• Journal of Ocean Engineering and Technology
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• v.26 no.2
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• pp.58-67
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• 2012

Recently, both the integration of product data during design and production and the effective management of information during full lifecycles have attracted attention from shipyards and ship owners as a result of recycling regulations and a desire for efficient operations. Generally, PLM (Product Lifecycle Management) supports a collaborative environment during the BOL (Beginning of Life) stage, while an ALM (Asset Lifecycle Management) system provides all of the information required to maintain, overhaul, and discard/recycle all or part of a vessel during the MOL (Middle of Life) and EOL (End of Life) stages. The main goal of this paper is to suggest the fundamental configuration of a PALM (Product Asset Lifecylce Management) system and a method that can be used to utilize a marine vessel's lifecycle information during the MOL, emphasizing the maintenance information during the middle of life. The authors also suggest a PALM system configuration in which lifecycle information can be collected by a PEID (Product Embedded Information Device) integrating a microcomputer, sensors, and wireless network communication. Through a prototype PALM system, the suggested features and PALM system configuration are implemented.

• The KIPS Transactions:PartC
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• v.13C no.7 s.110
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• pp.851-858
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• 2006

The risk analysis's aim is analyze the risk for the asset of organization with asset assessment, vulnerability assessment, threat assessment. existing TTA risk analysis methodology model propose to overall flow, but can not propose to detail behavior or each level. That is, step of risk analysis is insufficient in classification of threat and detail proposal of considered the risk with classified threat. So this paper propose that analysis and evaluate the vulnerability and threat assessment with determinated quantity. this paper consider current national information system and threat of environment and technology. So can estimate the risk with determinated quantity. Finally, analyze the asset risk of organization.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.761-768
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• 2023

In the 4th Industrial Revolution, AI(Artificial Intelligence) and IoT(Internet of Things) technologies are being applied to across various fields, with particularly prominence in asset management, disaster management, and meteorological observation. In these fields, it is necessary to accurately determine the real-time and precise tracking of the object's location and status, and to collect various data even in situations that are difficult to detect with existing sensors. In order to address these demands, the use of GNSS(Global Navigation Satellite System) is essential, and this technology enables the efficient management of assets, disaster prevent and response, and accurate weather forecasting. In this paper, we provide the investigated results for the latest trends in the application of GNSS in the fields of asset management, disaster management, and weather observation, among various fields incorporating AI and IoT and analyze them.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.248-258
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• 2020

This study proposed a breakdown structure for maintenance and management technologies under the concept of comprehensive asset management at the life cycle level of infrastructure based on benchmarking with other developed countries. For this purpose, a comparative case study was performed to review and analyze the existing definitions and hierarchies for infrastructure maintenance, repair, and rehabilitation (MR&R) systems under major industrialized countries and South Korea. In accordance with the ratio of maintenance costs to GDP, the U.S., U.K, and Japan were selected to review their systems. The classifications and definitions of MR&R technologies under the laws were analyzed. The result showed that most developed countries differentiate maintenance and repair from improvement and constitute a system centered on preventive maintenance activities. On the other hand, Korea's system for facility management is not definitely classified and still focused on reactive structures, which need to be improved. In this study, as proposed, a breakdown structure established the concept of Maintenance and Management, Maintenance & Repair, and Performance Improvement. Consequently, this study could be used as the basis for the implementation of preventive MR&R activities and reasonable resource allocations from an asset management point of view.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.153-158
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• 2018

At the Bank of Korea, capital stock statistics were created by the PIM (perpetual inventory method) with fixed capital formation data. Asset classifications also included 2 categories in residential buildings, 4 non-residential buildings, 14 constructions, 9 transportation equipment, 28 machinery, and 2 intangible fixed assets. It is the Korean government accounting system which is developed much with the field of the national accounts including the valuation, but until 2008 it was consistent with single-entry bookkeeping. Many countries, including Korea, were single-entry bookkeeping, not double-entry bookkeeping which can be aggregated by government accounting standard account. There was no distinction in journaling between revenue and capital expenditure when it was consistent with single-entry bookkeeping. For example, we would like to appropriately divide the past budget accounts and the settlement accounts data that have been spent on dredging into capital expenditure and revenue expenditure. It, then, tries to add the capital expenditure calculated to FCF (fixed capital formation), because revenue expenditure is cost for maintenance etc. This could be a new direction, especially, in the estimation of capital stock by the perpetual inventory method for infrastructure (SOC, social overhead capital). It should also be noted that there are differences not only between capital and income expenditure but also by other factors. How long will this difference be covered by the difference between the 'new series' and 'old series' methodologies? In addition, there is no large difference between two series by the major asset classification level. If this is treated as a round-off error, this is a problem.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.747-763
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• 2018

Information Technology (IT) plays an increasingly important role for small and medium-sized enterprises. It has become fundamental for these companies to protect information and IT assets in relation to risks and threats that have grown in recent years. This study aims to understand the importance and structure of an information security policy, using a quantitative study that intends to identify the most important and least relevant elements of an information security policy document. The findings of this study reveal that the top three most important elements in the structure of a security policy are the asset management, security risk management and define the scope of the policy. On the other side, the three least relevant elements include the executive summary, contacts and manual inspection. Additionally, the study reveals that the importance given to each element of the security policy is slightly changed according to the sectors of activity. The elements that show the greatest variability are the review process, executive summary and penalties. On the other side, the purpose of the policy and the asset management present a stable importance for all sectors of activity.

• Journal of Information Technology Services
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• v.15 no.2
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• pp.51-63
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• 2016

In this study, we conducted a comparative analysis of R&D investment efficiency and operational efficiency of IT firms using Data Envelopment Analysis (DEA). We categorized thirteen sample firms into two groups-IT manufacturing and IT service-after an extensive literature review on IT industry classification. We adopted an output-oriented two-stage DEA model suggested by Banker et al. (1984) with total asset and R&D investment as input variables. Then, we constructed investment efficiency and operational efficiency by using Return on Equity (ROE) and Return on Asset (ROA) as intervening variables and operating income and Earnings Per Share (EPS) as output variables. The outcome of the analysis is summarized as follows. First of all, IT manufacturing firms were more efficient (57% on average) than IT service firms. To be specific, IT service firms showed decreasing returns to scale (DRS) with diseconomy of scale. In contrast, IT service firms showed higher operational efficiency (81.5% on average) than IT manufacturing firms. Also, we conducted a Mann-Whitney U test to compare the output of IT service firms and IT manufacturing firms. Lastly, we found a negative correlation ($R^2$ = -.754) between R&D investment efficiency and operational efficiency which infers the trade-off between two constructs

• East Asian Journal of Business Economics (EAJBE)
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• v.7 no.4
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• pp.41-49
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• 2019

Purpose - This paper empirically investigates what factors contribute to corporate investments under financial constraint condition in the Korean stock market. In the paper, tangible assets' growth rate and fixed assets' growth rate were employed as investment performance and total assets were also used for comparison purpose. Research design and methodology - Samples are constructed by manufacturing firms listed on the stock market of Korea as well as those who settle accounts in December from 2001 to 2018. Financial institutions are excluded from the sample as their accounting procedures, governance and regulations differ. This study adopted a fixed panel regression model to assess the sample construction including yearly and cross-sectional data. Results - This results support the literatures that major shareholders showed positive significance to investment in financially unconstrained firms and no significance to investment in financially constrained firms. ROA showed positive significance to investment in financially unconstrained and constrained firms, whereas firm size showed negative significance to investment in financially unconstrained and constrained firms. Debt showed no positive significance to investment in financially unconstrained firms and negative significance to investment in financially constrained firms. Conclusions - This paper documented evidence that ROA and firm size are important factors to investment irrespective of firms' financial constraints. And this paper also supports that major shareholders give positive impact to investments in financially unconstrained firms. This means that financial constraints itself rule corporate' investment decision in financially constrained firms.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.39-49
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• 2020

This study empirically examines herd behavior for fast moving consumer goods (FMCG) sector stocks under varied market return conditions and the period during the global financial crisis and its aftermath. We examine the sample of stocks trading on the Nifty FMCG Index of the Indian equity market from January 2008 up to December 2018 using the dispersion measure of cross sectional absolute deviation and examine its relationship with the market return to explore herd phenomenon. Quantile regression estimate is used and the results of the study validate rational asset pricing models as the sector does not display herding. In contrast, anti-herd behavior at lower and median quantile values is observed. A possible reason can be the non-cyclical nature of the industry where investors rely more on the fundamentals rather than crowd chasing. We also findthe absence of herd phenomenon during the market asymmetries of bull and bear phases, extreme movements, the period of the global financial crisis, and afterward. We further examine herding under the impact of the information technology (IT) industry and conclude that significant return movements in IT sector impact dispersions in the FMCG industry. Also, there is a co-varying risk between the two sectors confirming the spillover in an integrated market.