• Convergence Security Journal
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• v.15 no.6_1
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• pp.49-57
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• 2015

Changes in the national intelligence security industry is becoming increasingly rapidly changing due to the development of the network and the use of the Internet. Information also can be called by critical information assets, as well as social infrastructure of the country's reality is that individuals at risk. These professionals make to prevent terrorists to destroy national defense system and network was absolutely necessary. But, Cyber Terror Response NCOs to be responsible for cyber terrorism requires a professional NCOs with advanced knowledge. National Competency Standards(NCS) using a national information security field tutors to conduct training courses teaching - learning model to develop and to apply.

• Electronics and Telecommunications Trends
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• v.35 no.7
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• pp.46-55
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• 2020

As artificial intelligence (AI) technology becomes more important, the demand for AI talent is increasing. However, there is a shortage of AI talent around the world, and it is difficult to secure. Therefore, it has become more important to nurture the AI workforce. The private sector and government in Korea and other countries are making an effort to cultivate AI talent, and ETRI has proposed "Nurturing AI Professionals" as ETRI AI Strategy #5 to meet both internal and national demands for AI talent. ETRI has suggested three key tasks to implement AI Strategy #5. The first one is to create a "top-notch AI talent training project: the ETRI AI Academy" to strengthen AI research capabilities. The second one is "nurturing AI engineers specialized in local-based industries: the ETRI AI Business School" to help supply the necessary AI workforce in the industry. The third one is the "contribution to AI education service for people: ETRI AI Literacy" to raise the public's understanding and utilization of AI.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1117-1122
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• 2017

Pedestrian detection is a very important and valuable part of artificial intelligence and computer vision. It can be used in various areas for example automatic drive, video analysis and others. Many works have been done for the pedestrian detection. The accuracy of pedestrian detection on multiple pedestrian image has reached high level. It is not easily get more progress now. This paper proposes a new structure based on the idea of HOG and convolutional filters to do the pedestrian detection in single pedestrian image. It can be a method to increase the accuracy depend on the high accuracy in single pedestrian detection. In this paper, we use Multiblock HOG and magnitude of the pixel as the feature and use convolutional filter to do the to extract the feature. And then use NEWFM to be the classifier for training and testing. We use single pedestrian image of the INRIA data set as the data set. The result shows that the Convolutional Multiblock HOG we proposed get better performance which is 0.015 miss rate at 10-4 false positive than the other detection methods for example HOGLBP which is 0.03 miss rate and ChnFtrs which is 0.075 miss rate.

• Journal of Intelligence and Information Systems
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• v.4 no.2
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• pp.23-34
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• 1998

Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.131-141
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• 2012

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.73-82
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• 2000

This study examined the characteristicso f the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms CHAID (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) since logistic regression has assumed a major position in the healthcare field as a method for predicting or classifying health outcomes based on the specific characteristics of each individual case. This comparison was performed using the test set of 4,588 beneficiaries and the training set of 13,689 beneficiaries that were used to develop the models. On the contrary to the previous study CHAID algorithm performed better than logistic regression in predicting hypertension but C5.0 had the lowest predictive power. In addition CHAID algorithm and association rule also provided the segment characteristics for the risk factors that may be used in developing hypertension management programs. This showed that data mining approach can be a useful analytic tool for predicting and classifying health outcomes data.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.495-503
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• 2017

Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict the progression of LOCAs in nuclear power plants. Therefore, accurate information on the LOCA break position and size should be provided to efficiently manage the accident. In this paper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of the CFNN model are the time-integrated values of each measurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• Journal of Korean Academy of Nursing
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• v.48 no.6
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• pp.708-719
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• 2018

Purpose: This study was conducted to examine the effects of a communication empowerment program based on situated learning theory for nursing students. Methods: A non-equivalent control group pretest-posttest design was used. The study participants were 61 nursing students (31 in the experimental group and 30 in the control group) from G city. Data were collected from November 3, 2015 to December 10, 2015. The experimental group received eight sessions of the program, which were scheduled twice a week, with each session lasting two hours. The data were analyzed using chi-square test, Fisher's exact test, and an independent t-test using SPSS/WIN 20.0. Results: There were significant increases in self-efficacy for communication (t=2.62, p=.011), emotional intelligence (t=2.66, p=.010), and interpersonal communication competence (t=2.87, p=.006) in the experimental group compared to the control group. Conclusion: Based on the findings, our study suggests a need to include content from communication curricula or clinical communication training programs for improving undergraduate nursing students' communication skills in practice settings.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1363-1369
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• 2018

Breast cancer is one of the most common cancers in women worldwide. In Korea, breast cancer is most common cancer in women followed by thyroid cancer. The purpose of this study is to evaluate the possibility of using deep - run model for segmentation of breast masses and to identify the best deep-run model for breast mass segmentation. In this study, data of patients with breast masses were collected at Asan Medical Center. We used 596 images of mammography and 596 images of gold standard. In the area of interest of the medical image, it was cut into a rectangular shape with a margin of about 10% up and down, and then converted into an 8-bit image by adjusting the window width and level. Also, the size of the image was resampled to $150{\times}150$. In Deconvolution net, the average accuracy is 91.78%. In U-net, the average accuracy is 90.09%. Deconvolution net showed slightly better performance than U-net in this study, so it is expected that deconvolution net will be better for breast mass segmentation. However, because of few cases, there are a few images that are not accurately segmented. Therefore, more research is needed with various training data.