• Korean journal of food and cookery science
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• v.3 no.1
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• pp.64-70
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• 1987

The effects of salting on the compositional and textural changes of Korean cabbage were studied. The optimum brining conditions were established ana the dietary fiber composition, mineral contents and moisture content of raw and salted Korean cabbage were determined. The cutting test of cabbage was made by Rheometer and the brittleness and chewiness were evaluated organoleptically. The optimum condition for brining was at 20% NaCl concentration for 6 hours. In the compositional changes of Korean cabbage by salting at 20% NaCl solution for one month, the content of hot water soluble pectin (HW-P) increased from 43.6% to 55.9% and that of hexametaphosphate soluble pectin (HM-P) decreased from 35.9% to 29.5%. The contents of cellulose and hemicellulose increased, but that of lignin decreased slightly by salting, showing no significant differences in raw and salted cabbage. The content of Na increased significantly and those of Ca, Mg and K decreased by salting. And also moisture content decreased from 91% to 79%. In the textural changes of Korean cabbage by salting, the maximum cutting force and cutting work increased five times and two and half times respectively. And organoleptic test did show significant increase in chewiness and decrease in brittleness. The maximum cutting force by Rheometer was well correlated with the sensory parameters. The results taken together showed that the changes in textural properties during salting are relevant to the changes in pectic substances, moisture content and mineral contents, but relatively irrelevant to the changes in cellulose, hemi-cellulose ana lignin. And it is considered that the maximum cutting force by cutting test is good means for the expression of texture of Korean cabbage.

• Journal of The Korean Association For Science Education
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• v.18 no.4
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• pp.589-600
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• 1998

The present study tested the hypothesis that adolescent's prefrontal lobe growth plateau and spurt exists and that this plateau and spurt influence students' ability to reason scientifically and to learn theoretical science concepts. In theory, maturation of the prefrontal lobes during early adolescence allows for improvements in students' abilities to inhibit task-irrelevant information and coordinate task-relevant information, which along with both physical and social experience, influences scientific reasoning ability and the ability to reject scientific misconceptions and accept scientific conceptions. Two hundred six students ages 13 to 16 years enrolled in four Korean secondary schools were administered tests of prefrontal lobe functions, scientific reasoning, and theoretical concepts derived from kinetic-molecular theory. A series of 14 lessons designed to teach the concepts were then taught. The concepts test was then re-administered following instruction. As predicted among the 14-year-olds, performance on the measures of prefrontal lobe functions, scientific reasoning, and conceptual change remained similar or regressed. Performance then improved considerably among the 15 and 16-year-olds. Because so few of the present students were able to undergo this apparently necessary conceptual change, the value of introducing theoretical concepts to early adolescent is questioned.

• The Korean Journal of Archival Studies
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• no.65
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• pp.227-251
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• 2020

The documentation of a city can never be complete with only the documentation of the administrative domain, and requires that of its citizens, who are living in the city in different ways. This study attempts to present the documentation of the memories of the citizens, which either have never been produced or have been damaged and thus are difficult to be collected. From the Archival Activist point of view, this study suggests documentary as its research method, in order to leave trace of various experiences of Seoul, which are not recorded in document but are rooted in its people's memories and their daily lives. Documentaries are characterized by their narrative. This can be somewhat arbitrary, but it is due to their narrative that this study suggests documentaries, rather than oral statements, as a new form of method. While, due to its self-historicality, oral records are subject to producing redundant or irrelevant memories, documentaries enable the documentation of data relevant to the topic of collection. First, the study presents the narrative-based archiving, which is the same method of collection suggested by Seoul Metropolitan Archives, and then explores the role and significance of documentary archiving. It further presents the conditions in which documentary archiving is required in the context of narrative-based collection. The study presents the planning and implementation of documentary archiving and introduces one of the three documentaries produced by 2019 Seoul Archiving Project.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Journal of the Korean Geotechnical Society
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• v.25 no.5
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• pp.53-64
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• 2009

Many slope failures are induced by rainfall infiltration. A lot of recent researches are therefore focused on rainfall-induced slope instability and the rainfall infiltration is recognized as the important triggering factor. The rainfall infiltrates into the soil slope and makes the matric suction lost in the slope and even the positive pore water pressure develops near the surface of the slope. They decrease the resisting shear strength. In Korea, a few public institutions suggested conservative slope design guidelines that assume a fully saturated soil condition. However, this assumption is irrelevant and sometimes soil properties are misused in the slope design method to fulfill the requirement. In this study, a more relevant slope stability evaluation method is suggested to take into account the real rainfall infiltration phenomenon. Unsaturated soil properties such as shear strength, soil-water characteristic curve and permeability for Korean weathered soils were obtained by laboratory tests and also estimated by artificial neural network models. For real-time assessment of slope instability, failure warning criteria of slope based on deterministic and probabilistic analyses were introduced to complement uncertainties of field measurement data. The slope stability evaluation technique can be combined with field measurement data of important factors, such as matric suction and water content, to develop an early warning system for probably unstable slopes due to the rainfall.

• Journal of the Korean Geographical Society
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• v.38 no.3
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• pp.426-443
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• 2003

Electronic Commerce (EC) has been at the center of discussion as a symbol of the integration of unprecedentedly developed Information and Communication Technologies (ICTs) and traditional commerce. In spite of much attention to EC, the research from a spatial perspective has not proliferated yet. EC was regarded to have aspatial characteristics based on the expectation for a global expansion of business activities in a digital economy. This paper attempts to figure out the spatial characteristics of public Business-to-Business electronic marketplaces (public B2B e-MPs), as one of the most evolving forms of B2B EC, regardless of the low proportion in B2B EC at present. Many of the firms operating public B2B e-MPs in Korea were located in Seoul, especially in Gangnam-gu. The analysis of three spatial indices showed their extreme spatial concentration. The analysis on the location factors of Public B2B e-MP firms in Korea demonstrated that location factors of public B2B e-MP firms were differentiated by regional groups: Gangnam-gu, Seoul except Gangnam-gu, and the provinces. It was againt an initial extreme expectation that the firms relevant to B2B EC will not care about physical locations because they mainly do businesses in electronic space. The differences between those in Gangnam-gu and in the provinces were strikingly prominent. Such differentiated location factors by region were closely related to the different attributes of the public B2B e-MPs by region. In conclusion, public B2B e-MPs are not irrelevant to physical space and physical proximity, at least at a current stage. Customized spatial strategies are required for successful online businesses.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.