• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.384-392
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• 2019

Big data-related research that deals with the amount of explosive information in the era of the Fourth Industrial Revolution is actively underway. Big data is an essential element that promotes the development of artificial intelligence with a wide range of data that become learning data for machine learning, or deep learning. The use of deep learning and big data in various fields has produced meaningful results. In this paper, we have investigated the use of Big Data in the cultural arts industry, focusing on video contents. Noteworthy is that big data is used not only in the distribution of cultural and artistic contents but also in the production stage. In particular, we first looked at what kind of achievements and changes the Netflix in the US brought to the OTT business, and analyzed the current state of the OTT business in Korea. After that, Netflix analyzed the success stories of 'House of Cards', which was produced / circulated through 'Deep Learning' cinematique, which is a prediction algorithm, through accumulated customer data. After that, FGI (Focus Group Interview) was held for cultural and artistic contents experts. In this way, the future prospects of Big Data in the domestic culture and arts industry are divided into technical aspect, creative aspect, and ethical aspect.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.1-7
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• 2022

Recently, as the use of applications such as big data programs and machine learning programs that are driven while generating large amounts of data in the program itself becomes common, the existing main memory alone lacks memory, making it difficult to execute the program quickly. In particular, the need to derive results more quickly has emerged in a situation where it is necessary to analyze whether the entire sequence is genetically altered due to the outbreak of the coronavirus. As a result of measuring performance by applying large-capacity data to a computing system equipped with a self-developed memory pool MOCA host adapter instead of processing large-capacity data from an existing SSD, performance improved by 16% compared to the existing SSD system. In addition, in various other benchmark tests, IO performance was 92.8%, 80.6%, and 32.8% faster than SSD in computing systems equipped with memory pool MOCA host adapters such as SortSampleBam, ApplyBQSR, and GatherBamFiles by task of workflow. When analyzing large amounts of data, such as electrical dielectric pipeline analysis, it is judged that the measurement delay occurring at runtime can be reduced in the computing system equipped with the memory pool MOCA host adapter developed in this research.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.161-183
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• 2022

Opinion polls have become a powerful means for election campaigns and one of the most important subjects in the media in that they predict the actual election results and influence people's voting behavior. However, the more active the polls, the more often they fail to properly reflect the voters' minds in measuring the effectiveness of election campaigns, such as repeatedly conducting polls on the likelihood of winning or support rather than verifying the pledges and policies of candidates. Even if the poor predictions of the election results of the polls have undermined the authority of the press, people cannot easily let go of their interest in polls because there is no clear alternative to answer the instinctive question of which candidate will ultimately win. In this regard, we attempt to retrospectively grasp public opinion on the 20th presidential election by applying the 'YouTube Analysis' function of Sometrend, which provides an environment for discovering insights through online big data. Through this study, it is confirmed that a result close to the actual public opinion (or opinion poll results) can be easily derived with simple YouTube data results, and a high-performance public opinion prediction model can be built.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.896-905
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• 2021

In the field of SIEM(Security information and event management), many studies try to use a feedback system to solve lack of completeness of training data and false positives of new attack events that occur in the actual operation. However, the current feedback system requires too much human inputs to improve the running model and even so, those feedback from inexperienced analysts can affect the model performance negatively. Therefore, we propose "active model improving feedback technology" to solve the shortage of security analyst manpower, increasing false positive rates and degrading model performance. First, we cluster similar predicted events during the operation, calculate feedback priorities for those clusters and select and provide representative events from those highly prioritized clusters using XAI (eXplainable AI)-based event visualization. Once these events are feedbacked, we exclude less analogous events and then propagate the feedback throughout the clusters. Finally, these events are incrementally trained by an existing model. To verify the effectiveness of our proposal, we compared three distinct scenarios using PKDD2007 and CSIC2012. As a result, our proposal confirmed a 30% higher performance in all indicators compared to that of the model with no feedback and the current feedback system.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.383-400
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• 2023

Air pollution-related diseases are escalating worldwide, with the World Health Organization (WHO) estimating approximately 7 million annual deaths in 2022. The rapid expansion of industrial facilities, increased emissions from various sources, and uncontrolled release of odorous substances have brought air pollution to the forefront of societal concerns. In South Korea, odor is categorized as an independent environmental pollutant, alongside air and water pollution, directly impacting the health of local residents by causing discomfort and aversion. However, the current odor management system in Korea remains inadequate, necessitating improvements. This study aims to enhance the odor management system by analyzing 1,010,749 data points collected from odor sensors located in Osong, Chungcheongbuk-do, using an Ensemble-Based Multi-Region Integrated Odor Concentration Prediction Model. The research results demonstrate that the model based on the XGBoost algorithm exhibited superior performance, with an RMSE of 0.0096, significantly outperforming the single-region model (0.0146) with a 51.9% reduction in mean error size. This underscores the potential for increasing data volume, improving accuracy, and enabling odor prediction in diverse regions using a unified model through the standardization of odor concentration data collected from various regions.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.3
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• pp.71-79
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• 2023

It is not efficient to install a maintenance system that measures seismic acceleration and displacement on all bridges and buildings to evaluate the safety of structures after an earthquake occurs. In order to maintain this, an on-site investigation is conducted. Therefore, it takes a lot of time when the scope of the investigation is wide. As a result, secondary damage may occur, so it is necessary to predict the safety of individual structures quickly. The method of estimating earthquake damage of a structure includes a finite element analysis method using approved seismic information and a structural analysis model. Therefore, it is necessary to predict the seismic information generated at arbitrary location in order to quickly determine structure damage. In this study, methods to predict the ground response spectrum and acceleration time history at arbitrary location using linear estimation methods, and artificial neural network learning methods based on seismic observation data were proposed and their applicability was evaluated. In the case of the linear estimation method, the error was small when the locations of nearby observatories were gathered, but the error increased significantly when it was spread. In the case of the artificial neural network learning method, it could be estimated with a lower level of error under the same conditions.