• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.279-282
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• 2006

IP망을 이용한 IPTV 방송 서비스가 새로운 수익 모델로 인정받고 현재 국내의 KT, SKT 등이 IPTV 시범서비스를 준비하거나 진행 중에 있다 이 IPTV 서비스는 이전의 단방향 방송과는 달리 사용자와의 인터렉션을 중시하는 양방향 방송을 표방하기 때문에 지금까지의 방송과는 다른 혁신적인 방송서비스가 기대된다. 하지만 IPTV 서비스에 있어서 여러 통신사와 방송사가 참여할 수 있을 것으로 보여지는 것과는 달리 실상은 몇몇 거대 통신기업이 자신들의 망을 이용하는 가입자들을 상대로 한정된 사업을 벌이고 있다. 이는 IPTV 서비스를 위한 인프라가 구축되어 있지 않고 방통융합망의 개념을 만족시키기 위해 서비스 개발자가 알아야 할 프로토콜들이 너무나 많기 때문이다. 따라서 본 논문에서는 이러한 상황을 타개할 수 있는 수단을 Open API로 제안한다. 맞춤형 방송을 위한 시나리오를 TV-Anytime의 벤치마킹과 유저 시나리오를 참고하여 재구성하고 이 시나리오로부터 IPTV 방송 서비스를 위한 방통융합망의 기본적이고 강력한 기능들을 Open API 함수로 정의하였다. 여기에서의 방송 서비스는 NDR, EPG, 개인 맞춤형 광고 서비스를 말하며 각 서비스를 위한 서버는 통합망 위에 존재하고 이 서버들이 개방하는 API들은 다른 응용프로그램에 의해 사용되는 것이기 때문에 가장 기본적인 기능을 정의하게 된다. 또한, 제안한 Open API 함수를 이용하여 개인 맞춤형 방송 응용 서비스를 구현함으로써 서비스 검증을 하였다. Open API는 웹서비스를 통해 공개된 기능들로써 게이트웨이를 통해 다른 망에서 사용할 수 있게 된다. Open API 함수의 정의는 함수 이름, 기능, 입 출력 파라메터로 이루어져 있다. 사용자 맞춤 서비스를 위해 전달되는 사용자 상세 정보와 콘텐츠 상세 정보는 TV-Anytime 포럼에서 정의한 메타데이터 스키마를 이용하여 정의하였다.가능하게 한다. 제안된 방법은 프레임 간 모드 결정을 고속화함으로써 스케일러블 비디오 부호화기의 연산량과 복잡도를 최대 57%감소시킨다. 그러나 연산량 감소에 따른 비트율의 증가나 화질의 열화는 최대 1.74% 비트율 증가 및 0.08dB PSNR 감소로 무시할 정도로 작다., 반드시 이에 대한 검증이 필요함을 알 수 있었다. 현지관측에 비해 막대한 비용과 시간을 절약할 수 있는 위성영상해석방법을 이용한 방법은 해양수질파악이 가능할 것으로 판단되며, GIS를 이용하여 다양하고 복잡한 자료를 데이터베이스화함으로써 가시화하고, 이를 기초로 공간분석을 실시함으로써 환경요소별 공간분포에 대한 파악을 통해 수치모형실험을 이용한 각종 환경영향의 평가 및 예측을 위한 기초자료로 이용이 가능할 것으로 사료된다.염총량관리 기본계획 시 구축된 모형 매개변수를 바탕으로 분석을 수행하였다. 일차오차분석을 이용하여 수리매개변수와 수질매개변수의 수질항목별 상대적 기여도를 파악해 본 결과, 수리매개변수는 DO, BOD, 유기질소, 유기인 모든 항목에 일정 정도의 상대적 기여도를 가지고 있는 것을 알 수 있었다. 이로부터 수질 모형의 적용 시 수리 매개변수 또한 수질 매개변수의 추정 시와 같이 보다 세심한 주의를 기울여 추정할 필요가 있을 것으로 판단된다.변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은 존재하므로 이러한 전략을 개발 및 활용할 수 있으며, 특히, 한국주식시장에 적합한 거래전략은 반전거래전략이고, 이 전략의 유용성은 투자자가 설정한 투자기간보다

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.41-54
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• 2020

This study was conducted to provide basic data for a transparent and fair bidding system by identifying problems and suggesting improvement measures through an analysis of the bidding status for construction projects and service-related landscaping of multi-family housing. To this end, we used the data from the "Multi-Family Housing Management Information System (K-apt)" that provides the history of apartment maintenance, bidding information, and the electronic bidding system to examine the winning bid status and amount, along with the size and trends of the winning bids by year, and the results of the selection of operators by construction type. As a result, it was found that out of the total number of successful bids (36,831), 4.4% (16,631) were in the landscaping business, and the average winning bid value was found to be about 24 million won. According to the data, 73% of the landscaping cases were valued between 3 million won and 30 million won, and 58.6% of the cases were in the field of "pest prevention and maintenance". 36% of the total number of bids were awarded from February to April, with "general competitive bidding" accounting for 59.8% of the bidding methods. As for the method of selecting the winning bidder, 55% adopted the "lowest bid" and "electronic bidding method," and 45% adopted the "qualification screening system" and "direct bidding method." As an improvement to the problems derived from the bidding status data, the following are recommended: First, the exception clause to the current 'electronic bidding method' application regulations must be minimized to activate the electronic bidding method so that a fair bidding system can be operated. Second, landscaping management standards for green area environmental quality of multi-family housing must be prepared. Third, the provisions for preparing design books, such as detailed statements and drawings before the bidding announcement, and calculating the basic amount shall be prepared so that fair bidding can be made by specifying the details of the project concretely and objectively must be made. Fourth, for various bidding conditions in the 'business operator selection guidelines', detailed guidelines for each condition, not the selection, need to be prepared to maintain fairness and consistency. These measures are believed to beuseful in the fair selection of landscaping operators for multi-family housing projects and to prepare objective and reasonable standards for the maintenance of landscaping facilities and a green environment.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.97-110
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• 2019

This study analyzed various factors of influence affecting the will to start a business and established and empirically analyzed a research model to see which factors significantly affect the will to start a business. To this end, we investigated the general characteristics and experiences of individuals, conducted a study on the will to start a business, and analyzed the entrepreneurship passion for startups, the ability to find business opportunities, and the ability to connect with partner companies. The intent to start a business survey was investigated in a recertive style with a 7 point scale, and the reliability and feasibility review were analyzed through the PLS analysis method, which enables the implementation of a measurement model and a structural model. To collect valid data, the survey was conducted using an entrepreneurial curriculum class hours to collect and analyze 421 data. In summary, the results are as follows: First, college students have many opportunities to develop their capabilities through competitions held by universities and support institutions, and by utilizing them, they have no fear of starting a business. Second, the ability of students to discover product clients themselves has been improved by fostering entrepreneurship in the special lectures on startup in universities. Third, it can be seen that it has received various information on startups from support agencies to enhance its commitment to startups. The implications are as follows. First, they should foster entrepreneurship among college students by offering practical oriented courses that can broaden their understanding of startups. Second, it needs to be improved from entrepreneurial enthusiasm to a program that can grow into a company that can collaborate with partner companies and confirm its commitment to corporate establishment and product development and determine market opportunities. Third, it is necessary to establish an ecosystem of start-ups that can carry out systematic planning and performance management as it is weak to carry out projects with will to startups.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.49-71
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• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Korea Science and Art Forum
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• v.23
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• pp.111-122
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• 2016

The purpose of this multi-disciplinary convergent study is to establish Image Copyright Archive Model for Museums to protect image copyright and vitalize the use of images out of necessity of research and development on copyright services over the life cycle of art contents created by the museums and out of the necessity to vitalize distribution market of image copyright contents in creative industry and to formulate management system of copyright services. This study made various suggestions for enhancement of transparency and efficiency of art contents ecosystem through vitalization of use and recycling of image copyright materials by proposing standard system for calculation, distribution, settlement and monitoring of copyright royalty of 1,000 domestic museums, galleries and exhibit halls. First, this study proposed contents and structure design of image copyright archive model and, by proposing art contents distribution service platform for prototype simulation, execution simulation and model operation simulation, established art contents copyright royalty process model. As billing system and technological development for image contents are still in incipient stage, this study used the existing contents billing framework as basic model for the development of billing technology for distribution of museum collections and artworks and automatic division and calculation engine for copyright royalty. Ultimately, study suggested image copyright archive model which can be used by artists, curators and distributors. In business strategy, study suggested niche market penetration of museum image copyright archive model. In sales expansion strategy, study established a business model in which effective process of image transaction can be conducted in the form of B2B, B2G, B2C and C2B through flexible connection of museum archive system and controllable management of image copyright materials can be possible. This study is expected to minimize disputes between copyright holder of artwork images and their owners and enhance manageability of copyrighted artworks through prevention of such disputes and provision of information on distribution and utilization of art contents (of collections and new creations) owned by the museums. In addition, by providing a guideline for archives of collections of museums and new creations, this study is expected to increase registration of image copyright and to make various convergent businesses possible such as billing, division and settlement of copyright royalty for image copyright distribution service.

• Pediatric Infection and Vaccine
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• v.31 no.1
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• pp.113-121
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• 2024

Purpose: In response to the surge in coronavirus disease 2019 (COVID-19) omicron variant cases, we have implemented preemptive testing for preschool and school. The purpose is to quickly detect COVID-19 cases using a rapid antigen test (RAT) kit so that normal school activities can continue. Methods: The results entered in The Healthcare Self-Test App were merged with the information on the status of confirmed cases in the COVID-19 Information Management System by Korea Disease Control and Prevention Agency (KDCA) for preschool and school of students and staffs March 2 to May 1, 2022 to analyze the RAT positive rate and positive predictive value of RAT. Results: In preschool and school 19,458,575 people were tested, weekly RAT positive rate ranged from 1.10% to 5.90%, positive predictive value of RAT ranged from 86.42% to 93.18%. By status, RAT positive rate ranged from 1.13% to 6.16% for students, 0.99% to 3.93% for staffs, positive predictive value of RAT ranged from 87.19% to 94.03% for students, 77.55% to 83.10% for staffs. RAT positive rate by symptoms ranged from 76.32% to 88.02% for those with symptoms and 0.34% to 1.11% for those without symptoms. As a result of preschool and school RAT, 943,342 confirmed cases were preemptively detected, before infection spread in preschool and school. Conclusions: RAT was well utilized to detect confirmed cases at an early stage, reducing the risk of transmission to minimize the educational gap in preschool and school. To compensate for the limitations of RAT, further research should continue to reevaluate the performance of RAT as new strains of viruses continue to emerge. We will have to come up with various ways to utilize it, such as performing periodic and repeated RAT and parallel polymerase chain reaction.

• Information Systems Review
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• v.25 no.2
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• pp.145-162
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• 2023

Asset management is a complex and difficult field that requires insight into numerous variables and even human psychology. Thus, it has traditionally been the domain of professionals, and these services have been expensive to obtain. Changes are taking place in these markets, and the driving force is the digital revolution, so-called the fourth industrial revolution. Among them, the Robo-Advisor service using artificial intelligence technology is the highlight. The reason is that it is possible to popularize investment advisory services with convenient accessibility and low cost. This study aims to clarify what factors are critically important when selecting robo-advisors for service users and providers in Korea, and what perception differences exist in the selection factors between user and provider groups. The framework of the study was based on the marketing mix 4C model, and the design and analysis of the model used Delphi survey and AHP. Through the study design, 4 main criteria and 15 sub-criteria were derived, and the findings of the study are as follows. First, the importance of the four main criteria was in the order of customer needs > customer convenience > customer cost > customer communication for both groups. Second, looking at the 15 sub-criteria, it was found that investment purpose coverage, investment propensity coverage, fee level and accessibility factors were the most important. Third, when comparing between groups, the user group found that the fee level and accessibility factors were the most important, and the provider group recognized the investment purpose coverage and investment propensity coverage factors as important. This study derived useful implications in practice. First, when designing for the spread of the robo-advisor service, the basis for constructing a user-oriented system was prepared by considering the priority of importance according to the weight difference between the four main criteria and the 15 sub-criteria. In addition, the difference in priority of each sub-criteria shown in the group comparison and the cause of the sub-criteria with large weight differences were identified. In addition, it was suggested that it is very important to form a consensus to resolve the difference in perception of factors between those in charge of strategy and marketing and system development within the provider group. Academically, it is meaningful in that it is an early study that presented various perspectives and perspectives by deriving a number of robo-advisor selection factors. Through the findings of this study, it is expected that a successful user-oriented robo-advisor system can be built and spread in Korea to help users.

• Korean Security Journal
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• no.36
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• pp.177-200
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• 2013

As we enter a high industrial society that widens the gap between the rich and poor, demand for the security services has grown explosively. With the growth in quantitative expansion of security services, people have also placed increased requirements on more sophisticated and diversified security services. Consequently, market outlook for private security services industry is positive. However, Korea's private security services companies are experiencing difficulties in finding a direction to capture this new market opportunity due to their small sizes and lack of management-strategic thinking skills. Therefore, we intend to offer a direction of development for our private security services industry using a management-strategy theory and the Analytic Hierarchy Process(AHP), a structured decision-making method. A resource-based theory is one of the important management strategy theories. It explains that a company's overall performance is primarily determined by its competitive resources. Using this theory, we could analyze a company's unique resources and core competencies and set a strategic direction for the company accordingly. The usefulness and validity of this theory has been demonstrated as it has often been subject to empirical verification since 1990s. Based on this theory, we outlined a set of basic procedures to establish a management strategy for the private security services companies. We also used the AHP method to identify competitive resources, core competencies, and strategies from private security services companies in contrast with public companies. The AHP method is a technique that can be used in the decision making process by quantifying experts' knowledge and unstructured problems. This is a verified method that has been used in the management decision making in the corporate environment as well as for the various academic studies. In order to perform this method, we gathered data from 11 experts from academic, industrial, and research sectors and drew distinctive resources, competencies, and strategic direction for private security services companies vis-a-vis public organizations. Through this process, we came to the conclusion that private security services companies generally have intangible resources as their distinctive resources compared with public organization. Among those intangible resources, relational resources, customer information, and technologies were analyzed as important. In contrast, tangible resources such as equipment, funds, distribution channels are found to be relatively scarce. We also found the competencies in sales and marketing and new product development as core competencies. We chose a concentration strategy focusing on a particular market segment as a strategic direction considering these resources and competencies of private security services companies. A concentration strategy is the right fit for smaller companies as a strategy to allow them to focus all of their efforts on target customers in a single segment. Thus, private security services companies would face the important tasks such as developing a new market and appropriate products for such market segment and continuing marketing activities to manage their customers. Additionally, continuous recruitment is required to facilitate the effective use of human resources in order to strengthen their marketing competency in a long term.