• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.535-546
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• 2019

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.259-277
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• 2023

This study aims to identify the academic landscape of the field of Library and Information Science by analyzing the research areas of the four major domestic journals using structural topic modeling and network analysis. The results show that each journal focuses on different research areas. The Journal of the Korean Society for Library and Information Science covers the most comprehensive range of research areas in the field, while the Journal of the Korean Biblia Society for Library and Information Science shows a similar research trend but with a higher preference for research areas related to library management and library programs. The Journal of Korean Library and Information Science Society deals more with topics related to school libraries and reading education and the Journal of the Korean Society for Information Management focuses more on information technology and information science. This study is able to provide valuable foundational data for researchers in submitting their papers and for the topical specialization and diversification of the journals in the field of Library and Information Science.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.1
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• pp.36-45
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• 2012

This study locates factors that affect the intention to use fashion SNS (social network service) and intention for word-of-mouth on fashion SNS. Independent variables were fashion shopping orientation, attitude toward innovative products, fashion leadership, and demographics. A questionnaire method was used to collect data on college students while factor analyses, multiple regression, $x^2$ analyses, and Pearson correlation coefficients were applied in analyzing data. Factor analyses resulted in four factors for fashion shopping orientation, three on attitude toward innovative products and two on fashion leadership. Multiple regression analyses showed that information compatibility of attitude toward innovative products had a significant impact on two models of intention to use fashion SNS and two models of intention for word-of-mouth on fashion SNS. Opinion leadership and gender were significant factors for two models of intention to use fashion SNS, which means that women are likely to have more intention to use fashion SNS. Meanwhile, fashion innovativeness was found to be a significant factor on two models of intention for word-of-mouth on fashion SNS. Shopping orientation factors were not important for any model. $x^2$ analyses showed that women rather than men wanted more information on online fashion shows, general fashion information, and user participation programs. Fashion major students wanted more information on online fashion shows and user participation programs than non-fashion major students.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.4 s.117
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• pp.18-36
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• 2006

The purpose of this research was to establish a green-networks from the perfective of landscape ecology in order to improve the function of urban green spaces. The study site was Dalsu-Gu in Daegu City. This research consisted of three phases. In the first phase, field surveys were carried out in order to understand existing distribution pattern of green spaces in the study site. 533 green spaces surveyed in the first phase were classified into 7 patterns and 24 types. The total area of the green spaces in Dalsu-gu was 3,329ha. Specifically the area of the 'urban nature parks' type was 57.49% of the total area of green spaces in Dalsu-gu, and it was expected that 'urban nature parks' type can play important roles in the green-networks in Dalsu-gu. Two analysis with green spaces in 9 types including 'urban nature parks', 'rivers' and 'neighborhood parks' were performed to establish a basic network frame of the green-networks. In the second phase, 'mutual connectivity analysis' and 'mutual matrix analysis' were performed to select core green spaces of a green-networks using 'areas of each green space and a distance between each space' and 'a rate of green spaces and a rate of water permeable pavement'. The results of the second phase indicated that, in mutual connectivity analysis, large green spaces apart from each other were evaluated as having higher mutual connectivity than small green spaces near to each other. In mutual matrix analysis, the green spaces with higher mutual connectivity and the small green spaces near to each other were evaluated as having better mutual matrix. In the last phase, we structured a basic frame of the green-networks in Dalsu-Gu. The results suggested that the basic frame of the green-networks in Dalsu-Gu was composed on four green-network axes and its shape mirrored a cruciform(+) of northwest${\longleftrightarrow}$southeast directions and southwest${\longleftrightarrow}$northeast directions, The Duryu neighborhood park is at the central point of this green-networks.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.94-105
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• 1993

In this paper propose a Koaren character recognition system using a neural network is proposed. This system is a multilayer neural network based on the masking field model which consists of a input layer, four feature extraction layers which extracts type, direction, stroke, and connection features, and an output layer which gives us recognized character codes. First, 4x4 subpatterns of an NxN character pattern stored in the input buffer are applied into the feature extraction layers sequentially. Then, each of feature extraction layers extracts sequentially features such as type, direction, stroke, and connection, respectively. Type features for direction and connection are extracted by the type feature extraction layer, direction features for stroke by the direction feature extraction layer and stroke and connection features for stroke by the direction feature extraction layer and stroke and connection features for the recongnition of character by the stroke and the connection feature extractions layers, respectively. The stroke and connection features are saved in the sequential buffer layer sequentially and using these features the characters are recognized in the output layer. The recognition results of this system by tests with 8 single consonants and 6 single vowels are promising.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.57-65
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• 2018

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS : For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS : The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination ($R^2$) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as $R^2$ had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.55-64
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• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.189-196
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• 2011

The main issues of the researches are monitoring environment such as weather or temperature variation and natural accident, and sensor gateways which have mobile device, applications for mobile health care. In this paper, we propose the SFMS(Smart Factory Management System) that can effectively monitor and manage a green smart factory area based on M2M service and smart phone with android OS platform. The proposed system is performed based on the TinyOS-based IEEE 802.15.4 protocol stack. To validate system functionality, we built sensor network environments where were equipped with four application sensors such as Temp/Hum, PIR, door, and camera sensor. We also built and tested the SFMS system to provide a novel model for event detection systems with smart phone.

• Industry Promotion Research
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• v.6 no.1
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• pp.1-7
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• 2021

We investigated multi-layer perceptrons and supervised learning algorithms, and also examined how to model functional relationships between covariates and response variables using a package called neuralnet. The algorithm applied in this paper is characterized by continuous adjustment of the weights, which are parameters to minimize the error function based on the comparison between the actual and predicted values of the response variable. In the neuralnet package, the activation and error functions can be appropriately selected according to the given situation, and the remaining parameters can be set as default values. As a result of using the neuralnet package for the infertility data, we found that age has little influence on infertility among the four independent variables. In addition, the weight of the neural network takes various values from -751.6 to 7.25, and the intercepts of the first hidden layer are -92.6 and 7.25, and the weights for the covariates age, parity, induced, and spontaneous to the first hidden neuron are identified as 3.17, -5.20, -36.82, and -751.6.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.