices can be made available one day in advance of the official price publication, thus extending the time available to plan …

Novel Approach To Integrate Can Based Vehicle Sensors With Gps Using Adaptive Filters To Improve Localization Precision In Connected Vehicles From A Systems Engineering Perspective, Abhijit Vasili

USF Tampa Graduate Theses and Dissertations

Research and development in Connected Vehicles (CV) Technologies has increased exponentially, with the allocation of 75 MHz radio spectrum in the 5.9 GHz band by the Federal Communication Commission (FCC) dedicated to Intelligent Transportation Systems (ITS) in 1999 and 30 MHz in the 5.9 GHz by the European Telecommunication Standards Institution (ETSI). Many applications have been tested and deployed in pilot programs across many cities all over the world.

CV pilot programs have played a vital role in evaluating the effectiveness and impact of the technology and understanding the effects of the applications over the safety of road users. The …

Treatment Selection Using Prototyping In Latent-Space With Application To Depression Treatment, Akiva Kleinerman, Ariel Rosenfeld, David Benrimoh, Robert Fratila, Caitrin Armstrong, Joseph Mehltretter, Eliyahu Shneider, Amit Yaniv-Rosenfeld, Jordan Karp, Charles F. Reynolds, Gustavo Turecki, Adam Kapelner

Publications and Research

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not …

Dynamic Wireless Sensor Network Simulation, Mitchell Clay

Student Theses and Dissertations

Wireless sensors have become fairly ubiquitous, having a wide variety of applications. Commonly, sensors are deployed as solitary devices, and usually in a fixed position. The ability to network several wireless sensors together as one large network, especially with moving nodes, provides solutions to data gathering that might otherwise be impossible. These additions add complexity, however, and development times and costs can be significantly higher than with stand-alone static nodes. The ability to simulate combinations of hardware, software, and networking algorithms is useful for system planning and development. Tools exist to simulate some aspects of these dynamic wireless sensor networks, …

Artificial Intelligence Algorithms For Medical Imaging And Healthcare, Jonathan William Stubblefield

Student Theses and Dissertations

In this dissertation, we studied several applications of artificial intelligence applications to healthcare. In the first chapter, we examined a machine learning algorithm for classifying patients presenting to the emergency department with acute respiratory distress syndrome (ARDS). Patients presenting with this life-threatening condition require a quick and accurate assessment of whether the condition is infectious or cardiac in etiology as the treatments for these etiologies of ARDS differ significantly. We used a transfer learning approach to develop our model. The model used a combination of clinical data and a chest x-ray as its input and achieved an accuracy 0.675 on …