• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.1-13
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• 2021

This study tried to suggest ways to improve the indicator system to strengthen the personal information protection. For this purpose, the components of indicator system are derived through domestic and foreign literature, and it was selected as main the diagnostic indicators through FGI/Delphi analysis for personal information protection experts and a survey for personal information protection officers of public institutions. As like this, this study was intended to derive an inspection standard that can be reflected as a separate index system for personal information protection, by classifying the specific IT technologies of the 4th industrial revolution, such as big data, cloud, Internet of Things, and artificial intelligence. As a result, from the planning and design stage of specific technologies, the check items for applying the PbD principle, pseudonymous information processing and de-identification measures were selected as 2 common indicators. And the checklists were consisted 2 items related Big data, 5 items related Cloud service, 5 items related IoT, and 4 items related AI. Accordingly, this study expects to be an institutional device to respond to new technological changes for the continuous development of the personal information management level diagnosis system in the future.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.81-91
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• 2019

Recently, the nation has been suffering from a variety of problems, such as the rapid aging of the population and the weakening of the family's role due to rapid industrialization, such as the problem of supporting the elderly or the decline in the quality of supporting them. Among them, the issue of supporting the sentiment of the elderly is a major issue in terms of the quality of the stimulus. The best solution would be to resolve this issue of emotional support through various physical and human support. However, due to various limitations, access to efficient utilization of resources is being sought, among which support efforts through the convergence of digital technologies need to be noted. In this study, we took note of the problems of aging population shortage due to aging phenomenon and the problems of the emotional side of the problem of declining quality of the service, and analyzed the types of digital technology applied to support the emotional side through the convergence of digital technology. Among them, the Commission proposed emotional support through smart speakers, confirming the possibility of supporting the elderly through smart speakers. In addition, the Commission proposed guidelines for smart speaker communication systems to support the sentiment of older adults by conducting an in-depth interview with the In-Depth interview with the evaluation of the usability of smart speakers for older people. Based on the results of this study, it is expected that it will be the basic data for designing a communication system when developing smart speakers to support the emotions of the elderly.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.36-41
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• 2021

Although the technology for prediction or analysis using artificial intelligence is constantly developing, a black-box problem does not interpret the decision-making process. Therefore, the decision process of the AI model can not be interpreted from the user's point of view, which leads to unreliable results. We investigated the problems of artificial intelligence and explainable artificial intelligence using Blockchain to solve them. Data from the decision-making process of artificial intelligence models, which can be explained with Blockchain, are stored in Blockchain with time stamps, among other things. Blockchain provides anti-counterfeiting of the stored data, and due to the nature of Blockchain, it allows free access to data such as decision processes stored in blocks. The difficulty of creating explainable artificial intelligence models is a large part of the complexity of existing models. Therefore, using the point cloud to increase the efficiency of 3D data processing and the processing procedures will shorten the decision-making process to facilitate an explainable artificial intelligence model. To solve the oracle problem, which may lead to data falsification or corruption when storing data in the Blockchain, a blockchain artificial intelligence problem was solved by proposing a blockchain-based explainable artificial intelligence model that passes through an intermediary in the storage process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.49-57
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• 2022

The government is making great efforts to prevent crimes such as missing children by using public CCTVs. However, there is a shortage of operating manpower, weakening of concentration due to long-term concentration, and difficulty in tracking. In addition, applying real-time object search, re-identification, and tracking through a deep learning algorithm showed a phenomenon of increased parameters and insufficient memory for speed reduction due to complex network analysis. In this paper, we designed the network to improve speed and save memory through the application of Yolo v4, which can recognize real-time objects, and the application of Batch and TensorRT technology. In this thesis, based on the research on these advanced algorithms, OSNet re-ranking and K-reciprocal nearest neighbor for re-identification, Jaccard distance dissimilarity measurement algorithm for correlation, etc. are developed and used in the solution of CCTV national safety identification and tracking system. As a result, we propose a solution that can track objects by recognizing and re-identification objects in real-time within situation of a Korean public multi-CCTV environment through a set of algorithm combinations.

• Journal of the Korean Wood Science and Technology
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• v.32 no.5
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• pp.75-85
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• 2004

The natural dyes from Gardenia jasminoides, Carthamus tinctorius L., Rhus javanica, Lithospermum erythrorhizon, Caesalpinia sappan L., and Castanea crenata were extracted under different pH in distilled water, As the pH in distilled water went from acid to alkali, the much deeper colors in the same color tone were generated from the individual plant species. Before dyeing, wood species were treated by different mordants including AI, Cu, Cr and Fe for color-fixing between wood and the natural dyes. Each mordant could develop independent color on the surface of the woods. The wood species dyed by the natural dyes created deep-tone colors according to higher pH and temperature of the dyeing solution, leading to deeper penetration of the dyes into the wood tissues. Finally through the computer modelling of natural-dyed wood furniture, it was confirmed that the colored furniture can adequately be compatible with the current interior spaces of diverse colors.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Knowledge Management Research
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• v.23 no.2
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• pp.193-210
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• 2022

The rapid development of mobile technology and the improvement of network speed are providing convenience to various services, and mobile shopping malls are no exception. Although efforts are being made to promote sales by combining various technologies such as customized recommendations using big data and specialized personalization services based on artificial intelligence, most mobile shopping malls have the same detailed page information structure including detailed product information. In this context, in this study, it was determined that the content layout of the product detail page and the mobile product detail page layout tailored to the consumer's preference should be presented according to the consumer's preference. Based on Higgins' Regulatory Focus Theory, a study of consumer propensity revealed that the content layout arrangement on a product detail page, when presented in an F-shape, informs the consumer that it is organized. If presented in a Z-shape, vivid information was recognized, and it was examined whether the product attitude and purchase intention were affected. As a result, when the content layout composition was presented as a layout arrangement in the form of a sense of unity and organization, prevention-focused consumers were positively affected by product attitudes and purchase intentions, and promotion-oriented consumers felt freedom. When presented in an arrangement, it was confirmed that the product attitude and purchase intention were affected.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1140-1147
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• 2022

With the development of digital technology, the marine environment is expected to change rapidly. In the case of autonomous vessels, technology is being developed in many countries, and the international community has begun to discuss ways to operate it. Changes in ships cause changes in the marine traffic environment and urge changes to aids to navigation. This study aims to analyze the cyber security management awareness of VTS personnel to improve the cyber security system for aids to navigation. To this end, the current status of cyber security management was reviewed with a focus on VTS, and a survey was conducted on VTS personnel. The survey analysis used the IPA methodology, and as a result of the analysis, a clear difference was observed in the perception of cybersecurity between those with experience in security and those without experience. In addition, technical measures related to cyber-attack detection and blocking should be implemented with the highest priority. The results of this study can be used as basic data for improving the cyber security management system for aids to navigation.