• Spatial Information Research
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• v.4 no.1
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• pp.21-42
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• 1996

The Anyang-cheon is one of the Han River tributaries in Seoul Metropolitan area. It is 35.1km long, has a basin area of 287km2 and touches seven cities of Kyounggi Province and part of Seoul. The purpose of this study were 1) to reconstruct the ancient stream network and to investigate the change of landuse in Anyang-cheon watershed between 1957 and 1991,2) to measure the change of the hydrologic ¬acteristics with urbanization, 3) to suggest the institutional solutions to reduce natural hazard as the area has urbanizedThe main results are as follows: 1.Anyang-cheon river basin has experienced the rapid urbanization and industrialization since 1957. Anyang-cheon stream network was oversimplified in the watershed. The total stream length decreased atributaries in the upper part of river basin have eliminated or buried undergrolmd in pipes. 2.Urbanization impacted to all of the area of Anyang-cht'On watershed. Urbanization in Anyang-cheon watershed corresponds to the large portion of flat area, especially flood - prone zone of river side, and the small portion of Greenbelt to constrain urban expantion in cities. 3.The urbanization of Anyang-cheon watershed produces fundamental changes in watershed hydrology. As infiltration is reduced by the creation of extensive pavement, concrete surface, and sewer pipe, runoff moves more quickly from upland to stream. As a result, runoff from the watershed is flashier, increasing flood hazardAs urban area continue to grow we will need to better utilize stream by protecting and enhancing stream systems.otecting and enhancing stream systems.tems.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.3
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• pp.1-16
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• 2012

Under the international business circumstance, global supply chain management is considered a vital strategic challenge to small and medium-sized enterprises(SMEs) suffering from deficient resources and capabilities to exploit overseas markets comparing with large corporations. That is because they can expand their business domains into overseas markets by establishing strategic alliances with global supply chain partners. Although a wide range of previous researches have emphasized the cooperative networks in the chain, most are ignoring the importance of developing relational characteristics such as trust and reciprocity with the partners. Besides, verifying the relational factors influencing firms' export performances, some studies proposed different and inconsistent factors. According to the social capital theory, which is the social quality and networks facilitating close cooperation of inter-individual and inter-organization, provides the integrated view to identify the relational characteristics in the aspects of network, trust and reciprocal norm. Meanwhile, a number of researchers shows that global entrepreneurship is the internal and intangible resource necessary to promote SMEs' internationalization. Upon closer examination, however, they cannot explain clearly its influencing mechanism in the inter-firm cooperative relationships. This study is to verify the effect of social capital accumulated within global supply chain on SMEs' qualitative and quantitative export performance. In addition, we shed new light on global entrepreneurship expected to be concerned with the formation of social capital and the enhancement of export performances. For this purpose, the questionnaires, developed through literature review, were collected from 192 Korean SMEs affiliated in Korean Medium Industries Association and Global Chief Executive Officer's Club focusing on their memberships' international business. As a result of multi-regression analysis, the social capital - network, trust and reciprocal norm shared with global supply chain partner - as well as global entrepreneurship - innovativeness, proactiveness and risk-taking - have positive effect on SMEs' export performances. Also global entrepreneurship affects positively social capital which has mediating effect partially in the relationship between global entrepreneurship and performances. These results means that there is a structural process - global entrepreneurship(input), social capital(output), and export performances(outcome). In other words, a firm should consistently invest in and develop the social capital with global supply chain partners in order to achieve common goals, establish strategic collaborations and obtain long-term export performances. Furthermore, it is required to foster the global entrepreneurship in an organization so as to build up the social capital. More detailed practical issues and discussion are made in the conclusion.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.297-306
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• 2019

Soil moisture monitoring is an important task to cope with climate change, and soil water prediction can provide large-scale soil moisture information. Therefore, this study was conducted to evaluate the relationship between the measured and predicted soil water content, and to estimate the correlation between the soil characteristics and soil water content. The selected sites in soil moisture monitoring network were 76, and the soil with high sand content (sand, loamy sand, and sandy loam in soil texture) accounted for 77% of the total. Organic matter and bulk density were 0.03 to 3.50% and 1.01 to 1.69 Mg m^-3, respectively. Predicting values of field capacity and wilting point were lower than the measured soil water content, and the correlation coefficient between the measured and predicted values were low as 0.548 to 0.748. However, a significantly high positive correlation (p<0.01) found between the measured and predicted soil water content. Soil water (field water capacity and wilting point) content was highly positively correlated with silt, clay, and organic matter (p<0.01) and highly negatively correlated with sand (p<0.01).

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.126-138
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• 2017

Recently, the national interest in environment is increasing and for dealing with water environment-related issues swiftly and accurately, the demand to facilitate the analysis of water environment data using a GIS is growing. To meet such growing demands, a spatial network data-based stream network analysis map(Korean Reach File; KRF) supporting spatial analysis of water environment data was developed and is being provided. However, there is a difficulty in delineating catchment areas, which are the basis of supplying spatial data including relevant information frequently required by the users such as establishing remediation measures against water pollution accidents. Therefore, in this study, the development of a computer program was made. The development process included steps such as designing a delineation method, and developing an algorithm and modules. DEM(Digital Elevation Model) and FDR(Flow Direction) were used as the major data to automatically delineate catchment areas. The algorithm for the delineation of catchment areas was developed through three stages; catchment area grid extraction, boundary point extraction, and boundary line division. Also, an add-in catchment area delineation module, based on ArcGIS from ESRI, was developed in the consideration of productivity and utility of the program. Using the developed program, the catchment areas were delineated and they were compared to the catchment areas currently used by the government. The results showed that the catchment areas were delineated efficiently using the digital elevation data. Especially, in the regions with clear topographical slopes, they were delineated accurately and swiftly. Although in some regions with flat fields of paddles and downtowns or well-organized drainage facilities, the catchment areas were not segmented accurately, the program definitely reduce the processing time to delineate existing catchment areas. In the future, more efforts should be made to enhance current algorithm to facilitate the use of the higher precision of digital elevation data, and furthermore reducing the calculation time for processing large data volume.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.95-114
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• 2020

This paper, a meta-analysis of the effect of the start-up support policy on the start-up performance was conducted to examine the effect of the start-up support policy on the start-up performance of beneficiary companies and to provide theoretical and practical implications to support organizations and practitioners. To this end, 35 papers containing the correlation coefficient, which is a positive statistical value, were selected from the previous studies in academic journals and dissertations published in Korea from 2007 to 2020. In the preceding study of the start-up support policy, the independent variables include funding, education support, facility/equipment support, network support, mentoring support, consulting support, marketing support, management support, technical support, manpower support, and finance as a dependent variable. The effect size of the impact on aptitude and non-financial performance was reviewed. The pattern of the effect size was presented as a forest plot for easy visual understanding, and outliers were verified through sensitivity analysis for small-study-effect data with publication convenience. As a result of analyzing the effect size of the government-supported policy, it was verified that the effect size was generally medium or higher, affecting the entrepreneurial performance. Among the independent variables, the factor that has the greatest effect on startup performance is manpower support, followed by technical support, marketing support, management support, facility/equipment support, education support, mentoring support, funding, network support, and consulting support. It was analyzed that the effect size was large in order. As the 「Small and Medium Business Startup Support Act」 was recently reorganized from the manufacturing industry to digital transformation and smartization on October 8, 2020, the start-up support policy should consider the start-up stage and verify the priorities to organize the budget.

• Asia Marketing Journal
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• v.13 no.1
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• pp.51-84
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• 2011

It seems essential to examine the factors that may affect relationship commitment of subcontractors to parent companies in the industrial market in Korea in an effort to construct a win-win-type cooperative network among them. Lots of studies have been focusing on the consumer goods market. Relatively few studies have been focused on industrial market. In the industrial goods market subcontractors used to sell their parts or services only to a small number of parent companies in a large quantity, resulting in decisive control of subcontractors over the quality of parent companies' finished goods. This is why relationship between subcontractors and parent companies is extremely important. From this viewpoint, this study aims to survey and analyze empirically the paths leading to relationship commitment of subcontractors toward the parent companies which are required to incite them to build up a collaborative network by means of subcontractors' entrepreneurship. For this aim, market orientation effects of entrepreneurship as well as factors of performance and trust are particularly set forth as the bases of developing hypotheses in this study in order to explore the paths from entrepreneurship to relationship commitment as follows. First, the path of entrepreneurship-market orientation-communication-trust- relationship commitment; second, the path of entrepreneurship-market orientation-performance-relationship commitment; third, the path of entrepreneurship-market orientation-transaction specific asset investment -trust-relationship commitment; and fourth, the path in which the entrepreneurship is expected to promote direct transaction specific asset investment by parent companies to induce their trust and, eventually, relationship commitment of subcontractors. The outcomes of the empirical analysis in this study may be summed up as follows: First, the conclusions of preceding studies are also supported here by the fact that the entrepreneurship of subcontractors promotes their market orientation (hypothesis 9), indicating that the entrepreneurship can facilitate collection, proliferation of and response to market informations. On the contrary, however, the assumption that the entrepreneurship of subcontractors might directly accelerate transaction specific asset investment by parent companies (hypothesis 8) is rejected. Second, although the influence of subcontractors' entrepreneurship on parent companies' investment of assets peculiar to their transactions is not affirmed, the assumption is found to be supported that subcontractors' market orientation would expedite the parent companies' investment of assets peculiar to their transactions. Moreover, it is also confirmed that parent companies' investment of assets peculiar to transactions would promote subcontractors' trust toward the parent companies (hypothesis 6), signifying that parent companies may level up their trust in subcontractors when they make great amount of efforts to invest in the assets peculiar to transactions, not behaving opportunistically, Third, the hypotheses 4 and 5 also turn out to be supported by the analysis as the former assumes that market orientation could promote communication and the latter relates that the communication between subcontractors and parent companies would prompt trust, both results in affirming that market orientation could introduce open communication to speed up sharing of information and that sharing of information by way of communication might give an impetus to trust. Fourth, the assumption that subcontractors' market orientation would expedite performance (hypothesis 3) is also proved favorably to the significant level equivalent to that of preceding studies. Fifth, same as preceding studies, it is also verified in this study that the benefit (outcomes) awarded by parent companies to subcontractors will be a direct cause exercising a positive impact upon relationship commitment(hypothesis 2) and that the trust of subcontractors toward parent companies may have affirmative influence on the relationship commitment(hypothesis 1). Overall, the first, second and third paths are identified as being supported by the hypotheses among constituent factors, while the fourth path is deemed meaningless since it is shown that the entrepreneurship exercises no effects on parent companies' investment in the assets peculiar to transactions.