• Journal of the Korean Geographical Society
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• v.29 no.2
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• pp.117-136
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• 1994

Contemporary global space economy is so dynamic that any one specific structural force can not explain the whole dynamic processes or trajectories of spatial industrial development. The major purpose of this paper is extending the traditional notion of industrial districts to functioning and development of new industrial districts with relation to the development of high technology industries. Several dynamic forces, which are dominated in new industrial districts in the modern space economy, are incorporated in the formation and dynamic aspects of new industrial districts. Even though key forces governing Marshallian industrial district are localization of small firms, division of labor between firms, constructive cooperation, and industrial atmosphere, Marshall points out a possibility of growing importance of large firms and non-local networks in the districts with changes of external environments. Some of Italian industrial districts can be regarded as Marshallian industrial districts in broader context, but the role of local authorities or institutions and local embeddedness seem to be more important in the Italian industrial districts. More critical implication form the review of Marshallian industrial districts and Italian industrial districts is that the industrial districts are not a static concept but a dynamic one: small firm based industrial districts can be regarded as only a specific feature evolved over time. Dynamic aspects of new industrial districts are resulting from coexistence of contrasting forces governing the functioning and formation of the districts in contemporary global space economy. The contrasting forces governing new industrial districts are coexistence of flexible and mass production systems, local and global networks, local and non-local embeddedness, and small and large firms. Because of these coexistence of contrasting forces, there are various types of new industrial districts. Nine types of industrial districts are identified based on local/non-local networks and intensity of networks in both suppliers and customers linkages. The different types of new industrial districts are described by differences in production systems, embeddedness, governance, cooperation and competition, and institutional factors. Out of nine types of industrial districts, four types - Marshallian; suppliers hub and spoke; customers hub and spoke; and satellite - are regarded as distinctive new industrial districts and four additional types - advanced hub and spoke types (suppliers and customers) and mature satellites (suppliers and customers) - can be evolved from the distinctive types and may be regarded as hybrid types. The last one - pioneering high technology industrial district - can be developed from the advanced hub and spoke types and this type is a most advanced modern industrial district in the era of globalization and high technology. The dynamic aspects of the districts are related with the coexistence of the contrasting forces in the contemporary global space economy. However, the development trajectory is not a natural one and not all the industrial districts can develop to the other hybrid types. Traditionally, localization of industries was developed by historical chances. In the process of high technology industrial development in contemporary global space economy, however, policy and strategies are critical for the formation and evolution of new industrial districts. It needs formation of supportive tissues of institutions for evolution of dyamic pattern of high technology related new industrial districts. Some of the original distinctive types of new industrial districts can not follow the path or trajectory suggested in this paper and may be declined without advancing, if there is no formation of supportive social structure or policy. Provision of information infrastructure and diffusion of an entrepreneurship through the positive supports of local government, public institutions, universities, trade associations and industry associations are important for the evolution of the dynamic new industrial districts. Reduction of sunk costs through the supports for training and retraining of skilled labor, the formation of flexible labor markets, and the establishment of cheap and available telecommunication networks is also regarded as a significant strategies for dynamic progress of new industrial districts in the era of high technology industrial development. In addition, development of intensive international networks in production, technology and information is important policy issue for formation and evolution of the new industrial districts which are related with high technology industrial development.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.4
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• pp.36-48
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• 2018

This study surveyed 599 elementary schools in Seoul to provide measures for the quantitative expansion and sustainable operation of environmentally-friendly school garden. Of all schools, 161 schools had formed and were operating school gardens. The total area of school gardens was $166,901m^2$ and the mean area was $131.2m^2$ in elementary, junior high and high schools in Seoul. Meanwhile, the total area of school gardens was $65,493m^2$ and the mean area was $363m^2$ in 161 schools that participated in the survey, indicating $1.15m^2$ per student. Of these schools, 11.8% were operating gardens themselves, while 50.3% were operating gardens that had been newly renovated or environmentally improved by institutional support projects after initially managing gardens themselves. According to the locations of school gardens, mixed-type gardening (a combination of school gardening and container vegetable gardening) accounted for 34.8%, followed by school gardening at 32.9%, container vegetable gardening at 29.2%, and suburb community gardening at 3.1%. Those in charge of garden operations were teachers at 51.6%, comprising the largest percentage. Facilities built when forming the garden included storage facilities for small-scale greenhouses and farming equipment at 26.1%, accounting for the largest percentage. No additional facilities constructed accounted for 21.7%. The greatest difficulty in operating gardens was garden management at 34.2%. The most needed elements for the sustainable operation of gardens were improvement in physical environment and the need for hiring a paid garden, each accounting for 32%. The most important purpose for school gardening was creating educational environments (81.6%). The major source for gaining information on garden management was consultation from acquaintances (67.8%). Schools that utilize plant waste from gardens as natural fertilizers accounted for 45.8% of all schools. Responses to the impact of operating school gardens for educational purpose were positive in all schools as 'very effective' in 63.2% and 'effective' in 36.8%. This study was meaningful in that it intended to identify the current status of the operation of school gardens in elementary schools in Seoul, support the formation of school gardens appropriate for each school with sustainable operation measures, implement a high-quality education program, develop teaching materials, expand job training opportunities for teachers in charge, devise measures to support specialized instructors, and propose the need for a garden management organization.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.76-82
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• 2017

Purpose Whole body bone scan is one of the most frequently performed in nuclear medicine. Basically, both the anterior and posterior views are acquired simultaneously. Occasionally, it is difficult to distinguish the lesion by only the anterior view and the posterior view. In this case, accurate location of the lesion through SPECT / CT or additional static scan images are important. Therefore, in this study, various improvement activities have been carried out in order to enhance the work capacity of technologists. In this study, we investigate the effect of technologist training and standardized work process processes on bone scan error reduction. Materials and Methods Several systems have been introduced in sequence for the application of new processes. The first is the implementation of education and testing with physicians, the second is the classification of patients who are expected to undergo further scanning, introducing a pre-filtration system that allows technologists to check in advance, and finally, The communication system called NMQA is applied. From January, 2014 to December, 2016, we examined the whole body bone scan patients who visited the Department of Nuclear Medicine, Asan Medical Center, Seoul, Korea Results We investigated errors based on the Bone Scan NMQA sent from January 2014 to December 2016. The number of tests in which NMQA was transmitted over the entire bone scan during the survey period was calculated as a percentage. The annual output is 141 cases in 2014, 88 cases in 2015, and 86 cases in 2016. The rate of NMQA has decreased to 0.88% in 2014, 0.53% in 2015 and 0.45% in 2016. Conclusion The incidence of NMQA has decreased since 2014 when the new process was applied. However, we believe that it will be necessary to accumulate data continuously in the future because of insufficient data until statistically confirming its usefulness. This study confirmed the necessity of standardized work and education to improve the quality of Bone Scan image, and it is thought that update is needed for continuous research and interest in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.3
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• pp.420-436
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• 2016

The purpose of this study was to identify the status of task performance and working conditions for nutrition teachers in order to determine new ways to improve the current nutrition system and increase job satisfaction among teachers. From the 14th of July to the 26th of September 2014, email questionnaires were distributed to and collected from 311 nutrition teachers nationwide, including teachers at elementary, middle, and high schools. The results are as follows. First, over 90% of nutrition teachers indicated that their work was demanding. In particular, 63.5% of nutrition teachers at high schools worked more than 40 hours of overtime per month. Second, 73% of nutrition teachers provided nutrition education, but the percentage significantly decreased at upper levels of school. Nutrition teachers had difficulties teaching due to the absence of standard teaching materials and a lack of time due to excessive work. Teachers also wanted 30 hours of job training once per year during their vacation organized by the regional Department of Education. Third, around half of the nutrition teachers considered that promotion and transfer professions are necessary for systematic foodservice and education. An additional allowance was demanded by nutrition teachers at schools that serve two or three meals per day. Considering the results, alleviating the workload of nutrition teachers and more time preparing nutrition classes for student are required. Fair evaluation of job performance that considers work conditions of nutrition teachers should be considered.

• The Korean Journal of Archival Studies
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• no.12
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• pp.137-178
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• 2005

The anticommunist guerrillas' activities that aretheconcern of this article took place largely in North Korea or behind the enemy-held lines. Verifying their history is accordingly difficult and requires careful attention, but despite their active operations the military as well as the scholarly community have been lax in studying them. The Korean War came to be perceived as a traditional, limited war with regular battles, so that the studies addressed mostly the regular operations, and guerrilla warfare is remembered as an almost 'exclusive property' of the communist invaders; a small wonder that the anticommunist guerrillas have not been studied much and the collection of materials neglected. Therefore, in contrast with the witness accounts concerning regular battles, witness resources were of a small volume about these "patriots without the service numbers." For the above reasons the guerrilla participants and their later-organized fellowships took to the task of leaving records and compiling the histories of their units. They became active preservers of history in order to inform later generations of their works and also to secure deserved benefits from the government, in a world where none recognized their achievements. For instance, 4th Donkey Unit published witness accounts in addition to a unit history, and left video-recordings of guerrilla witnesses before any institute systematized the oral history of the guerrillas. In the case of Kyulsa ("Resolved to Die") Guerrilla Unit, the unit history was 10 times revised and expanded upon for publication, contributing substantially to the recovery of anticommunist guerrilla history which had almost totally lacked documented resources. Now because the guerrilla-related witness accounts were produced through fellowship societies and not individually, it often took the form of 'collective memory.' As a result, though thousands of former guerrillas remain surviving, the scarcity of numerous versions of, or perspectives upon, an event renders difficult an objective approach to the historical truth. Even requests to verify the service of a guerrilla member or to apply for decoration or government benefits for those killed in action, the process is taken care of not at the hands of the first party but the veteran society, so that a variety of opinions are not available for consideration. Moreover, some accounts were taken by American military personnel, and since some historians, unaware of official documents or evaluation of achievements, tended to center the records around their own units and especially to exaggerate the units' performances, they often featured factual errors. Thefollowing is the means to utilize positively the aforementioned type of witness accounts in military history research. It involves the active use of military historical detachments (MHD). As in the examples of those dispatched by the American forces during the Korean War, experts should be dispatched during, and not just after, wartimes. By considering and investigating the differences among various perspectives on the same historical event, even without extra documented resources it is possibleto arrive at theerrors or questionable points of the oral accounts, supplementing the additional accounts. Therefore any time lapses between witness accounts must be kept in consideration. Moreover when the oral accounts come from a group such as participants in the same guerrilla unit or operation, a standardized list of items ought to be put to use. Education in oral history is necessary not just for the training of experts. In America wherethefield sees much activity, it is used not only in college or graduate programs but also in elementary and lifetime educational processes. In comparison in our nation, and especially in historical disciplines, methodological insistence upon documented evidences prevails in the main, and in the fields of nationalist movement or modern history, oral accounts do not receive adequate attention. Like ancient documents and monuments, oral history also needs to be made a regular part of diverse resource materials at our academic institutes for history. Courses in memory and history, such as those in American colleges, are available possibilities.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.221-228
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• 2021

This study aims to provide basic data for developing hand functional training programs using a keyboard to see if there is a relationship between the smart keyboard input speed using both hands, the Dexterity of the hand and the eye-hand coordination ability. The smartphone keyboard input speed, Purdue Pegboard, Grooved Pegboard Test, and Korean-Developmental-Test of Visual Perception-Adolescent were evaluated for 40 university students Province. An independent sample t-test and one-way ANOVA were conducted to identify differences in smartphone keyboard input speed, dexterity, eye-hand coordination ability and visual-motion using both hands according to the general characteristics of the subjects. Pearson correlation was also conducted to find out the relationship between hand-used smartphone keyboard input speed, hand dexterity, eye-hand coordination ability and visual-motor. As a result, the smartphone keyboard input speed using both hands showed a correlation with the dominant hand in the Purdue Pegboard Test (r=-.313, p<.05). In addition, the input speed of the smartphone keyboard is Copying(r=-.333, p<.05), Visual Motor Search(r=.455, p<.01), Visual Motor speed(r=-.453, p<.01) and Form Constancy (r=-.341, p<.05) in the item of K-DTVP-A. Therefore, it is believed that it will be helpful in the development of a treatment program using a smartphone, and it is expected that the effectiveness of a treatment program using a smartphone will be proven through additional experimental studies in the future.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.