Numerical Analysis and Scientific Computing Commons^™

A Method For Battlefield Situation Information Ontology Construction Based On Top-Down And Bottom-Up Integration, Cong Zhou, Sihang Zhou, Jian Huang, Dong Wang

Journal of System Simulation

Abstract: The construction of the unified expression model of battlefield situational information is challenging due to the complexity of data sources and the significant differences in data structures and expression methods. Ontologies, as semantic conceptual models, are often used to describe concepts, relationships, and attributes within knowledge domains. An ontology construction method for the battlefield situational information domain based on a top-down and bottom-top integration is proposed. The top-down method is used to construct the upper ontology, in which a conceptual hierarchy model with a clear top-down structure is designed to establish the hierarchical relationships and semantic associations. A bottom-up …

Just-In-Time Learning Energy Consumption Predictive Modeling Method In Multi-Condition Production Process, Sheng Wei, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the problem that the global energy consumption prediction model is only suitable for part of the prediction sample and the model is computationally intensive, the idea of just-in-time learning is introduced, and the local weighted partial least squares method combined with the energy consumption model is used to establish a temporary local energy consumption prediction model. The inertia weights of the particle swarm algorithm are improved, considering the effects of particle fitness, number of iterations and population size on the convergence speed and convergence accuracy of the particle swarm algorithm, a nonlinear change adaptive inertia weight strategy …

Unsupervised Complex Condition Recognition Based On Stochastic Neighborhood Embedding, Lin Huang, Shanjun Liu, Wei Wang, Li Gong

Journal of System Simulation

Abstract: Modern industrial production equipment usually has a complex structure and runs alternately in different working conditions. Accurate working conditions identification based on monitoring data is the basis of health monitoring of the system, but the monitoring data of the system usually has a high dimension and a large data volume. To identify the complex equipment operating conditions, an unsupervised operating condition identification method based on stochastic neighborhood embedding is proposed. The stochastic neighborhood embedding algorithm can simultaneously preserve the local and global structural characteristics of the data, and also calculate the probability similarity of data points in high-dimensional and …

Hyper-Heuristic Approach With K-Means Clustering For Inter-Cell Scheduling, Yanlin Zhao, Yunna Tian

Journal of System Simulation

Abstract: According to the actual production situation of China's manufacturing industry, a hyperheuristic algorithm based on K-means clustering is proposed for inter-cell scheduling problem of flexible job-shop. K-means clustering is applied to group entities with similar attributes into the corresponding work cluster decision blocks, and the ant colony algorithm is used to select heuristic rules for each decision block. The optimal scheduling solutions are generated by using corresponding heuristic rules for scheduling of entities in each decision block. Computational results show that, the computational granularity is properly increased by the form of decision blocks, and the computational efficiency of the …

Machine Learning And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane

Dissertations

High-throughput technologies such as DNA microarrays and RNA-seq are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed into Gene Co-expression Networks (GCNs). GCNs are analyzed to discover gene modules. GCN construction and analysis is a well-studied topic, for nearly two decades. While new types of sequencing and the corresponding data are now available, the software package WGCNA and its most recent variants are still widely used, contributing to biological discovery.

The discovery of biologically significant modules of genes from raw expression data is …

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

Base-Package Recommendation Framework Based On Consumer Behaviours In Iptv Platform, Kuruparan Shanmugalingam, Ruwinda Ranganayanke, Chanka Gunawardhaha, Rajitha Navarathna

Research Collection School Of Computing and Information Systems

Internet Protocol TeleVision (IPTV) provides many services such as live television streaming, time-shifted media, and Video On Demand (VOD). However, many customers do not engage properly with their subscribed packages due to a lack of knowledge and poor guidance. Many customers fail to identify the proper IPTV service package based on their needs and to utilise their current package to the maximum. In this paper, we propose a base-package recommendation model with a novel customer scoring-meter based on customers behaviour. Initially, our paper describes an algorithm to measure customers engagement score, which illustrates a novel approach to track customer engagement …

Slashing Quality Index Modeling And Simulation Based On Data Dispersion Clustering, Yuxian Zhang, Xiaoyi Qian, Dong Xiao, Jianhui Wang

Journal of System Simulation

Abstract: For the sensitivity of noise and outliers data in the typical partitioning clustering algorithm, a clustering algorithm based on data dispersion was proposed. The data dispersion was defined and introduced to a non-Euclidean distance. The similarity metric was established, and the data clustering was realized. The optimal clustering number was obtained by the validity function based on improved partition coefficient. Then the proposed clustering algorithm was applied to quality index model in slashing process. A size add-on quality index model was built by radial basis function neural networks. The node number of hidden layer was determined and the center …

Key Technologies Of Precaution And Prediction Of Abnormal Spatial-Temporal Trajectory: A Review Of Recent Advances, Gongda Qiu, He Ming, Yang Jie, Yuting Cao, Jihong Sun

Journal of System Simulation

Abstract: The ex-post disposition of a major incident, which is expected to transform into prediction and precaution of abnormal behavior, is increasingly unable to meet the urgent needs of the society.Therapid development and popularization of sensor network and positioning technology lay the foundation for mining spatial-temporal trajectory data. With the key objective of prediction and precaution of abnormal trajectory based on big data mining, the future research directions and prospects on trajectory clustering and recognitionareanalyzed, discussed and elaboratedinthis paper.Temporal trajectory prediction applied in prediction and precaution of abnormal spatial-temporal trajectory is also presented, providing a reference for further research on …

The Evolving Fuzzy Clustering Approach For Discriminating Neutron And Gamma-Ray Pulses, Shirkhorshidi Ali Seyed

Student Works (2020-2029)

Having a significant amount of data is not useful unless the data can be processed for extracting knowledge and information. One of the elementary steps in crunching data is to break it down into groups. When the data is small and collected in a controlled manner, and when the training data is appropriately labelled, the trivial approach is to use supervised learning to perform the grouping. Supervised methods need training data and information about groups beforehand; however, in the current reality, with an avalanche of data, this information is not available. Nevertheless, the need for grouping data remains. Clustering, as …

Spatiotemporal Mode Analysis Of Urban Dockless Shared Bikes Based On Point Of Interests Clustering, Zhang Fang, Bin Chen, Yanghua Tang, Dong Jian, Chuan Ai, Xiaogang Qiu

Journal of System Simulation

Abstract: The city’s dockless shared bikes have developed rapidly, and its features of convenience, economy and efficiency have been widely welcomed. The digital footprint they generate reveals the movement of people in time and space within the city, which makes it possible to quantify the activities of people in the city using shared bikes. In this paper, based on the collected shared bikes data of Beijing, a clustering method based on the point of interests is proposed to divide the urban space, so as to construct a mobile network of urban shared bikes, and analysis the spatiotemporal mode of bike …

Topicsummary: A Tool For Analyzing Class Discussion Forums Using Topic Based Summarizations, Swapna Gottipati, Venky Shankararaman, Renjini Ramesh

Research Collection School Of Computing and Information Systems

This Innovative Practice full paper, describes the application of text mining techniques for extracting insights from a course based online discussion forum through generation of topic based summaries. Discussions, either in classroom or online provide opportunity for collaborative learning through exchange of ideas that leads to enhanced learning through active participation. Online discussions offer a number of benefits namely providing additional time to reflect and synthesize information before writing, providing a natural platform for students to voice their ideas without any one student dominating the conversation, and providing a record of the student’s thoughts. An online discussion forum provides a …

Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar

Research Collection School Of Computing and Information Systems

In this paper, we study the evolutionary trends in the collaborative review process of a large open source software system. As expected, the number of reviews, the number of reviews commented on, as well as the number of reviewers, and the interactions between them show increasing trends over time. But unexpectedly, levels of clustering between developers in their interaction networks show a decreasing trend, even as connections between them increase. In the context of our study, clustering is an indicator of developer collaboration, whereas connection points to how intensely developers work together. Thus the trends we observe can inform how …

Using Smart Card Data To Model Commuters’ Responses Upon Unexpected Train Delays, Xiancai Tian, Baihua Zheng

Research Collection School Of Computing and Information Systems

The mass rapid transit (MRT) network is playing an increasingly important role in Singapore's transit network, thanks to its advantages of higher capacity and faster speed. Unfortunately, due to aging infrastructure, increasing demand, and other reasons like adverse weather condition, commuters in Singapore recently have been facing increasing unexpected train delays (UTDs), which has become a source of frustration for both commuters and operators. Most, if not all, existing works on delay management do not consider commuters' behavior. We dedicate this paper to the study of commuters' behavior during UTDs. We adopt a data-driven approach to analyzing the six-month' real …

Clustering Method Based On Graph Data Model And Reliability Detection, Yanyun Cheng, Huisong Bian, Changsheng Bian

Journal of System Simulation

Abstract: For the data in feature space, traditional clustering algorithm can take clustering analysis directly. High-dimensional spatial data cannot achieve intuitive and effective graphical visualization of clustering results in 2D plane. Graph data can clearly reflect the similarity relationship between objects. According to the distance of the data objects, the feature space data are modeled as graph data by iteration. Cluster analysis based on modularity is carried out on the modeling graph data. The two-dimensional visualization of non-spherical-shape distribution data cluster and result is achieved. The concept of credibility of the clustering result is proposed, and a method is proposed, …

Automated Species Classification Methods For Passive Acoustic Monitoring Of Beaked Whales, John Lebien

LSU New Orleans Theses and Dissertations

The Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive …

A Conceptual Framework For Analyzing Students' Feedback, Venky Shankararaman, Swapna Gottipati, Sandy Gan

Research Collection School Of Computing and Information Systems

In academic institutions it is normal practice that at the end of each term,students are required to complete a questionnaire that is designed to gather students’perceptions of the instructor and their learning experience in the course. This questionnaire comprises of Likert-scale questions and qualitative questions.One of the important goals of this exercise is to enable the instructor and the senior management to examine the feedback and then enhance students’ learning experience. In most universities, including our own, a lot of attention is paid to the quantitative feedback, which is summarized and statistical comparisons are computed, analysed and presented. However, the …

Xic Clustering By Baseyian Network, Kyle J. Handy

Graduate Student Theses, Dissertations, & Professional Papers

No abstract provided.

Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper highlights and validates the use of shape analysis using Mathematical Morphology tools as a means to develop meaningful clustering of historical data. Furthermore, through clustering more appropriate grouping can be accomplished that can result in the better parameterization or estimation of models. This results in more effective prediction model development. Hence, in an effort to highlight this within the research herein, a Back-Propagation Neural Network is used to validate the classification achieved through the employment of MM tools. Specifically, the Granulometric Size Distribution (GSD) is used to achieve clustering of daily traffic flow patterns based solely on their …

Clustering Data Of Mixed Categorical And Numerical Type With Unsupervised Feature Learning, Dao Lam, Mingzhen Wei, Donald C. Wunsch

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains …

Evaluation And Improvement Of Procurement Process With Data Analytics, Melvin H. C. Tan, Wee Leong Lee

Research Collection School Of Computing and Information Systems

Analytics can be applied in procurement to benefit organizations beyond just prevention and detection of fraud. This study aims to demonstrate how advanced data mining techniques such as text mining and cluster analysis can be used to improve visibility of procurement patterns and provide decision-makers with insight to develop more efficient sourcing strategies, in terms of cost and effort. A case study of an organization’s effort to improve its procurement process is presented in this paper. The findings from this study suggest that opportunities exist for organizations to aggregate common goods and services among the purchases made under and across …

Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw

Research Collection School Of Computing and Information Systems

With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed bandit (MAB) is a framework to balance this trade-off. Most of the previous work in MAB either models a single bandit for the whole population, or one bandit for each user. We propose an algorithm to divide the population of users into multiple clusters, and to customize the …

Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data. to forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). the clustering results improve the accuracy of the proposed method of wind …

On Identifying And Analyzing Significant Nodes In Protein-­Protein Interaction Networks, Rohan Khazanchi, Kathryn Dempsey Cooper, Ishwor Thapa, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Network theory has been used for modeling biological data as well as social networks, transportation logistics, business transcripts, and many other types of data sets. Identifying important features/parts of these networks for a multitude of applications is becoming increasingly significant as the need for big data analysis techniques grows. When analyzing a network of protein-protein interactions (PPIs), identifying nodes of significant importance can direct the user toward biologically relevant network features. In this work, we propose that a node of structural importance in a network model can correspond to a biologically vital or significant property. This relationship between topological and …

On Mining Biological Signals Using Correlation Networks, Kathryn Dempsey Cooper, Ishwor Thapa, Claudia Cortes, Zack Eriksen, Dhundy Raj Bastola, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Correlation networks have been used in biological networks to analyze and model high-throughput biological data, such as gene expression from microarray or RNA-seq assays. Typically in biological network modeling, structures can be mined from these networks that represent biological functions; for example, a cluster of proteins in an interactome can represent a protein complex. In correlation networks built from high-throughput gene expression data, it has often been speculated or even assumed that clusters represent sets of genes that are coregulated. This research aims to validate this concept using network systems biology and data mining by identification of correlation network clusters …

Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao

Computer Science Faculty Publications

Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …