• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.227-227
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• 2015

The Wairarapa Valley occupies a predominantly rural area in the lower North Island of New Zealand. It supports a mix of intensive farming (dairy), dry stock farming (sheep and beef cattle) and horticulture (including wine grapes). The valley floor is traversed by the Ruamahanga River, the largest river in the Wellington region with a total catchment area of 3,430 km2. Environmental, cultural and recreational values associated with this Ruamahanga River are very high. The alluvial gravel and sand aquifers of the Wairarapa Valley, support productive groundwater aquifers at depths of up to 100 metres below ground while the Ruamahanga River and its tributaries present a further source of water for users. Water is allocated to users via resource consents by Greater Wellington Regional Council (GWRC). With intensifying land use, demand from the surface and groundwater resources of the Wairarapa Valley has increased substantially in recent times and careful management is needed to ensure values are maintained. This paper describes the approach being taken to manage water resources in the Wairarapa Valley and redefine appropriate limits of sustainable water use. There are three key parts: Quantifying the groundwater resource. A FEFLOW numerical groundwater flow model was developed by GWRC. This modelling phase provided a much improved understanding of aquifer recharge and abstraction processes. It also began to reveal the extent of hydraulic connection between aquifer and river systems and the importance of moving towards an integrated (conjunctive) approach to allocating water. Development of a conjunctive management framework. The FEFLOW model was used to quantify the stream flow depletion impacts of a range of groundwater abstraction scenarios. From this, three abstraction categories (A, B and C) that describe diminishing degrees of hydraulic connection between ground and surface water resources were mapped in 3 dimensions across the Valley. Interim allocation limits have been defined for each of 17 discrete management units within the valley based on both local scale aquifer recharge and stream flow depletion criteria but also cumulative impacts at the valley-wide scale. These allocation limits are to be further refined into agreed final limits through a community-led decision making process. Community involvement in the limit setting process. Historically in New Zealand, limits for sustainable resource use have been established primarily on the basis of 'hard science' and the decision making process has been driven by regional councils. Community involvement in limit setting processes has been through consultation rather than active participation. Recent legislation in the form of a National Policy Statement on Freshwater Management (2011) is reforming this approach. In particular, collaborative consensus-based decision making with active engagement from stakeholders is now expected. With this in mind, a committee of Wairarapa local people with a wide range of backgrounds was established in 2014. The role of this committee is to make final recommendations about resource use limits (including allocation of water) that reflect the aspirations of the communities they represent. To assist the committee in taking a holistic view it is intended that the existing numerical groundwater flow models will be coupled with with surface flow, contaminant transport, biological and economic models. This will provide the basis for assessing the likely outcomes of a range of future land use and resource limit scenarios.

• Korean System Dynamics Review
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• v.14 no.1
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• pp.5-29
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• 2013

This study tries to measure the direct and indirect effects of the Regional Strategic Industry (RSI) promotion projects in Chungbuk Province in Korea. In specific, it critically examines whether there exists policy consistency and connectivity between the hardware-oriented Stage I (2002~2007) and the software-centered Stage II (2008~2012) RSI promotion projects. Applying System Dynamics (SD) techniques, this study examines complex system characteristics of RSI promotion projects, all of which have been derived from the causal and stock-flow models and their simulated results. Major findings are as follows: Firstly, 'the continuous investment' is regarded as the most crucial policy leverage for the strategic industry promotion and regional economic growth. Secondly, without exceptions, the RSI promotion projects should switch their evaluation criteria to performance-oriented ones. Thirdly, in selecting their subprojects, the RSI promotion projects should pay due attention to evaluating technology value and marketability. Fourthly, it should put policy priority in strengthening cluster networking and interconnectivity among projects, inevitably supporting a selective number of virtuous network systems. Fifthly, auxiliary projects such as marketing, technology aid, and knowledge-based services should not be overlooked.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.81-90
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• 2020

This study investigates the nexus between the level of Corporate Social Responsibility Disclosures (CSRD) and Risk of Bankruptcy of companies that are listing in the Stock Exchanges of Vietnam. To investigate that relationship, this study collected secondary data from annual audited financial statements from 2014 to 2018 of listing companies. Applying two different regression models with two dependent variables and six independent and control variables, we find out that Vietnamese firms with higher level of CSRD performance can rapidly reduce their risk of bankruptcy. This phenomenon happens in the current year and in the coming years in all firms in the research sample. This result may be that the disclosures of social responsibility information can bring financial and non-financial benefits to the firms. In addition, the results also point out that there is a difference in risk of bankruptcy between the group of companies, which discloses and the one which does not disclose corporate social responsibility on their annual reports. This might be from the effects of various factors such as business size, financial leverage, market to book ratio, return on assets, cash flow from operations, etc. Our research results can be applied to other firms in Vietnam and in other similar jurisdictions.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.327-333
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• 2021

Accounting information disclosure by enterprises is important for third-party entities (suppliers, creditors, banks, regulators, etc.). Voluntary accounting information disclosure (VAID) refers to additional information related to business activities shown on the annual report above and beyond the required information about business results and financial position as well as cash flow. This supports the stakeholders gaining useful information to make proper business decisions. The article examines the influence of ownership structure on the voluntary accounting information disclosure of the top 100 Vietnamese listed companies (VN100). Data collected by authors on regular annual reports totaled 425 observations from 2015 to 2019. The article uses OLS to test multivariate regression models with time-series data. The research results show that there are three variables affecting voluntary accounting information disclosure, of which foreign ownership and institution ownership have a positive impact, while concentration ownership has an opposite impact. Accordingly, the managers of VN100 should raise awareness in order to demonstrate the obligation of information providers to users to ensure clarity and completeness. The state agencies should encourage VN100 to enhance voluntary accounting information disclosure. This contributes to improve the information level of Vietnamese listed companies to embrace the trend of international economic integration.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.259-265
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• 2018

Compartment models, a type of mathematical model, have been widely applied to characterize the changes in a dynamic system with sequential events or processes, such as the spread of an epidemic disease. A compartment model comprises compartments, and the relations between compartments are depicted as boxes and arrows. This principle is similar to that of the system dynamics (SD) approach to constructing a simulation model with stocks and flows. In addition, both models are structured using differential equations. With this mutual and translatable principle, this study, in terms of SD, translates a reference SEIR model, which was developed in a recent study to characterize the transmission of the Middle East respiratory syndrome (MERS) in South Korea. Compared to the replicated result of the reference SEIR model (Model 1), the translated SEIR model (Model 2) demonstrates the same simulation result (error=0). The results of this study provide insight into the application of SD relative to constructing an epidemic compartment model using schematization and differential equations. The translated SD artifact can be used as a reference model for other epidemic diseases.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.5
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• pp.355-363
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• 2007

The Korean traditional 'Ondol' system has been a target for innovation to meet the requirements of sustainable domestic building and low carbon emission energy utilization. Simulation techniques provide designers and researchers with powerful tools to predict heating load and thermal behaviour of Ondol systems installed in various contexts. However, there are few studies on Ondol models, especially associated with multi-stories buildings of which type covers about 50% of Korean housing stock. In this study, we analyzed the double floor Ondol system on the multi-stories buildings using the ESP-r program. On the basis of the double floor Ondol system, we suggested the new modelling method that is composed of the Vent zone and Ondol zone. Using the this model, sensitivity analysis was carried out to refine the applicability of the model taking account of control conditions, constructions, air change and air flow network method and CFD analysis using the FLUENT. The air layer has enough temperature to use in heating zone. It is suggested that the simplicity of the model will allow building designers and mechanical engineers easily to implement scenario-based assessments of design options as well as control strategies. Later, we will simulate the real buildings and analyze the air distributions using the Fluent according to the various conditions.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of the Korean Society for Library and Information Science
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• v.4
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• pp.43-71
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• 1975

Operations research has developed rapidly since its origins in World War II. Practitioners of O. R. have contributed to almost every aspect of government and business. More recently, a number of operations researchers have turned their attention to library and information systems, and the author believes that significant research has resulted. It is the purpose of this essay to introduce the library audience to some of these accomplishments, to present some of the author's hypotheses on the subject of library management to which he belives O. R. has great potential, and to suggest some future research directions. Some problem areas in librianship where O. R. may play a part have been discussed and are summarized below. (1) Library location. It is usually necessary to make balance between accessibility and cost In location problems. Many mathematical methods are available for identifying the optimal locations once the balance between these two criteria has been decided. The major difficulties lie in relating cost to size and in taking future change into account when discriminating possible solutions. (2) Planning new facilities. Standard approaches to using mathematical models for simple investment decisions are well established. If the problem is one of choosing the most economical way of achieving a certain objective, one may compare th althenatives by using one of the discounted cash flow techniques. In other situations it may be necessary to use of cost-benefit approach. (3) Allocating library resources. In order to allocate the resources to best advantage the librarian needs to know how the effectiveness of the services he offers depends on the way he puts his resources. The O. R. approach to the problems is to construct a model representing effectiveness as a mathematical function of levels of different inputs(e.g., numbers of people in different jobs, acquisitions of different types, physical resources). (4) Long term planning. Resource allocation problems are generally concerned with up to one and a half years ahead. The longer term certainly offers both greater freedom of action and greater uncertainty. Thus it is difficult to generalize about long term planning problems. In other fields, however, O. R. has made a significant contribution to long range planning and it is likely to have one to make in librarianship as well. (5) Public relations. It is generally accepted that actual and potential users are too ignorant both of the range of library services provided and of how to make use of them. How should services be brought to the attention of potential users? The answer seems to lie in obtaining empirical evidence by controlled experiments in which a group of libraries participated. (6) Acquisition policy. In comparing alternative policies for acquisition of materials one needs to know the implications of each service which depends on the stock. Second is the relative importance to be ascribed to each service for each class of user. By reducing the level of the first, formal models will allow the librarian to concentrate his attention upon the value judgements which will be necessary for the second. (7) Loan policy. The approach to choosing between loan policies is much the same as the previous approach. (8) Manpower planning. For large library systems one should consider constructing models which will permit the skills necessary in the future with predictions of the skills that will be available, so as to allow informed decisions. (9) Management information system for libraries. A great deal of data can be available in libraries as a by-product of all recording activities. It is particularly tempting when procedures are computerized to make summary statistics available as a management information system. The values of information to particular decisions that may have to be taken future is best assessed in terms of a model of the relevant problem. (10) Management gaming. One of the most common uses of a management game is as a means of developing staff's to take decisions. The value of such exercises depends upon the validity of the computerized model. If the model were sufficiently simple to take the form of a mathematical equation, decision-makers would probably able to learn adequately from a graph. More complex situations require simulation models. (11) Diagnostics tools. Libraries are sufficiently complex systems that it would be useful to have available simple means of telling whether performance could be regarded as satisfactory which, if it could not, would also provide pointers to what was wrong. (12) Data banks. It would appear to be worth considering establishing a bank for certain types of data. It certain items on questionnaires were to take a standard form, a greater pool of data would de available for various analysis. (13) Effectiveness measures. The meaning of a library performance measure is not readily interpreted. Each measure must itself be assessed in relation to the corresponding measures for earlier periods of time and a standard measure that may be a corresponding measure in another library, the 'norm', the 'best practice', or user expectations.

• Progress in Medical Physics
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• v.10 no.3
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• pp.159-168
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• 1999

Cardiac disease is one of the leading causes of death in Korea. In quantitative analysis of cardiac function and morphological information by three-dimensional reconstruction of magnetic resonance images, left ventricle provides an important role functionally and physiologically. However, existing procedures mostly rely on the extensive human interaction and are seldom evaluated on clinical applications. In this study, we developed a system which could perform automatic extraction of enpicardial and endocardial contour and analysis of cardiac function to evaluate reliability and stability of each system comparing with the result of ARGUS system offered 1.5T Siemens MRI system and manual method performed by clinicians. For various aspects, we investigated reliability of each system by compared with left ventricular contour, end-diastolic volume (EDV), end-systolic volume (ESV), stock volume (SV), ejection fraction (EF), cardiac output (CO) and wall thickness (WT). When comparing with manual method, extracted results of developed process using minimum error threshold (MET) method that automatically extracts contour from cardiac MR images and ARGUS system were demonstrated as successful rate 90% of the contour extraction. When calculating cardiac function parameters using MET and comparing with using correlation coefficients analysis method, the process extracts endocardial and epicardial contour using MET, values from automatic and ARGUS method agreed with manual values within :t 3% average error. It was successfully demonstrated that automatic method using threshold technique could provide high potential for assessing of each parameters with relatively high reliability compared with manual method. In this study, the method developed in this study could reduce processing time compared with ARGUS and manual method due to a simple threshold technique. This method is useful for diagnosis of cardiac disease, simulating physiological function and amount of blood flow of left ventricle. In addition, this method could be valuable in developing automatic systems in order to apply to other deformable image models.