• The KIPS Transactions:PartC
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• v.15C no.5
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• pp.351-358
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• 2008

Recently, as network flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems(IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network environment. In this paper we propose a lightweight and fast detection mechanism for traffic flooding attacks. Firstly, we use SNMP MIB statistical data gathered from SNMP agents, instead of raw packet data from network links. Secondly, we use a machine learning approach based on a Support Vector Machine(SVM) for attack classification. Using MIB and SVM, we achieved fast detection with high accuracy, the minimization of the system burden, and extendibility for system deployment. The proposed mechanism is constructed in a hierarchical structure, which first distinguishes attack traffic from normal traffic and then determines the type of attacks in detail. Using MIB data sets collected from real experiments involving a DDoS attack, we validate the possibility of our approaches. It is shown that network attacks are detected with high efficiency, and classified with low false alarms.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.550-559
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• 2018

The effectiveness of deep neural networks (DNNs) for detection and classification of micro-Doppler signals generated by human walking and background noise sources is investigated. Previous research included a complex process for extracting meaningful features that directly affect classifier performance, and this feature extraction is based on experiences and statistical analysis. However, because a DNN gradually reconstructs and generates features through a process of passing layers in a network, the preprocess for feature extraction is not required. Therefore, binary classifiers and multiclass classifiers were designed and analyzed in which multilayer perceptrons (MLPs) and DNNs were applied, and the effectiveness of DNNs for recognizing micro-Doppler signals was demonstrated. Experimental results showed that, in the case of MLPs, the classification accuracies of the binary classifier and the multiclass classifier were 90.3% and 86.1%, respectively, for the test dataset. In the case of DNNs, the classification accuracies of the binary classifier and the multiclass classifier were 97.3% and 96.1%, respectively, for the test dataset.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.180-190
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• 2014

Social network services are increasingly being used in organizational settings to improve relationships among employees and enhance prospects for information exchange and cooperative work. Social Networking Service(SNS) has deeply penetrated organizational job settings, influencing multiple aspects of employee's life. This study is designed to explore the impact of SNS engagement on the job performance mediated as team cooperation and decision making quality effects. Data were collected from 146 employees who use organizational SNS in there company. Factor analysis and structural equation method are employed. Results from a survey accompanied by the substantial impacts of organizational employee's social networking engagement on social learning processes and outcomes. SNS engagement not only directly influences organizational employee's job performance, but also helps their team cooperation and decision making quality from others and adapt to organizational culture, both of which play prominent roles in improving their job performance.

• Journal of Korean Neurosurgical Society
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• v.63 no.3
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• pp.386-396
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• 2020

Objective : To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. Methods : GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results : The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). Conclusion : This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.

• Korean Journal of Human Ecology
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• v.6 no.1
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• pp.111-127
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• 1997

This study was to examine home economics (HE) teachers' and the 1st-grade students' needs perception toward the purposes of HE education in middle school which has been practced since 1995 for both male and female students. This study, attempted (1) to analyze needs priority among the educational purposes of HE subject in relation to three systems of actions; (2) to compare differences between HE teachers' and students' perception concerning the degree of importance and achievement of the educational purposes of HE subject: and (3) to examine what they conceive as the problems In the current HE education. The survey was conducted with the samples of 600 1st-grade middle school students and 101 middle school HE teachers during the period of February-March 1996. The questionnaire used in this study was a modified version which had already been developed along with the 6th HE curriculum. For data analyses, SAS program was utilized to get Means and to perform both discrepancy test and t-test. The findings of this study were summarized as follows: first, with respect to each group's perception of the importance of the purposes related to three systems of action, HE teachers emphasized the importance of the purposes related to emancipatory action, while students placed more emphasis on the purposes related to technical action. Second, in terms of the degree of achievement, students had more positive perception on the degree of achievement of the purposes related to technical action than HE teachers did. Both groups marked low level of recognition on the degree of achievement of the purposes related to emancipatory action. Third, with respect to needs priority, HE teachers placed the first priority on emancipatory action, the second on technical action, and the last on communicative action: in the case of students, the first priority was on technical action, the second on communicative action, and the last on emancipatory action. In addition, the analysis of the opinions on the 6th curriculum revealed that most respondents found it necessary to secure adequate amount of classes for HE education. Also they shared the recongnition that HE curriculum should be renovated into the one which would fully appreciate the purposes of HE education from the perspective of the practical concerns of action which are distinct from the functional and technical concerns of passive learning. The findings of this study can serve as basic data for establishing the new purposes of HE education which put more emphasis on the purposes related to emancipatory action: as well as for developing an enhanced curriculum and reinforcing the identity of HE education.

• Journal of the Korean Institute of Rural Architecture
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• v.19 no.1
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• pp.11-22
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• 2017

The purpose of this study is to analyze the actual situation of residents' participation in rural development projects, to identify their performance and problems, and suggest ways to develop desirable villages in rural areas. From reviewing relevant pre-studies, this study was done by interview, questionnaire, and observation targeting 140 leaders and residents of exampled village of the project in Jeollanam-do, as well as by listening opinions of relative experts. This study is largely classified into 4 parts, review of character and appearing background of village development project, review of theoretical discussion about residents' participation, evaluation of accomplishments and analysis of participation, and establishing a model for habitants-participating village development project and how to improve it. As a result of questionnaire, it was found urgent for habitants to convert their thinking about village development and their participation in it, to realize a model of this project, as well as political stimulus to promote that. Therefore, measures must be required to improve current village development projects and to promote them. First of all, a preparation period is required to sufficiently provide the village where habitants are willing to participate in, from the state of place selection. Besides, it is required to run away from profit-making businesses aiming at foreign people, to improvement of residents' welfare in a long term, and enhanced resources management in a broad view. Waste of working expenses seems to be solved through direct operation by a corporation in charge of profit-making businesses, under superstition of residents' community. Finally conclusion, expansion and practicalization of education to residents are essential, to promote their participation in rural development projects. Especially it must be practical education for habitants such as 'community-ship' or 'technology education in each interesting part,' rather than tour of other villages and unilateral lectures from experts. Along with this, a long term plan and systematic participation is more essential. Since planning itself can be mutual learning to enhance residents' capacity, a chance must be established to discuss and plan each part including resources-research, by making them participate in.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.3
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• pp.29-38
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• 2008

This study intends to analyze the value of school forests, one type of urban forest. For this purpose, four attributes of school forests were investigated, considering ecological, educational, social and economic values using a conjoint model as the stated preference. Based on literature reviews, the levels of the four attributes were selected, and a questionnaire survey was given to 279 urban residents divided into 2 groups: those impacted by school forests and those not. The study results suggest that the most important attribute of school forests is economic value, and next is ecological, social and educational value according to the part-worth model. The fitness level of the model is 0.900(total group) which is very significant. As for the economic value, free and 1,000 won are more critical factors than the other 2 levels, 5,000 won and 10,000 won and air pollution purification and making the school landscape are more critical factors than small habitats and microclimate factors. In addition, regarding the social value related to residents' leisure activities，the utility of nature observation is higher than walking and exercising. Finally, for educational value, understanding nature's importance is more critical than the emotions and learning of students. The estimated WTP per household/month is 3,580 won, the group related to school forestsis 3,650 won and the non-related group is 3,540 won. Based on these results, the estimated total economic value of all households per year is 6,820 hundred million won. The group related to school forests is 6,970 hundred million won and the non-related group is 6,750 hundred million won.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.177-185
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• 2009

Cluster analysis or clustering is a kind of unsupervised learning method in which a set of data points is divided into a given number of homogeneous groups. Fuzzy clustering method, one of the most popular clustering method, allows a point to belong to all the clusters with different degrees, so produces more intuitive and natural clusters than hard clustering method does. Even more some of fuzzy clustering variants have noise-immunity. In this paper, we improved the Possibilistic Fuzzy C-Means (PFCM), which generates a membership matrix as well as a typicality matrix, using Gath-Geva (GG) method. The proposed method has a focus on the boundaries of clusters, which is different from most of the other methods having a focus on the centers of clusters. The generated membership values are suitable for the classification-type applications. As the typicality values generated from the algorithm have a similar distribution with the values of density function of Gaussian distribution, it is useful for Gaussian-type density estimation. Even more GG method can handle the clusters having different numbers of data points, which the other well-known method by Gustafson and Kessel can not. All of these points are obvious in the experimental results.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.270-283
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• 2012

With the advent of multi-channel TV, IPTV and smart TV services, excessive amounts of TV program contents become available at users' sides, which makes it very difficult for TV viewers to easily find and consume their preferred TV programs. Therefore, the service of automatic TV recommendation is an important issue for TV users for future intelligent TV services, which allows to improve access to their preferred TV contents. In this paper, we present a recommendation model based on statistical machine learning using a collaborative filtering concept by taking in account both public and personal preferences on TV program contents. For this, users' preference on TV programs is modeled as a latent topic variable using LDA (Latent Dirichlet Allocation) which is recently applied in various application domains. To apply LDA for TV recommendation appropriately, TV viewers's interested topics is regarded as latent topics in LDA, and asymmetric Dirichlet distribution is applied on the LDA which can reveal the diversity of the TV viewers' interests on topics based on the analysis of the real TV usage history data. The experimental results show that the proposed LDA based TV recommendation method yields average 66.5% with top 5 ranked TV programs in weekly recommendation, average 77.9% precision in bimonthly recommendation with top 5 ranked TV programs for the TV usage history data of similar taste user groups.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.