• Journal of the Korean earth science society
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• v.42 no.3
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• pp.296-310
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• 2021

El Niño is a typical ocean and atmospheric interaction phenomenon that causes climate variability on a global scale, so it has been used as a very important teaching and learning material in the field of earth science. This study summarized the distribution and dynamics of the equatorial current system. The variability of the equatorial current system during the El Niño period and the associated misconceptions were also investigated. The North Equatorial Current, South Equatorial Current, and Equatorial Under Current significantly weaken during El Niño years. However, the variability of the North Equatorial Counter Current (NECC) during the El Niño period cannot be generalized because the NECC shows southward movement with weakening in the northern area and strengthening in the southern area, along its central axis. In the western Pacific, the NECC is further south during El Niño years, and thus, it has an eastward flow in the equatorial western Pacific. Our analysis of a mass media science article, a secondary school exam, and a survey for incumbent teachers confirmed disparate ideas about the equatorial current system's variability during El Niño periods. This is likely due to inaccurate interpretations of the existing El Niño schematic diagram and insufficient understanding of the equatorial current and wave dynamics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.1-8
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• 2021

This study developed low-cost and high-efficiency pavement condition monitoring technology to produce the key information required for pavement management. A noise and artificial intelligence-based monitoring system was devised to compensate for the shortcomings of existing high-end equipment that relies on visual information and high-end sensors. From idea establishment to system development, functional definition, information flow, architecture design, and finally, on-site field evaluations were carried out. As a result, confidence in the high level of artificial intelligence evaluation was secured. In addition, hardware and software elements and well-organized guidelines on system utilization were developed. The on-site evaluation process confirmed that non-experts could easily and quickly investigate and visualized the data. The evaluation results could support the management works of road managers. Furthermore, it could improve the completeness of the technologies, such as prior discriminating techniques for external conditions that are not considered in AI learning, system simplification, and variable speed response techniques. This paper presents a new paradigm for pavement monitoring technology that has lasted since the 1960s.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.19-26
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• 2021

Cultural assets excavated in historical areas have their own characteristics based on the background of the times, and it can be seen that their patterns and characteristics change little by little according to the history and the flow of the spreading area. Cultural properties excavated in some areas represent the culture of the time and some maintain their intact appearance, but most of them are damaged/lost or divided into parts, and many experts are mobilized to research the composition and repair the damaged parts. The purpose of this research is to learn patterns and characteristics of the past through artificial intelligence neural networks for such restoration research, and to restore the lost parts of the excavated cultural assets based on Generative Adversarial Network(GAN)[1]. The research is a process in which the rest of the damaged/lost parts are restored based on some of the cultural assets excavated based on the GAN. To recover some parts of dammed of cultural asset, through training with the 2D image of a complete cultural asset. This research is focused on how much recovered not only damaged parts but also reproduce colors and materials. Finally, through adopted this trained neural network to real damaged cultural, confirmed area of recovered area and limitation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1091-1104
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• 2022

Climate change is predicted to increase the frequency and intensity of rainfall worldwide, and the pattern is changing due to inundation damage in urban areas due to rapid urbanization and industrialization. Accordingly, the impact assessment of climate change is mentioned as a very important factor in urban planning, and the World Meteorological Organization (WMO) is emphasizing the need for an impact forecast that considers the social and economic impacts that may arise from meteorological phenomena. In particular, in terms of traffic, the degradation of transport systems due to urban flooding is the most detrimental factor to society and is estimated to be around £100k per hour per major road affected. However, in the case of Korea, even if accurate forecasts and special warnings on the occurrence of meteorological disasters are currently provided, the effects are not properly conveyed. Therefore, in this study, high-resolution analysis and hydrological factors of each area are reflected in order to suggest the depth of flooding of urban floods and to cope with the damage that may affect vehicles, and the degree of flooding caused by rainfall and its effect on vehicle operation are investigated. decided it was necessary. Therefore, the calculation formula of rainfall-immersion depth-vehicle speed is presented using various machine learning techniques rather than simple linear regression. In addition, by applying the climate change scenario to the rainfall-inundation depth-vehicle speed calculation formula, it predicts the flooding of urban rivers during heavy rain, and evaluates possible traffic network disturbances due to road inundation considering the impact of future climate change. We want to develop technology for use in traffic flow planning.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.109-118
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• 2022

The data of the missing section among the vertex surface sea temperature observation data was imputed using the Bidirectional Recurrent Neural Network(BiRNN). Among artificial intelligence techniques, Recurrent Neural Networks (RNNs), which are commonly used for time series data, only estimate in the direction of time flow or in the reverse direction to the missing estimation position, so the estimation performance is poor in the long-term missing section. On the other hand, in this study, estimation performance can be improved even for long-term missing data by estimating in both directions before and after the missing section. Also, by using all available data around the observation point (sea surface temperature, temperature, wind field, atmospheric pressure, humidity), the imputation performance was further improved by estimating the imputation data from these correlations together. For performance verification, a statistical model, Multivariate Imputation by Chained Equations (MICE), a machine learning-based Random Forest model, and an RNN model using Long Short-Term Memory (LSTM) were compared. For imputation of long-term missing for 7 days, the average accuracy of the BiRNN/statistical models is 70.8%/61.2%, respectively, and the average error is 0.28 degrees/0.44 degrees, respectively, so the BiRNN model performs better than other models. By applying a temporal decay factor representing the missing pattern, it is judged that the BiRNN technique has better imputation performance than the existing method as the missing section becomes longer.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.57-74
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• 2022

Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.

• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.307-324
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• 2022

This study examined the content diversity of seven authorized science textbooks by comparing the characteristics of the science concept description and the contents of inquiry activities in the "weight of objects" unit. For each textbook, the flow of concept description content and the uniqueness of the concept description process were analyzed, and the number of nodes and links and words with high connections were determined using language network analysis. In addition, for the inquiry activities described in each textbook, the inquiry subject, inquiry type, science process skill, and uniqueness were investigated. Results showed that the authorized textbooks displayed no more diversity than expected in their scientific concept description method or their inquiry activity composition. The learning elements, inclusion of subconcepts, and central words were similar for each textbook. The comparison of inquiry activities showed similarities in their contents, inquiry types, and scientific process skills. Specifically, these textbooks did not introduce any research topics or experimental methods that were absent in previous textbooks. However, despite the fact that the authorized textbook system was developed based on the same curriculum, some efforts were made to make use of its strengths. Since the sequence of subconcepts to explain the core contents differed across textbooks, this explanation process was divided into several types, and although the contents of inquiry activities were the same, the materials for inquiry activities were shown differently for each textbook to improve and overcome the difficulties in the existing experiments. These findings necessitate the continuation of efforts to utilize the strengths of certified textbooks.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.4
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• pp.133-142
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• 2023

In this paper, we built a data-driven artificial intelligence model using RNN-LSTM (Recurrent Neural Networks-Long Short-Term Memory) to predict the Lorenz system, and examined the possibility of whether this model can replace chaotic dynamic models. We confirmed that the data-driven model reflects the chaotic nature of the Lorenz system, where a small error in the initial conditions produces fundamentally different results, and the system moves around two stable poles, repeating the transition process, the characteristic of "deterministic non-periodic flow", and simulates the bifurcation phenomenon. We also demonstrated the advantage of adjusting integration time intervals to reduce computational resources in data-driven models. Thus, we anticipate expanding the applicability of data-driven artificial intelligence models through future research on refining data-driven models and data assimilation techniques for data-driven models.