• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.1-8
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• 2004

It's a very meaningful experimental study to image restoration of ancient villages vanished at the real life spatial world. Focused on Cheung-Pyung Lake around where most part were flooded by the Chung-Ju large dam founded in early 1980s, we used remote sensing technique in this study in order to restore topographical features before the flood with 3 dimensional effects. It was gathered comparatively good satellite photos and remotely sensed digital images, then its made a new color image from these and the topographical map which had been made before filled water. This task was putting together two kinds of different timed images. And then, we generated DEM(digital elevation model) including the outskirts of that area as harmonizing current contour lines with the map. That could be a perfect 3D image of Cheung-Pyung around before when it had been flood by making perspective images from all directions, north, south, east and west, for showing there in three dimensions. Also, flying simulation we made for close visiting can bring us to experience their real space at that time.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.147-154
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• 2006

Effective and efficient representation of color features of multiple video frames is an important vet challenging task for visual information management systems. This paper Proposes a Video Browsing Service(VBS) that provides both the video content retrieval and the video browsing by the real-time user interface on Web. For the scene segmentation and key frame extraction of video sequence, we proposes an efficient scene change detection method that combine the RGB color histogram with the X2 (Chi Square) histogram. Resulting key frames are linked by both physical and logical indexing. This system involves the video editing and retrieval function of a VCR's. Three elements that are the date, the need and the subject are used for video browsing. A Video Browsing Service is implemented with MySQL, PHP and JMF under Apache Web Server.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.85-92
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• 2021

In this paper, a method for effectively detecting rotated face and rotation angle regardless of the rotation angle is proposed. Rotated face detection is a challenging task, due to the large variation in facial appearance. In the proposed polar coordinate transformation, the spatial information of the facial components is maintained regardless of the rotation angle, so there is no variation in facial appearance due to rotation. Accordingly, features such as HOG, which are used for frontal face detection without rotation but have rotation-sensitive characteristics, can be effectively used in detecting rotated face. Only the training data in the frontal face is needed. The HOG feature obtained from the polar coordinate transformed images is learned using SVM and rotated faces are detected. Experiments on 3600 rotated face images show a rotation angle detection rate of 97.94%. Furthermore, the positions and rotation angles of the rotated faces are accurately detected from images with a background including multiple rotated faces.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.539-547
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• 2019

This study investigated the effect of small-world structure in team processes on team performance. I discussed the theoretical relationship between small-world structure in team processes and team performance and analyzed the relationship using pass data of soccer teams. I constructed the 128 pass networks from the pass data of the 2014 FIFA World Cup and then measured the structural features indicating small-world structure of the networks. Correlation analysis and regression analysis were performed in order to examine the strength and direction of the relationship. According to the results, the clustering has an exponential relationship with team performance and the connectivity has a log-function relationship with team performance. Finally, I found the positive effect of small-world structure in team processes on team performance. Through theoretical discussion and empirical analysis, this study found that small-world structure in team processes increase team performance by facilitating task coordination and collaboration between team members.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.191-197
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• 2021

Ensuring the economic security of agro-industrial complexes of Ukrainian regions has become a top-priority task of state regional policy, as their stable functioning is an essential element of economic security of the whole country. It is overcoming threats to the development of the agro-industrial complex that ensures its further effective functioning and has a significant impact on the economic security of our state. Methods: logical method; methods of system analysis; synthesis; economic and statistical method; method of expert assessment; SWOT analysis; economic and mathematical modelling and planning. Results. Characteristic features of economic security have been given. The essence and significance of the agro-industrial complex in improving the economic security of the state have been determined. It has been noted that in recent years, the agro-industrial complex, which acts as a driver of the domestic economy and has a direct impact on the development of the country, has been growing (in 2019 the cereal and legume harvest exceeded 75 million tons, 20,269 thousand tons of potatoes were dug, more than 15 million tons of sunflower, 9,688 thousand tons of vegetables and 2,119 thousand tons of fruits and berries were harvested, meat and egg production increased by 137.5 thousand tons (or 5.8%) and 545.5 million pieces (or 3.4%), respectively, the number of employed population in agriculture increased by 139.8 thousand people (or 4.9%), the labour productivity in crop production increased by UAH 294.4 thousand (or 44.6%), in livestock production - by UAH 311.3 thousand (or 61.8%)). Based on the system of production and economic indicators, the analysis of the state of the agro-industrial complex has been carried out. Taking into account the results of the obtained data and using SWOT-analysis, the major threats to the development of the agro-industrial complex have been identified. Ways of overcoming threats enhancing the economic security of Ukraine have been proposed.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.4
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• pp.103-111
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• 2018

The Soswaewon Garden, which was constructed by Yang San-Bo around 1520, is a private traditional garden in Korea. Soswaewon Garden has long been and still is the focus of continuous research. However, relatively few studies have been conducted on the restoration of buildings that were lost in the past. The Goam-Jeongsa and Boohwondang buildings, which were lost in 1597 during the Japanese invasion, were restored in 1614 and depicted in a picture of Soswaewon Garden in 1755. However, they eventually disappeared and no longer exist in the present. Therefore, the restoration of these two buildings is considered an urgent task. This research aims to search for the original location of these buildings and estimate their size and shape in terms of physical space. Several years ago, an estimation of the building site was carried out, providing a stepping stone on this matter. This can be studied through "Soswaewon's 48 quatrains with five Chinese characters in each line," "Soswaewon-sasil," and "Soswaewon's drawing." Some parts of the shape of Soswaewon also appear in the "Yuseoseokrok," which is helpful. Thus, in this research, information on the Goam-Jeongsa and Boowondang buildings appearing in research results and literature to date are collectively analyzed. The location and architectural features of both buildings are identified by focusing on excavations. Also, for the purpose of restoration planning, base data on the location, size, and shape of both buildings are presented. In line with this process, a valid restoration plan is presented by analyzing the abovementioned historical research materials and comparing empirical data, such as excavation results.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.233-246
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• 2021

The present study aimed to investigate whether there would be differences in the severity of PTSD symptoms, self and others concepts between trauma types. Among 166 university students, 61 (simple trauma's n = 31, complex trauma's n = 30) finally met the criteria and completed the Life Events Checklist, Impact of the Event Scale-Revised, and the emotional Stroop task. The results were as follows: firstly, PTSD symptoms were higher in complex trauma group than single trauma group. Secondly, response time in the complex trauma group was longer in the condition that the negative word related to 'self' was presented for 2 seconds compared to the single trauma group. These results suggest that the complex trauma group has different features at least in the severity of PTSD symptoms and the concept of the self, compared with the single trauma group. Finally, the therapeutic implications and limitations of the study were discussed.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.1-15
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• 2022

The MobileNetV3 is specially designed for mobile devices with limited memory and computing power. To reduce the network parameters and improve the network inference speed, a new lightweight network is proposed based on MobileNetV3. Firstly, to reduce the computation of residual blocks, a partial residual structure is designed by dividing the input feature maps into two parts. The designed partial residual structure is used to replace the residual block in MobileNetV3. Secondly, a dual-path feature extraction structure is designed to further reduce the computation of MobileNetV3. Different convolution kernel sizes are used in the two paths to extract feature maps with different sizes. Besides, a transition layer is also designed for fusing features to reduce the influence of the new structure on accuracy. The CIFAR-100 dataset and Image Net dataset are used to test the performance of the proposed partial residual structure. The ResNet based on the proposed partial residual structure has smaller parameters and FLOPs than the original ResNet. The performance of improved MobileNetV3 is tested on CIFAR-10, CIFAR-100 and ImageNet image classification task dataset. Comparing MobileNetV3, GhostNet and MobileNetV2, the improved MobileNetV3 has smaller parameters and FLOPs. Besides, the improved MobileNetV3 is also tested on CPU and Raspberry Pi. It is faster than other networks