• 품질경영학회지
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• 제47권4호
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• pp.713-723
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• 2019

Purpose: The purpose of this study is to develop a statistical program for capability analysis of measuring system and measurement process based upon KS Q ISO 22514-7. Methods: R is a powerful open source functional programming language that provides high level graphics and interfaces to other languages. Therefore, in this study, we will develop the statistical program using R language. Results: The R program developed in this study consists of the following five modules. ① Measuring system capability analysis with Type 1 study data: MSCA_Type1.R ② Measuring system capability analysis with Linearity study(Type 4 study) data: MSCA_Type4.R ③ Measurement process capability analysis with Type 1 study & Gage R&R study data: MPCA_T1GRR.R ④ Measurement process capability analysis with Type 4 study & Gage R&R study data: MPCA_T4GRR.R ⑤ Attribute measurement processes capability analysis : AttributeMP.R Conclusion: KS Q ISO 22514-7 evaluates measuring systems and measurement processes on the basis of the measurement uncertainty that was determined according to the GUM(KS Q ISO/IEC Guide 98-3). KS Q ISO 22514-7 offers precise procedures, however, computations are more intensive. The R program of this study will help to evaluate the measurement process.

• 한국유기농업학회지
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• 제22권4호
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• pp.581-591
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• 2014

This study analyze the policy efficiency of the environment-friendly farming zone program using data envelopment analysis. On analyzed results, the average policy efficiency is 0.185 and 98.3% of zones are inefficient. 97.8% of zones are in increasing returns to scales. The empirical results indicate that more work need to be done to increase the efficiency of the program.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 C
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• pp.1969-1971
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• 2000

This paper presents a data acquisition and analysis program modules that has been developed using the Labview program. These program modules can use to acquire and analyze leakage current data of insulator under different contamination conditions. The experimental hardware made up salt fog chamber, AC/DC power supply, transducer (shunt resistor), measurement devices and PC with the GPIB (General Purpose Interface Bus). And analysis program modules can calculate pulse level count, accumulated charge, average leakage current, power spectrum, and harmonics. The result of applying these analytical program modules to polymeric insulator is available for evaluation of leakage current characteristic of outdoor insulators.

• 대한의용생체공학회:의공학회지
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• 제19권5호
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• pp.477-486
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• 1998

본 연구에서는 요분석용 스트립을 이용하여 요의 상태를 정량적·정성적으로 측정할 수 있는 요분석시스템을 구현하였다. 요분석시스템의 분석 알고리듬은 온도 변화, 전원 노이즈 통의 외란에 강인한 특성을 나타내기 위하여 퍼지 논리를 적용하였다. 강인하고 안정적인 요분석시스템을 설계하기 위하여 스트립 9가지 패드의 분강학적 특성을 검토하였다. 요분석시스템 하트웨어와 소프트웨어로 구성되었다. 요분석시스템의 하드웨어는 단일칩 마이크로프로세서를 사용하였고, 주변장치들로는 광하부, 트레이 제어, 전치증폭부, PC와의 통신, 열전사 프린터 및 동작 상태 표시기로 구성하였다. 요분석시스템의 소프트웨어는 시스템 프로그램과 분류 프로그램으로 구성하였다. 시스템 프로그램은 시스템 제어와 데이터 취득 및 분석을 수행하도록 하였다. 분규 프로그램은 퍼지추론부와 멤버쉽함수 발생기로 구성되었다 멤버쉽함수 발생기는 정도관리의 통계학적 방법을 이용하여 삼각형 멤버쉽함수를 생성하였다. 측정된 데이터는 PC로 전송되고, 전송된 데이터는 C++로 작성된 데이터 관리 및 취득 프로그램에 의해 저장된다. 요분석시스템의 정확도와 퍼지분류기의 안정성은 표준시료를 이용하여 평가하였다. 실험결과는 검사항목과 만족한 일치를 보였다.

• 한국안전학회지
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• 제21권4호
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• pp.95-101
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• 2006

It is well known that the thermal load in PSC(prestressed concrete) box-girder bridge is the principal cause of detrimental crack. The longitudinal stress caused by the lateral stress from the temperature gradient in slab of PSC box-girder bridge has a considerable influence on the durability and economy of bridge structures. As the basic study for the rational consideration of thermal load and the derivation of design guide, the inverse thermal analysis program for PSC box-girder bridges using field measurement data is developed. In this paper, thermal analyses are performed using field monitoring data for the sample PSC box-girder bridge. It is proposed that the link between monitoring program and the inverse analysis program is available.

• 한국공작기계학회논문집
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• 제14권1호
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• pp.15-23
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• 2005

For reliability assessment for machine tools, failure mode analyses by two viewpoints were studied in this paper. First, this study developed the reliability data analysis program, which searches f3r optimal failure distribution like failure rate or MTBF(Mean Time Between Failure) using failure data and reliability test data of mechanical parts in the web. Moreover, this data analysis program saves both failure data or reliability data and their failure rate or MTBF for database establishment. Second, this paper conducted failure mode analysis through such performance tests as circular movement test and vibration testing for machine tools when reliability data is not available. A developed web-based analysis program shows correlations between failure mode and performance test result and also accumulates all the data. These kinds of data analysis programs and stored data furnish valuable information for improving the reliability of mechanical system.

• 한국기계연구소 소보
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• 통권9호
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• pp.183-191
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• 1982

This report describes the analysis method of the full-scale propulsive performance by using the data of model test and the full-scale speed trial. The model test data were analyzed by the computer program "PPTT" based on "1978 ITTC Performance Prediction Method for Single Screw Ships." Also the full-scale speed trial data were analyzed by the computer program "SSTT" based on the newly proposed “SRS-KIMM Standard Method of Speed Trial Analysis." An analysis of model and full-scale test data was carried out for a 60.000 DWT Bulk Carrier and the correlation between model and full-scale ship was stuied.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• 한국태양에너지학회 논문집
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• 제28권6호
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• pp.64-69
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• 2008

In this study the thermal performance and economic analysis program of solar heating system for district heating was developed. This program, named SOLAN-DHS and based on TRNSYS, consisted of four modules like as user's interface for system input/output, library, and utilities and a calculating engine. SOLAN-DHS simplifies user's input data through the database of most system engineering data including weather data of 17 areas in Korea. Five different types of solar systems which can be applicable to district heating system were presented in this program. Due to the user-friendly layout, all design parameters can be changed quickly and easily for the influence on system efficiency. The reliability of SOLAN-DHS was finally verified by the experiments.

• 한국컴퓨터정보학회논문지
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• 제22권11호
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• pp.1-7
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• 2017

In this paper, we introduce three monitoring filtering techniques which reduce the overheads of dynamic data race detection. It is well known that detecting data races dynamically in multi-threaded programs is quite hard and troublesome task, because the dynamic detection techniques need to monitor all execution of a multi-threaded program and to analyse every conflicting memory and thread operations in the program. Thus, the main drawback of the dynamic analysis for detecting data races is the heavy additional time and space overheads for running the program. For the practicality, we also empirically compare the efficiency of three monitoring filtering techniques. The results using OpenMP benchmarks show that the filtering techniques are practical for dynamic data race detection, since they reduce the average runtime overhead to under 10% of that of the pure detection.