• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.49-63
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• 2015

Keyword indexed in multidisciplinary journals show trends about science and technology innovation. Nature and Science were selected as multidisciplinary journals for our analysis. In order to reduce the effect of plurality of keyword, stemming algorithm were implemented. After this process, we fitted growth curve of keyword (stem) following bass model, which is a well-known model in diffusion process. Bass model is useful for expressing growth pattern by assuming innovative and imitative activities in innovation spreading. In addition, we construct keyword co-occurrence network and calculate network measures such as centrality indices and local clustering coefficient. Based on network metrics and yearly frequency of keyword, time series analysis was conducted for obtaining statistical causality between these measures. For some cases, local clustering coefficient seems to Granger-cause yearly frequency of keyword. We expect that local clustering coefficient could be a supportive indicator of emerging science and technology.

• IE interfaces
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• v.25 no.2
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• pp.255-263
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• 2012

In order to ensure the reliability and specialty of weapon system test results, a policy of extending certified testing service institutions has been driven by applying accreditation system of the ones in defense industry. Bass and Logistic models are used to apply the policy effectively and forecast the diffusion pattern of certified testing service institutions. The parameters for diffusion forecast are estimated using the diffusion pattern of certified testing service institutions in non-defense industry, and these are applied to forecast the diffusion of certified ones in defense industry. Coefficients of innovation and imitation of Bass model are analyzed to derive the factors influencing the early adoption and diffusion patterns. The more increasing the coefficients, the earlier adoption occurred. Diffusion pattern due to coefficient of imitation, internal factor, has larger effect on sensitivity of diffusion pattern. This means that the self recognition of necessity is more effectively worked than the policy or regulations driven by government.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.345-352
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• 2022

Conducting AI-based fusion business due to the increment of ICT fusion medical device has been expanded. In addition, AI-based medical devices help change existing medical system on treatment into the paradigm of customized treatment such as preliminary diagnosis and prevention. It will be generally promoted to the change of medical device industry. Although the current demand forecasting of medical biotechnology commercialization is based on the method of Delphi and AHP, there is a problem that it is difficult to have a generalization due to fluctuation results according to a pool of participants. Therefore, the purpose of the paper is to predict demand forecasting for identifying promising technology based on building up big data in medical biotechnology. The development method is to employ candidate technologies of keywords extracted from SCOPUS and to use word2vec for drawing analysis indicator, technological distance similarity, and recommended technological similarity of top-level items in order to achieve a reasonable result. In addition, the method builds up academic big data for 5 years (2016-2020) in order to commercialize technology excavation on demand perspective. Lastly, the paper employs global data studies in order to develop domestic and international demand for technology excavation in the medical biotechnology field.

• Journal of Technology Innovation
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• v.31 no.2
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• pp.173-214
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• 2023

Investments in new technologies have grown significantly in size, and science and technology have a large and complex impact on society at large. With people's great interest in technology, the government has the duty to accurately assess the influence of new technologies on society to facilitate their acceptance in society. For this purpose, technology impact assessment should be performed to facilitate a social consensus. There has been research on the initial methods of technology assessment for 50 years. Following various academic studies and discussions based on numerous new technology response policies, coupled with the examination of trends and changes over time, academia and policymakers around the world have paid attention to the multilateral analysis of the impact of new technologies on future society. This study focuses on research changes such as the stage of forecasting factors that should consider the technology assessment of new technologies, despite differences between the development methods for the assessment between developed countries and South Korea. The analysis yielded three factors of technological understanding of awareness, professionalism, and gender characteristics, in addition to a previously identified factor. The three factors are then suggested as forecasting factors for new technology. The findings of this study provide both academic and policy evidence for technology assessment based on the country's Framework Act on Science and Technology.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of Information Technology Applications and Management
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• v.21 no.1
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• pp.177-184
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• 2014

This research analyzes the effects of factors on the demands for outbound to the countries such as Japan, China, the United States of America, Thailand, Philippines, Hong Kong, Singapore and Australia, the countries preferred by many Koreans. The factors for this research are (1) economic variables such as Korea Composite Stock Price Index (KOSPI), which could have influences on outbound tourism and exchange rate and (2) unpredictable events such as diseases, financial crisis and terrors. Regression analysis was used to identify relationship based on the monthly data from January 2001 to December 2010. The results of the analysis show that both exchange rate and KOSPI have impacts on the demands for outbound travel. In the case of travels to the United States of America and Philippines, Korean tourists usually have particular purposes such as studying, visiting relatives, playing golf or honeymoon, thus they are less influenced by the exchange rate. Moreover, Korean tourists tend not to visit particular locations for some time when shock reaction happens. As the demands for outbound travels are different from country to country accompanied by economic variables and shock variables, differentiated measure to should be considered to come close to the target numbers of tourists by switching as well as creating the demands. For further study we plan to build outbound tourism forecasting models using Artificial Neural Networks.

• Korean journal of applied entomology
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• v.42 no.1
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• pp.57-63
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• 2003

Accurate forecasting of occurrence time and abundance of insect pests is essential for developing technology of integrated pest management system. Digital image processing algorithms were utilized to automatically recognize rice planthoppers which are major insect pests in the rice cultivation field and were subsequently used to estimate densities in the field for efficient forecasting of insect pests. To the images taken in the rice field, image decomposition, top-hat transformation, threshold, and minimum and maximum filter were implemented for patterning individually the brown planthopper specimens attached at the bottom area of rice stems. In average 95.8cio of images were correctly recognized for estimating densities by the developed system, and the recognition rate was higher than that obtained from direct observations by experienced observers. Furthermore, the size of the recognized specimens was measured and was used for estimating the age structure in the observed brown planthopper populations.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1209-1212
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• 2007

Urban flooding with surcharges in sewer system was investigated because of unexpected torrential storm events these days, causing significant amounts of human and economic damages. Although there are limitations in forecasting and preventing natural disasters, integrated urban flooding management system using the SWMM(Storm Water Management Model) engine and Web technology will be an effective tool in securing safety in operating rubber-tired transportation system. In this study, the study area, located in Chuncheon, Kangwon province, was selected to evaluate the applicability of the SWMM model in forecasting urban flooding due to surcharges in sewer system The catchment are 21.10 ha in size and the average slope is 2% in lower flat areas. Information of subcatchment, conjunctions, and conduits was used as the SWMM interface to model surface runoff generation, water distribution through the sewer system and amount of water overflow. Through this study, the applicability of the SWMM for urban flooding forecasting was investigated and probability distribution of storm events module was developed to facilitate urban flooding prediction with forecasted rainfall amounts. In addition, this result can be used to the establishment of disaster management system for rainfall safety of rubber-tired tram in the future.

• Journal of Information Management
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• v.37 no.3
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• pp.85-97
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• 2006

In a Digital Age contents are comprized of knowledge and information. Also distinction between the sinner and loser is very clear thus only those who can rise up to challenge of changes can become the strong and a principal player. In this study this author presented a model for forecasting information service program(FISP) which is devised for the purpose of innovation of service pattern related to providing information which is ultimate goal of a library. This model is an innovative strategy escaping boldly from negative and inactive information service pattern known in analog age. Through this model this author attempted to outline methodology for heightening ideal of professional librarians and for assuring success in organizational system.