• The korean journal of orthodontics
• /
• v.31 no.6 s.89
• /
• pp.601-610
• /
• 2001

The purpose of this retrospective study was to evaluate the level oi alveolar bone support of the erupted Permanent canine through the reconstructed cleft region compared to the contralateral canine on the non-cleft side. This study was limited to children with complete unilateral cleft lip and palate who underwent secondary alveolar iliac bone gvaft and the apices of the erupted canine roots were closed at the time of evaluation. With these criteria the study included 21 children whose average age at the time of bone graft reconstruction was 9.8 years, with a minimum of 12.4 years of age at the time of the evaluation. The study was limited to the use of iliac cancellous bone as the autograft material for reconstruction of the alveolar cleft. Cranial bone graft and other autogenous bone sources were excluded. The periapical radiographs were used to evaluate alveolar bone level of each canine. The percentages of root supported by the bone were established by dividing the amount of root covered with the bone by the anatomic root length. The canine oi the non-cleft side was used as an internal control and the canine on the cleft side was used as an experimental. There was a statistically significant difference in the alveolar bone support ratio between the control ($92.9\%$) and experimental canines ($8.7\%$). An average of $95\%$ level of alveolar bone support was achieved for the experimental canine in comparison to the control canine. Neither the presence of lateral incisor, nor the stage of root development of the canine at the time of the bone graft appeared to have affected the alveolar bone support ratio of the canine after the secondary bone graft.

• Journal of Food Hygiene and Safety
• /
• v.20 no.4
• /
• pp.253-257
• /
• 2005

This study was performed to develop management system of artificial flavor in Korea that considered the usage and management of artificial flavor within or outside (Europe, USA and JECFA) and to offer a yardstick for judgement and prevent from confusing when manufacture or import artificial flavoring substances. In questionnaire survey for flavoring manufacture form, ideal management system and others in companies related artificial flavor, the replier answered that artificial flavor was mainly used to drinks as water soluble from and that the countries exporting flavoring substances most frequently to Korea were Japan. Europe and America sequentially. On the basis of above results, we prepared the positive list (proposal) on about 1800 artificial flavoring substances for application to regulations in Korea Food Additives Code.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.3
• /
• pp.301-308
• /
• 2018

This study explores an analytical mathematical model designed to estimate the optimal debt ratio of the Korean automobile industry, which has a more significant effect on the national economy than that of other industries, and attempts to estimate the optimal debt ratio based on objective data. The analytical model is based on ROA and ROE which uses the debt ratio as an independent variable and employs ROS, TAT, and NFCL as the related parameters. Regarding the NFCL, the optimal debt ratio is usually defined as the debt ratio that maximizes the ROA and ROE and is calculated using analytical procedures, such as by adding an equation that considers the debt ratio and the linearity relationship to the analytical model. This is because the optimal debt ratio can be calculated reliably by making use of an estimated value within a certain range, which is derived from more than two calculations rather than a single estimation starting from one calculation formula. In this study, for the estimation of the optimal debt ratio, the ROA and ROE are expressed as a quadratic equation with the debt ratio as the independent variable. Using this analysis procedure, the optimal debt ratio obtained using the data from the Korean automobile industry over a sixteen year period, which would optimize the profitability of the Korean automobile industry, was found to be 188% of the debt ratio in the ROA and 213% of the debt ratio in the ROE. This result was obtained by overcoming the problem of the reliability of the estimation value in spite of the limitations of the logical theory of this study, and can be interpreted as meaning that maintaining a debt ratio of 188% to 213% can enhance the profitability and reduce the risks in the Korean automobile industry. Furthermore, this indicates that the existing debt ratio of the Korean automobile industry is lower than the optimal value within the estimated range. Consequently, it is necessary for corporations to change their future debt ratio policies, given that the purpose of debt ratio management is to maintain safety and increase profitability, and to take into account the characteristics of the specific industry.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.7
• /
• pp.1-8
• /
• 2017

As the IT environment becomes more sophisticated, various threats and their associated serious risks are increasing. Threats such as DDoS attacks, malware, worms, and APT attacks can be a very serious risk to enterprises and must be efficiently managed in a timely manner. Therefore, the government has designated the important system as the main information communication infrastructure in consideration of the impact on the national security and the economic society according to the 'Information and Communication Infrastructure Protection Act', which, in particular, protects the main information communication infrastructure from cyber infringement. In addition, it conducts management supervision such as analysis and evaluation of vulnerability, establishment of protection measures, implementation of protection measures, and distribution of technology guides. Even now, security consulting is proceeding on the basis of 'Guidance for Evaluation of Technical Vulnerability Analysis of Major IT Infrastructure Facilities'. There are neglected inspection items in the applied items, and the vulnerability of APT attack, malicious code, and risk are present issues that are neglected. In order to eliminate the actual security risk, the security manager has arranged the inspection and ordered the special company. In other words, it is difficult to check against current hacking or vulnerability through current system vulnerability checking method. In this paper, we propose an efficient method for extracting diagnostic data regarding the necessity of upgrading system vulnerability check, a check item that does not reflect recent trends, a technical check case for latest intrusion technique, a related study on security threats and requirements. Based on this, we investigate the security vulnerability management system and vulnerability list of domestic and foreign countries, propose effective security vulnerability management system, and propose further study to improve overseas vulnerability diagnosis items so that they can be related to domestic vulnerability items.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.11 no.4
• /
• pp.233-238
• /
• 1978

Rainbow trout were reared in a stainless steel aquarium from Nov. 11, 1977 to June 12, 1978, and the following results were obtained : 1. The volume of water was about $400\iota$ in a aquarium measuring $1m\;(Length)\times1m\;(Width)\times67cm(Height)$ and water depth 40 cm. Water was supplied for about 16 hours daily at a rate $3\iota/min$ and was drained through the conical settling part in the middle of the aquarium bottom. Filter tank was about $23cm(W)\times23cm(L)\times40cm(D)$ and contained pebbles 30 cm in depth. Water recirculation rate was at)out $1,030\iota/hr$, or 2.6 turn-over per hour. 2. During the first period (77 days), the trout grew from 88.3g to 229g in average, the total weight attaining 30.7kg. The food coefficient was 1.249, average daily increment 243.3g, average daily growth rate 1.245%, and the mortality was 2 smallest fish weighing 53 g, owing to unknown reason. During the second period (135 days), the trout grew from 239g to 555g in average, the total weight attaining 57.2 kg. The food coefficient was 1.447, average daily increment 279.8g, average daily growth rate $0.65\%$ and the mortality was 31 fish weighing 11,255 g, owing partly to miss-handling and partly to disease. 3. The feed consisting of fully domestic materials was prepared in this laboratory, and the feed conversion was not inferior to high protein commercial feed available in foreign countries. 4. The result of whole period for 212 days was 56.5 kg in gross increment, and based on this result, when $1\iota/min$ full day inflowing water available, the net production will become 28.25 kg. So, if a 5000kg production is planned, $180\iota/min$ or about $10.8m^3/hr$ be reauired, and the production in value frill become 15million won at local price at the expense of about 5.3 million won. From the result of this experiment, rainbow trout is feasible for commercial production in Korea with relatively small amount of well water and simplified water recirculation system.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.157-173
• /
• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.131-142
• /
• 2011

In recent years, Social Network Service, which is defined as a web-based service that allows an individual to construct a public or a semi-public profile within a bounded system, articulates a list of other users with whom they share connections, and traverses their list of connections. For example, Facebook and Twitter are the representative sites of Social Network Service, and these sites are the big issue in the world. A lot of people use Social Network Services to connect and maintain social relationship. Recently the users of Social Network Services have increased dramatically. Accordingly, many organizations become interested in Social Network Services as means of marketing, media, communication with their customers, and so on, because social network services can offer a variety of benefits to organizations such as companies and associations. In other words, organizations can use Social Network Services to respond rapidly to various user's behaviors because Social Network Services can make it possible to communicate between the users more easily and faster. And marketing cost of the Social Network Service is lower than that of existing tools such as broadcasts, news papers, and direct mails. In addition, Social network Services are growing in market place. So, the organizations such as companies and associations can acquire potential customers for the future. However, organizations uniformly communicate with users through Social Network Service without consideration of the characteristics of the networks although networks have different effects on information deliveries. For example, members' cohesion in an offline communication is higher than that in an online communication because the members of the offline communication are very close. that is, the network of the offline communication has a strong tie. Accordingly, information delivery is fast in the network of the offline communication. In this study, we compose two networks which have different characteristic of communication in Twitter. First network is constructed with data based on an offline communication such as friend, family, senior and junior in school. Second network is constructed with randomly selected data from users who want to associate with friends in online. Each network size is 250 people who divide with three groups. The first group is an ego which means a person in the center of the network. The second group is the ego's followers. The last group is composed of the ego's follower's followers. We compare the networks through social network analysis and follower's reaction analysis. We investigate density and centrality to analyze the characteristic of each network. And we analyze the follower's reactions such as replies and retweets to find differences of information delivery in each network. Our experiment results indicate that density and centrality of the offline communicationbased network are higher than those of the online-based network. Also the number of replies are larger than that of retweets in the offline communication-based network. On the other hand, the number of retweets are larger than that of replies in the online based network. We identified that the effect of information delivery in the offline communication-based network was different from those in the online communication-based network through experiments. So, you configure the appropriate network types considering the characteristics of the network if you want to use social network as an effective marketing tool.

• Journal of Intelligence and Information Systems
• /
• v.15 no.4
• /
• pp.1-22
• /
• 2009

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component based software development to promote application interaction and integration both within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web service repositories not only be well-structured but also provide efficient tools for developers to find reusable Web service components that meet their needs. As the potential of Web services for service-oriented computing is being widely recognized, the demand for effective Web service discovery mechanisms is concomitantly growing. A number of techniques for Web service discovery have been proposed, but the discovery challenge has not been satisfactorily addressed. Unfortunately, most existing solutions are either too rudimentary to be useful or too domain dependent to be generalizable. In this paper, we propose a Web service organizing framework that combines clustering techniques with string matching and leverages the semantics of the XML-based service specification in WSDL documents. We believe that this is one of the first attempts at applying data mining techniques in the Web service discovery domain. Our proposed approach has several appealing features : (1) It minimizes the requirement of prior knowledge from both service consumers and publishers; (2) It avoids exploiting domain dependent ontologies; and (3) It is able to visualize the semantic relationships among Web services. We have developed a prototype system based on the proposed framework using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web service registries. We report on some preliminary results demonstrating the efficacy of the proposed approach.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.23-45
• /
• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Korean Society of Environmental Engineers
• /
• v.37 no.2
• /
• pp.87-91
• /
• 2015

As negative effects of climate change have been visualized and its direct damages to economy have been realized, the global efforts to respond to climate change by reducing greenhouse gas emission were accelerated. Korea's Carbon Footprint Labeling gets a lot of attention as one of the effective methods to contribute to national GHG reduction goal, and for enterprises to show customers how much effort the company put into global warming prevention. Consumers' interest on low-carbon products has been increasing. This study uses Life Cycle Assessment method to calculate the amount of carbon emission of dishwashing detergent, LG Household & Healthcare, which reduced carbon emissions by using raw materials that has relatively lower environment load. Life Cycle Assessment Method is based on guidelines of Carbon Footprint Labeling, Ministry of Environment, and pre-manufacturing, manufacturing, and disposal phase are included while use phase of the product is excluded from assessment. In order to understand the effects of eco-design on carbon emissions, the dishwashing detergent's carbon emissions are compared before and after the change of main raw materials. The result shows the improvement from $0.47kgCO_2eq/kg$ to $0.38kgCO_2eq/kg$ per product, and this means the main raw materials' carbon emissions could be reduced by around 9.4%, which is equivalent to 916tons of GHG emissions per year.