• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.1-6
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• 2021

This study designed an artificial intelligence education program using localized public data as an educational method for improving computational thinking in elementary school students. Program design and development was carried out based on the results of pre-requisite analysis on elementary school students according to the ADDIE model. Based on localized public data, the program was organized to learn the principles of artificial intelligence by utilizing "Machine Learning for Kids" and "Scratch" and to solve problems and improve computational thinking skills through abstracting public data for purpose.Through subsequent research, it is necessary to put this education program into the field and verify the change in students' computational thinking as a result.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Ecology and Resilient Infrastructure
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• v.5 no.1
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• pp.35-44
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• 2018

Social, economic and environmental problems caused by rapid urbanization have been recently overcome by various civic participation projects. Local governance and resident - led partnership through field - based cooperative operating systems from urban regeneration to village projects are considered success factors. Among these, the village greening project which directly affects the residents and requires spontaneity requires the role and cooperation of the various participating actors due to the sharing of public space and private space. Social capital plays a key role in the sustainability and participation of the above - mentioned business as a relational capital centered on trust and participation, network and norms. Therefore, empirical research is needed. In this study, basic research was carried out to build a formation model of social capital in participation - type greening project expanding urban green space system to living area. We analyzed the elements of participation, the components of business progress, and the factors of social capital formation through literature review and in - depth interviews with participating experts. The purpose of this study is to provide basic data of social capital formation model for analyzing sustainability and activation strategies in the future.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.526-532
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• 2023

Falls are fatal accidents that occur more than 420,000 times a year worldwide. Therefore, to study patients with falls, we found the association between extrinsic injury codes and principal diagnosis S-codes of patients with falls, and developed a prediction model to predict extrinsic injury codes based on the data of principal diagnosis S-codes of patients with falls. In this study, we received two years of data from 2020 and 2021 from Institution A, located in Gangneung City, Gangwon Special Self-Governing Province, and extracted only the data from W00 to W19 of the extrinsic injury codes related to falls, and developed a prediction model using W01, W10, W13, and W18 of the extrinsic injury codes of falls, which had enough principal diagnosis S-codes to develop a prediction model. 80% of the data were categorized as training data and 20% as testing data. The model was developed using MLP (Multi-Layer Perceptron) with 6 variables (gender, age, principal diagnosis S-code, surgery, hospitalization, and alcohol consumption) in the input layer, 2 hidden layers with 64 nodes, and an output layer with 4 nodes for W01, W10, W13, and W18 exogenous damage codes using the softmax activation function. As a result of the training, the first training had an accuracy of 31.2%, but the 30th training had an accuracy of 87.5%, which confirmed the association between the fall extrinsic code and the main diagnosis S code of the fall patient.

• Korean Journal of Weed Science
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• v.7 no.2
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• pp.236-242
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• 1987

The study was conducted to interprete the fluctuation of weed vegetation in plant-sociological impacts as affected by the mixture use of oxyfluorfen and glyphosate with various dosages. Also, intended to know the real interaction between two herbicides in weeding efficacies. The better efficacy from the above mixture was recognized than from the oxyfluorfen + paraquat mixture on the perennial-sites. In lower rate mixture of oxyfluorfen, the dominance index was increased by the annual grass species (ie. Digitaria), and of glyphosate by the biennial Stellaria and perennial species (ie. Artemisia). Also, the positive maximum action of both oxyfluorfen and glyphosate in various mixture rates was categorized upto 0.55kg ai/ha for oxyfluorfen and 0.35kg ai/ha for glyphosate, respectively. However, the interaction between the above two herbicides recognized actually as negative. Consequently, the use of mixture compound of oxyfluorfen with glyphosate are expected rather to promote the control efficacy of specific weed species, to enlarge the weeding spectrum and to prolong the weeding periods than to reduce the application rate of both chemicals depending on any synergic interactions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.211-219
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• 2019

Accomplishing information system, techniques, methodology have been studied continuously and give much help to auditors who are using them. Additionally audit report which is the conclusion of accomplishing ISA(information system audit), has law of a basis and phase with ITA/EA Law(Electronic Government Law). This paper is for better quality of ISA report. But it has more errors about sentence and Grammatical structures. In this paper, to achieve quality improvement objectives, it is necessary to recognize the importance of an audit report by investigating on objectives, functionality, structures and usability of a report firstly, and a legal basis, the presence of report next. Several types of audit reports were chosen and the reports errors were divided into several categories and analyzed. After grasping reasons of those errors, the methods for fixing those errors and check-lists model was provided. And based on that foundation, the effectiveness validation about real audit reports was performed. The necessity for efforts to improve the quality of audit reports was emphasized and further research subject(AI Automatic tool) of this paper conclusion. We also expect this paper to be useful for the organization to improve on ISA in the future.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.242-264
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• 2019

This study focuses on considering the factors affecting the user intention to adopt an intelligent service - A.I. speaker services. Currently there can be a considerable difference between the expectation and the realized diffusion of IT-based intelligent services. This study aims to find out this gap based on the idea of diver previous researches including TAM and UTAUT studies and to identify the direct and indirect effects of diverse factors such as security issues, perceived time pressure, service innovativeness, and the experience of these IT-based intelligent services. And this study considers the expected impact of perceived time pressure factor on the user acceptance of A.I. speaker services. In analysis results, not only the traditional factors such as the perceived usefulness and the hedonic/utilitarian motives but also the perceived time pressure, the perceived security issues, and the experience of the services should be considered as meaningful factors to affect the users adopting A.I. speaker services.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.4
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• pp.27-43
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• 1990

This study aimed at developing a generalized model on the estimation of the long - term run - off volume for practical purpose. During the research period of last 3 years( 1986-1988), 3 types of estimation model on the long - term run - off volume(Effective rainfall model, unit hydrograph model and barne's model for dry season) had been developed by the author. In this study, through regressional analysis between determinant factors (bi of effective rainfall model, ai of unit hydrograph model and Wi of barne's model) and catchment characteris- tics(catchment area, distance round the catchment area, massing degree coefficient, river - exte- nsion, river - slope, river - density, infiltration of Watershed) of 11 test case areas by multiple regressional method, a new methodology on the derivation of determinant factors from catchment characteristics in the watershed areas having no hydrological station was developed. Therefore, in the resulting step, estimation equations on run - off volume for practical purpose of which input facor is only rainfall were developed. In the next stage, the derived equations were applied on the Kang - and Namgye - river catchment areas for checking of their goodness. The test results were as follows ; 1. In Kang - river area, average relative estimation errors of 72 hydrographs and of continuous daily run - off volume for 245 days( 1/5/1982 - 31/12) were calculated as 6.09%, 9.58% respectively. 2. In Namgye - river area, average relative estimation errors of 65 hydrographs and of conti- nuous daily run - off volume for 2fl days(5/4/1980-31/12) were 5.68%, 10.5% respectively. In both cases, relative estimation error was averaged as 7.96%, and so, the methodology in this study might be hetter organized than Kaziyama's formula when comparing with the relative error of the latter, 24~54%. However, two case studies cannot be the base materials enough for the full generalization of the model. So, in the future studies, many test case studies of this model should he carries out in the various catchment areas for making its generalization.

• Journal of the Korean association of regional geographers
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• v.1 no.1
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• pp.61-68
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• 1995

The writers study on the development and the prospect of computer map based on the most recent computer mapping technical aspects. We also study domestic prospect of computer map in connection with the present condition of domestic GIS. The main results are as follows: (1) Computer map has rapidly developed in spite of its short history. We expect that computer map will be improved more in the future owing to the development of computer hardware and software. Most mapping processes will be possible sooner or later owing to Artificial Intelligence(AI) and more improved scanner without human effort. (2) Computer map can be used for various industrial fields and its development can give a great help for technical advance in correlated industries. (3) Computer map has really developed in the country since 1980, when GIS was introduced. Especially, government planned to digitalize all the basic topographical maps covering the whole country between 1996-1998. We think that there is an epoch-making change in the development history of computer map in the future. (4) The development of GIS is closely connected with one of computer map, but the recent technical levels of GIS is not perfect. So there is an urgent need for technical supplement to produce good computer maps. (5) The government had better construct GIS database in order to cut down expenses derived from overlapping input of data by individual users and there is a need for data standard.