• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• Journal of the Korean Society for Railway
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• v.19 no.5
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• pp.617-628
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• 2016

This paper proposes the structure of a smart transformer to improve the performance of the 60Hz main power transformer for rolling stock. The proposed smart transformer is a kind of solid state transformer that consists of semiconductor switching devices and high frequency transformers. This smart transformer would have smaller size than the conventional 60Hz main transformer for rolling stock, making it possible to operate AC electrified track efficiently by power factor control. The proposed structure employs a cascade H-Bridge converter to interface with the high voltage AC single phase grid as the rectifier part. Each H-Bridge converter in the rectifier part is connected by a Dual-Active-Bridge (DAB) converter to generate an isolated low voltage DC output source of the system. Because the AC voltage in the train system is a kind of medium voltage, the number of the modules would be several tens. To control the entire smart transformer, the inner DC voltage of the modules, the AC input current, and the output DC voltage must be controlled instantaneously. In this paper, a control algorithm to operate the proposed structure is suggested and confirmed through computer simulation.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.55-64
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• 2013

Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original or counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from previous researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better than previous researches.

• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.1-14
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• 2019

A full scale hydrodynamic simulation that requires an accurate reproduction of shock-induced detonation was conducted for design of an energetic component system. A detailed hydrodynamic analysis SW was developed to validate the reactive flow model for predicting the shock propagation in a train configuration and to quantify the shock sensitivity of the energetic materials. The pyrotechnic device is composed of four main components, namely a donor unit (HNS+HMX), a bulkhead (STS), an acceptor explosive (RDX), and a propellant (BPN) for gas generation. The pressurized gases generated from the burning propellant were purged into a 10 cc release chamber for study of the inherent oscillatory flow induced by the interferences between shock and rarefaction waves. The pressure fluctuations measured from experiment and calculation were investigated to further validate the peculiar peak at specific characteristic frequency (${\omega}_c=8.3kHz$). In this paper, a step-by-step numerical description of detonation of high explosive components, deflagration of propellant component, and deformation of metal component is given in order to facilitate the proper implementation of the outlined formulation into a shock physics code for a full scale hydrodynamic simulation of the energetic component system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.583-593
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• 2022

The harvesting system in South Korea faces the problems of aging workers and high wages, so it is necessary to improve the operation system and train workers to use high-performance forestry machines. This study compared the effectiveness and costs of yarding and processing operations between a multi-operation system using a tower yarder (HAM300) and a processor (KESLA 20SH) with those of a single-system using a forestry combi-machine. A whole-tree (cable) yarding operation was conducted in the clear-cutting area located at Compartment 15, Gwangneung Experimental Forest, National Institute of Forest Science, and the productivity and cost of multi- and single-system were analyzed. The productivity of the single-system was 1.5 m³/PMH and 1.6 m³/PMH higher than that of the multi- system because the single-system produced 1 log/cycle more than the multi-system in the yarding operation. The cost was approximately 12.1% lower for the single-system (￦36,113/m³) than for the multi-system (￦41,065/m³). The costs of the single-system and multi-system were decreased by maximums of 22.6% and 15.9%, respectively, by decreasing the idle time.

• Journal of The Korean Association For Science Education
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• v.35 no.1
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• pp.159-167
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• 2015

RI-Biomics is a promising radiation convergence technology that combines radiation with bio science as new growth power technology. Many developed countries are focusing active support and constant exertion to dominate the RI-Biomics market in advance. In order to achieve global leadership in the RI-Biomics field, we need more highly advanced technologies and professional manpower. In fact, we have less manpower compared to technology we currently hold. In this study, we established a basic infrastructure to train professional manpower in the RI-Biomics field by developing/operating optimum training program through expert interviews and survey. The developed program has four organized sections to understand overall procedure of RI-Biomics. To evaluate our training program, we performed test operations with eight students who have a major related to RI-Biomics for three weeks in KARA (Seoul) and KAERI (Jung-eup). In detail, radioisotope usage and safety management were conducted for one week as basic course, RI-Biomics application technology was conducted for two weeks as professional course. To verify performance results of training program, we conducted to journal research, daily reports, and survey on participants. The results show a high level of satisfaction with training programs and continuous intention of involvement in our program. We also need to develop an intensive course to train high-quality human resources and to operate training program continuously. This training program will be used as basic materials for the development of RI-Biomics curriculum for university. Hence, we will expect that our training program contributes in training a professional manpower and develop RI-Biomics technology.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.