• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.121-133
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• 2005

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of the useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program based on GLCM algorithm is newly implemented. As well, texture imaging modules for GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV Texture imaging parameters, it composed of six types of second order texture functions such as Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality in GLCM/GLDV, two direction modes such as Omni-mode and Circular mode newly implemented in this program are provided with basic eight-direction mode. Omni-mode is to compute all direction to avoid directionality complexity in the practical level, and circular direction is to compute texture parameters by circular direction surrounding a target pixel in a kernel. At the second phase of this study, some case studies with artificial image and actual satellite imagery are carried out to analyze texture images in different parameters and modes by correlation matrix analysis. It is concluded that selection of texture parameters and modes is the critical issues in an application based on texture image fusion.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.57-65
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• 2010

This paper describes the method of efficient container number recognition in colored container image with number plate at ATC(Automatic Transfer Crane) in container terminal yard. At the Sinseondae terminal gate in Busan, the container number recognition system is installed by "intelligent port-logistics system technology development", that is government research and development project. It is the method that it sets up the tunnel structure inside camera on the gate and it recognizes the container number in order to recognize the export container cargo automatically. However, as the automation equipment is introduced to the container terminal and the unmanned of a task is gradually accomplished, the container number recognition system for the confirmation of the object of work is required at ATC in container terminal yard. Therefore, the container number recognition system fitted for it is necessary for ATC in container terminal yard in which there are many intrusive of the character recognition through image including a sunlight, rain, snow, shadow, and etc. unlike the gate. In this paper, hardware components of the camera, illumination, and sensor lamp were altered and software elements of an algorithm were changed. that is, the difference of the brightness of the surrounding environment, and etc. were regulated for recognize a container number. Through this, a shadow problem, and etc. that it is thickly below hung with the sunlight or the cargo equipment were solved and the recognition time was shortened and the recognition rate was raised.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.2
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• pp.263-277
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• 2021

Taint analysis techniques are popularly used to detect web vulnerabilities originating from unverified user input data, such as Cross-Site Scripting (XSS) and SQL Injection, in web applications written in JavaScript. To detect such vulnerabilities, it would be necessary to trace variables affected by user-submitted inputs. However, because of the dynamic nature of JavaScript, it has been a challenging issue to identify those variables without running the web application code. Therefore, most existing taint analysis tools have been developed based on dynamic taint analysis, which requires the overhead of running the target application. In this paper, we propose a novel static taint analysis technique using symbol information obtained from the TypeScript (a superset of JavaScript) compiler to accurately track data flow and detect security vulnerabilities in TypeScript code. Our proposed technique allows developers to annotate variables that can contain unverified user input data, and uses the annotation information to trace variables and data affected by user input data. Since our proposed technique can seamlessly be incorporated into the TypeScript compiler, developers can find vulnerabilities during the development process, unlike existing analysis tools performed as a separate tool. To show the feasibility of the proposed method, we implemented a prototype and evaluated its performance with 8 web applications with known security vulnerabilities. We found that our prototype implementation could detect all known security vulnerabilities correctly.

• Journal of Korea Trade
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• v.25 no.8
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• pp.1-24
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• 2021

Purpose - This study categorizes factors that influence the utilization of preferential tariffs based on the characteristics of rules of origin (RoO) and identifies and analyzes the influence of these characteristics on the utilization of preferential tariffs and the trade performance of companies. Design/methodology - In this study, we categorized factors that have an influence on the utilization of preferential tariffs based on the characteristics of RoO and investigated and tested the influence of these characteristics on the utilization of preferential tariffs and the trade performance of companies. For empirical analysis, we categorized the characteristics of RoO into restrictiveness, complexity, and uncertainty. We then developed a research model and formulated hypotheses based on previous studies, and tested the hypotheses using statistical software-(SPSS 25.0 and AMOS 18.0.) Findings - Previous studies suggested that each characteristic of RoO is determined by unique features of a Regional Trade Agreement (RTA). This study conducted an empirical analysis on the influence of the characteristics of RoO on the utilization of preferential tariffs and trade performance. The results confirmed that, overall, the characteristics of preferential rules of origin (PRoO) are related to and influence Korean companies' utilization of preferential tariffs and trade performance. As for the degree of the influence, the characteristics were in the order of uncertainty> restrictiveness> complexity. Nevertheless, complexity turned out not to have an influence large enough to change a company's decision on the utilization of preferential tariffs. Based on these results, this study identified unique features of PRoO and related problems for Korean companies that want to utilize preferential tariffs and suggested countermeasures for their effective utilization of preferential tariffs in the future. Originality/value - Companies that want to use preferential tariffs in international trade have to satisfy PRoO. The issue of origin can be regarded as an essential part of an RTA and RoO, are a crucial criterion in using preferential tariffs. The rules are requirements to claim benefits of preferential trade agreements and are the primary reasons companies have trouble in utilizing preferential tariffs. In this sense, this study categorized the characteristics of RoO, which are a key part of an RTA, and surveyed working-level professionals in charge of international trade at Korean companies to investigate the relationship between these characteristics and the utilizations of preferential tariffs and trade performance of the companies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.561-564
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• 2016

If using crime map which represents criminal section that violent crimes targeting women frequently happened, the police could prevent additional crimes by positioning themselves intensively in expected crime zones and each individual could avoid being damaged by referring information of criminal zones. In this paper, by analyzing crimes targeting women and offender information which is provided in public-opened datum portal, we suppose a system which prevents crimes that calculates locational danger and, by considering location and age group of users, provides user-customized information of danger. By crawling the criminals datum which is provided in public-opened datum portal, It collects them. About the areas which happened sexual crimes, calculating danger of crime based on statistical crime information including criminal information, residence of offenders, areas which happened sexual crimes, sentences and the number of crime, this system is able to visualize the areas which sexual crimes happened based on information of danger grade representing on user's location. The score of danger calculated in location unit can provide criminal information according to location and ages of users by interacting GIS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.541-550
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• 2019

An emergency generator is key equipment for fire-fighting to supply power to fire-fighting facilities, which protect property and people in cases of fire accidents. A rated load test for emergency generators must be carried out by connecting an emergency load to the generator in accordance with related regulations. However, a no-load test has been performed for emergency generators in general since serious problems can occur when the main power is cut off, including the damage of customer devices and shut down of critical loads. Therefore, this paper proposes a load test method for an emergency generator using energy storage system (ESS) without the interruption of main power. The emergency power system was also modeled based on PSCAD/EMTDC software, and a 200-kW scale ESS load test device was implemented. The simulation and test results show that the load test method is useful and practical for an emergency power supply system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.512-520
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• 2019

It is quite useful to represent speeches visually for learners who study foreign languages as well as the hearing impaired who cannot directly hear speeches, and a number of researches have been presented in the literature. They remain, however, at the level of representing the characteristics of speeches using colors or showing the changing shape of lips and mouth using the animation-based representation. As a result of such approaches, those methods cannot tell the users how far their pronunciations are away from the standard ones, and moreover they make it technically difficult to develop such a system in which users can correct their pronunciation in an interactive manner. In order to address these kind of drawbacks, this paper proposes a speech visualization model based on the relative distance between the user's speech and the standard one, furthermore suggests actual implementation directions by applying the proposed model to the visualization of Korean vowels. The method extract three formants F1, F2, and F3 from speech signals and feed them into the Kohonen's SOM to map the results into 2-D screen and represent each speech as a pint on the screen. We have presented a real system implemented using the open source formant analysis software on the speech of a Korean instructor and several foreign students studying Korean language, in which the user interface was built using the Javascript for the screen display.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.115-122
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• 2019

For Korean phoneme recognition, Hidden Markov-Gaussian Mixture model(HMM-GMM) or hybrid models which combine artificial neural network with HMM have been mainly used. However, current approach has limitations in that such models require force-aligned corpus training data that is manually annotated by experts. Recently, researchers used neural network based phoneme recognition model which combines recurrent neural network(RNN)-based structure with connectionist temporal classification(CTC) algorithm to overcome the problem of obtaining manually annotated training data. Yet, in terms of implementation, these RNN-based models have another difficulty in that the amount of data gets larger as the structure gets more sophisticated. This problem of large data size is particularly problematic in the Korean language, which lacks refined corpora. In this study, we introduce CTC algorithm that does not require force-alignment to create a Korean phoneme recognition model. Specifically, the phoneme recognition model is based on convolutional neural network(CNN) which requires relatively small amount of data and can be trained faster when compared to RNN based models. We present the results from two different experiments and a resulting best performing phoneme recognition model which distinguishes 49 Korean phonemes. The best performing phoneme recognition model combines CNN with 3hop Bidirectional LSTM with the final Phoneme Error Rate(PER) at 3.26. The PER is a considerable improvement compared to existing Korean phoneme recognition models that report PER ranging from 10 to 12.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.25-40
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• 2019

Many companies are executing big data analysis and utilization projects to legitimize the development of new business areas or conversion of management or technical strategies. In Korea and abroad, however, such projects are failing because they are not completed within specified deadlines, which is not unrelated to the current situation in which the knowledge base for big data project risk management from an engineering perspective is grossly lacking. As such, the current study analyzes the risk factors of big data implementation and utilization projects, in addition to finding risk factors that are highly important. To achieve this end, the study extracts project risk factors via literature review, after which they are grouped using affinity methodology and sifted through expert surveys. The deduced risk factors are structuralize using factor analysis to develop a table that categorizes various types of big data project risk factors. The current study is significant that in it provides a basis for developing basic control indicators related to risk identification, risk assessment, and risk analysis. The findings from the study contribute greatly to the success of big data projects, by providing theoretical basis regarding efficient big data project risk management.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.213-222
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• 2019

Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.