Computer Sciences Commons^™

Design Of Drilling Jumbo Simulation Training System Based On Vega Prime, Youcai Wang, Zilong Guo, Xinhai Xiang, Honghai Luan

Journal of System Simulation

Abstract: A drilling jumbo simulation training system was developed based on the Vega Prime in the MFC framework. The interactive communication control platform was built based on PCI bus, and then multithreading technology was adopted to achieving the Synchronous data communication between acquisition card and computer. A variety of modeling software was studied to aid Creator modeling method, and then the motion simulation of the drilling jumbo double-arm realization method based on DOF nodes was studied, which realized the real-time dynamic interaction of the system. By using the active 3d stereo imaging principle, the realization method of 3d stereo display …

Boiler Combustion Optimization Based On Bayesian Neural Network And Genetic Algorithm, Haiquan Fang, Huifeng Xue, Li Ning, Fei Xi

Journal of System Simulation

Abstract: Neural network and genetic algorithm have been extensively used in boiler combustion optimization problems. But the traditional Back Propagation neural network's generalization ability is poor. The Bayesian regularization can improve the neural network's generalization ability. A boiler combustion multi-objective optimization method combining Bayesian regularization BP neural network and genetic algorithm （Bayes NN-GA）was researched. A number of field test data from a boiler was used to simulate the Bayesian neural network model. The results show that the thermal efficiency and NOx emissions predicted by the Bayesian neural network model show good agreement with the measured, and the optimal results show …

Attitude Control Of Three-Axis Flexible Satellite Based On Active-Disturbance-Rejection Sliding Mode Method, Yunjie Wu, Li Chen, Ma Zheng

Journal of System Simulation

Abstract: By using Kane method, a mathematical model with CMGs as actuators was built for the three-axis satellite with flexible appendages. An active-disturbance-rejection sliding mode controller was designed for the demand about quickness and stability in attitude control. This controller observed the disturbance by expanded state observer, and compensated the output. The slide mode method in the controller made the system quick and stable. The simulation experiments show that the controller could control the attitude of three-axis satellite efficiently and weaken the vibration of flexible solar panel. The active-disturbance-rejection sliding mode controller shows the merits of both active-disturbance-rejection method …

Least-Energy Maneuver Of Five-Link Manipulator Constrained Within Tunnel Space Using Direct Collocation, Xiuqiang Pan, Chengcai Mei, Junjie Chen

Journal of System Simulation

Abstract: Optimal control and designs least-energy maneuver control laws for a five-linked manipulator were applied in order to carry out designated tasks in a confined space. Lagrange-Euler equation described the relationships between the actuators and system dynamics. Euler-Lagrange formulation indicates how optimization can be achieved when optimum occurs. Direct collocation method was introduced in order to solve this highly nonlinear dynamic optimal control problem. Simulations were done to exploit how the manipulator reacted to the constraint. In this study, the diameter of the cylindrical space was shrunken each time by 0.1 meters. The value of the cost function and …

Research Of Multi-Views Based Weapon And Equipment Simulation Model Architecture, Chunguang Peng, Jianhui Deng, Zhang Bo

Journal of System Simulation

Abstract: ：It's an important approach to build reasonable and complete simulation model architecture for promoting the simulation interoperability, establishing simulation model standards and promoting reusability of models and codes. Some problems existed in the research of simulation model architecture were analyzed. The weapon and equipment simulation model architecture describing method based on multi-views was brought out by using the system engineering theory, which emphasized on the complicated relations between modes and its environment. The method could describe simulation model architecture from different views of different users. Therefore, based on DoDAF and the popular software architecture, five-view models of weapon …

Research On Equipment Operation Capabilities Requirements Based On Exploratory Simulation Experiment Methodology, Zongrui Yan, Yinhua Wu, Chen Yong, Jianshu Dong

Journal of System Simulation

Abstract: Analysis on equipment operation requirements is a very difficult problem which has included a lot of uncertain factors, and it can assist peoples to determine whether the equipment item should be started. Exploratory simulation experiment methodology which is synthetized by exploratory analysis and war fighting experimentation can provide a better service for many problems about the equipment demonstration. Through the summary on basic applications exploratory simulation experiments related out comes and its analysis for concrete implementation steps equipment demonstration areas, a tactical level exploratory simulation methodology and its concrete implementation steps were proposed for the operational requirements analysis. An …

Research And Development Of Eicas System Emulator Based On Idata, Baoming Zhao, Hongshu Cheng, Cancan Liu, Jiying Li

Journal of System Simulation

Abstract: Some modeling tools can't design simulation instruments efficiently and conveniently such as OpenGL, etc. A cockpit display development tool of IData was worked in which has graphical user interface and could develop a system easily and rapidly. IData is a method of designing maps and pictures, it can respond variables of application and controlling events in real time and develop graphical interface by clicking on the menu. So IData can reduce the time of graphic development and integration in simulation. In order to render maps in real time, IData used a powerful graphic editor to model for instruments, and …

Diagnosis Of Aircraft Control Surface Fault Based On Semi-Physical Simulation, Yunlong Li, Xia Jie, Zifang Shi

Journal of System Simulation

Abstract: The aircraft control surface defaults of rudders were mainly divided into jam fault and damage fault. On the basis of the relationship between motor toque and hinge moment, a new method of using the motor voltage was proposed to detect the control surface'faults of rudder. And the method could detect the damage degree of the faults. Torque motor was used to play the role of hinge moment. And the new method was simulated by Semi-Physical system which is consisted of dSPACE system, PC and simulated rudder actuator. The results show that, the damage faults and jam faults can be …

Research On Scenario-Driven Component-Based Model Composition Method, Jianchun Zhang, Fengju Kang

Journal of System Simulation

Abstract: To fulfil the requirements for fast, easy and integrated modeling under certain operation conditions, a model composition method of scenario-driven was proposed. Starting with corresponding composition framework, traditional component models were extended through corresponding meta-model and normalization description of scenario was given. Furthermore, a full combat simulation system could be constructed quickly by realizing integrated process including three processes: scenario parse, model search and match. By preliminarily performing underwater weapon system modeling and simulation application, the result shows efficiency of this composition method.

A Study Of Information Bots And Knowledge Bots, Amartya Hatua

Dissertations

In this dissertation, a study of different aspects of information bots and knowledge bots is done. The research contributes to a better understanding of the various characteristics of information bots as well as the different patterns and factors responsible for the information diffusion in a social network. This research also shows how these factors can be used to predict information diffusion for a particular topic in a social network. The second part of the research is focused on strategies for improving the knowledge base of knowledge bots, where two different approaches are studied. In the first approach, knowledge is transferred …

Empirical Studies Of Deep Learning On Information Diffusion On Social Networks And Collective Task Learning For Swarm Robotics, Trung T. Nguyen

Dissertations

Researchers in multiple disciplines have recently adopted deep learning because of its ability of high accuracy representation learning from big and complex data. My research goal in this thesis is developing deep learning models for information diffusion analysis on social networks and collective tasks learning in swarm robotics. Firstly, the information diffusion on social networks is modeled as a multivariate time series in three dimensions with ten features. Then, we applied time-series clustering algorithms with Dynamic Time Warping to discover different patterns of our models. Then, we build a prediction model based on LSTM, which outperforms traditional time-series prediction methods. …

A 3d Image-Guided System To Improve Myocardial Revascularization Decision-Making For Patients With Coronary Artery Disease, Haipeng Tang

Dissertations

OBJECTIVES. Coronary artery disease (CAD) is the most common type of heart disease and kills over 360,000 people a year in the United States. Myocardial revascularization (MR) is a standard interventional treatment for patients with stable CAD. Fluoroscopy angiography is real-time anatomical imaging and routinely used to guide MR by visually estimating the percent stenosis of coronary arteries. However, a lot of patients do not benefit from the anatomical information-guided MR without functional testing. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a widely used functional testing for CAD evaluation but limits to the absence of anatomical information. …

Applications Of Artificial Intelligence And Graphy Theory To Cyberbullying, Jesse D. Simpson

Graduate Theses/Dissertations

Cyberbullying is an ongoing and devastating issue in today's online social media. Abusive users engage in cyber-harassment by utilizing social media to send posts, private messages, tweets, or pictures to innocent social media users. Detecting and preventing cases of cyberbullying is crucial. In this work, I analyze multiple machine learning, deep learning, and graph analysis algorithms and explore their applicability and performance in pursuit of a robust system for detecting cyberbullying. First, I evaluate the performance of the machine learning algorithms Support Vector Machine, Naïve Bayes, Random Forest, Decision Tree, and Logistic Regression. This yielded positive results and obtained upwards …

Secure Mobile Computing By Using Convolutional And Capsule Deep Neural Networks, Rui Ning

Electrical & Computer Engineering Theses & Dissertations

Mobile devices are becoming smarter to satisfy modern user's increasing needs better, which is achieved by equipping divers of sensors and integrating the most cutting-edge Deep Learning (DL) techniques. As a sophisticated system, it is often vulnerable to multiple attacks (side-channel attacks, neural backdoor, etc.). This dissertation proposes solutions to maintain the cyber-hygiene of the DL-Based smartphone system by exploring possible vulnerabilities and developing countermeasures.

First, I actively explore possible vulnerabilities on the DL-Based smartphone system to develop proactive defense mechanisms. I discover a new side-channel attack on smartphones using the unrestricted magnetic sensor data. I demonstrate that attackers can …

Deep Learning For Remote Sensing Image Processing, Yan Lu

Computational Modeling & Simulation Engineering Theses & Dissertations

Remote sensing images have many applications such as ground object detection, environmental change monitoring, urban growth monitoring and natural disaster damage assessment. As of 2019, there were roughly 700 satellites listing “earth observation” as their primary application. Both spatial and temporal resolutions of satellite images have improved consistently in recent years and provided opportunities in resolving fine details on the Earth's surface. In the past decade, deep learning techniques have revolutionized many applications in the field of computer vision but have not fully been explored in remote sensing image processing. In this dissertation, several state-of-the-art deep learning models have been …

Applying Deep Learning For Cell Detection In Time-Lapse Microscopic Images, Jay Patel

Honors Theses

The budding yeast Saccharomyces cerevisiae is an effective model for studying cellular aging. We can measure the lifespan of yeast cells in two ways: replicative and chronological lifespans. Chronological focuses on the time that a cell can survive. The replicative lifespan (RLS) is the number of cell divisions that a single mother cell can go through before ceases to be dividing. RLS is a measurement of individual cells and is more informative on the aging process than in chronological lifespan. Many genes that influence yeast RLS have been shown to be highly conserved and have a similar effect on aging …

Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder, Gordan Meisam

Student Works (2020-2029)

One of the approaches for structural health monitoring (SHM) consists of two major components, i.e. a network of sensors to collect the response data and an extraction method to obtain information on the structural health condition. Data mining (DM) is a novel data extraction technology which can employ for development of inverse analysis. Implementation of DM techniques in different areas of civil engineering has recently given very good results. However, application of DM in SHM is not used as much as expected, thus, many challenges are still ahead. Therefore, it is necessary to develop the applicability of DM in SHM. …

Feature Pyramid Transformer, Dong Zhang, Hanwang Zhang, Jinhui Tang, Meng Wang, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

Feature interactions across space and scales underpin modern visual recognition systems because they introduce beneficial visual contexts. Conventionally, spatial contexts are passively hidden in the CNN’s increasing receptive fields or actively encoded by non-local convolution. Yet, the non-local spatial interactions are not across scales, and thus they fail to capture the non-local contexts of objects (or parts) residing in different scales. To this end, we propose a fully active feature interaction across both space and scales, called Feature Pyramid Transformer (FPT). It transforms any feature pyramid into another feature pyramid of the same size but with richer contexts, by using …

An Ensemble Of Epoch-Wise Empirical Bayes For Few-Shot Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. “Epoch-wise'' means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. ”Empirical'' means that the hyperparameters, e.g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent …

An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how …

Intersentiment: Combining Deep Neural Models On Interaction And Sentiment For Review Rating Prediction, Shi Feng, Kaisong Song, Daling Wang, Wei Gao, Yifei Zhang

Research Collection School Of Computing and Information Systems

Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and product-level features, and fails to capture sufficient interactions between them represented typically in a sparse matrix as CF can do. In this paper, we propose a novel, extensible review rating prediction model called InterSentiment by bridging the user-product interaction model and the sentiment model …

Commanding And Re-Dictation: Developing Eyes-Free Voice-Based Interaction For Editing Dictated Text, Debjyoti Ghosh, Can Liu, Shengdong Zhao, Kotaro Hara

Research Collection School Of Computing and Information Systems

Existing voice-based interfaces have limited support for text editing, especially when seeing the text is difficult, e.g., while walking or cooking. This research develops voice interaction techniques for eyes-free text editing. First, with a Wizard-of-Oz study, we identified two primary user strategies: using commands, e.g., “replace go with goes” and re-dictating over an erroneous portion, e.g., correcting “he go there” by saying “he goes there.” To support these user strategies with an actual system implementation, we developed two eyes-free voice interaction techniques, Commanding and Re-dictation, and evaluated them with a controlled experiment. Results showed that while Re-dictation performs significantly better …

An Analysis Of Sketched Irls For Accelerated Sparse Residual Regression, Daichi Iwata, Michael Waechter, Wen-Yan Lin, Yasuyuki Matsushita

Research Collection School Of Computing and Information Systems

This paper studies the problem of sparse residual regression, i.e., learning a linear model using a norm that favors solutions in which the residuals are sparsely distributed. This is a common problem in a wide range of computer vision applications where a linear system has a lot more equations than unknowns and we wish to find the maximum feasible set of equations by discarding unreliable ones. We show that one of the most popular solution methods, iteratively reweighted least squares (IRLS), can be significantly accelerated by the use of matrix sketching. We analyze the convergence behavior of the proposed method …

Waste Cooking Oil Classification Using Artificial Intelligence Technology, Kar Sin Lau

Student Works (2020-2029)

Palm oil – one of the most common edible oil consumed in Malaysia. It is because Malaysia is one of the countries which supply palm oil to the global market and it is cheap to obtain for the consumer in Malaysia. Most of the Malaysian consume it via food preparation such as deep-frying and cooking. However, due to widely available for Malaysians, consumers also lacking awareness in dealing after using the edible oil. Most of the household consumers discard excess waste cooking oil (WCO) into sewage and with courtesy, some of them stored them in containers and sell to NGOs. …

Application Of Artificial Intelligence And Geographic Information System For Developing Automated Walkability Score, Md Mehedi Hasan

Dissertations

Walking is considered as one of the major modes of active transportation, which contributes to the livability of cities. It is highly important to ensure walk friendly sidewalks to promote human physical activities along roads. Over the last two decades, different walk scores were estimated in respect to walkability measures by applying different methods and approaches. However, in the era of big data and machine learning revolution, there is still a gap to measure the composite walkability score in an automated way by applying and quantifying the activityfriendliness of walkable streets. In this study, a street-level automated walkability score was …

Machine Learning Enhanced Free-Space And Underwater Oam Optical Communications, Patrick L. Neary

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Communications, bandwidth, security, and hardware simplicity are principles of interest to society at large. Recent advances in optics and in understanding properties of light, such as orbital angular momentum (OAM), have provided new potential mediums for communication.

Machine learning has wound its way into a broad range of fascinating areas. An emerging field of research is the use of a unique property of lasers called orbital angular momentum (OAM). With the proper hardware, a laser can go from a Gaussian shaped distribution to a doughnut shaped pattern, where the radius can be changed. Multiple OAM patterns, or modes, can be …

An Investigation Into Multi-View Error Correcting Output Code Classifiers Applied To Organ Tissue Classification, Daniel Alvarez

UNLV Theses, Dissertations, Professional Papers, and Capstones

Large amounts of data is being generated constantly each day, so much data that it is difficult to find patterns in order to predict outcomes and make decisions for both humans and machines alike. It would be useful if this data could be simplified using machine learning techniques. For example, biological cell identity is dependent on many factors tied to genetic processes. Such factors include proteins, gene transcription, and gene methylation. Each of these factors are highly complex mechanism with immense amounts of data. Simplifying these can then be helpful in finding patterns in them. Error-Correcting Output Codes (ECOC) does …

Action Real-Time Strategy Gaming Experience Related To Enhanced Capacity Of Visual Working Memory, Yutong Yao, Ruifang Cui, Yi Li, Lu Zeng, Jinliang Jiang, Nan Qiu, Li Dong, Diakun Gong, Guojian Yan, Weiyi Ma, Tiejun Liu

General Human Environmental Sciences Faculty Publications and Presentations

Action real-time strategy gaming (ARSG)—a major genre of action video gaming (AVG)—has both action and strategy elements. ARSG requires attention, visual working memory (VWM), sensorimotor skills, team cooperation, and strategy-making abilities, thus offering promising insights into the learning-induced plasticity. However, it is yet unknown whether the ARSG experience is related to the development of VWM capacity. Using both behavioral and event-related potential (ERP) measurements, this study tested whether ARSG experts had larger VWM capacity than non-experts in a change detection task. The behavioral results showed that ARSG experts had higher accuracy and larger VWM capacity than non-experts. In addition, the …

Predictive Insights For Improving The Resilience Of Global Food Security Using Artificial Intelligence, Meng Leong How, Yong Jiet Chan, Sin Mei Cheah

Research Collection Lee Kong Chian School Of Business

Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, …

A Multicut Outer-Approximation Approach For Competitive Facility Location Under Random Utilities, Tien Mai, Andrea Lodi

Research Collection School Of Computing and Information Systems

This work concerns the maximum capture facility location problem with random utilities, i.e., the problem of seeking to locate new facilities in a competitive market such that the captured demand of users is maximized, assuming that each individual chooses among all available facilities according to a random utility maximization model. The main challenge lies in the nonlinearity of the objective function. Motivated by the convexity and separable structure of such an objective function, we propose an enhanced implementation of the outer approximation scheme. Our algorithm works in a cutting plane fashion and allows to separate the objective function into a …