• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.397-409
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• 2014

According to the Personal Information Protection Act(PIPA) which is legislated in March 2011, the individual or company that handles personal information, called Personal information processor, should encrypt some kinds of personal information kept in his Database. For convenience sake we call it DB Encryption in this paper. Law enforcement and the implementation agency accordingly are being strengthen the supervision that the status of DB Encryption is being properly applied and implemented as the PIPA. However, the process of DB Encryption is very complicate and difficult as well as there are many factors to consider in reality. For example, there are so many considerations and requirements in the process of DB Encryption like pre-analysis and design, real application and test, etc.. And also there are surely points to be considered in related system components, business process and time and costs. Like this, although there are plenty of factors significantly associated with DB Encryption, yet more concrete and realistic validation entry seems somewhat lacking. In this paper, we propose a realistic DB Encryption Assurance Framework that it is acceptable and resonable in the performance of the PIPA duty (the aspect of the individual or company) and standard direction of inspection and verification of DB Encryption (the aspect of law enforcement).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.2
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• pp.13-23
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• 2016

Many traffic centers are highly hesitant in employing existing Automatic Incident Detection Algorithms due to high false alarm rate, low detection rate, and enormous effort taken in maintaining algorithm parameters, together with complex algorithm structure and filtering/smoothing process. Concerns grow over the situation particularly in Freeway Incident Management Area This study proposes a new algorithm and introduces a novel concept, the Displaced Flow Index (DiFI) which is similar to a product of relative speed and relative occupancy for every execution period. The algorithm structure is very simple, also easy to understand with minimum parameters, and could use raw data without any additional pre-processing. To evaluate the performance of the DiFI algorithm, validation test on the algorithm has been conducted using detector data taken from Naebu Expressway in Seoul and following transferability tests with Gyeongbu Expressway detector data. Performance test has utilized many indices such as DR, FAR, MTTD (Mean Time To Detect), CR (Classification Rate), CI (Composite Index) and PI (Performance Index). It was found that the DR is up to 100%, the MTTD is a little over 1.0 minutes, and the FAR is as low as 2.99%. This newly designed algorithm seems promising and outperformed SAO and most popular AIDAs such as APID and DELOS, and showed the best performance in every category.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.245-256
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• 2021

Poor logistical infrastructure and agri-food supply chain management leads to significant food waste in logistic system. The concept of the sustainable value added agri-food chains requires defined approach to the analysis of the existing situation, possible improving strategies and also assessment of these changes impact on further development. The purpose of research is to provide scientific substantiation of theoretical and methodological principles and develop practical recommendations for the improvement of the agri-food logistics distribution system. A case study methodology is used in this article. The research framework is based on 4 steps: Value Stream Mapping (VSM), Gap and Process Analysis, Validation and Improvement Areas Definition and Imitation Modelling. This paper presents the appropriateness of LEAN logistics tools using, in particular, Value Stream Mapping (VSM) for minimizing logistic losses and Simulation Modeling of possible logistics distribution system improvement results. The algorithm of VSM analysis of the agri-food supply chain, which involves its optimization by implementing the principles of sustainable development at each stage, is proposed. The methodical approach to the analysis of possible ways for optimizing the operation of the logistics system of the agri-food distribution is developed. It involves the application of Value Stream Mapping, i.e. designing of stream maps of the creation of the added value in the agri-food supply chain for the current and future state based on the minimization of logistic losses. Simulation modeling of the investment project on time optimization in the agri-food supply chain and economic effect of proposed improvements in logistics product distribution system functioning at the level of the investigated agricultural enterprise has been determined. Improvement of logistics planning and coordination of operations in the supply chain and the innovative pre-cooling system proposed to be introduced have a 3-year payback period and almost 75-80% probability. Based on the conducted VSM analysis of losses in the agri-food supply chain, there have been determined the main points, where it is advisable to conduct optimization changes for the achievement of positive results and the significant economic effect from the proposed measures has been confirmed. In further studies, it is recommended to focus on identifying the synergistic effect of the agri-food supply chain optimization on the basis of sustainable development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.611-620
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• 2021

The maintenance industry is mainly progressing based on condition-based maintenance after corrective maintenance and preventive maintenance. In condition-based maintenance, maintenance is performed at the optimum time based on the condition of equipment. In order to find the optimal maintenance point, it is important to accurately understand the condition of the equipment, especially the remaining useful life. Thus, using simulation data (C-MAPSS), a prediction model is proposed to predict the remaining useful life of a turbofan engine. For the modeling process, a C-MAPSS dataset was preprocessed, transformed, and predicted. Data pre-processing was performed through piecewise RUL, moving average filters, and standardization. The remaining useful life was predicted using principal component analysis and the k-NN method. In order to derive the optimal performance, the number of principal components and the number of neighbor data for the k-NN method were determined through 5-fold cross validation. The validity of the prediction results was analyzed through a scoring function while considering the usefulness of prior prediction and the incompatibility of post prediction. In addition, the usefulness of the RUL prediction model was proven through comparison with the prediction performance of other neural network-based algorithms.

• Analytical Science and Technology
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• v.32 no.1
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• pp.7-16
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• 2019

The objective of this study was the development of a discrimination model for the cultivational origin of paper mulberry bast fiber and Hanji using near infrared (NIR) and mid infrared (MIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA). Paper mulberry bast fiber was purchased in 10 different regions of Korea, and used to make Hanji. PLS-DA was performed using pre-treated FT-NIR and FT-MIR spectral data for paper mulberry bast fiber and Hanji. PLS-DA of paper mulberry bast fiber and Hanji samples, using FT-NIR spectral data, showed 100 % performance in cross validation and the confusion matrix (accuracy, sensitivity, and specificity). The discrimination models showed four regional groups which demonstrated clearer separation and much superior score plots in the NIR spectral data-based model than in the MIR spectral data-based model. Furthermore, the discrimination model based on the NIR spectral data of paper mulberry bast fiber had highly similar score morphology to that of the discrimination model based on the NIR spectral data of Hanji.

• Korean Journal of Anesthesiology
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• v.71 no.6
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• pp.467-475
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• 2018

Background: The Quality of Recovery-40 (QoR-40) is a widely-used, self-rated, and self-completed questionnaire for postoperative patients. The questionnaire is intended to elicit information from each patient regarding the quality of recovery during the postoperative period. It is noteworthy, however, that an official Korean version of the QoR-40 (QoR-40K) has not been established. The purpose of this study was to develop the QoR-40K by translation and cultural adaptation process and to evaluate the validity and reliability of the QoR-40K. Methods: After pre-authorization from the original author of the QoR-40, the translation procedure was established and carried out based upon Beaton's recommendation to create a QoR-40K model comparable to the original English QoR-40. Two hundred surgical patients were enrolled, and each completed the questionnaire during the preoperative period, on the third day, and 1 month after surgery. The QoR-40K was compared with the visual analogue scale (VAS) and another health-related questionnaire, the Short-form Health Survery-36 (SF-36). The method of validation for QoR-40K included test-retest reliability, internal consistency, and level of responsiveness. Results: Spearman's correlation coefficient for test-retest reliability was 0.895 (P < 0.001), and Cronbach's alpha of the global QoR-40K on the third day after surgery was 0.956. A positive correlation was obtained between the QoR-40K and the mental component summary of SF-36 (${\rho}=0.474$, P < 0.001), and a negative correlation was observed between QoR-40K and VAS (${\rho}=-0.341$, P < 0.001). The standardized responsive mean of the total QoR-40K was 0.71. Conclusions: The QoR-40K was found to be as acceptable and reliable as the original English QoR-40 for Korean patients after surgery, despite the apparent differences in the respective patients' cultural backgrounds.

• Therapeutic Science for Rehabilitation
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• v.10 no.3
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• pp.137-149
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• 2021

Objective : This study aimed to verify the effectiveness of ICT-Living Lab-based programs to promote the health of elderly residents in small and medium-sized cities, thus, providing basic data for solving the health and quality of life problems faced by modern society. Methods : The tow-month program included 14 elderly individuals visiting senior center located in W City, from October to November 2019. The program was organized by consulting four senior experts and one ICT expert. The program consisted of 10 min for advance assessment, 10 min for preparation activities, 30 min for main activities, and 10 min for finishing, and 60 min for eight sessions over two months, once a week. Results : EQ-5D increased from 0.76 to 0.84, compared to pre-assessment(p=.009^*). EQ-VAS scores increased from 36.43 to 65.71 (p=.001^*). MMSE-DS increased from 21.21 to 24.14 (p=.000^*). SGDS-K decreased from 3.36 to 3.21, but this was not statistically significant (p=.854). Conclusion : The ICT·Living Lab-based program could be used as a basic material for future research as one of the ways to improve health and quality of life by preventing and addressing the problems faced by the elderly in modern society.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.83-92
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• 2023

The purpose of this research is to develop a creative education program that utilizes AI education program based on big data for general education high schools, and to investigate its effectiveness. In order to achieve the purpose of the research, we developed a creative education program using artificial intelligence based on big data for first-year general high school students, and carried out on-site classes at schools and a validation process by experts. In order to measure the creative problem-solving ability and class satisfaction of high school students, a creative problem-solving ability test was conducted before and after the program application, and a class satisfaction test was conducted after the program. The results of this study are as follows. First, AI education program based on big data were statistically effective to improve the creative problem solving ability according to independent sample t test about 'problem discovery and analysis', 'idea generation', 'execution plan', 'conviction and communication', and 'innovation tendency' except 'execution', 'the difference between pre- and post-scores of male student and female student' on first year high school students. Secondly, in satisfaction conducted after classes of AI education program based on big data, the average of 'Satisfaction', 'Interest', 'Participation', 'Persistence' were 3.56 to 3.92, and the overall average was 3.78. Therefore, it was investigated that there was a lesson effect of the AI education program based on big data developed in this research.

• The korean journal of orthodontics
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• v.52 no.4
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• pp.287-297
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• 2022

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.