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A Hybrid FMM-CART Model for Fault Detection and Diagnosis of Induction Motors

18th International Conference on Neural Information Processing (ICONIP)

作者： Seera, Manjeevan Lim, CheePeng Ishak, Dahaman Univ Sci Malaysia Sch Elect & Elect Engn George Town Malaysia Univ Sci Malaysia Sch Comp Sci George Town Malaysia

ISBN: (纸本)9783642249648;9783642249655

A new approach to detect and classify fault conditions of induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and the classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of experiments using real data measurements of motor currents from healthy and faulty induction motors is conducted. FMM-CART is able to detect and classify the associated inductor motor faults with good accuracy rates. Useful rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

关键词： Fault detection and diagnosis fuzzy min-max neural network classification and regression tree induction motor

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Toward Non-security Failures as a Predictor of Security Faults and Failures

Toward Non-security Failures as a Predictor of Security Faul...

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1st International Symposium on Engineering Secure Software and Systems

作者： Gegick, Michael Rotella, Pete Williams, Laurie North Carolina State Univ Dept Comp Sci 890 Oval Dr Raleigh NC 27695 USA Cisco Syst Inc Res Triangle Pk NC USA

ISBN: (纸本)9783642001987

In the search for metrics that can predict the presence of vulnerabilities early in the software life cycle, there may be some benefit to choosing metrics from the non-security realm. We analyzed non-security and security failure data reported for the year 2007 of a Cisco software system. We used non-security failure reports as input variables into a classification and regression tree (CART) model to determine the probability that a component will have at least one vulnerability. Using CART, we ranked all of the system components in descending order of their probabilities and found that 57% of the vulnerable components were in the top nine percent of the total component ranking, but with a 48% false positive rate. The results indicate that non-security failures can be used as one of the input variables for security-related prediction models.

关键词： Attack-prone classification and regression tree

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Machine Learning-Driven Snow Cover Mapping Techniques using Google Earth Engine 19

Machine Learning-Driven Snow Cover Mapping Techniques using ...

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19th IEEE-India-Council International Conference (INDICON)

作者： Panda, Saptarshi Anilkumar, Ritu Balabantaray, Bunil Kumar Chutia, Dibyajyoti Bharti, Rishikesh Natl Inst Technol Meghalaya Dept Civil Engn Shillong Meghalaya India Govt India Dept Space North Eastern Space Applicat Ctr Umiam Ri Bhoi India Natl Inst Technol Meghalaya Dept Comp Sci & Engn Shillong Meghalaya India Indian Inst Technol Guwahati Dept Civil Engn Amingaon Guwahati India

ISBN: (纸本)9781665473507

Snow cover is a significant factor influencing the surface albedo. Thus an accurate snow cover mapping algorithm is of utmost importance in energy balance, land surface and climate models. Traditional methods utilizing satellite imagery to determine snow cover used snow indices such as Normalized Difference Snow Index, Normalized Difference Forest Snow Index and S3 index. Recent studies have demonstrated improved performance of snow mapping using machine learning-driven supervised classification techniques. Through this study, the authors present a set of algorithms developed using Google Earth Engine for spatial and temporal extension of machine learning-driven snow cover estimation. Further, the study evaluates the use of machine learning models viz. classification and regression tree (CART), Support Vector Machine (SVM), Gradient tree Boosting and Random Forest for the generation of snow cover maps using accuracy metrics such as overall accuracy, producer accuracy, consumer accuracy, and kappa coefficient. Gradient tree Boosting consistently depicted the best spatial and temporal accuracies. All machine learning classifiers performed better than the conventional index-derived snow cover maps.

关键词： Snow cover classification and regression tree Support Vector Machine Gradient tree Boosting Random Forest Google Earth Engine Supervised classification

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Cruise Passengers in a Homeport: A Market Analysis

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TOURISM GEOGRAPHIES 2013年第1期15卷 68-87页

作者： Brida, Juan Gabriel Pulina, Manuela Riano, Eugenia Zapata Aguirre, Sandra Free Univ Bolzano Sch Econ & Management Competence Ctr Tourism Management & Tourism Econ I-39100 Bolzano Italy Univ Sassari Econ Dept DiSEA I-07100 Sassari Italy Univ Sassari CRENoS I-07100 Sassari Italy Inst Univ Colegio Mayor Antioquia Fac Management GIET Res Grp Medellin Colombia

The aim of this study is to examine cruise passengers' characteristics, preferences and their overall experience in a port of call. Based on 1,361 survey data collected from passengers in the port of call of Cartagena, during the third quarter of 2009, a three-step multivariate market segment analysis is employed. First, a correspondence analysis is run to reveal the underlying factors in the data;second, based on the correspondence analysis, a hierarchical cluster investigation is performed to segment the sample into homogeneous groups;third, a decision tree is computed to characterise each group. The cluster analysis identifies six distinct market segments differentiated by nationality, satisfaction, safety perception and expenditure. The findings imply several policy directions. In particular, institutions should enhance the perception of safety in Cartagena to guarantee repeated visits, an ad hoc marketing policy may encourage revisit by young South Americans, and managers should extend the inland visiting time that is likely to produce local multiplier effects.

关键词： Cruise tourism customer segmentation hierarchical cluster classification and regression tree market analysis policy implications South America Colombia

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Measurement and Modeling of Job Stress of Electric Overhead Traveling Crane Operators

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SAFETY AND HEALTH AT WORK 2015年第4期6卷 279-288页

作者： Krishna, Obilisetty B. Maiti, Jhareswar Ray, Pradip K. Samanta, Biswajit Mandal, Saptarshi Sarkar, Sobhan DuPont Co Inc Hyderabad Andhra Pradesh India Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Indian Inst Technol Dept Min Engn Kharagpur W Bengal India

Background: In this study, the measurement of job stress of electric overhead traveling crane operators and quantification of the effects of operator and workplace characteristics on job stress were assessed. Methods: Job stress was measured on five subscales: employee empowerment, role overload, role ambiguity, rule violation, and job hazard. The characteristics of the operators that were studied were age, experience, body weight, and body height. The workplace characteristics considered were hours of exposure, cabin type, cabin feature, and crane height. The proposed methodology included administration of a questionnaire survey to 76 electric overhead traveling crane operators followed by analysis using analysis of variance and a classification and regression tree. Results: The key findings were: (1) the five subscales can be used to measure job stress;(2) employee empowerment was the most significant factor followed by the role overload;(3) workplace characteristics contributed more towards job stress than operator's characteristics;and (4) of the workplace characteristics, crane height was the major contributor. Conclusion: The issues related to crane height and cabin feature can be fixed by providing engineering or foolproof solutions than relying on interventions related to the demographic factors. Copyright (C) 2015, Occupational Safety and Health Research Institute. Published by Elsevier Ltd.

关键词： classification and regression tree electrical overhead traveling crane operation job stress modeling

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Statistical classification of spikes in Benign Rolandic Epilepsy

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Brain Topography 1988年第2期1卷 123-129页

作者： Wong, Peter K.H. Bencivenga, Roberto Gregory, Donna Department of Diagnostic Neurophysiology British Columbia Children's Hospital Canada Department of Pediatrics The University of British Columbia Canada

The occurrence of spikes during an electroencephalogram is a basic feature of Benign Rolandic Epilepsy of Childhood (BREC). In this study we addressed the question of whether the interictal spike structure is different between "typical" and "atypical" BREC patients. Atypical BREC patients are characterized by having other neurological abnormalities in addition to the typical BREC symptoms. This question is of interest given the good prognosis associated with the typical form of BREC. We analyzed data from 12 typical and 12 atypical BREC patients using ten variables to describe spike morphology and topography. The non-parametric method of "classification and regression trees" (CART) was used to detect discriminating features and construct a classification rule. In this way the location and amplitude of the spike were found to provide satisfactory discrimination, suggesting that the two groups may be affected by different epileptic processes. The study showed the effectiveness of the CART methodology in dealing with the categorical and non-normal variables that can be obtained from EEG records. It illustrates a simple but logical approach to the analysis of potentially complex topographic data. © 1988 Human Sciences Press, Inc.

关键词： Benign Rolandic epilespy classification and regression tree EEG topography Spike classification Spike mapping

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The bank of things An empirical investigation on the profitability of the financial services of the future

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BUSINESS PROCESS MANAGEMENT JOURNAL 2016年第2期22卷 324-340页

作者： Del Giudice, Manlio Campanella, Francesco Dezi, Luca Paris Sch Business Paris France Univ Naples 2 Dept Econ Capua Italy Univ Naples Parthenope Dept Business & Quantitat Studies Naples Italy

Purpose - The purpose of this paper is to study the existing relationship between the products offered by the banks of things and the relative return on equity (ROE). This can be used to highlight the benefit of carrying out investments to transform traditional banks into banks of things. Design/methodology/approach - The sample used in this empirical research contained 3,692 banks that, in 2013, were located in 28 European countries. To determine whether the Internet of Things (IoT) has an effect on the banks' profitability, the authors employed the classification analysis method (classification and regression tree). Findings - The empirical analysis shows that a high ROE for banks is expressed by the following features: first, banks offer IoT retail services to customers;second, banks offer IoT corporate services to customers;third, banks offer customers a large number of home banking services;and finally, banks offer customers a large number of traditional investment services. Research limitations/implications - The survey covers a sample of European banks. It would be necessary to extend the sample to other geographical areas. It would be possible to generalize the results. Originality/value - Scientific research on the implications of the "Bank of things" are still few and fragmented.

关键词： Profitability Internet of things Financial services Bank of things classification and regression tree Return on equity

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An NCME Instructional Module on Data Mining Methods for classification and regression

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EDUCATIONAL MEASUREMENT-ISSUES AND PRACTICE 2016年第3期35卷 38-54页

作者： Sinharay, Sandip Pacific Metr Corp Monterey CA 93940 USA

Data mining methods for classification and regression are becoming increasingly popular in various scientific fields. However, these methods have not been explored much in educational measurement. This module first provides a review, which should be accessible to a wide audience in education measurement, of some of these methods. The module then demonstrates using three real-data examples that these methods may lead to an improvement over traditionally used methods such as linear and logistic regression in educational measurement.

关键词： bagging boosting classification and regression tree random forests

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Association between polypharmacy and the persistence of delirium: a retrospective cohort study

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BIOPSYCHOSOCIAL MEDICINE 2020年第1期14卷 25-25页

作者： Kurisu, Ken Miyabe, Daisuke Furukawa, Yoshiko Shibayama, Osamu Yoshiuchi, Kazuhiro Yokohama Rosai Hosp Dept Psychosomat Med Yokohama Kanagawa Japan Univ Tokyo Grad Sch Med Dept Stress Sci & Psychosomat Med Bunkyo Ku 7-3-1 Hongo Tokyo 1138655 Japan

Background Although the association between polypharmacy and the occurrence of delirium has been well studied, the influence of polypharmacy on the persistence of delirium remains unclear. We aimed to explore the effect of polypharmacy on the persistence of delirium. Methods This retrospective cohort study was conducted at a tertiary hospital. The medical records of patients diagnosed with delirium who were referred to the Department of Psychosomatic Medicine were reviewed. Presentation with delirium on day 3 was set as the outcome in this study. We counted the number of drugs prescribed on the date of referral, excluding general infusion fluids, nutritional or electrolytic products, and psychotropics. To define polypharmacy, we developed a classification and regression tree (CART) model and drew a receiver operating characteristic (ROC) curve. The odds ratio (OR) of polypharmacy for the persistence of delirium on day 3 was calculated using a logistic regression model with the propensity score as a covariate. Results We reviewed the data of 113 patients. The CART model and ROC curve indicated an optimal polypharmacy cutoff of six drugs. Polypharmacy was significantly associated with the persistence of delirium both before [OR, 3.02;95% confidence interval (CI), 1.39-6.81;P = 0.0062] and after (OR, 3.19;95% CI, 1.32-8.03;P = 0.011) propensity score adjustment. Conclusion We discovered an association between polypharmacy and worsening courses of delirium and hypothesize that polypharmacy might be a prognostic factor for delirium.

关键词： classification and regression tree Delirium Polypharmacy Propensity score Receiver operating characteristic curve

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analysis of the efficacy of the MSHA respirable dust sampling program in surface metal/nonmetal mining industry

analysis of the efficacy of the MSHA respirable dust samplin...

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作者： Lashgari, Ali PennState University Libraries

学位级别：Doctor of Philosophy

The mining industry continues to be an important contributor to the American economy. However, there are a variety of health concerns associated with mining. Exposure to respirable silica dust is one of the sources of health risks in surface mines. A variety of respiratory diseases and other ailments, including silicosis and other non-malignant respiratory diseases, may be caused by exposure to respirable silica dust. The Mine Act of 1977 requires the Mine Safety and Health Administration (MSHA) to inspect mines every year. While one goal of these inspections is to ensure compliance with occupational health and safety regulations, Congress’s overall intent was to improve the safety and health of America’s mine workers. MSHA conducts a vigorous respirable dust sampling program to ensure compliance with its regulations as part of its deterrence and compliance mandate. MSHA inspections are resource intensive and consume a significant percentage of the agency’s annual appropriation. The question of whether MSHA’s compliance-sampling program is accomplishing the intended outcome is one of continued interest to the Government Accountability Office, taxpayers, organized labor, industry, and mine workers. The goal of this research is to determine the efficacy of the MSHA compliance-sampling program for respirable silica dust in surface metal and nonmetal mining operations. It is difficult to evaluate the efficacy of the program in reducing silicosis among mine workers because of the incomplete knowledge of health outcomes among miners and many confounding variables. The consequences of excess silica exposure can take years to develop. It is often impossible to accurately determine where and when exposures occurred, and whether exposures are fully attributable to occupational sources. Therefore, other surrogate metrics are required to assess the extent to which MSHA’s sampling program and enforcement activity is contributing to the intended outcome. Two surrogate measures, i

关键词： Surface Mining Occupational Health and Safety Respirable Dust Silica Compliance Sampling classification and regression tree Metal/Nonmetal Mining Fugitive Dust Dust Exposure Quartz

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