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2022

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Articles 31 - 60 of 3613

Full-Text Articles in Computer Sciences

Research On Iterative Calculation And Optimization Methods Of Aero-Engine On-Board Model, Xinghua Luo, Jia Geng, Ming Li, Bei Liu, Lei Wang, Zhiping Song Dec 2022

Research On Iterative Calculation And Optimization Methods Of Aero-Engine On-Board Model, Xinghua Luo, Jia Geng, Ming Li, Bei Liu, Lei Wang, Zhiping Song

Journal of System Simulation

Abstract: Aeroengine is a complex and time-varying multivariable thermophysical system. The research on the convergence accuracy and rate of the component-level model is of great significance to the model-based engine health management, performance and fault-tolerant control. The existing engine component-level models are generally based on the traditional quasi-Newton method to solve the equilibrium equations simultaneously. Compared with the traditional Newton-Raphson method (N-R method), the convergence speed is optimized, but it is difficult to meet accuracy and real-time requirements of the dynamic model airborne applications within the full envelope. An adaptive variable step factor quasi-Newton method is proposed, which can reduce …

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Reliability Parameter Optimization Complex System Simulation Based On R-Vikor Method, Zhiguang Wang, Baiting Liu, Xiaolei Wang, Tao Liu, Zhaowei Yang Dec 2022

Reliability Parameter Optimization Complex System Simulation Based On R-Vikor Method, Zhiguang Wang, Baiting Liu, Xiaolei Wang, Tao Liu, Zhaowei Yang

Journal of System Simulation

Abstract: The operation of complex simulation system is not isolated, and always affected by multiple external factors and its own performance. In simulation reliability calculation, parameters need to be chosen, and different performance indexes need to be taken into account when comparing with the reference model. The research is transformed into the multi-attribute decision. An system simulation trusted parameter optimization method based on R-VIKOR(resist rank reversal of visekriterijumska optimizacija | kompromisno resenje) is proposed. Multiple sets of aerodynamic parameters of the new model are obtained through model migration theory, and similarity calculation is carried out with the optimal parameters of …

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Simulation Research On Appearance Detection Of Ampoules Based On Lightweight Network And Model Compression, Zhihao Zhu, Yan Wang, Zhicheng Ji Dec 2022

Simulation Research On Appearance Detection Of Ampoules Based On Lightweight Network And Model Compression, Zhihao Zhu, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the large scale and redundant parameters of target detection network model, which result in the difficult to deploy the ampoule bottle appearance defect detection model to edge devices, an LC-Faster R-CNN defect detection algorithm based on lightweight network and model compression is proposed. MobileNet-V2 is used as the backbone, and the redundant channels in the convolutional network are trimmed by model pruning strategy. The floating-point parameters are quantized into integers through saturation truncation mapping. Knowledge distillation is used to restore the accuracy of the compressed network. Tested on the self-built ampoule appearance defect dataset, the model volume …

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Long-Term Resilience Simulation On Low-Carbon Urban Grid Based On Evolutionary Game, Zhengda Cui, Weiqiang Yao, Qin Xu, Chen Fang, Ying Chen Dec 2022

Long-Term Resilience Simulation On Low-Carbon Urban Grid Based On Evolutionary Game, Zhengda Cui, Weiqiang Yao, Qin Xu, Chen Fang, Ying Chen

Journal of System Simulation

Abstract: Because of the more frequent extreme disasters, the resilience of urban grid becomes more important. In the background of carbon neutralization policy, the decarbonization transition of urban grid also affects the development of grid resilience. An evolutionary game is used to simulate the resilience evolution of low-carbon urban grid and the evolution model is constructed. The decision to install photovoltaic and energy storage system for residents and to upgrade the grid for resilience is considered in the model and the stability conditions of equilibriums of the evolutionary game are analyzed. The resilience evolution simulation model considering disaster stochasticity is …

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Research On Low Computational Predictive Control Strategy Of Three-Level Four-Arm Apf, Guifeng Wang, Jinxing Guo Dec 2022

Research On Low Computational Predictive Control Strategy Of Three-Level Four-Arm Apf, Guifeng Wang, Jinxing Guo

Journal of System Simulation

Abstract: Four-arm active power filter(APF) is an ideal device to the power quality of three-phase four-wire distribution network. Owing to the sufficient number of base vectors, three-level four-arm APF has better current tracking performance, but the prediction calculation amount is too large when model predictive current control is applied. A model prediction voltage control strategy based on the idea of space stratification is proposed. Based on the deadbeat control idea and the discrete mathematical model in αβγ coordinate system, the expected reference voltage is predicted from the reference current, and the multiple current predictions are converted into the single voltage …

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Research On Emotional Contagion And Intervention Strategy Of Indoor Evacuation Based On Risk Perception, Yang Zeng, Jinling Li, Haixiang Guo, Weiming Chen Dec 2022

Research On Emotional Contagion And Intervention Strategy Of Indoor Evacuation Based On Risk Perception, Yang Zeng, Jinling Li, Haixiang Guo, Weiming Chen

Journal of System Simulation

Abstract: Aiming at the panic emotion contagion in the indoor emergency evacuation with multi-exit and multi-obstacle, an emotional contagion model is constructed on personality traits, risk perception differences of age and gender, and consciousness regulation. The simulation is carried out by using AnyLogic, which combines individual emotions with evacuation speed to realize the real-time updating of emotional state and speed. That personnel intervention in the process of evacuation can effectively alleviate the spread of panic emotion, is verified and can provide the theoretical basis for panic contagion in the process of emergency evacuation. The results show that the degree of …

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Machine Learning And Protein Allostery, Sian Xiao, Gennady M. Verkhivker, Peng Tao Dec 2022

Machine Learning And Protein Allostery, Sian Xiao, Gennady M. Verkhivker, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric …

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Big Data Technology Enabling Legal Supervision, Qingjie Liu, Shuo Liu, Yirong Wu, Yueqiang Weng, Yihao Wen, Ming Li Dec 2022

Big Data Technology Enabling Legal Supervision, Qingjie Liu, Shuo Liu, Yirong Wu, Yueqiang Weng, Yihao Wen, Ming Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

Legal supervision plays an important role in the national governance system and capacity. In the era of digital revolution, the rapid development of digital procuratorial work with big data legal supervision as the core promotes to reshape the legal supervision and governance system. In this study, the inherent need of legal supervision for active prosecution in the new era, and the innovative role of new public interest litigation in comprehensive social governance, are firstly analyzed. Then, the core meaning and reshaping role of big-data-enabling-legalsupervision and supervision-promoting-national-governance of digital prosecution are discussed. After summarizing the practical experiences and challenges of big …

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Challenges And Measurements For Governance Of Modern Cyber Space Society, Pinghui Wang, Hongbin Pei, Junzhou Zhao, Tao Qin, Chao Shen, Dongliang Liu, Xiaohong Guan Dec 2022

Challenges And Measurements For Governance Of Modern Cyber Space Society, Pinghui Wang, Hongbin Pei, Junzhou Zhao, Tao Qin, Chao Shen, Dongliang Liu, Xiaohong Guan

Bulletin of Chinese Academy of Sciences (Chinese Version)

The rapid development of information technology has unprecedentedly created a prosperous cyber society and greatly enhanced productivity facilitated by social interaction. At the same time, many problems emerge in the cyber society, such as telecom fraud, privacy leakage, Internet pollution, and algorithmic discrimination. The problems bring new challenges to social order and security. In order to find the way of cyber society governance and promote the modernization of national governance, this paper first presents the analyses on the new problems encountered in the cyber society in three typical scenarios, i.e., identity governance, behavior governance, and algorithm governance, as well as …

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Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander Dec 2022

Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander

School of Business: Faculty Publications and Other Works

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, …

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Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn Dec 2022

Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn

Masters Projects

This project applied software specification gathering, architecture, work planning, and development to a real-world development effort for a local business. This project began with a feasibility meeting with the owner of Zeal Aerial Fitness. After feasibility was assessed the intended users, needed functionality, and expected user restrictions were identified with the stakeholders. A hybrid software lifecycle was selected to allow a focus on base functionality up front followed by an iterative development of expectations of the stakeholders. I was able to create various specification diagrams that express the end projects goals to both developers and non-tech individuals using a standard …

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Defining Traffic Scenarios For The Visually Impaired, Judith Jakob, Kordula Kugele, József Tick Dec 2022

Defining Traffic Scenarios For The Visually Impaired, Judith Jakob, Kordula Kugele, József Tick

The Qualitative Report

For the development of a transfer concept of camera-based object detections from Advanced Driver Assistance Systems to the assistance of the visually impaired, we define relevant traffic scenarios and vision use cases by means of problem-centered interviews with four experts and ten members of the target group. We identify the six traffic scenarios: general orientation, navigating to an address, crossing a road, obstacle avoidance, boarding a bus, and at the train station clustered into the three categories: Orientation, Pedestrian, and Public Transport. Based on the data, we describe each traffic scenario and derive a summarizing table adapted from software engineering …

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A Hybrid Artificial Intelligence Model For Detecting Keratoconus, Zaid Abdi Alkareem Alyasseri, Ali H. Al-Timemy, Ammar Kamal Abasi, Alexandru Lavric, Husam Jasim Mohammed, Hidenori Takahashi, Jose Arthur Milhomens Filho, Mauro Campos, Rossen M. Hazarbassanov, Siamak Yousefi Dec 2022

A Hybrid Artificial Intelligence Model For Detecting Keratoconus, Zaid Abdi Alkareem Alyasseri, Ali H. Al-Timemy, Ammar Kamal Abasi, Alexandru Lavric, Husam Jasim Mohammed, Hidenori Takahashi, Jose Arthur Milhomens Filho, Mauro Campos, Rossen M. Hazarbassanov, Siamak Yousefi

Machine Learning Faculty Publications

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity …

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Lens: A Novel Image Processing-Based Approach For Low-Cost Segmentation Of Computed Tomography Scans Of Lungs, Monica Hinga Dec 2022

Lens: A Novel Image Processing-Based Approach For Low-Cost Segmentation Of Computed Tomography Scans Of Lungs, Monica Hinga

Computer Science ETDs

A staggering amount of data has been collected and analyzed since the beginning of the COVID-19 pandemic. Some of that data, however, particularly computed tomography (CT) scans of lungs, are difficult to analyze computationally. Segmentation of COVID-19 lesions provides researchers with insights into where lesions form, their volume, and in the case of time course data, how lesions grow as disease progresses. Spatial information, which has received little attention in the literature, has the potential to provide valuable insight to within-host viral and immune dynamics. Unfortunately, state-of-the-art supervised deep learning methods require labeled data, which can be prohibitively expensive to …

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Two Studies On Binary Program Stylometry: A White-Box Analysis And A Deep Learning Analysis On Some Crypto Ransomware, Alexander Mitchell Dec 2022

Two Studies On Binary Program Stylometry: A White-Box Analysis And A Deep Learning Analysis On Some Crypto Ransomware, Alexander Mitchell

Masters Theses, 2020-current

Computer programmers often leave their individual programming styles in source code. Recent studies show that contrary to a popular belief, many of such programming styles can survive, in controlled environments, code compilation into binary. From the binary programming styles can be effectively retrieved for enhanced binary authorship attribution; such binary authorship attribution is often called binary program stylometry. In this thesis, we first perform a white-box impact analysis of various factors in code compilation on programming styles. For the MS Windows platform, we study the impact of multiple compilers, including gcc, Clang, and MSVC, their optimization levels, symbol stripping, and …

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Interpretable Network Representations, Shengmin Jin Dec 2022

Interpretable Network Representations, Shengmin Jin

Dissertations - ALL

Networks (or interchangeably graphs) have been ubiquitous across the globe and within science and engineering: social networks, collaboration networks, protein-protein interaction networks, infrastructure networks, among many others. Machine learning on graphs, especially network representation learning, has shown remarkable performance in network-based applications, such as node/graph classification, graph clustering, and link prediction. Like performance, it is equally crucial for individuals to understand the behavior of machine learning models and be able to explain how these models arrive at a certain decision. Such needs have motivated many studies on interpretability in machine learning. For example, for social network analysis, we may need …

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Utilizing Remote Sensing Technology To Relocate Lubra Village And Visualize Flood Damages, Ronan Wallace Dec 2022

Utilizing Remote Sensing Technology To Relocate Lubra Village And Visualize Flood Damages, Ronan Wallace

Mathematics, Statistics, and Computer Science Honors Projects

As weather patterns change worldwide, isolated communities impacted by climate change go unnoticed and we need community and habitat-conscious solutions. In Himalayan Mustang, Nepal, indigenous Lubra village faces threats of increasing flash flooding. After every flood, residual concrete-like sediment hardens across the riverbed, causing the riverbed elevation to rise. As elevation increases, sediment encroaches on Lubra’s agricultural fields and homes, magnifying flood vulnerability. In the last monsoon season alone, the village witnessed floods swallowing several fields and damaging two homes. One solution considers relocating the village to a new location entirely. However, relocation poses a challenging task, as eight centuries …

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Low-Resource Machine Learning Techniques For The Analysis Of Online Social Media Textual Data, Toktam Amanzadeh Oghaz Dec 2022

Low-Resource Machine Learning Techniques For The Analysis Of Online Social Media Textual Data, Toktam Amanzadeh Oghaz

Electronic Theses and Dissertations, 2020-2023

Low-resource and label-efficient machine learning methods can be described as the family of statistical and machine learning techniques that can achieve high performance without needing a substantial amount of labeled data. These methods include both unsupervised learning techniques, such as LDA, and supervised methods, such as active learning, each providing different benefits. Thus, this dissertation is devoted to the design and analysis of unsupervised and supervised techniques to provide solutions for the following problems: Unsupervised narrative summary extraction for social media content, Social media text classification with Active Learning (AL), Investigating restrictions and benefits of using Curriculum Learning (CL) for …

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Is Cybersecurity Training Practical Or Not?, Bhawnish Sharma Dec 2022

Is Cybersecurity Training Practical Or Not?, Bhawnish Sharma

Cybersecurity Undergraduate Research Showcase

With technology growing, there has been an increase in cybercrime. Because of this, private and public sectors face global problems, i.e., phishing, security breaches, and identity theft. With cybersecurity software available on the internet, anyone can access it. As technology advances, cybersecurity experts must answer the tough question of, “is cybersecurity training practical or not”?

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Travel Dashboard, Naveen Kumar Lalam Dec 2022

Travel Dashboard, Naveen Kumar Lalam

Masters Projects

Travel Dashboard is a one stop solution for all the travel needs of travelers and tourists visiting a new place. In today’s world travel has become a part of everyone’s life and we love to travel whenever there is a holiday or long a weekend. Earlier, the travel industry was mostly dictated by tour operators who used to plan and organize tours with standard itinerary, while tourists had very limited choices and needed to pick one of the itineraries given by operator as there was no other option left for them. Time have changed now as travelers love to plan …

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Muse: A Genetic Algorithm For Musical Chord Progression Generation, Griffin Going Dec 2022

Muse: A Genetic Algorithm For Musical Chord Progression Generation, Griffin Going

Masters Projects

Foundational to our understanding and enjoyment of music is the intersection of harmony and movement. This intersection manifests as chord progressions which themselves underscore the rhythm and melody of a piece. In musical compositions, these progressions often follow a set of rules and patterns which are themselves frequently broken for the sake of novelty. In this work, we developed a genetic algorithm which learns these rules and patterns (and how to break them) from a dataset of 890 songs from various periods of the Billboard Top 100 rankings. The algorithm learned to generate increasingly valid, yet interesting chord progressions via …

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Building A Deep Model For Multi-Class Coral Species Discrimination, Hyeong Gyu Jang Dec 2022

Building A Deep Model For Multi-Class Coral Species Discrimination, Hyeong Gyu Jang

Masters Projects

The goal of this qualitative research project is to develop and optimize a multi-class discrimination model to identify different species of coral based on their digital images. Currently, there are artificial intelligence (AI) models that can distinguish between coral and other undersea objects such as sand or rocks, but to our knowledge the problem of multi-species classification has not yet been addressed. Given that coral reefs are a good indicator of overall ocean health, it is important to develop models that can classify the presence of different species in underwater images as a way to monitor the effects of climate …

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Advanced Deep Learning Methodologies For Deepfake Detection, Aminollah Khormali Dec 2022

Advanced Deep Learning Methodologies For Deepfake Detection, Aminollah Khormali

Electronic Theses and Dissertations, 2020-2023

The recent advances in the field of Artificial Intelligence (AI), particularly Generative Adversarial Networks (GANs) and an abundance of training samples along with robust computational resources have significantly propelled the field of AI-generated fake information in all kinds, e.g., deepfakes. Deepfakes are among the most sinister types of misinformation, posing large-scale and severe security and privacy risks targeting critical governmental institutions and ordinary people across the world. The fact that deepfakes are AI-generated digital content and not actual events captured by a camera implies that they still can be detected using advanced AI models. Although the deepfake detection task has …

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Torward Real-World Cross-View Image Geo-Localization, Sijie Zhu Dec 2022

Torward Real-World Cross-View Image Geo-Localization, Sijie Zhu

Electronic Theses and Dissertations, 2020-2023

Cross-view image geo-localization aims to determine the locations of street-view query images by searching in a GPS-tagged reference image database from aerial view. One fundamental challenge is the dramatic view-point/domain difference between the street-view query images and aerial-view reference images. Recent works have made great progress on bridging the domain gap with advanced deep learning techniques and geometric prior knowledge, i.e. the query is aligned at the center of one aerial-view reference image (spatial alignment) and the orientation relationship between the two views is known (orientation alignment). However, such prior knowledge of the geometry correspondence of the two views is …

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Big Data Analytics Of Medical Data, Ashwin Rajasankar Dec 2022

Big Data Analytics Of Medical Data, Ashwin Rajasankar

Masters Projects

Data has become a huge part of modern decision making. With the improvements in computing performance and storage in the past two decades, storing large amounts of data has become much easier. Analyzing large amounts of data and creating data models with them can help organizations obtain insights and information which helps their decision making. Big data analytics has become an integral part of many fields such as retail, real estate, education, and medicine. In the project, the goal is to understand the working of Apache Spark and its different storage methods and create a data warehouse to analyze data. …

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Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn Dec 2022

Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn

Masters Projects

This project applied software specification gathering, architecture, work planning, and development to a real-world development effort for a local business. This project began with a feasibility meeting with the owner of Zeal Aerial Fitness. After feasibility was assessed the intended users, needed functionality, and expected user restrictions were identified with the stakeholders. A hybrid software lifecycle was selected to allow a focus on base functionality up front followed by an iterative development of expectations of the stakeholders. I was able to create various specification diagrams that express the end projects goals to both developers and non-tech individuals using a standard …

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Exploring Coral Reefs With Interactive Geospatial Visualizations, David Nicolas Tonning Dec 2022

Exploring Coral Reefs With Interactive Geospatial Visualizations, David Nicolas Tonning

Masters Projects

This project uses geospatial data to generate custom polygons in an interactive setting to represent the size and location of coral reefs to extract insights from coral reef-centered data sets. Historically, the data used by the Reef Restoration Group Bonaire exists in disparate sources, making it difficult to track and analyze the outcomes of their restoration work. Additionally, this information is not available in a digestible format for other audiences who would be interested in this data, such as citizen scientists seeking coral reef health statistics, the general public wanting to better understand the coral reefs surrounding Bonaire or recreational …

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Devops: Course Development, James Lee Vanderzouwen Dec 2022

Devops: Course Development, James Lee Vanderzouwen

Masters Projects

DevOps has become somewhat of a buzzword amongst software engineers in the industry. Often developers do not have a dedicated DevOps engineer let alone a DevOps team. Developers benefit when they know what happens between ‘works on my machine’ and production. Making sure those steps make sense and are safe benefits the operations team. From compliance to code review to regression testing, understanding the full SDLC, employing DevOps concepts, and minimizing overhead from dependencies is quickly becoming a pre-requisite for the modern software engineer. This project attempts to bridge the gap between buzzword and best practice by developing a college-level …

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Notebooks Web Application, Vivekananda Marellali Dec 2022

Notebooks Web Application, Vivekananda Marellali

Masters Projects

In our day-to-day activities we often need to make quick short notes related to our work, study, or other activities. Later, those notes should be easy to access, modify, delete, share with others, or create PDF documents as and when required. It is ideal if the note content is rich text format such as HTML, as it provides many text formatting options and provide better view and readability. The Notes are better organized if they are grouped into notebooks where each notebook consists of related topic note pages and multiple users can create such notebooks and share with other users …

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Federated Learning And Applications In Cybersecurity, Ani Sreekumar Dec 2022

Federated Learning And Applications In Cybersecurity, Ani Sreekumar

Cybersecurity Undergraduate Research Showcase

Machine learning is a subfield of artificial intelligence that focuses on making predictions about some outcome based on information from a dataset. In cybersecurity, machine learning is often used to improve intrusion detection systems and identify trends in data that could indicate an oncoming cyber attack. Data privacy is an extremely important aspect of cybersecurity, and there are many industries that have more demanding laws to ensure the security of user data. Due to these regulations, machine learning algorithms can not be widely utilized in these industries to improve outcomes and accuracy of predictions. However, federated learning is a recent …

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