• Title/Summary/Keyword: Performance Model

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An Intelligent Decision Support System for Selecting Promising Technologies for R&D based on Time-series Patent Analysis (R&D 기술 선정을 위한 시계열 특허 분석 기반 지능형 의사결정지원시스템)

  • Lee, Choongseok;Lee, Suk Joo;Choi, Byounggu
    • Journal of Intelligence and Information Systems
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    • v.18 no.3
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    • pp.79-96
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    • 2012
  • As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

Content-based Recommendation Based on Social Network for Personalized News Services (개인화된 뉴스 서비스를 위한 소셜 네트워크 기반의 콘텐츠 추천기법)

  • Hong, Myung-Duk;Oh, Kyeong-Jin;Ga, Myung-Hyun;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
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    • v.19 no.3
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    • pp.57-71
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    • 2013
  • Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

A Study on the Characteristics of Enterprise R&D Capabilities Using Data Mining (데이터마이닝을 활용한 기업 R&D역량 특성에 관한 탐색 연구)

  • Kim, Sang-Gook;Lim, Jung-Sun;Park, Wan
    • Journal of Intelligence and Information Systems
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    • v.27 no.1
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    • pp.1-21
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    • 2021
  • As the global business environment changes, uncertainties in technology development and market needs increase, and competition among companies intensifies, interests and demands for R&D activities of individual companies are increasing. In order to cope with these environmental changes, R&D companies are strengthening R&D investment as one of the means to enhance the qualitative competitiveness of R&D while paying more attention to facility investment. As a result, facilities or R&D investment elements are inevitably a burden for R&D companies to bear future uncertainties. It is true that the management strategy of increasing investment in R&D as a means of enhancing R&D capability is highly uncertain in terms of corporate performance. In this study, the structural factors that influence the R&D capabilities of companies are explored in terms of technology management capabilities, R&D capabilities, and corporate classification attributes by utilizing data mining techniques, and the characteristics these individual factors present according to the level of R&D capabilities are analyzed. This study also showed cluster analysis and experimental results based on evidence data for all domestic R&D companies, and is expected to provide important implications for corporate management strategies to enhance R&D capabilities of individual companies. For each of the three viewpoints, detailed evaluation indexes were composed of 7, 2, and 4, respectively, to quantitatively measure individual levels in the corresponding area. In the case of technology management capability and R&D capability, the sub-item evaluation indexes that are being used by current domestic technology evaluation agencies were referenced, and the final detailed evaluation index was newly constructed in consideration of whether data could be obtained quantitatively. In the case of corporate classification attributes, the most basic corporate classification profile information is considered. In particular, in order to grasp the homogeneity of the R&D competency level, a comprehensive score for each company was given using detailed evaluation indicators of technology management capability and R&D capability, and the competency level was classified into five grades and compared with the cluster analysis results. In order to give the meaning according to the comparative evaluation between the analyzed cluster and the competency level grade, the clusters with high and low trends in R&D competency level were searched for each cluster. Afterwards, characteristics according to detailed evaluation indicators were analyzed in the cluster. Through this method of conducting research, two groups with high R&D competency and one with low level of R&D competency were analyzed, and the remaining two clusters were similar with almost high incidence. As a result, in this study, individual characteristics according to detailed evaluation indexes were analyzed for two clusters with high competency level and one cluster with low competency level. The implications of the results of this study are that the faster the replacement cycle of professional managers who can effectively respond to changes in technology and market demand, the more likely they will contribute to enhancing R&D capabilities. In the case of a private company, it is necessary to increase the intensity of input of R&D capabilities by enhancing the sense of belonging of R&D personnel to the company through conversion to a corporate company, and to provide the accuracy of responsibility and authority through the organization of the team unit. Since the number of technical commercialization achievements and technology certifications are occurring both in the case of contributing to capacity improvement and in case of not, it was confirmed that there is a limit in reviewing it as an important factor for enhancing R&D capacity from the perspective of management. Lastly, the experience of utility model filing was identified as a factor that has an important influence on R&D capability, and it was confirmed the need to provide motivation to encourage utility model filings in order to enhance R&D capability. As such, the results of this study are expected to provide important implications for corporate management strategies to enhance individual companies' R&D capabilities.

A study on the classification of research topics based on COVID-19 academic research using Topic modeling (토픽모델링을 활용한 COVID-19 학술 연구 기반 연구 주제 분류에 관한 연구)

  • Yoo, So-yeon;Lim, Gyoo-gun
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.155-174
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    • 2022
  • From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (

    ) were the topic modeling results for each research topic (
    ) was found to be derived from For example, as a result of topic modeling for papers related to 'vaccine', a new topic titled Topic 05 'neutralizing antibodies' was extracted. A neutralizing antibody is an antibody that protects cells from infection when a virus enters the body, and is said to play an important role in the production of therapeutic agents and vaccine development. In addition, as a result of extracting topics from papers related to 'treatment', a new topic called Topic 05 'cytokine' was discovered. A cytokine storm is when the immune cells of our body do not defend against attacks, but attack normal cells. Hidden topics that could not be found for the entire thesis were classified according to keywords, and topic modeling was performed to find detailed topics. In this study, we proposed a method of extracting topics from a large amount of literature using the LDA algorithm and extracting similar words using the Skip-gram method that predicts the similar words as the central word among the Word2vec models. The combination of the LDA model and the Word2vec model tried to show better performance by identifying the relationship between the document and the LDA subject and the relationship between the Word2vec document. In addition, as a clustering method through PCA dimension reduction, a method for intuitively classifying documents by using the t-SNE technique to classify documents with similar themes and forming groups into a structured organization of documents was presented. In a situation where the efforts of many researchers to overcome COVID-19 cannot keep up with the rapid publication of academic papers related to COVID-19, it will reduce the precious time and effort of healthcare professionals and policy makers, and rapidly gain new insights. We hope to help you get It is also expected to be used as basic data for researchers to explore new research directions.

  • The Effects on CRM Performance and Relationship Quality of Successful Elements in the Establishment of Customer Relationship Management: Focused on Marketing Approach (CRM구축과정에서 마케팅요인이 관계품질과 CRM성과에 미치는 영향)

    • Jang, Hyeong-Yu
      • Journal of Global Scholars of Marketing Science
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      • v.18 no.4
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      • pp.119-155
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      • 2008
    • Customer Relationship Management(CRM) has been a sustainable competitive edge of many companies. CRM analyzes customer data for designing and executing targeted marketing analysing customer behavior in order to make decisions relating to products and services including management information system. It is critical for companies to get and maintain profitable customers. How to manage relationships with customers effectively has become an important issue for both academicians and practitioners in recent years. However, the existing academic literature and the practical applications of customer relationship management(CRM) strategies have been focused on the technical process and organizational structure about the implementation of CRM. These limited focus on CRM lead to the result of numerous reports of failed implementations of various types of CRM projects. Many of these failures are also related to the absence of marketing approach. Identifying successful factors and outcomes focused on marketing concept before introducing a CRM project are a pre-implementation requirements. Many researchers have attempted to find the factors that contribute to the success of CRM. However, these research have some limitations in terms of marketing approach without explaining how the marketing based factors contribute to the CRM success. An understanding of how to manage relationship with crucial customers effectively based marketing approach has become an important topic for both academicians and practitioners. However, the existing papers did not provide a clear antecedent and outcomes factors focused on marketing approach. This paper attempt to validate whether or not such various marketing factors would impact on relational quality and CRM performance in terms of marketing oriented perceptivity. More specifically, marketing oriented factors involving market orientation, customer orientation, customer information orientation, and core customer orientation can influence relationship quality(satisfaction and trust) and CRM outcome(customer retention and customer share). Another major goals of this research are to identify the effect of relationship quality on CRM outcomes consisted of customer retention and share to show the relationship strength between two factors. Based on meta analysis for conventional studies, I can construct the following research model. An empirical study was undertaken to test the hypotheses with data from various companies. Multiple regression analysis and t-test were employed to test the hypotheses. The reliability and validity of our measurements were tested by using Cronbach's alpha coefficient and principal factor analysis respectively, and seven hypotheses were tested through performing correlation test and multiple regression analysis. The first key outcome is a theoretically and empirically sound CRM factors(marketing orientation, customer orientation, customer information orientation, and core customer orientation.) in the perceptive of marketing. The intensification of ${\beta}$coefficient among antecedents factors in terms of marketing was not same. In particular, The effects on customer trust of marketing based CRM antecedents were significantly confirmed excluding core customer orientation. It was notable that the direct effects of core customer orientation on customer trust were not exist. This means that customer trust which is firmly formed by long term tasks will not be directly linked to the core customer orientation. the enduring management concerned with this interactions is probably more important for the successful implementation of CRM. The second key result is that the implementation and operation of successful CRM process in terms of marketing approach have a strong positive association with both relationship quality(customer trust/customer satisfaction) and CRM performance(customer retention and customer possession). The final key fact that relationship quality has a strong positive effect on customer retention and customer share confirms that improvements in customer satisfaction and trust improve accessibility to customers, provide more consistent service and ensure value-for-money within the front office which result in growth of customer retention and customer share. Particularly, customer satisfaction and trust which is main components of relationship quality are found to be positively related to the customer retention and customer share. Interactive managements of these main variables play key roles in connecting the successful antecedent of CRM with final outcome involving customer retention and share. Based on research results, This paper suggest managerial implications concerned with constructions and executions of CRM focusing on the marketing perceptivity. I can conclude in general the CRM can be achieved by the recognition of antecedents and outcomes based on marketing concept. The implementation of marketing concept oriented CRM will be connected with finding out about customers' purchasing habits, opinions and preferences profiling individuals and groups to market more effectively and increase sales changing the way you operate to improve customer service and marketing. Benefiting from CRM is not just a question of investing the right software, but adapt CRM users to the concept of marketing including marketing orientation, customer orientation, and customer information orientation. No one deny that CRM is a process or methodology used to develop stronger relationships being composed of many technological components, but thinking about CRM in primarily technological terms is a big mistake. We can infer from this paper that the more useful way to think and implement about CRM is as a process that will help bring together lots of pieces of marketing concept about customers, marketing effectiveness, and market trends. Finally, a real situation we conducted our research may enable academics and practitioners to understand the antecedents and outcomes in the perceptive of marketing more clearly.

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    Effects of an intensive asthma education program on asthmatic children and their caregivers (천식 환아와 보호자를 대상으로 한 집중 교육 프로그램의 효과)

    • Seo, Kang Jin;Kim, Gun Ha;Yu, Byung Keun;Yeo, Yun Ku;Kim, Jong Hoon;Shim, Eu Ddeum;Yoon, Mi Ri;Yoo, Young;Choung, Ji Tae
      • Clinical and Experimental Pediatrics
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      • v.51 no.2
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      • pp.188-203
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      • 2008
    • Purpose : Asthma is one of the most common chronic childhood disease. Education of asthmatic children and their families about asthma and its management may improve disease control, reduce symptoms, and improve school performance. The aim of the study was to evaluate the effects of an intensive asthma education program in asthmatic children and their families on outcome measure of asthma management behavior scale, knowledge about asthma, self efficacy scale and quality of life. Methods : Fifteen asthmatic children and their families were invited the intensive asthma education program which including allergen avoidance, management of asthma, correct use of the inhalation devices and control of exercise-induced asthma (study group). Fifteen asthmatic children and their families those who did not participate this program were served as control group. Participants were asked to complete a written questionnaire before and 3-month after the program. Results : After completing the intensive education program, significant improvement of the childrens asthma management behavior scale (27.1 vs. 32.2, P=0.011), belief and knowledge about asthma (14.2 vs. 17.9, P<0.001), self efficacy (47.9 vs. 49.7, P=0.091) and quality of life (79.6 vs. 88.6, P<0.001) was noted in the study group by measuring questionnaires. There are increasing tendencies in parental asthma management behavior scale and knowledge about asthma. Conclusion : This intensive asthma education program is effective in improving asthma control, self efficacy and quality of life of asthmatic children. This should serve as a national model for family-based programs for asthmatic children and their families.

    Prediction of Chinese Cabbage Yield as Affected by Planting Date and Nitrogen Fertilization for Spring Production (정식시기와 질소시비 수준에 따른 봄배추의 생육량 추정)

    • Lee, Sang Gyu;Seo, Tae Cheol;Jang, Yoon Ah;Lee, Jun Gu;Nam, Chun Woo;Choi, Chang Sun;Yeo, Kyung-Hwan;Um, Young Chul
      • Journal of Bio-Environment Control
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      • v.21 no.3
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      • pp.271-275
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      • 2012
    • The average annual and winter ambient air temperatures in Korea have risen by $0.7^{\circ}C$ and $1.4^{\circ}C$, respectively, during the last 30 years. The continuous rise in temperature presents a challenge in growing certain horticultural crops. Chinese cabbage, one most important cool season crop, may well be used as a model to study the influence of climate change on plant growth, because it is more adversely affected by elevated temperatures than warm season crops. This study examined the influence of transplanting time, nitrogen fertilizer level and climate parameters, including air temperature and growing degree days (GDD), on the performance of a Chinese cabbage cultivar (Chunkwang) during the spring growing season to estimate crop yield under the unfavorable environmental conditions. The chinese cabbage plants were transplanted from Apr. 8 to May 13, 2011 when 3~4 leaves were occurred, at internals of 7 days and cultivated with 3 levels of nitrogen fertilization. The data from plants transplanted on Apr. 22 and 29, 2012 were used for the prediction of yield as affected by planting date and nitrogen fertilization for spring production. In our study, plant dry weight was higher when the seedlings were transplanted on 15th (168 g) than on 22nd (139 g) of April. There was no significant difference in the yield when plants were grown with different levels of nitrogen fertilizer. The values of correlation coefficient ($R^2$) between GDD and number of leaves, and between GDD and dry weight of the above-ground plant parts were 0.9818 and 0.9584, respectively. Nitrogen fertilizer did not provide a good correlation with the plant growth. Results of this study suggest that the GDD values can be used as a good indicator in predicting the top biomass yield of Chinese cabbage.

    Estimation of Ecological Carrying Capacity for Oyster Culture by Ecological Indicator in Geoje-Hansan Bay (생태지표를 이용한 거제한산만 굴양식장의 생태학적 수용능력 산정)

    • Lee, Won-Chan;Cho, Yoon-Sik;Hong, Sok-Jin;Kim, Hyung-Chul;Kim, Jeong-Bae;Lee, Suk-Mo
      • Journal of the Korean Society of Marine Environment & Safety
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      • v.17 no.4
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      • pp.315-322
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      • 2011
    • The importance of aquafarming is increasing all over the world, however the coastal environment in the semi-closed inner bay has been aggravated due to the long term production and the high stocking density. For the sustainable aquafarming, there is a requirement for a eco-friendly fishery management by the estimation of ecological carrying capacity. The model development and application is still in the initial step, because it has to consider the whole ecosystem and all culture activities. As an alternative, there is a requirement for ecological indicator to assess the ecological performance. This study tried the estimation of ecological carrying capacity using ecological indicator. The production and the facility of the oyster farms was 4,935M/T, $49ind./m^3$ in Geoje-Hansan Bay(2008). Filtration pressure indicator was 0.203 which could provide a guidance on the present level of culture development. According to the environmental characteristics and the present oyster farms in Geoje-Hansan Bay, the newly assessed filtration pressure for the acceptable ecological carrying capacity was 0.102. Consequently, ecological carrying capacity in Geoje-Hansan Bay was 2,480M/T, $25ind./m^3$ and this represents the level of culture that can be introduced into Geoje-Hansan Bay without leading to significant changes to ecological process, species, populations or communities. Our study utilized the ecological indicator to estimate ecological carrying capacity of oyster farming for sustainable productivity and this could be the scientific basis for the eco-friendly fishery management.

    A Comparative Study on the Effective Deep Learning for Fingerprint Recognition with Scar and Wrinkle (상처와 주름이 있는 지문 판별에 효율적인 심층 학습 비교연구)

    • Kim, JunSeob;Rim, BeanBonyka;Sung, Nak-Jun;Hong, Min
      • Journal of Internet Computing and Services
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      • v.21 no.4
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      • pp.17-23
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      • 2020
    • Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

    A Study on the Method of Producing the 1 km Resolution Seasonal Prediction of Temperature Over South Korea for Boreal Winter Using Genetic Algorithm and Global Elevation Data Based on Remote Sensing (위성고도자료와 유전자 알고리즘을 이용한 남한의 겨울철 기온의 1 km 격자형 계절예측자료 생산 기법 연구)

    • Lee, Joonlee;Ahn, Joong-Bae;Jung, Myung-Pyo;Shim, Kyo-Moon
      • Korean Journal of Remote Sensing
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      • v.33 no.5_2
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      • pp.661-676
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      • 2017
    • This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.


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