• Solar Energy
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• v.9 no.3
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• pp.81-91
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• 1989

This study represents the method to predict the flow duration curve and primary design specifications of small hydropower plant at hydropower site through analyzing the monthly rainfall data. Weibull distribution was selected to characterize the rainfall data and Thiessen method was used to calculate monthly average flowrate at site. Application of these results, primary design specifications such as design flowrate, annual average load factor and utility factor, annual average hydropower density and annual electric energy production were estimated and discussed for surveyed site located in Daigi-ri, Kangwon province. And performance characteristic model of small hydro-power plant was applied to estimate these specifications.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.43-46
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• 1997

Reliability engineering is regarded as the major and important roll for all industry. And advanced manufacturing systems with high sped and intelligent have been developing for betterment of machining ability. In this study, we have systemized evaluation of reliability for machinery system. We proposed the reliability assessment and designed and manufactured reliability test-bed to evaluate reliability. In addition we acquired reliability data using test-bed system and made database to handle reliability data. And also we not only use reliability data by analyzing reliability, but also apply design review method using analyzing critical units of machinery system. Form this study, we will expect to guide and increase the reliability engineering in developing and processing phase of high quality product.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.517-524
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• 2020

Recently, many researches have been conducted to automatically recognize license plates of vehicles and use the analyzed information to manage stolen vehicles and track the vehicle. However, such a system must eventually be investigated by people through direct monitoring. Therefore, in this paper, the system of tracking a vehicle is implemented by sharing the information analyzed by the vehicle image among cameras registered in the IoT environment to minimize the human intervention. The distance between cameras is indicated by the node and the weight value of the weighted-graph, and the eigenvector centrality is used to select the camera to search. It demonstrates efficiency by comparing the time between analyzing data using weighted graph searching algorithm and analyzing all data stored in databse. Finally, the path of the vehicle is indicated on the map using parsed json data.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.132-132
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• 2000

The reliability of EIC systems in hot rolling mill is indispensable and very important in order to maintain stable production. Signals obtained from sensors and control system should be analyzed to monitor the condition of down coiler in hot rolling mill. We develope a monitoring system of down coiler which is composed of three parts (1) data acquisition and MMI (2) signal processing and analyzing, and (3) automatic data saving. Also it is designed to enable to inform users the abnormal conditions of down coiler. This developed system is expected to make it possible to reduce long downtime, secure high facility precision, and maintain high control levels.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.367-378
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• 2004

Recently logistic regression is popular in a variety of fields so that a number of statistical packages are developed for analyzing the logistic regression. This paper briefly considers the several types of logistic regression models used depending on different types of data. In addition, when four statistical packages (MINTAB, SAS, SPSS and STATA) are used to apply logistic regression models to the real fields respectively, their scope and characteristics are investigated.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.167-172
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• 2005

This study based on the experimental data on the daylight responsive dimming systems performance. The purpose of this study increases the energy-saving effects by reducing excessive intensity of radiation of artificial lighting through analyzing incident daylight. The photosensor sends amounts of detected luminous flux to digital control unit(DCU) as a signal and then, it can decide dimming ratios, by received a proper dimming signal from DCU. Generally it is effective to control artificial lighting with the different control ratio of each row by setting a photosensor as same numbers and rows as artificial lighting. However, it is ineffective to do in initial costs of systems aspect in offices. By analyzing the data of this performance and finding regular ration between photosensors. we will execute different dimming ratios to each row of artificial lighting by a single photosensor.

• Journal of KIISE
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• v.42 no.2
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• pp.155-161
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• 2015

It is difficult and cumbersome to figure out whether a multithread program runs with concurrency bugs, such as data races and atomicity violations, because there are many possible executions of the program and a lot of the defects are hard to reproduce. Hence, monitoring techniques for collecting and analyzing the information from program execution, such as thread executions, memory accesses, and synchronization information, are important to locate data races for debugging multithread programs. This paper presents an efficient and practical monitoring tool, called VcTrace, that analyzes the partial ordering of concurrent threads and events during an execution of the program based on the vector clock system. Empirical results on C/C++ benchmarks using Pthreads show that VcTrace is a sound and practical tool for on-the-fly data race detection as well as for analyzing multithread programs.

• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.808-817
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• 2016

Recently, the wearable computing technology with bio-sensors has been rapidly developed and utilized in various areas such as personal health, care-giving for senior citizens who live alone, and sports activities. In particular, the wearable computing equipment to measure vital signs by means of digital yarns and bio sensors is noticeable. The wearable computing devices help users monitor and manage their health in their daily lives through the customized healthcare service. In this paper, we suggest a system for monitoring and analyzing vital signs utilizing smart healthcare clothing with bio-sensors. Vital signs that can be continuously acquired from the clothing is well-known as unstructured data. The amount of data is huge, and they are perceived as the big data. Vital sings are stored by Hadoop Distributed File System(HDFS), and one can build data warehouse for analyzing them in HDFS. We provide health monitoring system based on vital sings that are acquired by biosensors in smart healthcare clothing. We implemented a big data platform which provides health monitoring service to visualize and monitor clinical information and physical activities performed by the users.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.437-447
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• 2009

Recently, in addition to analyzing data with appropriate statistical methods, statistical analysts in the industrial fields face difficulties that they have to compose proper datasets for analysis objectives via extracting or generating processes from diverse data storage devices. In this paper we survey and compare many state-of-the-art data access technologies adopted by several commonly used statistical packages. More understanding of these technologies will help to reduce the costs occurring when analyzing large size of datasets in especially data mining works, and so to allow more time in applying statistical analysis methods.