• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.274-282
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• 2022

In order to secure pedestrians' right to walk, we have up-cycled second hand mobile phones to overcome limitations of the existing survey methods, analysis methods, and diagnosis to reduce pedestrian traffic accidents. Second hand mobile phones were up-cycled to produce mobile CCTVs and installed in areas where pedestrian deaths rate is high to secure image data sets for the period of more than 24 hours. It was analyzed by applying image visualization technology and clouding reporting technology, and more precise and accurate results were derived through modeling based on artificial intelligence learning and GIS-based diagnostic guidance. As a result, it was possible to analyze the risk factors and number of pedestrian safety, and even factors that were not known in the existing method could be derived. In addition, the traffic accident risk index was derived by converting data into one year to verify whether second hand mobile phone up-cycling mobile CCTV will be an objective tool for finding pedestrian risk factors. Up-cycling mobile CCTV of second hand mobile phones newly applied through research can be used as a new tool to find pedestrian risk factors, and it can be used as a service to protect the safety of the traffic vulnerable other than pedestrians.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.79-94
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• 2022

The demand for tunnel construction is increasing as part of underground space development due to urban saturation. The shield TBM method minimizes vibration and noise and minimizes ground deformation that occurs simultaneously with excavation, and shield TBM is generally applied to tunnel construction in urban areas. The importance of urban ground settlement prediction is increasing day by day, and in the case of shield TBM construction, ground deformation is minimized, but ground settlement due to tunnel excavation inevitably occurs. Therefore, in this study, the correlation between shield TBM, which is highly applicable to urban areas, and ground settlement is analyzed to suggest the shield TBM construction factors that have a major effect on ground settlement. Correlation analysis was performed between the shield TBM construction data and ground settlement measurement data collected at the actual site, and the degree of correlation was expressed as a correlation coefficient "r". As a result, the main construction factors of shield TBM affecting ground settlement were thrust force, torque, chamber pressure, backfill pressure and muck discharge. Based on the results of this study, it is expected to contribute to the presentation of judgment criteria for major construction data so that the ground settlement can be predicted and controlled in advance when operating the shield TBM in the future.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• Journal of Conservation Science
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• v.37 no.3
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• pp.289-301
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• 2021

When investigating old, printed documents, determining whether a work is printed on a woodblock or using a movable metal type is crucial. It is because the history of printing in Korea and across the world relies on determining the relevant printing invention used and the time of use of the movable metal type. Deciphering details from woodblock and metal prints requires various kinds of information regarding the imprint and the work's printing background, such as information on the characters in the printed document, the outline of the pages, the type of ink used, the production period of the ink, and the production period of the Korean paper. Analyzing such information can generally reveal the production period and the methods used on the old document. However, as such information is not documented systematically, relying on the researcher's judgment based on their experience and perception becomes inevitable. This study conducted an experimental investigation of the printing characteristics of woodblock prints using a replicated woodblock of the Wanpanbon edition of the Shimcheongjeon. Subsequently, the various phenomena and characteristics appearing on the woodblock prints were documented for future reference to determine the printing method of old documents. Finally, woodblock novels without an imprint may be used as a reference to estimate the printing dates by determining the degree of wear on the woodblock.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.139-159
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• 2022

As interest in health functional foods has increased since COVID-19, the importance of imported food safety inspections is growing. However, in contrast to the annual increase in imports of health functional foods, the budget and manpower required for inspections for import and export are reaching their limit. Hence, the purpose of this study is to propose a machine learning model that efficiently detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export inspections data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, in order to select features for the machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. In particular, the ensemble model was the best, and it was confirmed that the derived variables and models proposed in this study can be helpful to the system used in import/export inspections.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• Journal of Christian Education in Korea
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• v.70
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• pp.175-206
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• 2022

In this study, a mixed research method that collects and analyzes qualitative and quantitative data together was used to broaden the understanding of young children's faith. First of all, the contents of interviews with 18 research participants were divided into categories, and the frequency of each category was calculated. From 75 statements made in the interview, the importance of each was evaluated with a 5-point Likert scale. The means and standard deviations of the evaluation score were calculated, and sub-factors were identified through exploratory factor analysis. The frequency of categories mentioned in the qualitative interview was in the following order: faith manifested in difficulties, religious activities, Christian education at home, Christian education in the church, love and faith in God, and the formation of a Christian worldview (identity). The statement on the perception of God, theological concepts, religious activities, and Christian education at home and in the church received high scores. On the other hand, statements on developmentally difficult or abstract content, value judgment or conflict resolution based on the Word, and evangelism showed low scores. The sub-factors extracted through factor analysis were faith education through home and church, awareness of God and religious activities, identity based on the gospel, character based on the gospel, and overcoming a crisis through faith. In conclusion, the results of each of the mixed methods of looking at young children's faith were very similar-relationships with God, religious activities, Christian worldview (identity), Christian education at home and church were highlighted, even though various methods were used. This study is meaningful in that it suggests what and how to teach in early childhood Christian education.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.96-105
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• 2017

In this paper, we investigated the age classification from the speaker by analyzing the voice calls of the emergency center. We classified the adult and elderly from the call center calls using behavioral speech utterances and SVM(Support Vector Machine) which is a machine learning classifier. We selected two behavioral speech utterances through analysis of the call data from the emergency center: Silent Pause and Turn-taking latency. First, the criteria for age classification selected through analysis based on the behavioral speech utterances of the emergency call center and then it was significant(p <0.05) through statistical analysis. We analyzed 200 datasets (adult: 100, elderly: 100) by the 5 fold cross-validation using the SVM(Support Vector Machine) classifier. As a result, we achieved 70% accuracy using two behavioral speech utterances. It is higher accuracy than one behavioral speech utterance. These results can be suggested age classification as a new method which is used behavioral speech utterances and will be classified by combining acoustic information(MFCC) with new behavioral speech utterances of the real voice data in the further work. Furthermore, it will contribute to the development of the emergency situation judgment system related to the age classification.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.509-520
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• 2023

Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.841-849
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• 2023

Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.