• Journal of the Korean Society for Library and Information Science
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• v.6
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• pp.137-163
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• 1979

The Purpose of this study is analyzed that (l) How the public Libraries under the Japanese Rule since the end of the Yi-Dynasty were recepted and generated by the people and (2) How they were organiged and managed. (3) Also it examined that how they affected the development of the libraries of today. 1. The following are the analyzed results: Three types of the public Libraries under the Japanese Rule for a period of 36 years engaged busily in colonization were Private's Public Libraries, Local Self-Government's Libraries and the Central Governmental Libraries, and were in order established. 2. They were eatablished by individuals, Confucian School Foundation, Young Men's Clubs, School Associations, Korean brethren residing abroad, or The Press Centering around the Local Self-Governments and the Japanese Government-General. 3. In 1932 of the period of the Japanese Rule, the number of Libraies gained the summit and reached 80 Libraries. The Public Libraries including the Central Governmental Libraries remained in existence until the end of the War had been kept up the functions of the Libraries, but the Private Libraries operated by the Koreans were very small and poor. As a result, most of them were closed up and some Libraries transferred their controls to the public. Until the end of the war, only a little over 10 Private Libraries were Kept up. From the aspects of it's organization system, the most of their libraries replaced their chief librarians with non-professional county-headmen or Local supporters. From the aspect of collections, they wate mainly consists of Japanese books for the proper quidance of the public thought based on the ideology of Japanese Rule to Korea and on the industrial promotion rather than books about Koreanology or Western books. At that time, the Library users were with the jobless men and students as the central figures. And the next ranking by the social position of readers was children, farmers, merchants, industrialists, public servants, miscellaneous and educators. Their reading tendencies laid stress on linguistics and literature, physical sciences and medicine, While the reading trend of military sciences and medicine, while the reading trend of military sciences and engineering were very inactive. This was because the Japanese Government-General had not kept the military collections on file. Besides, they were reluctant to make Korean's learn the professional knowledge and so the main reference materials of technology not provited. Most of the Libraries put practiced in circulation services were very important circulation in withinder of the reading room rather than in outside of the Library building. On the other hand, their circulation services has above came with many limitations. As stated above, the public Libraries' managements and activitives under the period of Japanese Rule were the way and means to achive the colonial and imperialistic purpose of the Japanese Empire.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Appropriate Technology
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• v.6 no.1
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• pp.28-36
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• 2020

Sustainable appropriate technology requires user-centered design with consideration of the political, cultural and environmental aspects of the area. However, in the preparation of appropriate technology, there is a limit to the prior grasp of the user's intention and experience leading to the actual behavior of the user after the dissemination. As a result, appropriate technologies are often inconvenient for practical use or used for other purposes, contrary to the designer's intention. This study analyzes the case of appropriate technology with an analysis framework that reflects Maier's affordance theory, and proposes a design solution that can overcome the limitations of existing design. Affordance theory is the theory of factors that cause the user to identify and use features through interpretation based on prior knowledge and experience about things. The analysis cases in this study are the interviews with the designers, management education materials, and manager interviews for water purification systems at three of six schools in Binh Dinh Province, Vietnam, from August 2015 to January 2018. The case was attempted to be improved by periodic installation, maintenance, and inspection, but similar problems continued to occur. First, the facility inspections and manager interviews are compared with manager training materials distributed at the time of installation to find inconsistencies. Next, we analyze the designer's intended affordance and the affordances that actually influenced the management behavior. And then, we propose design solutions based on commonly found problems and affordances. This study suggests that it is necessary to apply the design considering the user's behavior before distributing the appropriate technology, and this study will be precedent in the process of finding the improvement through the analysis framework based on the affordance.

• Journal of the Korean Society for Marine Environment & Energy
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• v.8 no.4
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• pp.227-235
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• 2005

This study aimed at suggesting management policy directions for the uninhabited islands of Korea which are national land resources with economic potential for tourism and development and strategic value for boundary delineation of territorial waters and exclusive economic zone as well as their unique ecological status. Review of existing management arrangements related to the uninhabited islands revealed six management issues to be addressed: insufficient data and their low reliability, lack of management policy directions, increase in ecosystem deterioration and perturbation by human activities, lack of policy measures for meeting utilization and development demands, weak management base with insufficient personnel and budget, and legal measures not taking Into account their unique ecological and socioeconomic characteristics. The management policy directions to improve the management of the uninhabited islands of Korea include management directions and strategies, and suggestions for legal improvement. Considering the unique ecological value of the uninhabited islands, management directions suggested are anti-degradation in which current and future demands for their utilization and development do not degrade the ecological potential of the uninhabited islands and integration in which land and sea areas are managed as an integrated management unit. Four strategies proposed to follow the management directions are enhancement of the knowledge base through a comprehensive survey, development and legislation of guidelines for the rational management of utilization and development demands, establishment of the comprehensive island debris collection and disposal system, and enhancement of management capacity. Legal improvement for the effective implementation of the management policy directions should include comprehensive uninhabited islands survey, legal utilization restraints and management guidelines based on classification of the islands, management boundary, and improvement of regulations on designated islands.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.