• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.41-53
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• 2020

The production of near- and off-shore fisheries in South Korea is decreasing due to rapid changes in the fishing environment, particularly including higher sea temperature in recent years. To improve the competitiveness of the fisheries, it is necessary to provide fish catch information that changes spatiotemporally according to the sea state. In this study, artificial intelligence models that predict the CPUE (catch per unit effort) of mackerel, anchovies, and squid (Todarodes pacificus), which are three major fish species in the near- and off-shore areas of South Korea, on a 15-km grid and daily basis were developed. The models were trained and validated using the sea surface temperature, rainfall, relative humidity, pressure,sea surface wind velocity, significant wave height, and salinity as input data, and the fish catch statistics of Suhyup (National Federation of Fisheries Cooperatives) as observed data. The 10-fold blind test results showed that the developed artificial intelligence models exhibited accuracy with a corresponding correlation coefficient of 0.86. It is expected that the fish catch models can be actually operated with high accuracy under various sea conditions if high-quality large-volume data are available.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.899-908
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• 2019

Massive in-depth neural networks with numerous parameters are powerful machine learning methods, but they have overfitting problems due to the excessive flexibility of the models. Dropout is one methods to overcome the problem of oversized neural networks. It is also an effective method that randomly drops input and hidden nodes from the neural network during training. Every sample is fed to a thinned network from an exponential number of different networks. In this study, instead of feeding one sample for each thinned network, two or more samples are used in fitting for one thinned network known as a Hybrid Dropout. Simulation results using real data show that the new method improves the stability of estimates and reduces the minimum error for the verification data.

• Information Systems Review
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• v.13 no.1
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• pp.61-74
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• 2011

Software industry still has many deep-seated problems. As a natural consequence, it may have to learn from best practices in more mature industry like manufacturing. An example is 'lean' software development which is defined as translation of 'lean manufacturing' principles to the software development domain. The principles include 'eliminate waste' and 'amplify learning.' It was much asserted that these principles are worth applying. Not much study, however, was done on how to practically implement these principles into software development practice. In this study we attempt to present a method in which project lead time and software defects are regarded as major targets of management and are visualized using Cumulative Flow Diagram. We further applied this method on actual projects. The result confirms that agile is positively effective on reducing wastes.

• Korean journal of food and cookery science
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• v.28 no.6
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• pp.829-837
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• 2012

This study was investigated seafoods provided by school food service and students' preferences for and perceptions of seafoods. The subjects were 275 second grade(age 14-16) students of 4 middle schools in Gyeongnam. The results were as follows. The most main seafoods intake place was 'home'(65.8%). 'School food service' took meaningful ratio(20.7%) of students' seafoods intakes. In the intake amount of seafoods provided by school food service, 'all' took 22.5%(male 31.6%, female 14.1%), 'more than provided' took 1.5%(male 3.0%, female 0%). Male students ate seafoods more than female students did(p<.001). In seafoods providing frequency, '2~3 times a week' took 74.5%, '4~5 times a week' took higher ratio in males' schools, while '0~1 times a week' took higher ratio in females'(p<.05). In perceptions of seafoods, most subjects had positive perceptions as 'good for health'(3.95), 'various kinds'(3.75) except 'good peculiar smell' got smallest point(2.85). In means of learning about seafoods names, 'by looking at everyday menu' took 64.6%. In taking nutrition education, 'no nutrition education' took 69.5%. In preferences for seafoods using 5-point scale, males' preferences were higher than females'(p<.001). 48.1% of males got higher than 4 point, while 14.1% of females did. In improvement measures of seafoods, 'provide various kinds'(47.3%) took highest ratio. In preferences for seafoods by seafoods kinds, preference for 'crustacean' was highest while preferences for 'shell fish' and 'fish' were relatively low. Both male and female students highly preferred laver, shrimp, swimming crab, small octopus, fish cake and tuna canned goods. Male students' preferences were higher than female students' for most kinds of seafoods. In preferences for seafoods by cooking methods, preferences for 'grilled', 'stir fried', 'pan fried' were relatively high, 'braised', 'deep fried', 'steamed' were relatively low. Males' preferences were higher than females' for every cooking method except 'steamed'.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.661-670
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• 2018

This study aims to develop a methodology of convolutional neural networks (CNNs) to produce thematic maps from remote sensing data. Optimizing the image size for CNNs was studied, since the size of the image affects to accuracy, working as hyper-parameter. The selected study area is Mt. Ung, located in Dangjin-si, Chungcheongnam-do, South Korea, consisting of both coniferous forest and deciduous forest. Spatial structure analysis and the classification of forest type using CNNs was carried in the study area at a diverse range of scales. As a result of the spatial structure analysis, it was found that the local variance (LV) was high, in the range of 7.65 m to 18.87 m, meaning that the size of objects in the image is likely to be with in this range. As a result of the classification, the image measuring 15.81 m, belonging to the range with highest LV values, had the highest classification accuracy of 85.09%. Also, there was a positive correlation between LV and the accuracy in the range under 15.81 m, which was judged to be the optimal image size. Therefore, the trial and error selection of the optimum image size could be minimized by choosing the result of the spatial structure analysis as the starting point. This study estimated the optimal image size for CNNs using spatial structure analysis and found that this can be used to promote the application of deep-learning in remote sensing.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.61-67
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• 2017

In this paper we introduce two experimental results from classification of Instagram images and some valuable lessons from them. We have tried some experiments for evaluating the competitive power of Convolutional Neural Network(CNN) in classification of real social network images such as Instagram images. We used AlexNet and ResNet, which showed the most outstanding capabilities in ImageNet Large Scale Visual Recognition Challenge(ILSVRC) 2012 and 2015, respectively. And we used 240 Instagram images and 12 pre-defined categories for classifying social network images. Also, we performed fine-tuning using Inception V3 model, and compared those results. In the results of four cases of AlexNet, ResNet, Inception V3 and fine-tuned Inception V3, the Top-1 error rates were 49.58%, 40.42%, 30.42%, and 5.00%. And the Top-5 error rates were 35.42%, 25.00%, 20.83%, and 0.00% respectively.

• Analytical Science and Technology
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• v.33 no.2
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• pp.98-107
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• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.

• Journal of Elementary Mathematics Education in Korea
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• v.20 no.2
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• pp.239-257
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• 2016

The Common Core States Standards was developed by building on the best state standards in the U.S.; examining the expectations of other highperforming countries around world; and carefully studying the research and literature available on what students need to know. The Common Core States Standards for Mathematics are reshaping the teaching and learning of mathematics in California classroom using the California Common Core States Standards for Mathematics(CA CCSSM). The aim of this study is to observe CA CCSSM. The CA CCSSM were established to address the problem of having a curriculum that is 'a mile wide and an inch deep'. And it have two types of standards. One is standards for mathematical practice which are the same at each grade level, the other is standards for mathematical content which are different at each grade level. This study focused on standards for mathematical content, in particular, on Geometry domain in elementary level, using Mathematics Framework for California Public Schools.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.55-63
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• 2017

One of the purposes of Information Technology (IT) is to support human response to natural and social problems such as natural disasters and spread of disease, and to improve the quality of human life. Recent climate change has happened worldwide, natural disasters threaten the quality of life, and human safety is no longer guaranteed. IT must be able to support tasks related to disaster response, and more importantly, it should be used to predict and minimize future damage. In South Korea, the data related to the damage is checked out by each local government and then federal government aggregates it. This data is included in disaster reports that the federal government discloses by disaster case, but it is difficult to obtain raw data of the damage even for research purposes. In order to obtain data, information extraction may be applied to disaster reports. In the field of information extraction, most of the extraction targets are web documents, commercial reports, SNS text, and so on. There is little research on information extraction for government disaster reports. They are mostly text, but the structure of each sentence is very different from that of news articles and commercial reports. The features of the government disaster report should be carefully considered. In this paper, information extraction method for South Korea government reports in the word format is presented. This method is based on patterns and dictionaries and provides some additional ideas for tokenizing the damage representation of the text. The experiment result is F1 score of 80.2 on the test set. This is close to cutting-edge information extraction performance before applying the recent deep learning algorithms.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.