• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.7-14
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• 2016

Recently, as cloud computing technologies are developed, it enable to work anytime and anywhere by smart phone and computer. Also, cloud computing technologies are suited to reduce costs of maintaining IT infrastructure and initial investment, so cloud computing has been developed. As demand about cloud computing has risen sharply, problems of power consumption are occurred to maintain the environment of data center. To solve the problem, first of all, power consumption has been measured. Although using power meter to measure power consumption obtain accurate power consumption, extra cost is incurred. Thus, we propose prediction method about power consumption without power meter. To proving accuracy about proposed method, we perform CPU and Hard disk test on cloud computing environment. During the tests, we obtain both predictive value by proposed method and actual value by power meter, and we calculate error rate. As a result, error rate of predictive value and actual value shows about 4.22% in CPU test and about 8.51% in Hard disk test.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6A
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• pp.765-778
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• 2008

Performance Profiles are essential to predict the performance variation over time for the bridge management system (BMS) based on risk management. In general, condition profiles based on experts opinion and/or visual inspection records have been used widely because obtaining profiles based on real performance is not easy. However, those condition profiles usually don't give a good consistency to the safety of bridges, causing practical problems for the effective bridge management. The accuracy of performance evaluation is directly related to the accuracy of BMS. The reliability of the evaluation is important to produce the optimal solution for distributing maintenance budget reasonably. However, conventional methods of bridge assessment are not suitable for a more sophisticated decision making procedure. In this study, a method to compute quantitative performance profiles has been proposed to overcome the limitations of those conventional models. In Bridge Management Systems, the main role of performance profiles is to compute and predict the performance of bridges subject to lifetime activities with uncertainty. Therefore, the computation time for obtaining an optimal maintenance scenario is closely related to the efficiency of the performance profile. In this study, the Response Surface Method (RSM) based on independent and important design variables is developed for the rapid computation. Steel box bridges have been investigated because the number of independent design variables can be reduced significantly due to the high dependency between design variables.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.568-574
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• 2021

According to the Framework Act on Sustainable Infrastructure Management, which has been enforced since 2020, reservoirs should be managed to minimize life cycle costs caused by aging through preemptive management such as systematic maintenance and performance improvement. For maintenance in consideration of the life cycle, it is essential to derive the end of life due to continuous performance degradation as the common period increases. For this purpose, it is necessary to develop a performance-predicting model for reservoirs. In this study, a reservoir was divided into main complex facilities to develop a model for the maintenance of the life cycle. A model was developed for each facility. For model development, maintenance information data were collected under management by the Rural Community Corporation. The data available for model development were selected by analyzing the collected data. The developed model was used to predict the expected life expectancy of the reservoir in the current maintenance system and the expected life expectancy in the case of no action. By using the developed model, it is expected that it will be possible to support decision making in operation management and maintenance while considering the life cycle of the reservoir.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.351-360
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• 2016

Sensors equipped in Internet-of-Thing (IoT) devices are used to measure the surrounding contexts, and IoT applications analyze the contexts to infer situations and provide situation-specific smart services. There are different context acquisition schemes including pulling, pushing, and broadcasting. Most IoT devices support only one of the schemes. Hence, there can be an incompatible issue on data acquisition schemes between applications and devices, and consequently it could result in an increased development cost and inefficiency on application maintenance. This paper presents design patterns which can effectively remedy the incompatibility problem. By applying the patterns, IoT applications with incompatibility can be systematically and effectively developed. And, also its maintainability is expected to increase.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1127-1136
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• 2003

Estimation of software project cost, effort and duration in the early stage of software development cycle is a difficult and key problem in software engineering. Most of models estimate the development effort using the function point that is measured from the requirement specification. This paper presents optimal team size and duration prediction based on function point in order to provide information that can be used as a guide in selecting the most Practical and productive team size for a software development project. We introduce to productive metrics and cost for decision criteria of ideal team size and duration. The experimental is based on the analysis of 300 development and enhancement software project data. These data sets are divide in two subgroups. One is a development project; the other is a maintenance project. As a result of evaluation by productivity and cost measured criteria in two subgroups, we come to the conclusion that the most successful projects has small teams and minimum duration. Also, I proposed that predictive model for team sire and duration according to function point size based on experimental results. The presented models gives a criteria for necessary team site and duration according to the software size.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.219-224
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• 2019

The number of solar power installation companies including domestic small scale (50kW or less) is increasing rapidly, but the efficient operation system and management are insufficient. Therefore, a new type of operating system is needed as a maintenance management aspect to maximize the generation amount in the current state rather than the additional function which causes the increase of the generation cost. In this paper, we utilize Big Data and sensor network to maximize the operating efficiency of solar power plant and analyze the expert system to develop power generation prediction technology, module unit fault detection technology, life prediction of inverter components and report technology, maintenance optimization And to develop a smart monitoring system that enables optimal operation of photovoltaic power plants such as development of algorithms and economic analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.298-298
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• 2020

하천 혹은 그 인근에 설치된 시설물은 수문, 통문, 제방, 댐, 보, 배수펌프장, 상·하수도, 하구둑 등이 존재한다. 이러한 하천 시설물은 홍수나 가뭄 등 수해를 저감하는 역할을 한다. 그러나 많은 하천 시설물은 노후화, 기후변화, 하천환경변화 등으로 구조적 혹은 기능적 안정성의 저하가 우려되는 실정이다. 시설물 유형별 고령화율을 살펴보면, 댐, 하천, 상하수도 순으로 조사되었으며, 준공연수가 30년을 초과한 하천 시설물은 약 40%를 넘어섰다. 그럼에도 불구하고 하천시설물의 관리 구조는 시설물 설치단계까지만 치중되었으며, 이후 계획 재수립 단계까지의 평가 및 모니터링, 유지관리, 정보관리 등에 이르는 선순환 구조가 미흡한 실정이다. 시설물의 노후화에 따라 유지관리 비용이 증가하며, 대형사고로 이어질 수 있기 때문에 적절한 시기에 시설물 점검 및 유지보수가 매우 중요하다. 우리나라의 경우 시설물의 안전 및 유지관리에 관한 특별법에 따라 국가주요시설물은 안전점검을 실시하고 있으며, 시설물통합정보관리시스템(Facility Management System; FMS)에 안전등급을 제공하고 있다. 본 연구에서는 FMS의 하천 시설물 안전등급 현황을 기반으로 시설물의 효과적인 생애주기관리를 위해 하천 시설물의 성능평가모델을 제안하였다. 성능평가모델은 하천 시설물의 사용연수에 따른 안전등급의 예측이 가능하며, 관리자 측면에서 예산투입 등의 의사결정 시 활용이 가능할 것으로 판단된다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.220-227
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• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.802-809
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• 2021

To cope with the increasing maintenance costs due to aging, the maintenance cost was evaluated from the perspective of asset management. The maintenance cost can be predicted based on the condition of the bridge, and the life cycle cost is used as an index. In general, the condition of a bridge has a wide distribution characteristic depending on the deterioration, load, and material characteristics. In this paper, to evaluate the effect of the bridge conditions on the life cycle cost, condition prediction models were constructed considering the service life, deterioration rate, and inspection error, which are the main variables of the bridge condition and life cycle cost calculation. In addition, condition prediction models were constructed based on the distribution of the health index to estimate the upper and lower bounds of the life cycle costs that can occur in individual bridges. Life cycle cost analysis showed that the life cycle cost differed significantly according to the condition of the bridge. Accordingly, research will be needed to increase the reliability of predicting the life cycle cost of individual bridges.