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48h Asilomar Conference on Signals, Systems and Computers

作者： Chen, Yang Xie, Le Kumar, P. R. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9781479982974

This paper reports our recent work on dimensionality reduction of synchrophasor data and subsequent engineering analysis of the results. Principal component analysis (PCA) based dimensionality reduction is first applied to explore the underlying dimensionality of power systems from the data of massively deployed PMUs. Then the physical interpretations are provided with the power engineering insights: spatial interpretation suggests the coherency of generator groups;temporal analysis indicates the time-scale hierarchy of power system operations. Numerical examples using both synthetic and realistic PMU data are conducted to illustrate the potential value of combining PMU data-driven and physics-based analytics in real-time grid operations.

关键词： Dimensionality reduction phasor measurement unit data-driven analytics physics-based analytics principal component analysis

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A data-analytics Approach for Risk Evaluation in Peer-to-Peer Lending Platforms

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IEEE INTELLIGENT SYSTEMS 2020年第3期35卷 85-94页

作者： He, Feng Li, Yuelei Xu, Tiecheng Yin, Libo Zhang, Wei Zhang, Xiaotao Tianjin Univ Finance & Econ Sch Finance Tianjin Peoples R China Tianjin Univ Coll Management Tianjin Peoples R China Singapore Management Univ Sch Accountancy Business Adm Accounting & Finance Singapore Singapore Cent Univ Finance & Econ Beijing Peoples R China Tianjin Univ Coll Management & Econ Tianjin Peoples R China

The goal of this article is to investigate the roles of individual behavior characteristics and Internet finance industry risk in the light of bank run theory for P2P. We know that risk evaluation is clearly important for peer-to-peer (P2P) lending platforms in China, as during the last two years, the industry has experienced thousands of platform crashes. Traditional approaches to evaluate enterprise risk are increasingly ineffective in this industry, due to the difficulty of assessing the real information. In addition, the Internet business model makes it possible to record new kinds of information. By applying a data-driven analytics method, we build an intelligent risk evaluation model for P2P platforms that have comparable targeting platforms. The case study shows that our risk evaluation method can generate early warning signals regarding platform or industry risk, which is able to provide effective supporting for P2P business in practice.

关键词： Internet Industries Finance Risk management Peer-to-peer computing Computer crashes Peer-to-Peer lending data-driven analytics risk evaluation crash

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data analytics during pandemics: a transportation and location planning perspective

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ANNALS OF OPERATIONS RESEARCH 2023年第1期328卷 193-244页

作者： Bozkaya, Elif Eriskin, Levent Karatas, Mumtaz Natl Def Univ Turkish Naval Acad Dept Comp Engn TR-34940 Istanbul Turkey Natl Def Univ Turkish Naval Acad Dept Ind Engn TR-34940 Istanbul Turkey

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

关键词： Transportation Location data-driven analytics Pandemic COVID-19

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Prescriptive Maintenance of Railway Infrastructure: From data analytics to Decision Support 6

Prescriptive Maintenance of Railway Infrastructure: From Dat...

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6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Consilvio, Alice Sanetti, Paolo Anguita, Davide Crovetto, Carlo Dambra, Carlo Oneto, Luca Papa, Federico Sacco, Nicola Univ Genoa DIME Genoa Italy Univ Genoa DIBRIS Genoa Italy Hitachi Rail STS Genoa Italy

ISBN: (纸本)9781538694848

One of the main benefits of the railways digital transformation is the possibility of increasing the efficiency of the Asset Management process through the combination of data-driven models and decision support systems, paving the road towards an Intelligent Asset Management System (IAMS). The paper describes the whole IAMS decisional process based on a real railway signaling use case: from field data acquisition to decision support. The process includes data collection, preparation and analytics to extract knowledge on current and future assets' status. Then, the extracted knowledge is used within the decision support system to prioritize asset management interventions in a fully-automated way, by applying optimization logics and operational constraints. The target is to optimize the scheduling of maintenance activities, to maximize the service reliability and optimize both usage of resources and possession times, avoiding (or minimizing) contractual penalties and delays. In this context, a real use case related to signaling system and, in particular, to track circuits, is presented, applying the proposed methodology to an Italian urban rail network and showing the usefulness of the approach and its possible further developments.

关键词： railway prescriptive maintenance data-driven analytics decision support decision automation

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Innovation in Big data analytics Applications of Mathematical Programming in Medicine and Healthcare 5

Innovation in Big Data Analytics Applications of Mathematica...

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IEEE International Conference on Big data (IEEE Big data)

作者： Lee, Eva K. Georgia Inst Technol NSF I UCRC Ctr Hlth Org Transformat Ind & Syst En Ctr Operat Res Med & HealthCare Atlanta GA 30332 USA

ISBN: (纸本)9781538627150

Risk and decision models and predictive analytics have long been cornerstones for advancement of business analytics in industrial, government, and military applications. In particular, multi-source data system modeling and big data analytics and technologies play an increasingly important role in modern business enterprise. Many problems arising in these domains can be formulated into mathematical models and can be analyzed using sophisticated optimization, decision analysis, and computational techniques. In this talk, we will share some of our successes in healthcare, defense, and service sector applications through innovation in predictive and big data analytics through the modeling and computational advances in integer programming. Specifically, the first model is a discrete support vector machine predictive model that incorporates comprehensive factors related to demographics and socioeconomic status, clinical and hospital resources, operations and utilization, and patient complaints and risk factors for global prediction of readmission and treatment outcome of patients. The second model describes an outcome-driven personalized treatment planning model for cancer patients.

关键词： data-driven analytics integer programing predictive models classification machine learning discrete support vector machine personalized treatment planning cancer multi-objective optimization

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Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2024年第1期23卷 264-281页

作者： Ngo, Ngoc-Tri Truong, Ngoc-Son Truong, Thi Thu Ha Pham, Anh-Duc Huynh, Nhat-To Univ Danang Univ Sci & Technol Fac Project Management Hanoi Vietnam Univ Danang Univ Technol & Educ Dept Civil Engn Da Nang City Vietnam Univ Danang Univ Sci & Technol Fac Project Management 54 Nguyen Luong Bang Danang Vietnam

Improving energy efficiency in buildings is a challenge during operation and maintenance. The work proposes a cloud artificial intelligence-based building energy management (cloud AI-BEM) system for predicting building energy consumption. The proposed system includes the data layer, the AI-analytics layer, and the decision support information layer. The data layer collects and stores data in the cloud database management system. The analytics layer performs applied a hybrid AI model which was developed and deployed in this layer that enables predict future energy consumption in buildings. The hybrid AI model, namely the SAMFOR model was developed based on the integration of the seasonal autoregressive integrated moving average (SARIMA) model and the firefly algorithm (FA) and least-squares support vector regression (LSSVR). The web-based layer visualizes insights for users. As insights, the cloud AI-BEM system enables to monitor and to compare the energy consumption among buildings;to predict one-day-ahead energy use in buildings, to produce key performance indicators of energy use;to visualize energy data and outdoor temperature data;to easily interact with data from the system interface. Average accuracy in terms of RMSE values ranged from 1.36 kW per 30 min. The R values were higher than 0.957 and very close to 1.

关键词： data-driven analytics building energy efficiency artificial intelligence machine learning building energy management system

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data-driven fuel consumption estimation: A multivariate adaptive regression spline approach

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2017年第Oct.期83卷 134-145页

作者： Chen, Yuche Zhu, Lei Gonder, Jeffrey Young, Stanley Walkowicz, Kevin Natl Renewable Energy Lab 15013 Denver West Pkwy Golden CO 80401 USA

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never considered before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. A statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： data-driven analytics Fuel consumption estimation Multivariate adaptive regression spline Eco-routing

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Automatic summarization of risk factors preceding disease progression an insight-driven healthcare service case study on using medical records of diabetic patients

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2015年第4期18卷 1163-1175页

作者： Hsueh, Pei-Yun S. Zhu, Xin Xin Hsiao, Mark J. H. Lee, Selina Y. F. Deng, Vincent Ramakrishnan, Sreeram IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA IBM Res Collaboratory Taipei Taiwan

In this study we consider the problem of how to derive insight from medical records to define and improve healthcare services. As noted in many guidelines, risk factors are important to determining the care plan of chronic disease patients, e.g., pre-diabetic or diabetic patients who have started on hemoglobin A1c (HbA1c) control medications. Whereas the traditional management of chronic disease relies on a predetermined set of risk factors, without regard to patient-specific status, literature and recently released guidelines have suggested a less-prescriptive approach that allows flexibility in disease management plans to account for patient-centric information shown in medical records. However, methods of systematically summarizing medical records into risk factors have not been evaluated to support such a patient-centric focus in healthcare services. In this study, we evaluated automatic methods that can identify risk factors important for classifying Diabetic patients at risk of worsen disease progression. In particular, we used the prescription of cardiovascular disease (CVD) medication as the indicator of CVD co-morbidity development in Diabetic patients. We evaluated the summaries obtained with different sources of health information on the risk stratification task and examined the quality of the generated summaries using various proposed intrinsic metrics. In addition, we evaluated to what extent we can reduce the whole medical records into a small set of risk factors. The evaluation illustrates the potential of risk factor summarization and hints on how it can be used to enable practitioners in care planning and to support complex follow-up services at both the point of care and the extended care settings.

关键词： Healthcare service design Case study Disease management data-driven analytics Medical record summarization

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Investigating the ability of deep learning to predict welding depth and pore volume in hairpin welding

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JOURNAL OF LASER APPLICATIONS 2024年第4期36卷

作者： Darwish, Amena Ericson, Stefan Ghasemi, Rohollah Andersson, Tobias Lonn, Dan Lassila, Andreas Andersson Salomonsson, Kent Univ Skovde Sch Engn Sci Virtual Mfg Proc Kaplansgatan 11 SE-54134 Skovde Sweden

To advance quality assurance in the welding process, this study presents a deep learning (DL) model that enables the prediction of two critical welds' key performance characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a wide range of laser welding key input characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were obtained from hairpin welding experiments. Two DL networks are employed with multiple hidden dense layers and linear activation functions to investigate the capabilities of deep neural networks in capturing the complex nonlinear relationships between the welding input and output variables (KPCs and KICs). Applying DL networks to the small numerical experimental hairpin welding dataset has shown promising results, achieving mean absolute error values of 0.1079 for predicting welding depth and 0.0641 for average pore volume. This, in turn, promises significant advantages in controlling welding outcomes, moving beyond the current trend of relying only on defect classification in weld monitoring to capture the correlation between the weld parameters and weld geometries.

关键词： data-driven analytics deep learning hairpin welding quality assurance of laser welding welding similar materials

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When You Work with a Superman, Will You Also Fly? An Empirical Study of the Impact of Coworkers on Performance

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MANAGEMENT SCIENCE 2019年第8期65卷 3495-3517页

作者： Tan, Tom Fangyun Netessine, Serguei Southern Methodist Univ Cox Business Sch Dallas TX 75275 USA Univ Penn Wharton Sch Philadelphia PA 19104 USA

We examine a large operational data set in a casual restaurant setting to study how coworkers' sales ability level affects other workers' sales performance. We find that waiters react nonlinearly to their coworkers' ability. In particular, when coworkers' overall sales ability is low, increasing this ability may prompt waiters to redouble both upselling and cross-selling efforts. When overall coworkers' ability is high, however, further increasing their ability may trigger waiters to reduce sales efforts. Our empirical findings imply that, to maximize sales, managers should mix waiters with heterogeneous ability levels during the same shift. Through a counterfactual analysis, we find that considering the inverted U-shaped peer effects when optimizing current waiters' schedules without changing their utilization may increase total sales by approximately 2.48% at no extra cost.

关键词： peer effects behavioral operations management scheduling/rostering empirical labour decisions in service operations restaurant operations people-centric operations data-driven analytics

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