Physical Sciences and Mathematics Commons^™

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …

Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong

Electrical Engineering Theses and Dissertations

Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.

Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia

University of New Orleans Theses and Dissertations

In this thesis I describe a method where an experience manager chooses actions for non-player characters (NPCs) in intelligent interactive narratives through story graph representation and pruning. The space of all stories can be represented as a story graph where nodes are states and edges are actions. By shaping the domain as a story graph, experience manager decisions can be made by pruning edges. Starting with a full graph, I apply a set of pruning strategies that will allow the narrative to be finishable, NPCs to act believably, and the player to be responsible for how the story unfolds. By …

A Qualitative Representation Of Spatial Scenes In R2 With Regions And Lines, Joshua Lewis

Electronic Theses and Dissertations

Regions and lines are common geographic abstractions for geographic objects. Collections of regions, lines, and other representations of spatial objects form a spatial scene, along with their relations. For instance, the states of Maine and New Hampshire can be represented by a pair of regions and related based on their topological properties. These two states are adjacent (i.e., they meet along their shared boundary), whereas Maine and Florida are not adjacent (i.e., they are disjoint).

A detailed model for qualitatively describing spatial scenes should capture the essential properties of a configuration such that a description of the represented objects …

Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain

Master's Projects

Research in the field of malware classification often relies on machine learning models that are trained on high level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this research, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code. Specifically, we visualize malware samples as images and employ image analysis techniques. In this context, we focus on two machine learning models, namely, Convolutional Neural Networks (CNN) and Extreme …

Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar

Master's Projects

A fundamental problem in malware research consists of malware detection, that is, dis- tinguishing malware samples from benign samples. This problem becomes more challeng- ing when we consider multiple malware families. A typical approach to this multi-family detection problem is to train a machine learning model for each malware family and score each sample against all models. The resulting scores are then used for classification. We refer to this approach as “cold fusion,” since we combine previously-trained models—no retraining of these base models is required when additional malware families are considered. An alternative approach is to train a single model …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg

Master's Projects

Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed. Second, …

Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten

Master's Projects

Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …

Ordinal Hyperplane Loss, Bob Vanderheyden

Doctor of Data Science and Analytics Dissertations

This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize …

Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin

Master of Science in Computer Science Theses

This paper attempts to answer the question of if it’s possible to produce a simple, quick, and accurate neural network for the use in upper-limb prosthetics. Through the implementation of convolutional and artificial neural networks and feature extraction on electromyographic data different possible architectures are examined with regards to processing time, complexity, and accuracy. It is found that the most accurate architecture is a multi-entry categorical cross entropy convolutional neural network with 100% accuracy. The issue is that it is also the slowest method requiring 9 minutes to run. The next best method found was a single-entry binary cross entropy …

Graph Deep Learning: Methods And Applications, Muhan Zhang

McKelvey School of Engineering Theses & Dissertations

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone's life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as …

Rehabilitation Training System Of Vr-Based Speech Apraxia, Huanhuan Jiao, Zhigeng Pan, Shuchang Xu, Qingshu Yuan

Journal of System Simulation

Abstract: Aiming at the problem that the enthusiasm of active training in patients with traditional speech apraxia rehabilitation is gradually weakened, a virtual reality-based rehabilitation training system for speech apraxia is designed. The system simulates the 26 monosyllabic pronunciation mouths shapes made by virtual human in Chinese and uses it for the learning module. It is proposed to identify 26 monosyllabic words by using the isolated word speech recognition algorithm, which improves the accuracy of speech recognition. The system includes a training module and a scene module for assisting the patient's pronunciation rehabilitation training. The evaluation results show that …

Fault Prediction Of Satellite Attitude Control System Based On Neural Network, Xiaofan Meng, Song Hua

Journal of System Simulation

Abstract: A new method based on BP neural network(BPNN), wavelet neural network(WNN) and wavelet decomposition-LSTM(wLSTM) network is proposed for predicting faults in the satellite attitude control system. Normal satellite attitude data is used to train BPNN which is used as the standard model of satellite attitude control system. The real-time attitude residuals is obtained by subtracting the BPNN output attitude angle from the real-time data of satellite attitude. The time series of the residuals are used to build WNN and wLSTM models to predict the faults of satellite attitude control system. A conclusion is given according to comparing the …

Parallel Tasks Optimization Scheduling In Cloud Manufacturing System, Chenwei Feng, Wang Yan

Journal of System Simulation

Abstract: To solve the problem of unbalanced resource requirements and low resource utilization when the same type of tasks are executed in parallel in the cloud manufacturing system, a task resource scheduling model with the goal of minimizing cost, minimizing time, maximizing reliability and optimizing quality is established. A non-dominated sorting genetic algorithm based on reference points (NSGA-III) is adopted to solve the model by combining real number matrix coding and crossover and mutation based on real number coding instead of common evolutionary strategy. And an optimal decision strategy based on combination of analytic hierarchy process and entropy value method …

Research On Mechanical Configuration Of Automatic Container Terminals Based On Simulation Optimization Method, Shaozheng Yu, Dongshi Sun, Chen Jing, Yongchao Li

Journal of System Simulation

Abstract: In the study of mechanical configuration problem of automated container terminal, a system simulation model based on discrete event simulation is constructed. The key parameters and interference factors that affect the performance of the system are fully considered in the model and a mechanical scheduling algorithm interface to ensure the effectiveness of the simulation model is designed. On the premise of ensuring the reliability of the container terminal system, a cycle optimization method of mechanical configuration scheme is proposed to minimize the cost. An example is given to solve the mechanical configuration scheme of an automatic container terminal. …

Multi-Objective Optimization Design Of Aerodynamic Layout For Twin Swept-Wing Aircraft, Yuchang Lei, Dengcheng Zhang, Yanhua Zhang, Guangxu Su, Luo Hao, Zhan Ren

Journal of System Simulation

Abstract: Multi-objective optimization of aerodynamic layout is a key technology in the design of vehicles. The overall configuration of the shape parameters is optimized with a double swept-shaped wave shape as the basic configuration. We use NSGA-Ⅱ multi-objective genetic algorithm, take the aircraft double sweep angle as the design variable, consider the maximum takeoff weight, range, volume ratio and other performance indicators, use Elman neural network to establish the relationship between shape parameters and performance parameters, and establish constraints based on mission planning requirements. The Pareto optimal solution set is obtained by using optimized design and the individuals with …

Dynamic Simulation Of Marine Pasture Water Quality Monitoring Network Based On Aug, Wenxia Xue, Bin Lin, Hu Xu, Jingbo Zhang

Journal of System Simulation

Abstract: The development of marine pastures puts forward higher requirements for water quality monitoring. In order to achieve large-scale and long-term underwater water quality monitoring, AUG-based Underwater Sensor Network (AUSN) is studied. The AUG is introduced as the underwater sink node of the sensor network and the sleep scheduling mechanism is adopted for the sensors, which can effectively reduce the total energy consumption of the network. Based on the analysis of energy consumption characteristics of underwater sensor networks, the energy consumption calculation formula of underwater sensor networks is discussed. Combined with the virtual visualization technology, the underwater sensor network simulation …

Overview Of Cooperative Target Assignment, Ou Qiao, Xiaoyuan He, Jiuyang Tao

Journal of System Simulation

Abstract: The concept and connotation of combat coordination are briefly analyzed. The historical origin of cooperative target allocation and its orientation in command and control process are expounded. The research status of collaborative target allocation in model building and algorithm solving is analyzed comprehensively, the shortcomings of current research is pointed out. It is proposed that for the next step we should pay more attention to large-scale collaborative target allocation under dynamic and confrontational conditions, and focus on the real-time, adaptability and iterative evolution mechanism of the algorithm.

Research On Intelligent Clustering Algorithm For Complex Water Wireless Network Surveillance, Hua Xiang, Hongtao Liang, Zhaoxin Dong, Wang Zhao, Hongjuan Yao, Baohua Li, Bingqing Jiang

Journal of System Simulation

Abstract: The clustering of irregular networks will cause load imbalance, which results in the phenomenon of “energy hot zone”. Aiming at the unreasonable topology of irregular network clustering, an intelligent clustering algorithm based on genetic strategy is proposed for the wireless network surveillance of complex water system. An irregular complex water topology model and an energy consumption model are built, and a genetic clustering strategy is designed via the principle of minimum energy consumption. The P matrix coding method is given, which avoids the squared increment of data calculation. Simultaneously, an adaptive genetic operator and a fuzzy modified operator are …

Design Of A Flexible System Simulation Evaluation Framework, Rusheng Ju, Zimin Cai, Wang Song, Wang Peng

Journal of System Simulation

Abstract: To meet variable evaluation requirement of complex system simulation, this paper puts forward a design strategy of flexible effectiveness evaluation framework. The composition structure of system simulation evaluation framework is analyzed. To help users design reference dynamically, the method of evaluation reference edit and display is introduced based on Web. To enhance the extensibility of evaluation model, the method of interface design and code generation is investigated. To ensure the flexibility and extensibility of evaluation framework, the relation and mapping mechanism of evaluation references, evaluation model and evaluation result is studied. The framework is realized and verified in …

A Temporal Action Detection Algorithm Based On Spatio-Temporal Feature Pyramid Network, Liu Wang, Jinyu Sun, Shiwei Ma

Journal of System Simulation

Abstract: In view of the discontinuity of motion timing detection in the frame-level prediction network structure, a novel algorithm based on spatio-temporal feature pyramid network (ST-FPN) is proposed. In the frame-level action prediction, several 3D convolution-de-convolution (CDC) networks are used to sample spatial feature down to 1 dimension and sample temporal feature up to corresponding proposal level. Then the prediction scores of different CDC networks are fused by non-maximum suppression (NMS). The softmax classifier is used to classify frame-level actions, and then temporal action detection is obtained. The experimental results on dataset THUMOS14 show that the proposed algorithm improves the …

Research On Control Technology Of Aeroengine Heat Exchanger, Qingyang Jiang, Song Hua

Journal of System Simulation

Abstract: In order to solve the high temperature problem caused by the high speed of the synergistic aspirating engine, this paper designs a control algorithm for the heat exchanger. In the process of designing the control algorithm, considering the strong nonlinearity, uncertainty and strong coupling of the engine, The decoupling study of the multi-input and multi-output linear model of the heat exchanger is carried out. The decoupling effect of two different decoupling methods on the strongly coupled heat exchanger is obtained. The fuzzy decoupling PID controller has short adjustment time and overshoot. Small, conclusions with better decoupling effects.

Attitude Control Of Hypersonic Vehicle Considering Input Saturation, Xiaocen Liu, Yunjie Wu, Xu Peng

Journal of System Simulation

Abstract: Aiming at the design of attitude control of hypersonic vehicles, the sliding mode disturbance observer is used to compensate the composite disturbance, meanwhile the dynamic surface control method is used to deal with the design of the nonlinear control system. Considering the input saturation problem, the control input is expanded into a new variable to design the controller, which is different from the direct limit of the rudder deviation. The simulation results show that the dynamic surface control method based on sliding mode observer has stronger robustness than the simple dynamic inversion control method, but the control inputs of …

Fault Identification Of High-Speed Train Bogie Based On Siamese Convolutional Neural Network, Yunpu Wu, Weidong Jin, Junxiao Ren

Journal of System Simulation

Abstract: The performance degradation and failure of high-speed train bogie components will threaten the operation security of train. This paper proposes a fault type identification method based on siamese convolutional neural network to address the scarcity of data and the high-dimension of monitoring signals. Deep residual network with one-dimension convolution layers is employed for features extraction and fusion of vibration signals from multiple sensors. The siamese structure is employed to obtain the similarities between samples. Fault types are identified by ranking similarities in the support set. The experimental results show that the proposed method can identify the fault types with …

Pedestrian Navigation Method Based On Uwb And Sins, Yang Yang, Li Qing

Journal of System Simulation

Abstract: In view of the problem that the inertial navigation error is accumulating with time and UWB indoor is hard to continue tracking problems in complex environment, an extended Kalman filter (EKF) fusing UWB and sins pedestrian navigation is proposed.The zero speed correction algorithm is used to trigger EKF at zero speed. The sins is corrected by the velocity error measurement provided by sins and the position error measurement provided by UWB restraining error accumulation.When UWB signal is interrupted, the position of UWB output without interruption is taken as the starting position. The experiment results of SINS autonomous navigation …

Comparative Study On Resistance Modeling Method In Yacht Simulator, Xiaochen Li, Yin Yong

Journal of System Simulation

Abstract: In order to solve the problem that the existing resistance modeling method of yacht has poor universality and the calculation accuracy is not high, approximate resistance modeling methods of high speed craft are analyzed and summarized. The actual yacht model is simplified into a prismatic hull. “ЦАГИ” method and SIT method are reproduced and the simulation results are compared with model test. The application of resistance approximate modeling in the yacht simulator is discussed. The results reveal that the resistance is in good agreement with the experimental value, and the SIT method with simplified hull form is more …

Research On The Value Accessing Method For Calibrating Micro Traffic Simulation Model Parameters, Chenjing Zhou, Rong Jian, Kwok Lam

Journal of System Simulation

Abstract: Parameter calibration is the precondition of the application of micro traffic simulation technology. This study focuses on the value accessing method for parameter calibration in order to further improve the parameter calibration process. The analysis of the distribution characteristics of each parameter calibration results shows that the parameters have different trends in the process of gradual iteration, and there are multiple optimal solutions for the model parameter calibration results. In this paper, the dispersion is used as the quantitative analysis index of the concentration degree of each parameter calibration result, and the parameter value of the model is determined …