• 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 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 Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Cartoon and Animation Studies
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• s.47
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• pp.273-294
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• 2017

Korea has suffered numerous casualties due to a lot of accidents caused by safety insufficiency in recent years. Therefore, safety education is more important than ever before, and 'how to educate with what contents' is an important subject. Especially, experience education is effective rather than theoretical education because of the nature of safety education. However, it is not easy to design and develop these safety education programs. There is not much opportunity to access first-aid training, which is a part of safety education, unless it is compulsory to learn through public institutions. As a result, program utilization on safety education in Korea is still insufficient to what it should be. With that taken into account, this study proposed an effective serious game with fun and immersion for medical first-aid education. To do this, we analyzed five medical games through 20 cases of first-aid applications and elicited five factors that enhance the usability of serious games. With an analysis of five medical games, we selected one game to borrow the game rules, and applied the elicited five elements in the forms of level-up structure, iterative learning, compensation outcomes, competition system, and information transfer. The proposed medical education functional games should have 1) a character that plays a role of a patient, 2) a narrative flow that shows the situation, 3) the user should judge the situation and induce first aid. 4) compensation, levels, and simple repetition should be designed, and 5) information should be shared with the others in the given community. The results of this study is believed to contribute to enhance the medical emergency education in Korea.

• 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.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.907-918
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• 2007

This study was conducted to explore key factors of expertise development of talented scientists who achieved outstanding research performance according to the stages of expertise development and dimensions of individual-domain-field. To fulfill the research purpose, 31 domestic scientists who were awarded major prizes in the field of science were interviewed in-depth from March to September, 2007. Stages of expertise development were analyzed in light of Csikszentmihalyi's IDFI (individual-domain-field interaction) model. Self-directed learning, multiple interests and finding strength, academic and liberal home environment, and meaningful encounter were major factors affecting expertise development in the exploration stage. In the beginner stage, independence, basic knowledge on major, and thirst for knowledge at university affected expertise development. Task commitment, finding flow, finding their field of interest and lifelong research topic, and mentor in formal education were the affecting factors in the competent stage. Finally, placing priority, communication skills, pioneering new domain, expansion of the domain, and evaluation and support system affected talented scientists' expertise development in the leading stage. The meaning of major patterns of expertise development were analyzed and described. Based on these analyses, educational implications for nurturing scientists were suggested.

• 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.18 no.9
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• pp.76-83
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• 2017

Fish respond sensitively to light, so it is possible to develop fish management technology using this feature. In this study, we developed a light-based fish barrier and analyzed itsblocking effect using the difference in illuminance for the major fish species in Korea, bass and bluegill. The light was generated by a light emitting diode and the facility was installed vertically from the bottom. Considering the fish's ability to travel upstream, the flow rate was divided into three stages (0.2, 0.1, and 0.05 m/s). To prevent the learning effect, an experiment was carried out with fish that had rested for more than one day in a rearing tank. The experiment was carried out in such a way as tocompare the number of fish which travelled upstream after the introductionof the fish barrier and that of the fish which travelled upstream after itsremoval. It was also carried out after sunset to increase the effectiveness of the barrier. According to the results of the experiment, the fish blocking effect depending on the difference in illuminance was high and, overall, the blocking rate for bass was lower than that for bluegill. Based on the total size of the experimental population, the blocking rates for bass and bluegill were 96.33% and 99.00%, respectively. Based on the number of fish that travelled upstream, the blocking rates for bass and bluegill were 91.73% and 98.73%, respectively.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.395-432
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• 2021

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MOD-FLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.