• KEPCO Journal on Electric Power and Energy
• /
• v.7 no.1
• /
• pp.73-77
• /
• 2021

As the big data analysis technologies has been developed worldwide, the importance of asset management for electric power facilities based data analysis is increasing. It is essential to secure quality of data that will determine the performance of the RISK evaluation algorithm for asset management. To improve reliability of asset management, asset data must be preprocessed. In particular, the process of cleaning dirty data is required, and it is also urgent to develop an algorithm to reduce time and improve accuracy for data treatment. In this paper, the result of the development of an automatic cleaning algorithm specialized in overhead transmission asset data is presented. A data cleaning algorithm was developed to enable data clean by analyzing quality and overall pattern of raw data.

• Korean Journal of Mathematics
• /
• v.22 no.3
• /
• pp.529-552
• /
• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

• KEPCO Journal on Electric Power and Energy
• /
• v.7 no.1
• /
• pp.79-84
• /
• 2021

The fundamental element to be kept for big data analysis, artificial intelligence technologies and asset management system is a data quality, which could directly affect the entire system reliability. For this reason, the momentum of data cleaning works is recently increased and data cleaning methods have been investigating around the world. In the field of electric power, however, asset data cleaning methods have not been fully determined therefore, automatic cleaning algorithm of asset data for transmission cables has been studied in this paper. Cleaning algorithm is composed of missing data treatment and outlier data one. Rule-based and expert opinion based cleaning methods are converged and utilized for these dirty data.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.9 no.1
• /
• pp.31-53
• /
• 2005

We analyze some real data and, from the background of analysis of data, we define a multi-dimensional jump-type asset model which is derived from short-sampling asset prices. We study some basic properties of this asset model.

• Journal of Families and Better Life
• /
• v.14 no.3
• /
• pp.109-120
• /
• 1996

While theories of portfolio selection have been developed very little is known about how individuals actually go about constructing their asset potfolios. This study investigates empirically the characteristics of household's assets and which factor associated with risky and safe asset amount. Data used in this study consisted of 2,164 households and the statistics employed to analyze the data are univariate procedure Logit analysis and OLS. The results of this study were as follows: Among 2,164 households 505 housholds(23.3%) had risky assets. Average risky asset amount is 8,351,500 won and average safe asset amount is 7,086,900 won. Region education and occupation of household head home ownership transfer and other income and total expenditure had significant effects on either and other income and total expenditure had significant effects on either household having risky asset or safe asset. Financial income transfer and other income and total expenditure had significantly p sitive relation with the risky asset amount. Whereas age and the occupation of household head the sense of economic wellbeing earned financial tranfer and other income and total expenditure had significantly positive relation with safe asset amount.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.361-364
• /
• 2007

A small sized device with computing, communicating, sensing capability is changing our life. It will be deployed in the world and acquire a lot of data from the world. It is used for various applications such as military surveillance, environmental monitoring, structure health monitoring, building management, asset tracking, etc. In this paper we focus on USN middleware for asset tracking. A mobile asset is moving here and there within a specific area. The USN middleware tracks the mobile assets in real-time by using sensor nodes and notify their current positions to a user. To achieve the goal, the USN middleware provides some features related to the positions of mobile assets.. They are storing location data by using 3D indexing method, retrieving them by using spatio-temporal query, making trace of an asset, and retrieving the history data of an asset. In the paper, we developed USN middleware to adapt the requirements of asset tracking. It can help users increase the efficiency of their business related to mobile assets and make a valuable decision.

• Journal of Information Processing Systems
• /
• v.1 no.1 s.1
• /
• pp.79-85
• /
• 2005

In this paper, we dealt with substantial asset analysis methodology applied to two-dimensional asset classification and qualitative evaluation method according to the business process. Most of the existent risk analysis methodology and tools presented classification by asset type and physical evaluation by a quantitative method. We focused our research on qualitative evaluation with 2-dimensional asset classification. It converts from quantitative asset value with purchase cost, recovery and exchange cost, etc. to qualitative evaluation considering specific factors related to the business process. In the first phase, we classified the IT assets into tangible and intangible assets, including human and information data asset, and evaluated their value. Then, we converted the quantitative asset value to the qualitative asset value using a conversion standard table. In the second phase, we reclassified the assets using 2-dimensional classification factors reflecting the business process, and applied weight to the first evaluation results. This method is to consider the organization characteristics, IT asset structure scheme and business process. Therefore, we can evaluate the concrete and substantial asset value corresponding to the organization business process, even if they are the same asset type.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.380-387
• /
• 2009

In the past, many state Departments of Transportation (DOTs) in the U.S. managed their highway assets on a "worst first" basis and planned their highway projects in a tactical rather than strategic fashion. Due to increasingly tight highway budgets and recognition of long term benefits of asset management systems, the Federal Highway Administration (FHWA) has strongly pushed and encouraged state DOTs to implement asset management for managing their highway assets and highway projects. Currently, many DOTs have actively implemented and are in the process of applying this asset management concept for their highway infrastructure. However, different DOTs are developing different asset management systems because of their different organizational structures, data management structures, relationship with the legislature, and investment priorities. This study first identifies asset management indicators which are essential to successfully implementing asset management systems for State highway assets. The research team conducted a survey of asset management experts and reviewed the practices and policies of leading DOTs in asset management. Based on these indicators, this study develops an Asset Management Assessment Model (AM²) for different asset management systems. This model can be used by different DOTs to evaluate their current asset management systems and identify their strong areas and also their weak areas to improve in order to fully benefit from the advanced concept of asset management.

• Korean Journal of Social Welfare Studies
• /
• v.40 no.1
• /
• pp.115-138
• /
• 2009

The primary focus of this study is to examine the characteristics of the asset poor and to empirically investigate those factors affecting the likelihood of the asset poor's poverty exit and entry. The 2nd wave through 8th wave data from KLIPS were used for analysis. The asset poverty lined of 50% of the household net asset was set up so that households below 50% are classified as the asset poor. The characteristics of the asset poor were examined in a static manner by analyzing only the 8th wave KLIPS data. To investigate those factors affecting the likelihood of asset poor's poverty transferal with a dynamic perspective, the authors employed two survival analysis methods, the life table analysis and the Time-dependent Cox regression analysis. Based on the findings, some recommendations were made for future policy efforts to support the asset poor and for the current poverty policies as well. In specific, if the 'Individual Development Account' is to be initiated in the future, it would be essential to build a systematic model to utilize accumulated asset by enhancing job competencies and ability to gain a decent job.

• Journal of Families and Better Life
• /
• v.17 no.4
• /
• pp.193-206
• /
• 1999

This study analyzed the household portfolio according to wealth levels using a survey data of 1997 Korea Household Panel Study. The major findings of this study are as follows: (1) A household in high wealth level has invested relatively large proportion of his asset into real estate (2) A household in middle wealth level has invested relatively large proportion of his asset into risky financial asset(3) A household in low wealth level has invested relatively large proportion of his asset into secure financial asset. These findings accorded with risky pyramid model.