Computer Sciences Commons^™

Variable Pitch Control Of Wind Power Generation System Based On Wiener Model, Yue Xu, Li Jia, Xuanyi Fu

Journal of System Simulation

Abstract: Strong nonlinearity and large fluctuation are the characteristics of wind power generation system. Quickly controlling the output power of wind turbines within the rated range under wind speed random changes is the major problem of wind power system control. Aiming at the pitch control of 5 MW wind turbines, a pitch control scheme for wind power generation systems based on the Wiener model is proposed. On the basis of the special structure in which the linear and nonlinear links of the Wiener model can be separated, the controlled object of a generalized wind power system with linear properties …

Parallel Live Performance Simulation Based On A Multidimensional Hierarchy And Application, Jingsi Yang, Tianyu Huang, Gangyi Ding, Lijie Li, Peng Li

Journal of System Simulation

Abstract: A parallel simulation method is proposed for modern live performance. By decomposing the live performance process from the top down, this method assists creators in delivering stage design and control with time and space constraints, which is unattainable for traditional live performances. A multi-layer constraint hierarchy is constructed to apply parallel simulation to art performances. The live performance procedure is continuously optimized by leveraging the circulation of data between virtual and physical stages. The parallel simulation method for stage space has been applied to a digital TV stage for ten years. The experiments show that parallel live performance simulation …

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 …

Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data, Hossein Mohammadian Foroushani

McKelvey School of Engineering Graduate Student Theses & Dissertations

Biological understanding of complex diseases such as stroke and obesity is critical for the advancement of medicine. Further knowledge discovery can provide effective biomarkers to improve disease diagnosis and prognosis, identify driver mutations, predict individual genetic susceptibility for early prevention and effective disease management, and facilitate development of personalized drugs. Stroke is the second leading cause of death and long-term disability in the world. Thus, stroke management is a time-sensitive emergency. The initial hours after stroke onset map the trajectory of subsequent neurologic complications. Cerebral edema develops hours to days after acute ischemic stroke and may result in midline shift …

Augmented Creativity: Leveraging Natural Language Processing For Creative Writing, Daniel Plate, James Hutson

Faculty Scholarship

Recent advances have moved natural language processing (NLP) capabilities with artificial intelligence beyond mere grammar and spell-checking functionality. One such new use that has arisen is the ability to suggest new content to writers to inspire new ideas by using “machine-in-the-loop” strategies in creative writing. In order to explore the possibilities of such a strategy, this study provides a model to be adopted in creative writing courses in higher education. An NLP application was created using Python and spaCy and deployed via Streamlit. The AI allowed students to see if their grammar aligned with those principles and techniques taught in …

Smart Sensing And Clinical Predictions With Wearables: From Physiological Signals To Mental Health, Ruixuan Dai

McKelvey School of Engineering Graduate Student Theses & Dissertations

Wearable devices such as smartwatches and wristbands are gaining adoption. Recent advances in technology in wearables enable remote health monitoring. However, there are challenges in exploiting wearables in healthcare applications. First, sensor readings from wearables are vulnerable to motion and noise artifacts. A robust pipeline is needed to extract reliable measurements from noisy signals. Second, while wearables support an increasing number of sensing modalities, there is a significant need to generate more clinically meaningful measurements with wearables. Finally, to incorporate wearables into clinical practice, we need to establish the link between wearable measurements and clinical outcomes, thus supporting clinical decisions. …

Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …

Secure Human Action Recognition By Encrypted Neural Network Inference, Miran Kim, Xiaoqian Jiang, Kristin Lauter, Elkhan Ismayilzada, Shayan Shams

Faculty, Staff and Student Publications

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average …

Human-Centered Machine Learning: Algorithm Design And Human Behavior, Wei Tang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Machine learning is increasingly engaged in a large number of important daily decisions and has great potential to reshape various sectors of our modern society. To fully realize this potential, it is important to understand the role that humans play in the design of machine learning algorithms and investigate the impacts of the algorithm on humans.

Towards the understanding of such interactions between humans and algorithms, this dissertation takes a human-centric perspective and focuses on investigating the interplay between human behavior and algorithm design. Accounting for the roles of humans in algorithm design creates unique challenges. For example, humans might …

Design And Analysis Of Strategic Behavior In Networks, Sixie Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Networks permeate every aspect of our social and professional life.A networked system with strategic individuals can represent a variety of real-world scenarios with socioeconomic origins. In such a system, the individuals' utilities are interdependent---one individual's decision influences the decisions of others and vice versa. In order to gain insights into the system, the highly complicated interactions necessitate some level of abstraction. To capture the otherwise complex interactions, I use a game theoretic model called Networked Public Goods (NPG) game. I develop a computational framework based on NPGs to understand strategic individuals' behavior in networked systems. The framework consists of three …

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 …

Asian Hate Speech Detection On Twitter During Covid-19, Amir Toliyat, Sarah Ita Levitan, Zeng Peng, Ronak Etemadpour

Publications and Research

Coronavirus disease 2019 (COVID-19) started in Wuhan, China, in late 2019, and after being utterly contagious in Asian countries, it rapidly spread to other countries. This disease caused governments worldwide to declare a public health crisis with severe measures taken to reduce the speed of the spread of the disease. This pandemic affected the lives of millions of people. Many citizens that lost their loved ones and jobs experienced a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, and panic. All of the aforementioned phenomena led to the spread of racism and …

Tfa Inference: Using Mathematical Modeling Of Gene Expression Data To Infer The Activity Of Transcription Factors, Cynthia Ma

McKelvey School of Engineering Graduate Student Theses & Dissertations

Transcription factors (TFs) are a set of proteins that play a key role in the information processing system that enables a cell to respond to changes in internal and external state. By binding near a gene in a cell’s DNA, a TF can influence that gene’s expression level, triggering the appropriate increase or decrease in production levels of proteins that are needed to handle stressors like a change in nutrient availability or damage to the cell’s internal structures. Transcription factor activity (TFA) is a measure of how much effect a TF has on its target genes in a given sample …

Scheduling For High Throughput And Small Latency In Parallel And Distributed Systems, Zhe Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Parallel and distributed systems are pervasive, such as web services, clouds, and cyber-physical systems. We often desire high throughput and small latency in the parallel and distributed system. However, since the system is distributed and the input is online, scheduling for high throughput while keeping the latency small is often challenging. In this dissertation, we developed scheduling algorithms, policies, and mechanisms to approach high throughput with small latency in various parallel and distributed applications. First, we developed AMCilk runtime system for running multi-programmed parallel jobs on many-processor machines. When running parallel jobs, the allocation of processors to the parallel jobs …

Dynamic Continuous Distributed Constraint Optimization Problems, Khoi Hoang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model multi-agent coordination problems that are distributed by nature. The formulation is suitable for problems where the environment does not change over time and where agents seek their value assignment from a discrete domain. However, in many real-world applications, agents often interact in a more dynamic environment and their variables usually require a more complex domain. Thus, the DCOP formulation lacks the capabilities to model the problems in such dynamic and complex environments. To address these limitations, researchers have proposed Dynamic DCOPs (D-DCOPs) to model how DCOPs dynamically …

Geometric Algorithms For Modeling Plant Roots From Images, Dan Zeng

McKelvey School of Engineering Graduate Student Theses & Dissertations

Roots, considered as the ”hidden half of the plant”, are essential to a plant’s health and pro- ductivity. Understanding root architecture has the potential to enhance efforts towards im- proving crop yield. In this dissertation we develop geometric approaches to non-destructively characterize the full architecture of the root system from 3D imaging while making com- putational advances in topological optimization. First, we develop a global optimization algorithm to remove topological noise, with applications in both root imaging and com- puter graphics. Second, we use our topology simplification algorithm, other methods from computer graphics, and customized algorithms to develop a high-throughput …

Integrating Physical Models And Deep Priors For Computational Imaging, Yu Sun

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation addresses integrating physical models and learning priors for computational imaging. The motivation of our work is driven by the recent discussion of learning-based methods that solve the imaging inverse problem by directly learning a measurement-to-image mapping from the existing data: they achieve superior performance over the traditional model-based methods but lack the physical model to impose sufficient interpretation and guarantee of the final image. We adopt the classic statistical inference as the underlying formulation and integrate learning models as implicit image priors, such that our framework is able to simultaneously leverage physical models and learning priors. Additionally, the …

Avist: A Benchmark For Visual Object Tracking In Adverse Visibility, Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha, Christoph Mayer, Hisham Cholakkal, Salman Khan, Luc Van Gool, Fahad Shahbaz Khan

Computer Vision Faculty Publications

One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects. We introduce AVisT, a dedicated benchmark for visual tracking in diverse scenarios with adverse visibility. AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios …