• Journal of Intelligence and Information Systems
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• 제25권2호
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• pp.57-75
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• 2019

In this paper, we conducted a study focusing on the innovation direction of the documentary model on the Internet of Things industry, which is the most actively industrialized among the core technologies of the 4th Industrial Revolution. Policy, economic, social, and technical issues were derived using PEST analysis for global trend analysis. It also presented future prospects for the Internet of Things industry of ICT-related global research institutes such as Gartner and International Data Corporation. Global research institutes predicted that competition in network technologies will be an issue for industrial Internet (IIoST) and IoT (Internet of Things) based on infrastructure and platforms. As a result of the PEST analysis, developed countries are pushing policies to respond to the fourth industrial revolution through cooperation of private (business/ research institutes) led by the government. It was also in the process of expanding related R&D budgets and establishing related policies in South Korea. On the economic side, the growth tax of the related industries (based on the aggregate value of the market) and the performance of the entity were reviewed. The growth of industries related to the fourth industrial revolution in advanced countries overseas was found to be faster than other industries, while in Korea, the growth of the "technical hardware and equipment" and "communication service" sectors was relatively low among industries related to the fourth industrial revolution. On the social side, it is expected to cause enormous ripple effects across society, largely due to changes in technology and industrial structure, changes in employment structure, changes in job volume, etc. On the technical side, changes were taking place in each industry, representing the health and medical sectors and manufacturing sectors, which were rapidly changing as they merged with the technology of the Fourth Industrial Revolution. In this paper, various management methodologies for innovation of existing business model were reviewed to cope with rapidly changing industrial environment due to the fourth industrial revolution. In addition, four criteria were established to select a management model to cope with the new business environment: 'Applicability', 'Agility', 'Diversity' and 'Connectivity'. The expert survey results in an AHP analysis showing that Business Model Canvas is best suited for business model innovation methodology. The results showed very high importance, 42.5 percent in terms of "Applicability", 48.1 percent in terms of "Agility", 47.6 percent in terms of "diversity" and 42.9 percent in terms of "connectivity." Thus, it was selected as a model that could be diversely applied according to the industrial ecology and paradigm shift. Business Model Canvas is a relatively recent management strategy that identifies the value of a business model through a nine-block approach as a methodology for business model innovation. It identifies the value of a business model through nine block approaches and covers the four key areas of business: customer, order, infrastructure, and business feasibility analysis. In the paper, the expansion and application direction of the nine blocks were presented from the perspective of the IoT company (ICT). In conclusion, the discussion of which Business Model Canvas models will be applied in the ICT convergence industry is described. Based on the nine blocks, if appropriate applications are carried out to suit the characteristics of the target company, various applications are possible, such as integration and removal of five blocks, seven blocks and so on, and segmentation of blocks that fit the characteristics. Future research needs to develop customized business innovation methodologies for Internet of Things companies, or those that are performing Internet-based services. In addition, in this study, the Business Model Canvas model was derived from expert opinion as a useful tool for innovation. For the expansion and demonstration of the research, a study on the usability of presenting detailed implementation strategies, such as various model application cases and application models for actual companies, is needed.

• Journal of Intelligence and Information Systems
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• 제23권1호
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• 제29권1호
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Journal of Intelligence and Information Systems
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• 제23권4호
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Internet Computing and Services
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• 제9권5호
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• pp.59-72
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• 2008

Computing systems in the modern era are expanding rapidly to include mobile-based businesses that make us of the various convergence distributed business process. This has lead to growing interest in the field of mobile embedded software development methodology, which has in turn lead to the proliferation of the embedded mobility. The use of CBD (Component Based Development) provides reusability, maintainability and portability, all of which are very important and focus issues to the business process. It also comes with the inherent productivity, quality and reliability of CBD. To make efficient use of CBD, though, clarified interface definitions for component integration are necessary. These definitions should be made up of collaborative hierarchical and horizontal architecture layers. Successful definitions should apply an effective framework made up of the architecture and process. In this paper, we describe an interface specification for small grained mobile embedded components(MEC) for the mobile embedded domain to meet maximum user requirements. We build and deploy the reconfigurable design patterns and components (in business domain categories) to make a component hierarchy and business logics for mobile embedded software. Proposed components specification plays a major role in development of the software for handling inconsistency in existing specification. It also includes plenty of specification information, using semantics and modeling based mechanisms to support business processes. We propose a development model of mobile embedded software using CBD for very complex and dynamic mobile business. We can apply it in a plug and play manner to develop the software. We verify that our framework supports very good productivity, quality and maintainability to meet the user's requirements in mobile business.

• Proceedings of the Speleological Society Conference
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• 한국동굴학회 1995년도 관광동굴의 관리운영 워크숍
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• pp.39-44
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• 1995

$\bigcirc$ 동굴생물은 식물과 동물, 미생물로 크게 구분되지만 태양광선이 완전히 차단된 동굴내 환경에서는 동굴 동물들이 주종을 이루고 있다. $\bigcirc$ 동굴내의 동물에 관한 연구는 1966년 고씨동굴, 용담굴을 시발로 고수굴, 천동굴, 노동굴, 백룡글, 대이굴, 환선굴 등 강원도내에 분포하는 동굴 중 약 30% 정도는 동물을 포함한 생물상이 조사되었다. $\bigcirc$ 강원도의 동굴동물로서 확인된 것은 9강 30목 141종이며 강별로는 곤충강이 9목 50종 주형강이 4목 49종 갑각강이 6목 18종의 순으로 우세하고 목별로는 거미목이 15과 39종, 톡토기목이 7과 20종, 딱정벌레목이 8과 15종으로 우세한 편이다(남, 1987). $\bigcirc$ 동굴생물에 관한 연구는 육상생태계와 격리된 환경에서 생리, 생태적 적응을 통한 종의 분화나 유전, 진화문제를 추구하며 지하생태계와 육상생태계의 생물적인 유연관계 규명에 중요한 분야로 인정되며 아울러 독특한 특성을 지닌 동굴생물의 생존을 위한 방안과 노력이 일부동굴의 개방 및 개발에 앞서 더욱 구체적으로 모색되고 증대되어야 한다. $\bigcirc$ 동굴환경의 주요 특성은 첫째 햇빛에 차단되어 암흑상태이며, 둘째 내부습도가 높고 기온이나 수온의 년중변화가 심하지 않고, 셋째 먹이연쇄에 필수적인 영양공급원이 제한되어 있다. 따라서 광합성작용으로 성장하는 녹색식물은 태양광선이 유입되는 곳이나 인공조명시설 지역을 제외한 곳에서는 서식이 불가능하고 대형동물이나 초식동물은 생존이 어렵다. $\bigcirc$ 동굴내에는 환경에 적응하는 종들만이 서식할 수 있고 이들을 생태적 특성에 따라 세가지로 구분 할 수 있다. 환경적응 요인으로는 광도, 습도, 온도 영양공급원과 섭식장소, 수중생물의 경우는 특히 수온, 수량 영양원등이다.(중략). 본 연구의 접근방법으로는 ASRS의 개념적인 Reference Model을 수립하고 이 Reference Model에 대한 Formal Model로 DEVS(Discrete Event System Specification)을 이용하여 시스템을 Modeling하였다. 이의 Computer Simulation을 위하여 DEVS형식론 환경에서의 Simulation Language인 DEVSim ++ⓒ를 이용하여 시스템을 구현하였다.. 실형 결과로는 먼저 선형 상미분방정식의 예로 mass-damper-spring system, 비선형 상미분방정식의 예로는 van der Pol 방정식, 연립 상미분방정식의 예로는 mixing tank problem 등을 보였으며, 그의 공학에서 일어나는 여러 가지 문제들도 다루었다.화물에 대한 방어력이 증가되어 나타난 결과로 여겨지며, 또한 혈청중의 ALT, ALP 및 LDH활성을 유의성있게 감소시키므로서 감잎 phenolic compounds가 에탄올에 의한 간세포 손상에 대한 해독 및 보호작용이 있는 것으로 사료된다.반적으로 홍삼 제조시 내공의 발생은 제조공정에서 나타나는 경우가 많으며, 내백의 경우는 홍삼으로 가공되면서 발생하는 경우가 있고, 인삼이 성장될 때 부분적인 영양상태의 불충분이나 기후 등에 따른 영향을 받을 수 있기 때문에 앞으로 이에 대한 많은 연구가 이루어져야할 것으로 판단된다.태에도 불구하고 [-wh]의미의 겹의문사는 병렬적 관계의 합성어가 아니라 내부구조를 지니지 않은 단순한 단어(minimal $X^{0}$ elements)로 가정한다. 즉, [+wh] 의미의 겹의문사는 동일한 구성요 소를 지닌 병렬적 합성어([$[W1]_{XO-}$ $[W1]_{XO}$ ]$_{XO}$)로 그리고 [-wh] 의미의 겹의문사는 중복된 발은을 지닌 한 단어로 ([W]$_{XO}$

• Journal of Korean Society of Transportation
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• 제35권4호
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

• Proceedings of the Speleological Society Conference
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• 한국동굴학회 1994년도 동굴의 개발과 보전 세미나
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• pp.106-110
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• 1994

$\bigcirc$ 동굴생물은 식물과 동물, 미생물로 크게 구분되지만 태양광선이 완전히 차단된 동굴내 환경에서는 동굴 동물들이 주종을 이루고 있다. $\bigcirc$ 동굴내의 동물에 관한 연구는 1966년 고씨동굴, 용담굴을 시발로 고수굴, 천동굴, 노동굴, 백룡굴, 대이굴, 환선굴 등 강원도내에 분포하는 동굴 중 약 30%정도는 동물을 포함한 생물상이 조사되었다. $\bigcirc$ 강원도의 동굴동물로서 확인된 것온 9종 30목141종이며 강별로는 곤충강이 9목 50종 주형강이 4목 49종 갑각강이 6목 18종의 순으로 우세하고 목별로는 거미목이 15과 39종, 톡토기목이 7과 20종, 딱정벌레목이 8과 15종으로 우세한 편이다 (남, 1987). $\bigcirc$ 동굴생물에 관한 연구는 육상생태계와 격리된 환경에서 생리, 생태적 적응을 통한 종의 분화나 유전, 진화문제를 추구하며 지하생태계와 육상생태계의 생물적인 유연관계 규명에 중요한 분야로 인정되며 아울러 독특한 특성을 지닌 동굴생물의 보존을 위한 방안과 노력이 일부동굴의 개방 및 개발에 앞서 더욱 구체적으로 모색되고 증대되어야 한다. $\bigcirc$ 동굴환경의 주요 특성은 첫째 햇빛이 차단되어 암흑상태이며, 둘째 내부습도가 높고 기온이나 수온의 연중변화가 심하지 않고, 셋째 먹이연쇄에 필수적인 영양공급원이 제한되어 있다. 따라서 광합성작용으로 성장하는 녹색식물은 태양광선이 유입되는 곳이나 인공조명시설 지역을 제외한 곳에서는 서식이 불가능하고 대형동물이나 초식동물은 생존이 어렵다. $\bigcirc$ 동굴내에는 환경에 적응하는 종들만이 서식할 수 있고 이들을 생태적 특성에 따라 세가지로 구분 할 수 있다. 환경적응 요인으로는 광도, 습도, 온도 영양공급원과 섭식장소, 수중생물의 경우는 특히 수온, 수량 영양원등이다.(중략)다. 본 연구의 접근방법으로는 ASRS의 개념적인 Reference Model을 수립하고 이 Reference Model에 대한 Formal Model로 DEVS(Discrete Event System Specification)을 이용하여 시스템을 Modeling하였다. 이의 Computer Simulation을 위하여 DEVS형식론 환경에서의 Simulation Language인 DEVSim ++ⓒ를 이용하여 시스템을 구현하였다.. 실형 결과로는 먼저 선형 상미분방정식의 예로 mass-damper-spring system, 비선형 상미분방정식의 예로는 van der Pol 방정식, 연립 상미분방정식의 예로는 mixing tank problem 등을 보였으며, 그의 공학에서 일어나는 여러 가지 문제들도 다루었다.화물에 대한 방어력이 증가되어 나타난 결과로 여겨지며, 또한 혈청중의 ALT, ALP 및 LDH활성을 유의성있게 감소시키므로서 감잎 phenolic compounds가 에탄올에 의한 간세포 손상에 대한 해독 및 보호작용이 있는 것으로 사료된다.반적으로 홍삼 제조시 내공의 발생은 제조공정에서 나타나는 경우가 많으며, 내백의 경우는 홍삼으로 가공되면서 발생하는 경우가 있고, 인삼이 성장될 때 부분적인 영양상태의 불충분이나 기후 등에 따른 영향을 받을 수 있기 때문에 앞으로 이에 대한 많은 연구가 이루어져야할 것으로 판단된다.태에도 불구하고 [-wh]의미의 겹의문사는 병렬적 관계의 합성어가 아니라 내부구조를 지니지 않은 단순한 단어(minimal $X^{0}$ elements)로 가정한다. 즉, [+wh] 의미의 겹의문사는 동일한 구성요 소를 지닌 병렬적 합성어([$[W1]_{XO-}$ $[W1]_{XO}$ ]$_{XO}$)로 그리고 [-wh] 의미의 겹의문사는 중복된 발은을 지닌 한 단어로 ([W]$_{XO}$

• Journal of Intelligence and Information Systems
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• 제28권4호
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• pp.211-227
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• 2022

Illegal gambling through online gambling sites has become a significant social problem. The development of Internet technology and the spread of smartphones have led to the proliferation of illegal gambling sites, so now illegal online gambling has become accessible to anyone. In order to mitigate its negative effect, the Korean government is trying to detect illegal gambling sites by using self-monitoring agents or reporting systems such as 'Nuricops.' However, it is difficult to detect all illegal sites due to limitations such as a lack of staffing. Accordingly, several scholars have proposed intelligent illegal gambling site detection techniques. Xu et al. (2019) found that fake or illegal websites generally have unique features in the HTML tag structure. It implies that the HTML tag structure can be important for detecting illegal sites. However, prior studies to improve the model's performance by utilizing the HTML tag structure in the illegal site detection model are rare. Against this background, our study aimed to improve the model's performance by utilizing the HTML tag structure and proposes Tag2Vec, a modified version of Doc2Vec, as a methodology to vectorize the HTML tag structure properly. To validate the proposed model, we perform the empirical analysis using a data set consisting of the list of harmful sites from 'The Cheat' and normal sites through Google search. As a result, it was confirmed that the Tag2Vec-based detection model proposed in this study showed better classification accuracy, recall, and F1_Score than the URL-based detection model-a comparative model. The proposed model of this study is expected to be effectively utilized to improve the health of our society through intelligent technology.

• Archives of design research
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• 제17권2호
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• pp.413-424
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• 2004

This paper defines the telepresence as a particular type of experience, rather than a collection of hardware. Defining telepresence in this way provides a means for examining telepresence in relation to other types of mediated experience. Presence refers to the natural perception of an environment, and telepresence refers to the mediated perception of an environment. Factors influencing whether a particular mediated environment will induce a sense of telepresence include the following: the combination of sensory stimuli employed in the environment, the ways in which participants are able to interact with the environment, and the characteristics of the individual experiencing the environment. Telepresence art invites the people from remote worlds to networked cyber space and creates the experience of 'being there' by making participants control the virtual reality system and receive feedback from their teleactions. It is the way to produce an open and engaging experience that manifests the cultural changes brought about by remote control, remote vision, telekinesis, and real-time exchange of audiovisual information. The principal determinants of telepresence are sensory immersion, sensory fidelity, cognitive fidelity and personal factors. This paper applies the 4 determinants to telepresence art works such as Ken Goldberg's Telegarden, Monika Fleischmann & Wolfgang Strauss' The Home of the Brain, Paul Sermon's Telematic Dreaming, Telematic Vision, Eduardo Kac's Uriapuru, Simon Penny's Traces and Paul Sermon & Andrea Zapp's A Body of Water.