• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.276-279
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• 2015

Along with the increase of the quantity of data in various industries, the construction industry has also developed various systems focusing on collecting data related to the construction performance such as productivity and costs achieved in construction job sites. Numerous researchers worldwide have been focusing on developing efficient methodologies to analyze such data. However, applications of such methodologies have shown serious limitations on practical applications due to lack of data and difficulty in finding appropriate analytic methodologies which were capable of implementing significant insights. With development of information technology, the new trend in analytic methodologies has been introduced and steeply developed with the new name of "big data analysis" in various fields in academia and industry. The new concept of big data can be applied for significant analysis on various formats of construction data such as structured, semi-structured, or non-structured formats. This study investigates preliminary application methods based on data collected from actual construction site. This preliminary investigation in this study expects to assess fundamental feasibility of big data analytic applications in construction.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 추계학술발표회 논문집
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• pp.257-262
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• 2004

The purpose of data fusion is collecting maximized information from combining the data attained from more than two same or different kind sensor systems. Data fusion of same kind sensor systems like optical imagery has been on focus, but recently, LIDAR emerged as a new technology for capturing rapidally data on physical surfaces and the high accuray results derived from the LIDAR data. Considering the nature of aerial imagery and LIDAR data, it is clear that the two systems provide complementary information. Data fusion is consisted of two steps, alignment and matching. However, the complementary information can only be fully utilized after sucessful alignment of the aerial imagery and lidar data. In this research, deal with centroid of building extracted from lidar data as control information for estimating exterior orientation parameters of aerial imagery relative to the LIDAR reference frame.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2006년도 춘계학술발표회 논문집
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• pp.533-540
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• 2006

In recent years, civil-engineering work is desired the terrain information to be more efficient in earthwork volume calculation. One method for collecting elevation data is LiDAR. Lidar data was used to produce rapidly an accurate digital elevation model of the terrain, compared with the conventional ground surveys, photogrammetty, and remote sensing. Raw Lidar data is combined with GPS positional data to georeference the data sets. Lidar data is edited and processed to generate surface models, elevation models, and contours. Here we can either create a Tin Volume Surface or a Gird Volume Surface. Triangulated Irregular Network(TIN) has complex data structure, but it can describe well terrain surface features. As we have seen, we search the efficiency for earthwork volume calculation using Lidar data. One conclusion we can draw from this study is that Lidar data is more accurate result than digital map in the calculation of earthwork volume.

• 한국태양에너지학회 논문집
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• 제25권3호
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• pp.11-18
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• 2005

The Korea Institute of Energy Research(KIER) has begun collecting horizontal global insolation data since May, 1982 at different locations. Because of a poor reliability of existing data, KIER's new data will be extensively used by the solar system users as well as by research institutes. But the quality of solar insolation data is not always good. This reports on an attempt to identify systematic error in such data using clear-day analysis for data rehabilitation. Clear-day analysis is successful in uncovering solar insolation data of questionable quality. It is not proven that rehabilitation process can improve the quality of data for daily or monthly means, but it is suggested that the method can be used to improve the quality of data for monthly means of several years for use in many applications of solar energy planning. Earlier studies finding a maximum ETR of about 0.80 are confirmed.

• 디지털산업정보학회논문지
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• 제6권2호
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• pp.19-27
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• 2010

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.

• 태양에너지
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• 제8권1호
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• pp.107-121
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• 1988

The Korea Institute of Energy and Resources (KIER) has begun collecting horizental insolation data since May, 1982 at different locations. Because of a poor reliability of existing data, KIER's new data will be extensively used by the solar system users as well as by research institutes. But, the quality of the solar radiation data is not always good. This reports on an attempt to identify systematic error in such data using clear-day analysis for data rehabilitation. Clear-day analysis is successful in uncovering solar radiation data of questionable quality. It is not proven that rehabilitation process can improve the quality of data for daily or monthly means but it is suggested that the method can be used to improve the quality of data for monthly means of several years for use in many applications of solar energy planning. Earlier studies finding a maximum ETR of about 0.80 are confirmed.

• Journal of information and communication convergence engineering
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• 제20권2호
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• pp.125-130
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• 2022

In modern society, as many data are used for research or commercial purposes, the value of data is gradually increasing. In related fields, research is being actively conducted to collect valuable data, but it is difficult to collect proper data because the value of collection is determined according to the performance of existing sensors. To solve this problem, a method to effectively reduce noise has been proposed, but there is a point in which performance is degraded due to damage caused by noise. In this paper, a device capable of collecting time series data was designed to correct such data noise, and a correction technique was performed by giving an error value based on the representatively collected ultrafine dust data, and then comparing before and after Compare performance. For the correction method, Kalman, LPF, Savitzky-Golay, and Moving Average filter were used. Savitzky-Golay filter and Moving Average Filter showed excellent correction rate as an experiment. Through this, the performance of the sensor can be supplemented and it is expected that data can be effectively collected.

• International Journal of Reliability and Applications
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• 제4권2호
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• pp.79-95
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• 2003

Manufacturers collect and analyze field reliability data to enhance the quality and reliability of their products and to improve customer satisfaction. To reduce the data collecting and maintenance costs, the amount of data maintained for evaluating product quality and reliability should be minimized. With this in mind, some industrial companies assemble warranty databases by gathering data from different sources for a particular time period. This “marginal count failure data” does not provide (i) the number of failures by when the product entered service, (ii) the number of failures by product age, or (iii) information about the effects of the operating season or environment. This article describes a method for estimating age-based claim rates from marginal count failure data. It uses covariates to identify variations in claims relative to variables such as manufacturing characteristics, time of manufacture, operating season or environment. A Poisson model is presented, and the method is illustrated using warranty claims data for two electrical products.

• 공학교육연구
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• 제17권5호
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• pp.51-58
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• 2014

This study proposes an effective data management method for easing the burden on self-study report by analyzing operational data on accredited engineering programs. Four analysis criteria are developed: variability, difficulty level of collecting, urgency of analysis, timeliness. After the operational data are analyzed in terms of the analysis criteria, the data which should be managed in time are extracted according to the analysis results. This study proposes a data management method in which tasks of managing the timely-managed data are performed based on the regular academic schedule, so that the result of this study may be used as a working-level reference material.

• 한국자동차공학회논문집
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• 제18권1호
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• pp.31-36
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• 2010

Collecting all failures during life cycle of vehicle is not easy way because its life cycle is normally over 10 years. Warranty period can help gathering failures data because most customers try to repair its failures during warranty period even though small failures. This warranty data, which means failures during warranty period, can be a good resource to predict initial reliability and permanence reliability. However uncertainty regarding reliability prediction remains because this data is censored. University of Wuppertal and major auto supplier developed the reliability prognosis model considering censored data and this model introduce to predict reliability estimate further "failure candidate". This paper predicts reliability of telecommunications system in vehicle using the model and describes data structure for reliability prediction.