• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.236-238
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• 2022

According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.59-68
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• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• The Korean Journal of Archival Studies
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• no.14
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• pp.153-190
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• 2006

Externally, business environment in public institution has being changed as government business reference model(BRM) appeared and business management systems for transparency of a policy decision process are introduced. After Records Automation System started its operation, dissatisfaction grows because of inadequacy in system function and the problems about authenticity of electronic records. With these backgrounds, National Archives and Records Service had carried out 'Information Strategy Planning for Reform to Records Management System' for 5 months from September, 2005. As result, this project reengineers current records management processes and presents the world-class system model. After Records and Archives Management Act was made, the records management in public institution has propelled the concept that paper records are handled by means of the electric data management. In this reformed model, however, we concentrates on the electric records, which have gradually replaced the paper records and investigate on the management methodology considering attributes of electric records. According to this new paradigm, the electric records management raises a new issue in the records management territory. As the major contents of the models connecting with electric records management were analyzed and their significance and bounds were closely reviewed, the aim of this paper is the understanding of the future bearings of the management system. Before the analysis of the reformed models, issues in new business environments and their records management were reviewed. The government's BRM and Business management system prepared the general basis that can manage government's whole results on the online and classify them according to its function. In this points, the model is innovative. However considering the records management, problems such as division into Records Classification, definitions and capturing methods of records management objects, limitations of Records Automation System and so on was identified. For solving these problems, the reformed models that has a records classification system based on the business classification, extended electronic records filing system, added functions for strengthening electric records management and so on was proposed. As regards dramatically improving the role of records center in public institution, searching for the basic management methodology of the records management object from various agency and introducing the detail design to keep documents' authenticity, this model forms the basis of the electric records management system. In spite of these innovations, however, the proposed system for real electric records management era is still in its beginning. In near feature, when the studies is concentrated upon the progress of qualified classifications, records capturing plans for foreign records structures such like administration information system, the further study of the previous preservation technology, the developed prospective of electric records management system will be very bright.

• Journal of Broadcast Engineering
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• v.5 no.2
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• pp.281-289
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• 2000

While acquiring images, the relative motion between the imaging device and the object scene seriously damages the image quality. This phenomenon is called motion blur. The peak-trace approach, which is our recent previous work, identifies important parameters to characterize the point spread function (PSF) of the blur, given only the blurred image itself. With the peak-trace approach the direction of the motion blur can be extracted regardless of the noise corruption and does not need much Processing time. In this paper stochastic peak-trace approaches are introduced. The erroneous data can be selected through the ML classification, and can be made small through weighting. Therefore the distortion of the direction in the low frequency region can be prevented. Using the linear prediction method, the irregular data are prohibited from being selected as the peak point. The detection of the second peak using the proposed moving average least mean (MALM) method is used in the Identification of the motion extent. The MALM method itself includes a noise removal process, so it is possible to extract the parameters even an environment of heavy noise. In the experiment, we could efficiently restore the degraded image using the information obtained by the proposed algorithm.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.10-17
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• 2017

This study is the first attempt to raise face recognition ability through machine learning algorithm and apply to CRM's information gathering, analysis and application. In other words, through face recognition of VIP customer in distribution field, we can proceed more prompt and subdivided customized services. The interest in machine learning, which is used to implement artificial intelligence, has increased, and it has become an age to automate it by using machine learning beyond the way that a person directly models an object recognition process. Among them, Deep Learning is evaluated as an advanced technology that shows amazing performance in various fields, and is applied to various fields of image recognition. Face recognition, which is widely used in real life, has been developed to recognize criminals' faces and catch criminals. In this study, two image analysis models, TF-SLIM and Inception-V3, which are likely to be used for criminal face recognition, were selected, analyzed, and implemented. As an evaluation criterion, the image recognition model was evaluated based on the accuracy of the face recognition program which is already being commercialized. In this experiment, it was evaluated that the recognition accuracy was good when the accuracy of the image classification was more than 90%. A limit of our study which is a way to raise face recognition is left as a further research subjects.

• Fashion & Textile Research Journal
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• v.16 no.2
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• pp.255-265
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• 2014

This study developed and proposed a pattern that can fit the body and compensate for the defects of the body type by applying shoulder princess line to the torso pattern and including formative characteristics of each object on the study of body shape's change in 40-59 year old middle aged women. The results of this study are as follows; First, according to the analysis of 'Size Korea 2010' such as average, standard deviation, minimum value, and maximum value of 48 items, women showed increase in most of the items except height as they became aged. Second, factor analysis was made to understand the shape component factors of middle aged women and to use them for the body type classification. Third, cluster analysis was made according to the shape of front and sides which should be considered in pattern production based on the factor analysis results, and the body type with the measurement values most similar to the average of direct measurement of 'Size Korea 2010' was set as standard. Fourth, in designing torso patterns through the $1^{st}$ and the $2^{nd}$ wearing experiments according to the body type, body shapes such as Chest Circumference, Waist Circumference, Hip Circumference, and Waist Back Length were considered in pattern design, goodness-of-fit was enhanced with difference in margin according to body type and different margins in front and back, and fitting satisfaction was improved by applying princess line.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1909-1916
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• 2017

Recently, deep learning algorithms have good performance in various fields, but they are not actively applied to sonar systems. In this study, we carried out experiments to classify active sonar returns into a metal object such as a mine and a rock using a convolutional neural network which is one of the deep learning algorithms. Data augmentation is applied on this paper to avoid overfitting and increase performance. And we analyzed performance variation depending on hyperparameter value and change of the number of training data through data augmentation. The experiments are performed with two training data; an aspect-angle independent and an aspect-angle dependent. As a result, the performances are 88.9% and 94.9% in aspect-angle independent and dependent, respectively. These are up to 4.5% point higher than the performance obtained by applying artificial neural network and support vector machine algorithm in the previous study.

• Asia pacific journal of information systems
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• v.9 no.4
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• pp.23-40
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• 1999

This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining neural network and rough set approach, We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables and objects (i.e., firms) is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. Through the reduction of information table, it is expected that the performance of the neural network improve. The rules developed by rough sets show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2,400 Korean firms during the period 1994-1996 were selected, and for the validation, k-fold validation was used.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.117-124
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• 2008

This paper introduces a novel approach for automated mapping of a map feature that is vinyl green house in high spatial resolution imagery Some map features have their unique spatial patterns. These patterns are normally detected in high spatial resolution remotely sensed data by human recognition system. When spatial patterns can be applied to map feature identification, it will improve image classification accuracy and will be contributed a lot to feature identification. In this study, an automated feature identification approach using spatial aucorrelation is developed, specifically for the vinyl green house that has distinctive spatial pattern in its array. The algorithm aimed to develop the method without any human intervention such as digitizing. The method can investigate the characteristics of repeated spatial pattern of vinyl green house. The repeated spatial pattern comes from the orderly array of vinyl green house. For this, object-based approaches are essential because the pattern is recognized when the shapes that are consists of the groups of pixels are involved. The experimental result shows very effective vinyl house extraction. The targeted three vinyl green houses were exactly identified in the IKONOS image for a part of Jeju area.

• Journal of Fashion Business
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• v.23 no.5
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• pp.31-47
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• 2019

This study focuses on women and fashion in Korea between the 1960s and 1970s, when the government regulated the socio-cultural aspects of individuals while achieving remarkable economic industrialization, particularly through the representative popular weekly magazine 'Sunday-Seoul'. The scope of this study included 168 issues from September 22, 1968 to December 26, 1971. Two research methods were applied, literature research and content analysis research. First, the literature on Korean society, culture, women's fashion, the sociological, feminine and popular cultural studies were reviewed. Thereafter, the contents, cover, articles, pictorials were collected and analyzed for classification and identification of the women's images and women's fashion. In the case of fashion articles, the contents of vocabulary and description texts were highlighted, and in the case of pictorials, the visual elements such as images, silhouettes of clothes, details of features, and patterns of materials were assessed. The images of women in Sunday Seoul's articles and pictorials exhibited extreme opposite, presenting the most important purpose of marriage, 'wise mother and good wife' and 'image of sexual object' for men. The two images of women differed; however, there was one more female image 'industrial laborer' which was placed in the blind spot of interest. The characteristics of fashion which appeared in 'Sunday-Seoul' were 'uniform modern elegance' based on neat mini-style, and 'sexual image of exposure fashion' which endeavored to selectively borrow from overseas pictorials and trend-oriented articles. This could be viewed as a 'transformation of traditional Hanbok', 'avant-garde trend' and 'de-sexualization & indifference of fashion'.