• 한국태양에너지학회 논문집
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• 제38권4호
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• pp.11-31
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• 2018

Nowadays, domestic photovoltaic system market has been expanded and the governmental dissemination policy has been continued. There is only PV system output performance analysis which is called Performance Ratio(PR) analysis. However, there exists many parameters that can affect PV system output. This papers shows the PV system energy performance analysis using meteorological monitoring data. The meteorological monitoring system was installed in the H university and we analyzed the PV system which installed in the H university. We also investigated other three PV systems which located less than 3 kilometers from H university. We evaluated total 4 PV systems through the field survey data, design drawing data and power generation data. Finally, we compared the actual measuring data with the simulation data using PVSYST software.

• 대한산업공학회지
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• 제39권1호
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• pp.1-9
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• 2013

The current manufacturers have generated tremendous amount of digitized product data to efficiently share and exchange it with other stakeholders or various software systems for product development. The digitized product data is a valuable asset for manufacturers, and has a potential to support high level strategic decision makings needed at many stages in product development. However, the lack of studies on extraction of key performance indicators(KPIs) from product data management(PDM) databases has prohibited manufacturers to use the product data to support the decision makings. Therefore this paper examines a possibility of an architecture that supports KPIs for evaluation of product development performances, by applying multidimensional product data model and on-line analytic processing(OLAP) to operational databases of product data management. To validate the architecture, the paper provides a prototype product data management system and OLAP applications that implement the multidimensional product data model and analytic processing.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.244-251
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• 2023

Engineering or humanities data are stored in databases and are often used for search services. While the latest deep-learning technologies, such like BART and BERT, are utilized for data analysis, humanities data still rely on traditional databases. Representative analysis methods include n-gram and lexical statistical extraction. However, when using a database, performance limitation is often imposed on the result calculations. This study presents an experimental process using MariaDB on a PC, which is easily accessible in a laboratory, to analyze the impact of the database on data analysis performance. The findings highlight the fact that the database becomes a bottleneck when analyzing large-scale text data, particularly over hundreds of thousands of records. To address this issue, a method was proposed to provide real-time humanities data analysis web services by leveraging the open source database, with a focus on the Seungjeongwon-Ilgy, one of the largest datasets in the humanities fields.

• 한국정보통신학회논문지
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• 제26권11호
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• pp.1608-1614
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• 2022

최근 학생 수 감소로 인한 대학 간 경쟁이 심화되면서 성과부진학생을 조기에 예측하고, 중도이탈을 예방하기 위해 다양한 노력을 기울이는 것은 대학의 필수 업무로 인식되고 있다. 이를 위해서는 학생의 성과를 정밀하게 예측하는 우수한 성능의 모델이 필수적이다. 본 논문은 성과부진학생을 식별하기 위한 분류 예측 모델에서 이상 데이터를 제거하거나 증폭을 통해 예측 성능을 향상시키는 방법에 대해 제안한다. 기존 이상데이터 처리방법은 주로 데이터를 삭제하거나 무시하는데 집중되었지만 이 논문에서는 잡음과 변화지표를 구분하는 기준을 제시하고, 데이터를 삭제하거나 증폭함으로써 예측 모델의 성능을 높이는데 기여한다. 제안 방법의 검증을 위해 공개된 학습 성과 데이터를 활용한 실험에서 기존 방법에 비해 제안방법이 분류 성능을 향상시킬 수 있는 다수의 사례를 발견할 수 있었다.

• Management Science and Financial Engineering
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• 제3권1호
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• pp.39-56
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• 1997

This paper applies the binary tree classifier and discriminant analysis methods to predicting failures of banks and insurance companies. In this study, discriminant analysis is generally better than the binary tree classifier in the classification of bank defaults; the binary tree is generally better than discriminant analysis in the classification of insurance company defaults. This situation can be explained that the performance of a classifier depends on the characteristics of the data. If the data are dispersed appropriately for the classifier, the classifier will show a good performance. Otherwise, it may show a poor performance. The two data sets (bank and insurance) are analyzed to explain the better performance of the binary tree in insurance and the worse performance in bank; the better performance of discriminant analysis in bank and the worse performance in insurance.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 컴퓨터소사이어티 추계학술대회논문집
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• pp.3-6
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• 2003

In this paper, we conduct a performance analysis for the composite scheme that is obtained by combining the data distribution and the data replication schemes usually used for the implementation of distributed directory service systems. The analysis results reveal that the composite model is a viable option to overcome the performance trade-off between the data distribution and the data replication model. In this paper, we present the performance model developed for the composite model by appling queuing modelling. Using the performance model, performance values for a variety of system execution environments are suggested which enable us to bring an efficient design for high performance distributed directories.

• 한국정보과학회논문지:시스템및이론
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• 제27권6호
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• pp.592-598
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• 2000

분산 공유 메모리(Distributed Shared Memory) 시스템에서 데이타 참조의 지역성은 시스템 성능에 중요한 영향을 미친다. 데이타 참조의 지역성을 고려하여 적절하게 데이타를 배치할 경우 전체적인 시스템 성능 향상을 가질 수 있다. 본 논문에서는 데이타 배치 방식을 효과적으로 적용할 수 있는 동적제한 디렉터리 기법에서 성능을 평가한다. 데이타 배치 방식 정보는 동적 제한 디렉터리 기법에서 존재 비트를 효과적으로 이용할 수 있다. 그리고 적절한 존재 비트의 사용은 메모리 오버헤드를 줄이고 디렉터리 풀을 효율적으로 사용하므로 성능을 향상시킬 수 있다. 성능 평가를 위해 서로 다른 공유 특성을 가진 3개의 응용 프로그램으로 모의 실험하였다. 모의 실험 결과 최적 배치 방식은 3.6 배의 성능을 향상시킬 수 있다.

• The Journal of Asian Finance, Economics and Business
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• 제10권2호
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• pp.15-27
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• 2023

The purpose of this research is to investigate the impact of Business Intelligence (BI) on the relation between Big Data Analytics (BDA) and Financial Performance (FP), at the beginning we reviewed the academic accounting and finance literature to develop the theoretical framework of business intelligence, big data and financial performance in terms of definition, motivations and theories, then we conduct an empirical analysis based on questionnaire-base survey data collected. The researchers identified the study population in the joint-stock companies listed on the Egyptian Stock Exchange and operating in the sectors and activities related to modern technologies in information systems, big data analytics, and business intelligence, in addition to the auditing offices that review the financial reports of these companies, and The sector closest to the research objective is the communications, media, and information technology sector, where the survey list was distributed among the sample companies with (15) lists for each company, and (15) lists for each audit office, so that the total sample becomes (120) individuals (with a response rate 83.3%), The results show, First, Big data analytics significantly affect organizations' financial performance, second, Business intelligence mediates (partial) the relationship between big data analytics and financial performance.

• 한국산업융합학회 논문집
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• 제24권1호
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• pp.69-77
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• 2021

The widely used low-cost design methodology for IoT devices is very popular. In such a networked device, memory is composed of flash memory, SRAM, DRAM, etc., and because it processes a large amount of data, memory design is an important factor for system performance. Therefore, each device selects optimized design factors such as function, performance and cost according to market demand. The design of a memory architecture available for low-cost IoT devices is very limited with the configuration of SRAM, flash memory, and DRAM. In order to process as much data as possible in the same space, an architecture that supports parallel processing units is usually provided. Such parallel architecture is a design method that provides high performance at low cost. However, it needs precise software techniques for instruction and data mapping on the parallel architecture. This paper proposes an instruction/data mapping method to support optimized parallel processing performance. The proposed method optimizes system performance by actively using hardware and software parallelism.

• ETRI Journal
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• 제45권6호
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• pp.1035-1045
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• 2023

We propose a novel graphics processing unit (GPU) algorithm that can handle a large-scale 3D fast Fourier transform (i.e., 3D-FFT) problem whose data size is larger than the GPU's memory. A 1D FFT-based 3D-FFT computational approach is used to solve the limited device memory issue. Moreover, to reduce the communication overhead between the CPU and GPU, we propose a 3D data-transposition method that converts the target 1D vector into a contiguous memory layout and improves data transfer efficiency. The transposed data are communicated between the host and device memories efficiently through the pinned buffer and multiple streams. We apply our method to various large-scale benchmarks and compare its performance with the state-of-the-art multicore CPU FFT library (i.e., fastest Fourier transform in the West [FFTW]) and a prior GPU-based 3D-FFT algorithm. Our method achieves a higher performance (up to 2.89 times) than FFTW; it yields more performance gaps as the data size increases. The performance of the prior GPU algorithm decreases considerably in massive-scale problems, whereas our method's performance is stable.