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作者： Maradani, Bhavana University of Massachusetts Lowell

学位级别：Ph.D., Doctor of Philosophy

Machine learning is a very powerful technique to analyze rapid data gleaned from various sources. Combined with the visual analytics, good exploratory data analysis paves a way to the right direction. It both shortens the process of machine learning and provides accurate outcomes. However, managing and understanding this vast body of data to tease actionable insights in the shortest possible time and with less human intervention is critical. Our dissertation aims at addressing the challenges of providing solutions with less expertise intervention and early detection of life threatening instances. In this dissertation, we focus on the following two problems: 1) Analyzing the performance of a subject while performing physical activity during Tele-Rehabilitation; 2) Estimating the health status and remaining useful “life” (RUL) of an aircraft engine. To address the problem for efficient visual analytics to provide insights into large streams of data, we considered the use case of Tele-rehabilitation. The data collected from the body sensor network needs to be presented in an understandable format to aid the caregiver to understand the movement of the subject. Most of the current research has developed models to estimate joint angles. However, research on visualizing the movement data has been limited and little has been done concerning the long-term performance of a subject. To fill this gap we proposed an interactive visualization to evaluate subjects’ upper limb movements using data acquired while performing various rehabilitation exercises over a period of time. The main contribution of this task presents interactive visualizations to demonstrate the amount of deviation from the normal range of motion and the time taken to finish a task. For the second task, we have considered the problem of detecting the health status and estimating the remaining useful life of an aircraft engine (RUL). Considering the high cost associated with engine failure, monitoring the degrad

关键词： Machine learning data-driven analytics Health Public safety

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Rule-Based data-driven analytics for Wide-Area Fault Detection Using Synchrophasor data

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2017年第3期53卷 1789-1798页

作者： Liang, Xiaodong Wallace, Scott A. Duc Nguyen Mem Univ Newfoundland Dept Elect & Comp Engn St John NF A1B 3X9 Canada Washington State Univ Sch Engn & Comp Sci Vancouver WA 99163 USA

Synchrophasor technology, also known as wide-area monitoring system technology, utilizes phasor measurement unit (PMU) to monitor real-time system data, which can provide unique insights into the operation of a power grid. In this paper, a rule-based data-driven analytics method for wide-area fault detection in a power system using synchrophasor data is proposed. As a data-driven approach, this method relies on rules created using PMU measurement data, and does not require knowledge of the power system's topology and model. It can detect fault location (bus and line) and fault type for a particular fault event. Three common types of short circuit faults in a power grid, single-line-to-ground, line-to-line, and three-phase faults, can be identified using the proposed method. Fault thresholds used in rules are determined based on theoretical values and recorded PMU data during fault events in Bonneville power administration (BPA)'s large power grid. The proposed method is validated by comparing with the recorded field data for fault events provided by BPA. It is found that it can effectively detect most faults with a great accuracy. It has been developed into a software program, and can be readily used by utility companies.

关键词： data-driven analytics fault detection phasor measurement unit (PMU) symmetrical component method synchrophasor technology voltage sags wide-area monitoring system

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Determine Q-V Characteristics of Grid-Connected Wind Farms for Voltage Control Using a data-driven analytics Approach

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2017年第5期53卷 4162-4175页

作者： Andalib-Bin-Karim, Chowdhury Liang, Xiaodong Khan, Nahidul Zhang, Huaguang Mem Univ Newfoundland Dept Elect & Comp Engn St John NF A1B 3X9 Canada Newfoundland & Labrador Hydro Syst Operat & Plann Energy Control Ctr St John NF A1B 0C9 Canada Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

Due to the varying and intermittent nature of wind resource, grid-connected wind farms pose significant technical challenges to power grid on power quality and voltage stability. Wind farm Q-V characteristic curve at the point of interconnection (POI) can offer valuable information for voltage control actions and provide essential indication about voltage stability. data-driven analytics is a promising approach to determine characteristics of a large complex system, a physical model of which is difficult to obtain. In this paper, data-driven analytics is used to determine Q-V curve of grid-connected wind farms based on measurement data recorded at the POI. Different curve-fitting models, such as polynomial, Gaussian, and rational, are evaluated, and the best fit is determined based on different graphical and numerical evaluation metrics. A case study is conducted using field measurement data at two grid-connected wind farms currently in operation in New-foundland and Labrador, Canada. It is found that the Gaussian (degree 2) model describes the Q-V relationship most accurately for the two wind farms. The obtained functions and processed data can be used in the voltage controller design. The plotted QV curve can also be used to determine the reactive margin at the POI for voltage stability evaluation. As a generic method, the proposed approach can be employed to determine Q-V characteristic curve of any grid-connected large wind farms.

关键词： Curve fitting data-driven analytics power quality Q-V curve voltage fluctuation voltage stability wind farms

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The data-driven analytics for investigating cargo loss in logistics systems

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INTERNATIONAL JOURNAL OF PHYSICAL DISTRIBUTION & LOGISTICS MANAGEMENT 2017年第1期47卷 68-83页

作者： Wu, Pei-Ju Chen, Mu-Chen Tsau, Chih-Kai Feng Chia Univ Dept Transportat Technol & Management Taichung Taiwan Natl Chiao Tung Univ Dept Transportat & Logist Management Taipei Taiwan Natl Chiao Tung Univ Degree Program Transportat & Logist Taipei Taiwan

Purpose - Cargo loss has been a major issue in logistics management. However, few studies have tackled the issue of cargo loss severity via business analytics. Hence, the purpose of this paper is to provide guidance about how to retrieve valuable information from logistics data and to develop cargo loss mitigation strategies for logistics risk management. Design/methodology/approach - This study proposes a research design of business analytics to scrutinize the causes of cargo loss severity. Findings - The empirical results of the decision tree analytics reveal that transit types, product categories, and shipping destinations are key factors behind cargo loss severity. Furthermore, strategies for cargo loss prevention were developed. Research limitations/implications - The proposed framework of cargo loss analytics provides a research foundation for logistics risk management. Practical implications - Companies with logistics data can utilize the proposed business analytics to identify cargo loss factors, while companies without logistics data can employ the proposed cargo loss mitigation strategies in their logistics systems. Originality/value - This pioneer empirical study scrutinizes the critical cargo loss issues of cargo damage, cargo theft, and cargo liability insurance through exploiting real cargo loss data.

关键词： Decision tree Cargo loss data-driven analytics Logistics risk management Logistics system

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ARTIFICIAL INTELLIGENCE IN data-driven analytics FOR THE PERSONALIZED HEALTHCARE

ARTIFICIAL INTELLIGENCE IN DATA-DRIVEN ANALYTICS FOR THE PER...

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11th International Conference of Computer Communication and Informatics (ICCCI)

作者： Sivabalaselvamani, D. Selvakarthi, D. Yogapriya, J. Thiruvenkatasuresh, M. P. Maruthappa, Muthukumaran Chandra, Akkaraju Sailesh Kongu Engn Coll Dept Comp Applicat Erode 638060 Tamil Nadu India Kongu Engn Coll Dept Elect & Instrumentat Engn Erode 638060 Tamil Nadu India Kongunadu Coll Engn & Technol Dept Comp Sci & Engn Trichy Tamil Nadu India Erode Sengunthar Engn Coll Dept Comp Sci & Engn Erode Tamil Nadu India Sunway Univ Sch Engn & Technol Petaling Jaya Selangor Malaysia Christ Univ Deemed Univ Inst Management Bengaluru 560085 Karnataka India

ISBN: (纸本)9781728158754

Among the various developments in progress over the last decade, we have seen the generous growth of information investigation to take care of, plan, and use a lot of information beneficially. Be that as it may, because the analysis of evidence will only operate for authentic information and have findings as predefined by individuals, explicit principle-based calculations have been developed to broaden the investigation of information, "Which is usually referred to as "AI". AI didn't expect PCs to be personalized unambiguously, which is a definite bit of leeway. In order to break down information and construct complicated equations to foresee models, which was called prescient analysis, AI was then joined with information inquiry. A set of laws characterized by persons, known as prescient equations, drive the prescient inquiry, and are used to break down genuine knowledge in order to predict potential outcomes.

关键词： smart healthcare Big data analytics data-driven medicine Digital health Health intelligence data-driven analytics Expert systems

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Harmonizing Heterogeneous Diagnostic data of a Vehicle Fleet for data-driven analytics 8

Harmonizing Heterogeneous Diagnostic Data of a Vehicle Fleet...

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8th IEEE International Conference on Connected Vehicles and Expo (ICCVE)

作者： Korper, Sidney Herberth, Roland Gauterin, Frank Bringmann, Oliver Dr Ing Hc F Porsche AG D-71287 Weissach Germany Univ Tubingen Wilhelm Schickard Inst Comp Sci D-72076 Tubingen Germany Karlsruhe Inst Technol Inst Vehicle Syst Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9781728101422

data-driven technologies, such as predictive maintenance, become increasingly important to today's automotive industry due to advancements of connected cars and Over-the Air technologies. A data source that has barely been used in the literature so far is diagnostic data, which is obtained by sending requests to the electronic control units of a vehicle. Diagnostic data can be collected cost-effectively and is already available on a large scale to car manufacturers today. However, the use of diagnostic data is associated with some difficulties. The set of measured variables differs greatly between different vehicles of the same type due to different configurations and therefore differences in the electronic control units. In this contribution, we show how diagnostic data can he harmonized for the use of data driven modeling. An heuristic three-step procedure is introduced to identify similar measured variables. Finally, our approach is verified on a synthetic data set. Future data-driven technologies are able to use larger and more cost-efficient data sets this way.

关键词： Diagnostic data Connected Cars Record Linkage data-driven analytics Predictive Maintenance

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A data-driven analytics Approach in the Study of Pneumonia's Fatalities

A Data-driven Analytics Approach in the Study of Pneumonia's...

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Proceedings of the 2015 IEEE International Conference on data Science and Advanced analytics (IEEE DSAA 2015)

作者： Santos, Maribel Yasmina Carvalheira, Antonio de Araujo, Artur Teles Univ Minho Dept Informat Syst ALGORITMI Res Ctr Guimaraes Portugal Portuguese Lung Fdn Lisbon Portugal

ISBN: (纸本)9781467382731

As huge amounts of data become available in organizations and society, specific data analytics skills and techniques are needed to explore this data and extract from it useful patterns, tendencies, models or other useful knowledge, which could be used to support the decision-making process, to define new strategies or to understand what is happening in a specific field. Only with a deep understanding of a phenomenon it is possible to fight it. In this paper, a data-driven analytics approach is used for the analysis of the increasing incidence of fatalities by pneumonia in the Portuguese population, characterizing the disease and its incidence in terms of fatalities, knowledge that can be used to define appropriate strategies that can aim to reduce this phenomenon, which has increased more than 65% in a decade.

关键词： data-driven analytics pneumonia dashboards data exploration data analysis

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Scalable data Management and data-driven analytics for Structural Condition Assessment using Structural Monitoring data

Scalable Data Management and Data-Driven Analytics for Struc...

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作者： Zhang, Yilan University of Michigan

学位级别：Ph.D.

Structural monitoring systems are an objective and quantitative-based management tool that have been developed to assist structure owners with their diagnostic and prognostic structural condition monitoring processes. As sensing technologies mature, the deployment of permanent sensing arrays in structures are becoming more popular resulting in increasing volumes of sensing data. This thesis focuses on the advancement of structural condition assessment by developing a scalable data management system for the curation and analysis of large-scale data sets associated with long-term structural monitoring systems. A hybrid database system termed SenStore is proposed consisting of a relational database system for the storage of structural information and a hierarchical data format (HDF) repository for the storage of time history sensor measurements. By storing sensor data in an HDF repository, sensor data can be queried at high speeds resulting in greater scalability of analytics to be performed on the data. The thesis also develops novel approaches to interrogating data associated with long-term structural monitoring systems. data-driven analytics are built on top of the SenStore data management platform to extract information associated with the performance and condition of the monitored structure from the curated data. Automated modal parameter extraction by stochastic subspace identification (SSI) is implemented to obtain the modal properties of structures monitored using accelerometers. Long term trends in modal parameters are modeled by Gaussian process regression (GPR). A load estimation algorithm is also proposed for highway bridges monitored using strain gages. The data-driven analytics developed in the thesis are evaluated using long-term monitoring datasets from the New Carquinez Bridge (Vallejo, CA) and the Telegraph Road Bridge (Monroe, MI).

关键词： data management data-driven analytics structural condition assessment structural monitoring data Thesis

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Hybrid Causality Analysis of ENSO's Global Impacts on Climate Variables Based on data-driven analytics and Climate Model Simulation

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FRONTIERS IN EARTH SCIENCE 2019年 7卷

作者： Song, Hua Tian, Jing Huang, Jingfeng Guo, Pei Zhang, Zhibo Wang, Jianwu Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21228 USA Towson Univ Dept Math Towson MD USA Sci Syst & Applicat Inc Lanham MD USA Univ Maryland Baltimore Cty Joint Ctr Earth Syst Technol Baltimore MD 21228 USA Univ Maryland Baltimore Cty Dept Phys Baltimore MD 21228 USA

Numerous studies have indicated that El Nino and the southern oscillation (ENSO) could have determinant impacts on remote weather and climate using the conventional correlation-based methods, which however, cannot identify the cause-and-effect of such linkage, and ultimately determine a direction of causality. This study employs the vector auto-regressive (VAR) model estimation method with the long-term observational sea surface temperature (SST) data and the NCEP/NCAR reanalysis data to demonstrate the Granger causality between ENSO and other climate attributes. Results showed that ENSO as the modulating factor can result in abnormal surface temperature, pressure, precipitation and wind circulation remotely, not vice versa. We also carry out the global climate model sensitivity simulations using the parallel computing techniques to double confirm the causality relations between ENSO and abnormal events in remote regions. Our statistical and climate model-based analyses may enrich our current understanding on the occurrences of extreme events worldwide caused by different ENSO strengths through teleconnections.

关键词： causality analysis ENSO data-driven analytics climate model simulation teleconnection

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Harmonizing Heterogeneous Diagnostic data of a Vehicle Fleet for data-driven analytics

Harmonizing Heterogeneous Diagnostic Data of a Vehicle Fleet...

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International Conference on Connected Vehicles and Expo (ICCVE)

作者： Sidney Körper Roland Herberth Frank Gauterin Oliver Bringmann Dr. Ing. H.C. F. Porsche AG Weissach Germany Institute of Vehicle Systems Technology Karlsruhe Institute of Technology Karlsruhe Germany Wilhelm-Schickard-Institute for Computer Science University of Tübingen Tübingen Germany

ISBN: (纸本)9781728101439

data-driven technologies, such as predictive maintenance, become increasingly important to today's automotive industry due to advancements of connected cars and Over-the-Air technologies. A data source that has barely been used in the literature so far is diagnostic data, which is obtained by sending requests to the electronic control units of a vehicle. Diagnostic data can be collected cost-effectively and is already available on a large scale to car manufacturers today. However, the use of diagnostic data is associated with some difficulties. The set of measured variables differs greatly between different vehicles of the same type due to different configurations and therefore differences in the electronic control units. In this contribution, we show how diagnostic data can be harmonized for the use of data-driven modeling. An heuristic three-step procedure is introduced to identify similar measured variables. Finally, our approach is verified on a synthetic data set. Future data-driven technologies are able to use larger and more cost-efficient data sets this way.

关键词： Diagnostic data Connected cars Record linkage data-driven analytics Predictive maintenance Predictive maintenance vehicle fleet electronic control unit data driven

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