• Journal of Navigation and Port Research
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• v.37 no.4
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• pp.391-400
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• 2013

Each country has recently established and operated so called Aids to Navigation(AtoN) AIS system collecting and transferring information on the sea state and AtoN's position. Korea has also decided to introduce the system and been establishing it. And it has already installed and operated this system in Incheon and Daesan. For the purpose of giving the national standards of AtoN AIS, this paper is to investigate the present states of AtoN AIS systems in the above two area and to suggest the improvement to the operation of AtoN AIS system. To make it, the paper deals with the international regulations of related AtoN AIS and the cases of foreign's AtoN AIS system installed and also investigates the operation states of the system, in five modules such as system management, system composition, information, function and cooperation. The followings are concluded; improving the inconsistency of system management, reducing operator's confusion caused by excessive display of AtoN AIS system, improving the problem of receiving unnecessary information, improving the functional problems caused by AIS communication inability and improving AtoN AIS information which is presently not used on board.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.63-70
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• 2009

Recently, in the security token based authentication system, there is an increasing trend of using fingerprint for the token holder verification, instead of passwords. However, the security of the fingerprint data is particularly important as the possible compromise of the data will be permanent. In this paper, we propose an approach for secure fingerprint verification by distributing both the secret and the computation based on the fuzzy vault(a cryptographic construct which has been proposed for crypto-biometric systems). That is, a user fingerprint template which is applied to the fuzzy vault is divided into two parts, and each part is stored into a security token and a server, respectively. At distributing the fingerprint template, we consider both the security level and the verification accuracy. Then, the geometric hashing technique is applied to solve the fingerprint alignment problem, and this computation is also distributed over the combination of the security token and the server in the form of the challenge-response. Finally, the polynomial can be reconstructed from the accumulated real points from both the security token and the server. Based on the experimental results, we confirm that our proposed approach can perform the fuzzy vault-based fingerprint verification more securely on a combination of a security token and a server without significant degradation of the verification accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.459-467
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• 2008

Because of the insufficiency of ground space, the utilization of underground is getting more and more in these days. Moreover, underground space is being used not only buildings but multipurpose space for movement, storage and shopping. However, ground space has vital weakness for fire compared to ground space. Especially in case of underground shopping center, there are various stuffs to burn and poisonous gas can be exposed on this count when the space is burned. A large number of casualties can be also occurred from conflagration as underground space has closed structures that prevent rapid evacuation and access. Therefore, this research proposes the guidance methodology for evacuation from conflagration in large-sized underground facilities. In addition, suggested methodology uses high technology wireless sensor information from up-to-date ubiquitous sensor networks. Fire information collected by sensors is integrated with existing underground facilities information and this is sent to guidance systems by inducing process. In the end, this information is used for minimum time paths finding algorithm considering the passageway capacity and distance. Also, usefulness and inadequacies of proposed methodology is verified by a case study.

• Smart Media Journal
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• v.13 no.3
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• pp.9-17
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• 2024

Due to the recent pandemic and the development of ICT technology, the use of non-face-to-face and unmanned systems is expanding, and it is very important to understand emotions in communication in non-face-to-face situations. As emotion recognition methods for various facial expressions are required to understand emotions, artificial intelligence-based research is being conducted to improve facial expression emotion recognition in image data. However, existing research on facial expression emotion recognition requires high computing power and a lot of learning time because it utilizes a large amount of data to improve accuracy. To improve these limitations, this paper proposes a method of recognizing facial expressions using age and gender, which are image meta information, as a method of recognizing facial expressions with even a small amount of data. For facial expression emotion recognition, a face was detected using the Yolo Face model from the original image data, and age and gender were classified through the VGG model based on image meta information, and then seven emotions were recognized using the EfficientNet model. The accuracy of the proposed data classification learning model was higher as a result of comparing the meta-information-based data classification model with the model trained with all data.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.1
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• pp.57-68
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• 2010

Increase in greenhouse gas emissions during the last century has led to remarkable changes in our environment and climate system. Many policy measures have been developed to reduce greenhouse gas emissions across the world, many of which require our lifestyle changes from energy-intensive to energy-saving. One of the changes in our living patterns is to consider food miles. A food mile is the distance food travels from where it is produced to where it is consumed. Providing information of food miles will help people choose low mileage food, helping promote a "green consumption" action and lead to a low carbon society with emission reduction systems. In this study, 10 items are selected from 23 Harmonized commodity description and 2-digit coding system (HS) to estimate their food miles, and $CO_2$ emissions released in the transportation of imported food. For the estimation, four countries are chosen-Korea, Japan, United Kingdom (UK) and France, with Korea and Japan's 2001, 2003, and 2007 trade statistics and UK and France's 2003 and 2007 trade statistics used. As a result, Korea showed in 2007 the highest level of food miles and $CO_2$ emissions per capita among 4 countries. That suggests that Korea should make an effort to purchase local food to reduce food miles and use low-carbon vehicles for food transport, contributing to reducing greenhouse gas emissions.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Women's Health Nursing
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• v.10 no.3
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• pp.190-199
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• 2004

This thesis explores the actual circumstances of Korean nursing by investigating its present situations. Ultimately, the intent of this study aims to establish a future direction of Korean Nursing. As such, the current conditions of Korean nursing is analyzed in the following categories: education, practice, research, nursing policy, expansion of nursing field, and entrepreneurship. In the final conclusion, an appropriate orientation of the future of Korean nursing is discussed. There are two primary Nursing programs, a three-year(63) and a four-year(53), in Korean Nursing education. Master's programs are available at 32 nursing schools or 32 professional graduate schools. A total of 15 nursing schools have a doctoral program in Korea. The ratio of graduates between the three-year and four year programs is 76:24. Hence, it is highly encouraged to expand four-year nursing programs, because it will help raise the social status of nursing professionals as well as the quality of nursing. In the clinical nursing field, independency and self regulation are critical. As such, organizational change, implementation of a standardized nursing m information system, appropriate workforce, and improvement of the reimbursement system in nursing is recommended. In community nursing, the following should be resolved to provide better nursing services: improvement of working condition and benefits, establishment of a law enforcing the hiring of nurses, and providing continuing education. The number of nursing research has increased and nursing studies are in great quantity. However, research in practices and theories are more in demanded. Hence, research that integrates theories and practices are very significant. Ultimately, it is critical to support nursing studies that will influence nursing policy. The Korean Nurses Association(KNA) is an organization that supervises the nation's nursing policy. The primary focus of KNA is to combine the three and four year undergraduate education systems into 4 years and to establish the Nursing Practice Act. The Ministry of Health and Welfare has adapted a system to educate and certify nurse specialists in 10 nursing areas in 21 nursing graduate schools expecting high-quality nursing services and a decrease of cost. The government also allowed nurses to operate facilities for health management or welfare agencies.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.4
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• pp.299-305
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• 2015

A statistical annoyance model to railway noise at platform was proposed by jury evaluation test performed in hearing laboratory. ITX-Saemaeul and Mugunghwa were chosen as the noise sources of the test, and announcement sound was included to simulate real situation. Logistic regression analysis produced %HA_LAB curve. Hosmer-Lemeshow test and classification accuracy test were used to verify the model's statistical significance. It was shown that the model which was generated from relatively small number of samples is statistically significant.