• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Management & Information Systems Review
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• v.37 no.4
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• pp.1-20
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• 2018

This study aimed to examine earnings quality of firms selected as Global Champs project which has been promoted by the government since 2013 to support small and medium sized enterprises, for the screening year(t-1) and selected year(t). Earing quality is measured as the value of discretionary accruals estimated by Dechow et al.(1995) adjusted Jones model and Kothari et al.(2005) model, respectively. I analyze the differences of earning quality between the Global Champ firms and the paired firms selected through criteria of the similar total assets and the same industry in the screening year and the selected year. This study is motivated by the needs of measurement of the performance of the Project from the accounting transparent point of view. As the results of this study, major findings are summarized as follows. Firstly the earnings quality of the selected firms was lower than that of the paired firms. This can be explained as a result of motivation of earnings management by companies eager to meet the requirements to be selected for the Project. Secondly, in the selected year, the earnings quality was proved to improve, comparing to the screening year. This can be explained by the efforts of companies to reinforce management innovation and transparent management, which in turn led to positive effects on the earnings quality. These findings were found to be consistent in the additional analyses, where the earning quality of the reconstructed sample with only selected companies was compared for the screening year and the selected year, based on the year before the screening year(t-2).

• Journal of Korean Society of Forest Science
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• v.98 no.4
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• pp.409-416
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• 2009

Forest biomass estimation is essential for greenhouse gas inventories and terrestrial carbon accounting. Remote sensing allows for estimating forest biomass over a large area. This study was conducted to estimate forest biomass and to produce a forest biomass map for Muju county using forest biomass conversion table developed by field plot data from the 5th National Forest Inventory and Landsat TM-5. Correlation analysis was carried out to select suitable independent variables for developing regression models. It was resulted that the height class, crown closure density, and age class were highly correlated with forest biomass. Six regression models were used with the combination of these three stand variables and verified by validation statistics such as root mean square error (RMSE) and mean bias. It was found that a regression model with crown closure density and height class (Model V) was better than others for estimating forest biomass. A biomass conversion table by model V was produced and then used for estimating forest biomass in the study site. The total forest biomass of the Muju county was estimated about 8.8 million ton, or 128.3 ton/ha by the conversion table.

• Journal of the Korea Institute of Building Construction
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• v.15 no.5
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• pp.507-514
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• 2015

In recent years, the number of construction projects carried out to repair and reinforce newly built structures and facilities has been on the rise compared to the number of new construction projects, accounting for more than 90 percents of all construction projects carried out by specialty construction companies. However, as some of the ordering parties fill out the required tasks incorrectly, the wrong information on construction bids is announced, and the specialty construction companies that hold a license and technology are unable to get the job at the right time. As such, it is critical to prevent unnecessary time and expense related to the correction of incorrect bid announcements by providing accurate information and definitions, because the tasks of each specialty construction work stipulated in the framework act of construction industry are vague. Therefore, the causes and problems were analyzed based on the correction cases of bid information, and a plan that can address the problem will be proposed. The result of this study can be utilized as fundamental data to achieve an institutional improvement in the bidding service for the specialty construction companies.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1171-1180
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• 2015

Multidimensional scaling (MDS) is a graphical technique of multivariate analysis to display dissimilarities among individuals into low-dimensional space. We often have two kinds of MDS which are metric MDS and non-metric MDS. Metric MDS can be applied to quantitative data; however, we need additional information about variables because it only shows relationships among individuals. Gower (1992) proposed a method that can represent variable information using trajectories of the pseudo-points for quantitative variables on the metric MDS space. We will call his method a 'replacement method'. However, the trajectory can not be represented even though metric MDS can be applied to binary data when we apply his method to binary data. Therefore, we propose a method to represent information of binary variables using pseudo-points called a 'partition method'. The proposed method partitions pseudo-points, accounting both the rate of zeroes and ones. Our metric MDS using the proposed partition method can show the relationship between individuals and variables for binary data.

• Management & Information Systems Review
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• v.28 no.3
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• pp.69-98
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• 2009

This study investigates in-sample and out-of-sample predictive abilities of accruals and accruals components with respect to future cash flows using models developed by Barth et al.(2001). In tests, data collected fromda62 Korean KOSPI and KOSDAQ listed firms for ccr4-2007 are used. Results of in-sample prediction tests are similar with those of Barth et al.(2001). Their accrual components model is better than other three models(NI only model, CF only model and NI-total accruals model) in future cash flows predictive ability. That is, in the case of in-sample prediction, accrual components excluding amortization have additional information contents for future cash flows. But in out-of-sample tests, the results are different. The model including operational cash flows(CF only model) shows best out-of-sample predictive ability with respect to future cash flows among above four prediction models. The accrual components model of Barth et al.(2001) has worst out-of-sample predictive ability. The results are robust to sensitivity analyses. In conclusion, we can't find the evidence that accruals and accrual components have predictive ability with respect to future cash flows in out-of-sample prediction tests. This results are consistent with results of Lev et al.(2005), and inconsistent with the belief of accounting standards formulating organizations such as FASB and KASB.

• Journal of agricultural medicine and community health
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• v.37 no.2
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• pp.76-83
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• 2012

Objectives: The purpose of this study was to define the underserved emergency medical services (EMS) areas in Daejeon metropolitan city, as well as to identify their distinctive characteristics in public health perspectives. Methods: An underserved EMS area was operationally defined as an area in which it is difficult to arrive at an emergency medical center within 30 minutes. Using a cost-weighted distance algorithm with a geographic information system (GIS), the underserved EMS area was calculated. The characteristics of the underserved areas were analyzed by the Chi-square test. The SPSS statistical software package was used to perform the statistical analysis. All statistical tests were two-sided, and a p-value<0.05 was considered statistically significant. Results: Twelve administrative sectors ('Dong' in Korean) were included in the underserved areas, accounting for a population of approximately 8,100 citizens. The relationships between underserved EMS area and populations of agriculture, fishery, and forestry; citizens who are recipients of national basic livelihood security program; disabled; or aged 65 or older were statistically significant. Conclusion: It was found that 12 administrative sectors were included in the underserved EMS areas. Revealing underserved EMS areas using GIS analysis based on a cost-weighted distance algorithm of road data was an effective analytic method. However, as this study was confined to Daejeon City, South Korea, a nation-wide study should be performed to provide a more accurate conclusion.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.163-175
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• 2016

The purpose of this study is to provide an efficient internal control system formation incentives for company and to confirm empirically usefulness of the internal accounting control system for financial institutions by analyzing whether the internal control vulnerabilities of companies related significantly to the classification and assessment of soundness of financial institutions. Empirical analysis covered KOSPI, KOSDAQ listed companies and unlisted companies with more than 100 billion won of assets which have trading performance with "K" financial institution from 2008 until 2013. Whereas non-internal control vulnerability reporting companies by the internal control of financial reporting received average credit rating of BBB on average, reporting companies received CCC rating. And statistically significantly, non-reporting companies are classified as "normal" and reporting companies are classified as "precautionary loan" when it comes to asset quality classification rating. Therefore, reported information of internal control vulnerability reduced the credibility of the financial data, which causes low credit ratings for companies and suggests financial institutions save additional allowance for asset insolvency prevention and require high interest rates. It is a major contribution of this study that vulnerability reporting of internal control in accordance with the internal control of financial reporting can be used as information significant for the evaluation of financial institutions on corporate soundness.

• Management & Information Systems Review
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• v.35 no.4
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• pp.37-56
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• 2016

This study investigates how adapting of K-IFRS effects NI and OCI affecting of credit rating on changing of the period and variable by using samples of around adapting of K-IFRS. First of all, after adapting of K-IFRS(2011-2013), it was noticeable that how NI affecting after adapting of K-IFRS(2007-2010) had been increased more than that of before affecting of K-IFRS. However, there was not a single difference in affecting OCI on credit rating comparing to the past of adapting of K-IFRS. Second, it seemed like NI affected more after adapting of K-IFRS(2011-2013). The first year of K-IFRS had bigger incremental effect than after adapting of K-IFRS. However, after adapting of K-IFRS, OCI affecting on credit rating had no ncremental effect. Third, it seemed like NI in the first year affected more than OCI on credit rating. After adapting(2012-2013) of K-IFRS, it seemed like NI and OCI do not affect on credit rating. To interpret this, NI and OCI affected the first year of adapting of K-IFRS; therefore, adapting of K-IFRS affected without affecting financial ratio on adapting credit rating. As the time goes on, it can be expected that adapting K-IFRS became stable; therefore, extra incremental effect will not be seen comparing to the early adaption. The implication of this study is when information users use credit rating, they have to concern of affecting of K-IFRS. This is because NI in financial ratio is affecting on credit rating.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.107-133
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• 2021

This study was performed in order to provide suggestions on how to strengthen librarian competency by evaluating and analyzing the competency value of librarians as information professions. First, the study divided the common competency value of librarians as human capital of libraries into skills, knowledge, behavior and attitude, and analyzed each area of competency value for librarians of the A-library. As a result, the average of the 'librarian's behavior and attitude' area was the highest, followed by the 'librarian's skill' area and the 'librarian's knowledge' area. Second, in terms of 'librarian's skill', A-library librarians' competence values were high in the order of 'communication', 'leadership', 'technology' and in the terms of 'librarian's knowledge' ones were high in the order of 'law and policy', 'marketing', 'learning and growth' and 'finance and accounting'. In addition, in areas of 'librarian's behavior and attitude', the factors were high in the order of 'ethics and values', 'interpersonal relationships' and 'customer service'. Third, the analysis of whether the average difference exists depending on the characteristics of A-library librarians on their evaluation of the competency value shows that only the 'working period' factor in the total competency value and the two factors 'age' and 'working period' were statistically significant in the 'librarian's knowledge' area. Forth, as a result of a regression analysis to identify the characteristics of A-library librarians and their impact on competency value, only the 'final education' factor was statistically significant for the competency value of the 'librarian's skill' area. Fifth, in the survey on problems and desirable improvement measures in increasing the competency value of librarians, the proportion of presenting problems and improvement plan in systemic aspects such as the 'librarian qualification system' and 'librarian training system' was high.