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Articles 1 - 20 of 20
Full-Text Articles in Systems Science
A Hybrid Data Processing, Computational Intelligence, And Complex Systems Modeling Approach For Describing And Predicting The Bitcoin Market, Oluwadamilare Akinpelu Omole
A Hybrid Data Processing, Computational Intelligence, And Complex Systems Modeling Approach For Describing And Predicting The Bitcoin Market, Oluwadamilare Akinpelu Omole
Doctoral Dissertations
"The Bitcoin market, like traditional financial markets, is a complex system with intricate interdependencies and nonlinear interactions among various market elements. This, coupled with the inherent uncertainty and high volatility present, makes predicting Bitcoin price movements difficult. The lack of understanding of the underlying market dynamics often results in significant losses for investors and traders. Existing studies have focused on the use of predictive models, which have not sufficiently captured the complexities of the market and cannot forecast extreme market events. This research endeavors to bridge this gap by combining data processing techniques, computational intelligence, and complex systems theory to …
Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu
Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu
Military Cyber Affairs
Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.
Asl-Catboost Method For Wind Turbine Fault Detection Integrated With Digital Twin, Hongtao Liang, Lingchao Kong, Guozhu Liu, Wenxuan Dong, Xiangyi Liu
Asl-Catboost Method For Wind Turbine Fault Detection Integrated With Digital Twin, Hongtao Liang, Lingchao Kong, Guozhu Liu, Wenxuan Dong, Xiangyi Liu
Journal of System Simulation
Abstract: In view of the low visibility of the current wind farm status monitoring and insufficient realtime operation and maintenance, based on the concept of digital twin five-dimensional model, the framework of wind farm digital twin five-dimensional model is constructed. Aiming at the insufficient fault detection capability of traditional algorithms and unbalanced positive and negative samples in fan fault data set, the improved ASL-CatBoost algorithm is proposed to achieve the accurate detection of fan fault status. Based on the digital twinning platform, combined with MATLAB/Simulink, the simulation mathematical model of doubly-fed wind turbine under the condition of blade mass imbalance …
Construction Of Machine Learning Data Set For Analyzing The Replay Of The Wargaming, Dayong Zhang, Jingyu Yang, Jun Ma, Chenye Song
Construction Of Machine Learning Data Set For Analyzing The Replay Of The Wargaming, Dayong Zhang, Jingyu Yang, Jun Ma, Chenye Song
Journal of System Simulation
Abstract: The first problem to be solved in the application of machine learning to the analysis of the replay of the wargaming is the construction of data sets. Due to the standardization requirements of machine learning for data structure, as well as the limitations of computing power and storage, building a machine learning data set through the wargaming data still faces many problems in terms of how to describe the wargaming situation, how to describe the wargaming process, how to handle high dimensional data, and how to prevent data distortion. To solve these problems, this paper constructs a mapping model …
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Austin T. Walden, Paul J. Thomas
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Austin T. Walden, Paul J. Thomas
Journal of Aviation/Aerospace Education & Research
Increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations: 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) developing an embedded machine learning framework. Data cleanup and …
Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening
Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
In recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI). These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a …
Learning-Based Ant Colony Optimization Algorithm For Solving A Kind Of Complex 2-Echelon Vehicle Routing Problem, Xue Chen, Rong Hu, Hui Wang, Zuocheng Li, Bin Qian, Yixu Li
Learning-Based Ant Colony Optimization Algorithm For Solving A Kind Of Complex 2-Echelon Vehicle Routing Problem, Xue Chen, Rong Hu, Hui Wang, Zuocheng Li, Bin Qian, Yixu Li
Journal of System Simulation
Abstract: Aiming at green 2-echelon vehicle routing problem with simultaneous pick-up and delivery, a learning-based ant colony optimization algorithm combined with clustering decomposition is proposed. The objective function to be minimized is total transportation cost wherein carbon emission cost is specially considered. Associated with the mutual coupling features of the 2-echelon vehicle routing problem, we propose a distance-based clustering method to decompose the original problem into a set of sub-problems. Then, a learning-based ant colony optimization algorithm is presented to find the solutions of the sub-problems based on which the solution of the original problem can be obtained. In the …
Graphical Models In Reconstructability Analysis And Bayesian Networks, Marcus Andrew Harris
Graphical Models In Reconstructability Analysis And Bayesian Networks, Marcus Andrew Harris
Dissertations and Theses
This research focuses on the investigation of two machine learning methodologies, Reconstructability Analysis (RA) and Bayesian Networks (BN). Both methods are probabilistic graphical modeling (PGM) methodologies. RA was developed in the systems community and has applications including time-series analysis, classification, decomposition, compression, pattern recognition, prediction, control, and decision analysis. BNs have origins in path models and have applications similar to those of RA. BNs are another graphical modeling approach for data modeling that is closely related to RA; where BN overlaps RA the two methods are equivalent, but RA and BN each has distinctive features absent in the other methodology. …
Advancing Internet Viewpoint Diversity: A Novel Algorithm And A Corpus Creation Tool, Jeffrey Harwell
Advancing Internet Viewpoint Diversity: A Novel Algorithm And A Corpus Creation Tool, Jeffrey Harwell
CGU Theses & Dissertations
A fundamental requirement for Western democracy is an informed and engaged electorate with access to a wide range of viewpoints. However, concerns have arisen regarding how information technology affects the diversity of viewpoints available. In response to an increasingly polarized society and worries surrounding filter bubbles and algorithmic bias, this research presents a novel tool for constructing internet-based topical corpora and an algorithm tailored for viewpoint detection and the curation of diverse search results.Following a comprehensive exploration of viewpoint diversity through the lenses of mass media, social psychology, and information retrieval, this dissertation presents an approach to operationalize viewpoint diversity …
Behavior Modeling For Computer Generated Forces Based On Machine Learning, Zhang Qi, Junjie Zeng, Xu Kai, Qin Long, Quanjun Yin
Behavior Modeling For Computer Generated Forces Based On Machine Learning, Zhang Qi, Junjie Zeng, Xu Kai, Qin Long, Quanjun Yin
Journal of System Simulation
Abstract: With the rapid development of Machine Learning, especially deep learning, it has become an important way of modeling Computer Generated Force (CGF) behavior by ML methods, which can overcome the challenges of traditional methods. The existing research and application of three typical learning methods in CGF behavior modeling are discussed, and the effects of introducing learning into different stages of the typical CGF applications are analyzed, and the function and performance requirements of CGF behavior modeling using machine learning are proposed. Four potential research directions in the field for future are proposed.
Computational Decision Support For The Covid-19 Healthcare Coalition, Andreas Tolk, Christopher Glazner, Joseph Ungerleider
Computational Decision Support For The Covid-19 Healthcare Coalition, Andreas Tolk, Christopher Glazner, Joseph Ungerleider
VMASC Publications
In the early months of 2020, the SARS-CoV-2 Coronavirus took the world by surprise, resulting in the COVID-19 pandemic that has caused significant loss of lives and challenged the sustainability of our health care systems. In mid-March, it became obvious that government and communities had to react immediately. Under the lead of the Mayo Clinic and The MITRE Corporation, the COVID-19 Healthcare Coalition (C19HCC) was established as a coordinated public-interest, private-sector response. The coalition brought healthcare organizations, technology firms, nonprofits, academia, and startups to support supply chains, inform coordinated social policies, and provide data-driven insights to protect people and preserve …
Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding
Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding
Electrical & Computer Engineering Faculty Publications
Model continuity plays an important role in applications like system identification, adaptive control, and machine learning. This paper provides sufficient conditions under which input-output systems represented by locally convergent Chen-Fliess series are jointly continuous with respect to their generating series and as operators mapping a ball in an Lp-space to a ball in an Lq-space, where p and q are conjugate exponents. The starting point is to introduce a class of topological vector spaces known as Silva spaces to frame the problem and then to employ the concept of a direct limit to describe convergence. The proof of the main …
Embedded Power Optimization Method Based On User Behavior, Wang Hai, Gao Ling, Dongqi Chen, Ren Jie
Embedded Power Optimization Method Based On User Behavior, Wang Hai, Gao Ling, Dongqi Chen, Ren Jie
Journal of System Simulation
Abstract: In recent years, with the rapid development of embedded device represented by mobile phone and tablet computer, low power technology has been one of the hotspots in the embedded research field. Because the battery capacity of embedded device is limited due to its restricted volume and weight, there are often users suffering the problem that their phone battery being dead. There are many research directions in embedded low power field at present. The relationship between low power and user behavior recognition was aimed, which started with recognizing user behavior using machine learning and then obtains the user’s daily usage …
Gep Automatic Clustering Algorithm With Dynamic Penalty Factors, Chen Yan, Kangshun Li, Yang Lei
Gep Automatic Clustering Algorithm With Dynamic Penalty Factors, Chen Yan, Kangshun Li, Yang Lei
Journal of System Simulation
Abstract: Various problems such as sensitive selection of initial clustering center, easily falling into local optimal solution, and determining numbers of clusters, still exist in the traditional clustering algorithm. A GEP automatic clustering algorithm with dynamic penalty factors was proposed. This algorithm combines penalty factors and GEP clustering algorithm, and doesn't rely on any priori knowledge of the data set. And a dynamic algorithm was proposed to generate the penalty factors according to the distribution characteristics of different data sets, which is a better solution for the impact of isolated points and noise points. According to four dataset, penalty factors' …
Monocular Depth Image Mark-Less Pose Estimation Based On Feature Regression, Chen Ying, Shen Li
Monocular Depth Image Mark-Less Pose Estimation Based On Feature Regression, Chen Ying, Shen Li
Journal of System Simulation
Abstract: Monocular camera mark-less pose estimation system suffers low accuracy, robustness and efficiency due to variety of action, self-occlusion of human body. A method of feature exaction from point clouds was proposed, in which a single-to-multiple (S2M) feature regressor and a joint position regressor were designed to quickly and accurately predict the 3D positions of body joints from a single depth image without any temporal information. Experiment result shows that the estimation accuracy is superior to that of state-of-the-arts and multi-camera based methods.
Chaotic Time Series Prediction Based On Gaussian Processes Mixture, Zhenjie Feng, Fan Yu
Chaotic Time Series Prediction Based On Gaussian Processes Mixture, Zhenjie Feng, Fan Yu
Journal of System Simulation
Abstract: Aiming at the problem that the existing learning algorithms of Gaussian processes mixture (GPM) model, such as Markov Chain Monte Carlo (MCMC), variation or leave one out, have high computational complexity, a hidden variables posterior hard-cut iterative training algorithm is proposed, which simplifies the training process of the model. The GPM model based on the proposed algorithm is applied to chaotic time series prediction. The effects of embedding dimension, time delay, learning sample number, and testing sample numbers on predictive ability are discussed. It is demonstrated by the experimental results that the prediction of the GPM model is more …
Characteristic Index Digging Of Combat Sos Capability Based On Machine Learning, Yongli Yang, Xiaofeng Hu, Rong Ming, Xiaojing Yin, Wenxiang Wang
Characteristic Index Digging Of Combat Sos Capability Based On Machine Learning, Yongli Yang, Xiaofeng Hu, Rong Ming, Xiaojing Yin, Wenxiang Wang
Journal of System Simulation
Abstract: Aiming at the two difficulties in characteristic index digging of combat system of systems (CSoS), namely operation data generation and digging method selection, this paper proposes a new digging method, that is, using the simulation testbed to generate operation data, then adopting the machine learning to dig characteristic index. Two methods of characteristic index digging based on machine learning are researched: (1) the method based on network convergence, divides the communities for fundamental indexes based on their relationship, and obtains the characteristic indexes by principal component analysis (PCA); this method is applied to dig the characteristic indexes of …
Intelligent Diagnosis Of Aircraft Electrical Faults Based On Rmbp Neural Network, Lishan Jia, Zhe Liu, Sun Yi
Intelligent Diagnosis Of Aircraft Electrical Faults Based On Rmbp Neural Network, Lishan Jia, Zhe Liu, Sun Yi
Journal of System Simulation
Abstract: To the characteristics of multiple properties, hard to remove and high cost of time and manpower of aircraft electrical faults maintenance in aircraft maintenance of civil aviation, construction of intelligent aircraft electrical faults diagnosis system using RMBP neural network is proposed. RMBP algorithm is used to study sample data in the intelligent faults diagnosis system as it can overcome the faults of long time of convergence and easy to go into local minima of common BP algorithm, and is suitable for training large-scale neural network,. Experience data are collected, samples are made, samples training and experiment are carried out. …
Research Of Nonlinear Time Series Prediction Method For Motion Capture, Tianyu Huang, Yunying Guo
Research Of Nonlinear Time Series Prediction Method For Motion Capture, Tianyu Huang, Yunying Guo
Journal of System Simulation
Abstract: In this paper, we study the nonlinear time series prediction method for action capture. A prediction method based on the capture data is studied and implemented by analyzing human motion data to solve the data loss and correction problem caused by sensor failure. Based on this research purpose, the simulation experiment assumes that a sensor in the sequence of actions fails, then uses eight kinds of machine learning methods, and evaluates them with six indexes. The prediction results of different methods are compared and the predicted motions are visualized. Through the experiments, data prediction accuracy by random forest, decision …
Optimizing Control Of Total Heat Supply Based On Machine Learning, Li Qi, Xingqi Hu, Jianmin Zhao
Optimizing Control Of Total Heat Supply Based On Machine Learning, Li Qi, Xingqi Hu, Jianmin Zhao
Journal of System Simulation
Abstract: The central heating system has complex structure, along with the characteristics of hysteresis, strong coupling and nonlinear. Contraposing the problem that the process is difficult to be identified and controlled by the mechanism modeling, an optimal control method of heat source total heat production based on machine learning is proposed. The heat source model of central heating system is established by BP neural network and long short-term memory neural network. Under the premise of meeting the demand of heating quality, with the total energy consumption as the optimization objective, the optimal control sequence of water supply temperature and water …