• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.279-294
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• 2009

This research evaluated Information Application Mechanism based on university student's preference. In other words, the research content is analyzing what is the most effective learning method for lectures. To obtain this research objective I've done some research in theoretical research and pervious research related to informational mechanism. I also analyzed the degree of understanding in learning-efficiency with students based on lectures using various information mechanisms. For the data gathering I've surveyed 82 students living in Busan, and used the survey sheets to gather data, I made the survey sheet with nominal and interval parts to make the analyzement of data easier. The research result is down below. First, It shows that the students with higher understanding in IT preferred lectures with "PPT"s. Second, The "PPT" preference group thought that the "PPT" teaching method is more effective than other methods. Third, The "WRT" preference group thought that the "WRT" teaching method is more effective than other methods. Forth, Both groups gave each method a 3.08 and 2.95 as a total mean value out on a 1-5 scale showing not much difference. However this research has a research limitation like other researches. In other words, there can be some distortion of information when using the interview method to gather data. The researches from now on will need to organize groups by information mechanisms and research the learning-efficiency with each mechanism.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.185-199
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• 2019

Korea's facility horticulture has developed remarkably in a short period of time. However, in order to secure international competitiveness in response to unfavorable surrounding conditions such as high operating costs and market opening, it is necessary to diagnose the problems of facility horticulture and prepare countermeasures through analysis. The purpose of this study was to analyze the case of leading farmers by introducing information and communication technology (ICT) in hydroponic cultivation agriculture and horticulture, and to examine how agricultural technology utilizing smart farm and big data of facility horticulture contribute to farm productivity. Crop growth information gathering and analysis solutions were developed to analyze the productivity change factors calculated from hydroponics tomato farms and strawberry farms. The results of this study are as follows. The application range of the leaf temperature was verified to be variously utilized such as house ventilation in the facility, opening and closing of the insulation curtain, and determination of the initial watering point and the ending time point. Second, it is necessary to utilize water content information of crop growth. It was confirmed that the crop growth rate information can confirm whether the present state of crops is nutrition or reproduction, and can control the water content artificially according to photosynthesis ability. Third, utilize EC and pH information of crops. Depending on the crop, EC values should be different according to climatic conditions. It was confirmed that the current state of the crops can be confirmed by comparing EC and pH, which are measured from the supplied EC, pH and draining. Based on the results of this study, it can be confirmed that the productivity of smart farm can be affected by how to use the information of measurement growth.

• Korean journal of communication and information
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• v.58
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• pp.7-30
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• 2012

The advent of Social Journalism coincided with the rise of social media to create and deliver news information; as a type of civic journalism, social journalism may be characterized as a new form of information gathering and news reporting which is fed by citizens creating news information through their use social networking services (SNSs). The current study analyzed a Facebook page called, to determine how this page was utilized during the onset of the citizen movement for the Egyptian democratic revolution to produce news, to facilitate interaction among the public and to deliver the news under the form of networked journalism. Each post uploaded onto the Facebook page from January 27 till February 2, 2011 was coded in its category, content and the contextual frame of the news. The results of the study showed that during the first week, straight news rather than those with opinions was produced most frequently. The research findings of the current study suggest that in a society of political turmoil, such as in Egypt and other Arabic countries, when the institutionalized media are controlled severely by the government or other forces, SNSs can perform journalistic media roles which create and distribute news information representing facts and reality, and simultaneously facilitate the public's interactions on social and political issues.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.5
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• pp.1191-1204
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• 2012

As attackers try to paralyze information security systems, many researchers have investigated security testing to analyze vulnerabilities of information security products. Penetration testing, a critical step in the development of any secure product, is the practice of testing a computer systems to find vulnerabilities that an attacker could exploit. Security testing like penetration testing includes gathering information about the target before the test, identifying possible entry points, attempting to break in and reporting back the findings. Therefore, to obtain maximum generality, re-usability and efficiency is very useful for efficient security testing and vulnerability hunting activities. In this paper, we propose a threat analysis based software security testing technique for evaluating that the security functionality of target products provides the properties of self-protection and non-bypassability in order to respond to attacks to incapacitate or bypass the security features of the target products. We conduct a security threat analysis to identify vulnerabilities and establish a testing strategy according to software modules and security features/functions of the target products after threat analysis to improve re-usability and efficiency of software security testing. The proposed technique consists of threat analysis and classification, selection of right strategy for security testing, and security testing. We demonstrate our technique can systematically evaluate the strength of security systems by analyzing case studies and performing security tests.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.449-466
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• 2022

The establishment of a complete geological model that includes information about all the various components at a site (such as underground structures and the compositions of rock and soil underground space) is difficult, and geological modeling is a developing field. This study uses commercial software for the relatively easy composition of geological models. Our digital modeling process integrates a model of Jeju Island's 3D geological information, models of cave shapes, and information on the state of a road at the site's upper surface. Among the numerous natural caves that exist in Jeju Island, we studied the Jaeamcheon lava tube near Hallim town, and the selected site lies below a road. We developed a digital model by applying the principles of building information modeling (BIM) to the cave (CaveBIM). The digital model was compiled through gathering and integrating specific data: relevant processes include modeling the cave's shape using a laser scanner, 3D geological modeling using geological information and geophysical exploration data, and modeling the surrounding area using drones. This study developed a global-scale model of the Hallim region and a local-scale model of the Jaeamcheon cave. Cross-validation was performed when constructing the LSM, and the results were compared and analyzed.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.27-37
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• 2012

Internet privacy has become a significant issue in recent years in light of the sharp increase in internet-based social and economic activities. The technology which collects, processes and disseminates personal information is improving significantly and the demand for personal information is rising given its inherent value in regard to targeted marketing and customized services. The high value placed on personal information has turned it into a commodity with economic worth which can be transacted in the marketplace. Therefore, it is strongly required to approach the issue of privacy from economic perspective in addition to the prevailing approaches. This article analyzes the behaviors of consumers and firms in gathering personal information, and shielding it from unauthorized access, using a game theory framework in which players strive to do their best under the given conditions. The analysis shows that there exist no market forces which require all firms to respect consumer privacy, and that government intervention in the form of a nudging incentive for information sharing and/or strict regulation is necessary.

• Journal of KIISE:Information Networking
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• v.35 no.2
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• pp.130-138
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• 2008

The scheme for recognizing the channel availability is one of the most important research issues in cognitive radio systems utilizing unused frequency bands. In this paper, we propose a novel scheme of selecting sensing channel in order to improve the sensing ability of frequency status in cognitive radio ad hoc networks. To fully exploit the sensing ability of each cognitive radio user, we adopt a master for a cluster which is made of several cognitive radio users. By gathering and analyzing the sensing information from cognitive radio users in the cluster, the cooperative sensing is realized. Since the transmission range of a licensed user is limited, it is possible that a master determines different sensing channels to each cognitive radio users based on their location. By making cognitive radio users sense different channels, the proposed scheme can recognize the state of wireless spectrum fast and precisely. Using the simulation, we compare the performance of the proposed scheme with those of two different compared schemes that one makes cognitive radio users recognize the frequency status based on their own sensing results and the other shares frequency status information but does not utilize the location information of licensed user. Simulation results show that the proposed scheme provides available channels as many as possible while detecting the activation of licensed user immediately.

• Korean Journal of Community Nutrition
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• v.18 no.6
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• pp.588-598
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• 2013

This study was conducted to assess needs of educational mobile application (App) development for nutritional management and information on pregnant women. A total of 105 pregnant women were investigated on general characteristics, dietary habits, health behavior and needs for contents and composition of the application. The mean age of the subjects was 31.9 years and the mean gestation period was 25.4 weeks. The rate of skipping meal was 39.0% and the rate of irregular meal time was 46.6%. The consciousness of the meal as balanced nutrition and health was 19.9%. Eating out at least forth a week was 35.3%. Obtaining information about pregnancy and childbirth were internet (35.3%), hospital or health center (19.9%), books (17.1%), experience (15.2%), mobile (8.6%) and friends or acquaintances (4.8%). If the application is developed, subject replied 'frequently use' (51.4%), 'when needed' (47.6%) respectively. The favour topic in developing application were 'nutrition information of pregnant and fetal' (36.2%), 'weight management, feeding' (33.3%), 'food choice and cooking' (21.9%), 'shopping' (5.7%), 'example of menu' (1.9%), 'effect of smoking, drinking, exercising' (1.0%). The favorite content was 'include sufficient amount about information' (44.8%). Depending on the age and education level, the best age for pregnancy group have significantly higher ability for utilize and information gathering than old age pregnant group. Also the best age for pregnancy group have high demands of design, convenience and various contents in App development. Therefore, mobile application (App) for pregnant women could be widely used as an effective dietary guide.