• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.4
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• pp.83-92
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• 2005

D-OCSP-KIS proposed by Koga and Sakurai not only reduces the number or OCSP Responder's certificate but also criers the certificate status validation about OCSP Responder to the client. Therefore, D-OCSP-KIS is an effective method that can reduce the communication cost, computational time and storage consumption in client, but it has some problems. In case an attacker accidentally acquires an OCSP Responder's session private key in a time period (e.g., one day), she can disguise as the OCSP Responder in the time period unless the OCSP Responder recognizes. She can offer the wrong response to the client using the hash value intercepted. And the server and user on I-commerce can have a serious confusion and damage. And the computation and releasing of hash chain can be a load to CA. Thus, we propose a method detecting immediately the exposure of an OCSP Responder's session private key and the abuse of hash value in D-OCSP-KIS.

• Management & Information Systems Review
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• v.34 no.5
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• pp.23-47
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• 2015

In the Korea stock market, individual investors have perceived stock as short arbitrage investment, not long-term investment strategy. In order to reinforce stock market transparency and soundness, it is important to enforce the measures for stock market management. Especially, stock market event caused by financial policy can be given individual investors negative information regarding a stock trading. Thus, it is a need for investigating whether comprehensive review of listing eligibility is influenced on individual investors' responses and stock behaviors in respect of effectiveness. The purpose of this study to examine the relations between such stock market management and transitional aspect of individual investors' trading types and response on the based of pre- and post-event occurrence. Using an dataset of user's text messages on 9 firms posted on the firm-based social media (i.e., Naver, Daum, Paxnet) over the period 2009 to 2014. And we performed text-clustering and topic modeling according to keywords for classifying into investors group and non-investors groups and two types of investors were categorized depending on main topic transition by event windows in Comprehensive review of listing eligibility. The results indicated that a variety of stockholders existed in the stock. And the ratio of non-investors group was on the decrease, on the other hand, the proportion of investors group veer onto the side of pre-pattern after comprehensive review of listing eligibility. A distinctive feature of our study is to explain the influence of stock market management on response changes of individual investors as well as to categorize in accordance with time progression. Implications an suggestions for future research were also discussed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.602-609
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• 2020

In the event of a large-scale disaster such as an earthquake, typhoon, landslide, and building collapse, the disaster situation awareness and timely disaster information sharing play a key role in the disaster response and decision-making stages for disaster management, such as disaster site control and evacuation of residents. In this paper, an exited field investigation system of NDMI (National Disaster Management Research Institute) was enhanced with an LTE-VPN- based wireless communication system to provide an effective on-site response in an urgent disaster situation and share observation data or analysis information acquired at the disaster fields in real-time. The required performance of wireless communication for the disaster field investigation system was then analyzed and evaluated. The experimental result for field data transmission performance of an advanced wireless communication investigation system showed that the UDP transmission performance of at least 4.1Mbps is required to ensure a seamless video conference system between disaster sites. In addition, a wireless communication bandwidth of approximately 10 Mbps should be guaranteed to smoothly share the communication and field data between the survey equipment currently mounted on the survey vehicle.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.839-846
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• 2006

Context-aware adaptive service for overcoming the limitations of wireless devices and maintaining adequate service levels in changing environments is becoming an important issue. However, most existing studies concentrate on an adaptation module on the client, proxy, or server. These existing studies thus suffer from the problem of having the workload concentrated on a single system when the number of users increases md, and as a result, increases the response time to a user's request. Therefore, in this paper the adaptation module is dispersed and arranged over the client, proxy, and server. The module monitors the contort of the system and creates a proposition as to the dispersed adaptation system in which the most adequate system for conducting operations. Through this method faster adaptation work will be made possible even when the numbers of users increase, and more stable system operation is made possible as the workload is divided. In order to evaluate the proposed system, a prototype is constructed and dispersed operations are tested using multimedia based learning content, simulating server overload and compared the response times and system stability with the existing server based adaptation method. The effectiveness of the system is confirmed through this results.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.132-141
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• 2024

In this study, the differences in user satisfaction and the variables influencing the satisfaction with demand response transport (DRT) by travel purpose were compared. The purpose of DRT travel was divided into commuting/school and shopping/leisure travel. A survey conducted on 'Shucle' users in Sejong City was used for the analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to minimize the overfitting problems of the multilinear model. The results of the analysis confirmed the possibility that the introduction of the DRT service could eliminate the blind spot in the existing public transportation, reduce the use of private cars, encourage low-carbon and public transportation revitalization policies, and provide optimal transportation services to people who exhibit intermittent travel behaviors (e.g., elderly people, housewives, etc.). In addition, factors such as the waiting time after calling a DRT, travel time after boarding the DRT, convenience of using the DRT app, punctuality of expected departure/arrival time, and location of pickup and drop-off points were the common factors that positively influenced the satisfaction of users of the DRT services during their commuting/school and shopping/leisure travel. Meanwhile, the method of transfer to other transport modes was found to affect satisfaction only in the case of commuting/school travel, but not in the case of shopping/leisure travel. To activate the DRT service, it is necessary to consider the five influencing factors analyzed above. In addition, the differentiating factors between commuting/school and shopping/leisure travel were also identified. In the case of commuting/school travel, people value time and consider it to be important, so it is necessary to promote the convenience of transfer to other transport modes to reduce the total travel time. Regarding shopping/leisure travel, it is necessary to consider ways to create a facility that allows users to easily and conveniently designate the location of the pickup and drop-off point.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.5
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• pp.28-39
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• 2015

The purpose of this study is to analyze users' behavior and facility satisfaction and to suggest the practical solution plans for environmental improvement of trails in Suseong recreation park in Daegu. Therefore, satisfaction factor analysis on trails was carried out based on a field and questionnaire survey in Suseong recreation park. First, from users' behavior, 48% of users visited Suseong recreation park for walking. The greatest response to number of visits was once or twice a week, and average use time per visit was 80.4 minutes. In terms of trails, the greater responses to the number of visits were once(38.1%) or more than 5 times(23.8%), and average use time was 45.4 minutes. According to the results from the analysis of facility satisfaction, management condition, length, slope, and adjacent natural landscape of trails were evaluated at a satisfaction rate higher than 3.4 points. However, water pollution, and number of exits and parking lots were analyzed at a low rate of 2.75 and 2.78 points, respectively. In terms of analyzing determining factors of facility satisfaction for trails, use of facilities, walking convenience, surrounding landscapes, amenities, and noise had a significantly positive effect on satisfaction. In particular, walking convenience was the highest effect factor; its standardized coefficient was 0.533. The findings from this study can contribute to the improvement of the physical environment for trails of Suseong recreation park and provide basic data for plan and maintenance of similar waterside trails.

• Journal of Korea Spatial Information System Society
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• v.1 no.2 s.2
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• pp.21-31
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• 1999

In accordance with the completion of the spatial database building works in the central and/or local government authorities as well as the rapid popularization of various information services through internet, it is heavily required to provide spatial information services through World Wide Web. To provide a qualified spatial information service, it is crucial to have a Web-based GIS (Geographic Information Service) service system equipped with the publicity, the convenient accessibility, and the easy-to-use user interface. In this paper, we introduce a new component system for Web-based spatial information services based on the OpenGIS Simple Feature specification for OLE/COM[3] and OLE DB specification[4]. The important functionality of a Web-based spatial information service system includes its accessibility of various existing GIS server system and huge databases in addition to the resolution of response-time delay problems caused by transmitting a large amount of digital maps via internet[6]. To cope with these problems, our component system has been designed to access heterogeneous databases in transparent manner and to support vector-based and/or image-based image production techniques for shortening transmission time.

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.51-58
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• 2007

This paper suggests prototype of dialog system combining VXML(VoiceXML) that is the W3C's standard XML format for specifying interactive voice dialogues between human and computer, and RSS(RDF Site Summary or Really Simple Syndication) that is representative technology of semantic web for syndication and subscription of updated web-contents. Merits of the proposed system are as following: 1) It is a new method that recognize spoken contents using ire and wireless telephone networks and then provide contents to user via STT(Speech-to-Text) and TTS(Text-to-Speech) instead of traditional method using web only. 2) It can apply advantage of RSS that subscription of updated contents is converted to VXML without modifying traditional method to provide RSS service, 3) In terms of users, it can reduce restriction on time-spate in search of contents that is provided by RSS because it uses ire and wireless telephone networks, not internet environment. 4) In terms of information provider, it does not need special component for syndication of the newest contents using speech recognition and synthesis technology. We implemented a news service system using VXML and RSS for performance evaluation of the proposed system. In experiment results, we estimated the response time and the speech recognition rate in subscription and search of actuality contents, and confirmed that the proposed system can provide contents those are provided using RSS Feed.