• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.6
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• pp.137-148
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• 2017

This study analyzed the opinions of residents for desirable urban regeneration and suggested an improvement plan for alley environments. This study conducted a questionnaire survey of the residents living in Alley Garden of Bisan 2,3-dong, Daegu Metropolitan City. In the analysis of the importance and satisfaction of Alley Garden components, items related to a safe, cleanly environment such as 'Lighting facility installation', 'Sewage and waste disposal' and 'CCTV installation' had a high level of importance. It was also found that items improved by the Residential Environment Improvement Project and Alley Garden such as 'Lighting facility installation', 'Quantity of herbaceous flowers' and 'Kinds of herbaceous flowers' had a high level of satisfaction. The IPA results showed that items such as 'Empty house maintenance', 'Rest facilities such as benches and pergolas', 'Space for resident interaction' and 'Public parking lot' had a high level of importance, but had a low level of satisfaction, which suggests that they should be improved by priority. As a result of factor analysis, Alley Garden components were classified into four factors: 'Safety and cleanliness', 'Greenness', 'Exchange and convenience facility' and 'Aesthetics renewal'. Based on this classification, a regression analysis was conducted regarding the effects of the four factors on overall satisfaction. Results showed that all four factors had a significant influence on the overall satisfaction and that 'Aesthetics renewal' and 'Safety and cleanliness', respectively showing levels of significance at 0.274 and 0.235, were highly influential to overall satisfaction. Therefore, it was concluded that spaces for resident interaction and rest facilities should be preferentially installed to improve the environment of alleys. For the improvement of the overall satisfaction of alley environments, it was also concluded that residents should be encouraged to engage in activities such as sculpture installation and mural drawing, along with the introduction of safety bells and crime prevention environment design and the implementation of alley beautification projects.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Food Engineering Progress
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• v.15 no.4
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• pp.346-354
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• 2011

This study was carried out to fortify the antimicrobial activity of yoghurt by adding liquorice extract to it. The liquorice extracts (1 mg/mL) showed relatively high antibacterial activity against H. pylori KCCM 40449 (p < 0.05). The solvent liquorice extracts of minimal inhibitory concentrations (MIC) against H. pylori KCCM 40449 were 25- 100 ${\mu}g$/mL. Lactobacillus amylovorus DU-21 with high EPS production ability were inoulated to milk after the addition of different amounts of liquorice extracts (0.0%, 0.05%, 0.1% and 0.2%). The physico-chemical characteristics of yoghurts added with liquorice extracts were examined. The initial pH, titratable acidity, viscosity and viable cell counts of the yoghurt added liquorice extracts were 3.41-3.51, 1.021-1.091%, 1,686-1,930 cp and 9.41-9.38 Log CFU/mL, respectively. The viscosity and syneresis of yoghurt were better than that of the control. Antimicrobial activity against H. pylori KCCM 40449 increased with increasing addition of liquorice extract. However, the sensory score of yoghurt added with different amounts of liquorice extracts was lower than that of the control (p < 0.05). As a result of the sensory evaluations, the flavor, taste, texture, color and overall acceptability of the yoghurt with 0.05% liquorice extract were found to be much better than those of the other groups (p < 0.05). Overall, the optimal amount of liquorice extract added in the manufacture of yoghurt was 0.05% of the total weight. Further studies on increment of antimicrobial activity and palatability of liquorice extract added yoghurt are necessary.

• The Korean Journal of Archival Studies
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• no.28
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• pp.35-81
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• 2011

Manhwa is a mass media (to expose all faces of an era such as politics, society, cultures, etc with the methodology of irony, parody, etc). Since the Manhwa records is primary culture infrastructure, it can create the high value-added industry by connecting with fancy, character, game, movie, drama, theme park, advertising business. However, due to lack of active and systematic aquisition system, as precious Manhwa manuscript is being lost every year and the contents hard to preserve such as Manhwa content in the form of electronic records are increasing, the countermeasure of Manhwa contents management is needed desperately. In this study, based on these perceptions, the need of Manhwa records management is examined, and the characteristics and the components of Manhwa records were analyzed. And at the same time, the functions of record management process reflecting the characteristics of Manhwa records were extracted by analyzing various cases of overseas Cartoon Archives. And then, the framework of record-keeping regime was segmented into each of acquisition management service areas and the general Manhwa records archiving strategy, which manages the Manhwa contents records, was established and suggested. The acquired Manhwa content records will secure the context among records and warrant the preservation of records and provide diverse access points by reflecting multi classification and multi-level descriptive element. The Manhwa records completed the intellectual arrangement will be preserved after the conservation in an environment equipped with preservation facilities or preserved using digital format in case of electronic records or when there is potential risk of damaging the records. Since the purpose of the Manhwa records is to use them, the information may be provided to diverse classes of users through the exhibition, the distribution, and the development of archival information content. Since the term of "Manhwa records" is unfamiliar yet and almost no study has been conducted in the perspective of records management, it will be the limit of this study only presenting acquisition strategy, management and service strategy of Manhwa contents and suggesting simple examples. However, if Manhwa records management strategy are possibly introduced practically to Manhwa manuscript repositories through archival approach, it will allow systematic acquisition, preservation, arrangement of Manhwa records and will contribute greatly to form a foundation for future Korean culture contents management.

• Korean Journal of Environment and Ecology
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• v.35 no.6
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• pp.621-631
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• 2021

The purpose of this study is to survey and analyze acorn production and characteristics of the Quercus acuta Thunb. according to the need for information on seed supply and seedling cultivation during the restoration of warm broad-leaved forests. For the survey, a total of 30 seed traps with a surface area of 1 m² were set up, 3 in each of 10 quadrats (8 in Wando, 1 in Haenam, and 1 in Jindo). The acorns that fell in the seed trap at the end of each month were collected from August to December each year between 2013 to 2016. The collected acorns were then classified into sound, damaged, decayed, or empty grade, and the number of acorns produced was calculated. In the case of sound acorns, acorn traits, such as length, diameter and weight of acorns without cupule, were measured. Duncan's multiple tests of acorn production and characteristics were conducted for comparative analysis of the annual average values with the values by year, stand, month, and treatment plot. The annual number of acorn dropped into the seed traps in each quadrat from 2013 to 2016 was 5-350 acorns/3 m² in 2013, 17-551 acorns/3 m² in 2014, 5-454 acorns/3 m² in 2015, and 14-705 acorns/3 m² in 2016. There was a large difference in acorn production between the quadrats, presumably attributed to the difference in the amount of light received due to the density of trees in the square. Annual acorn production per area was 335,000 acorns/ha in 2013, 932,000 acorns/ha in 2014, 556,000 acorns/ha in 2015, and 1,037,000 acorns/ha in 2016. That was a sharp variation of acorn production in the two-year cycle. As the fluctuation in the production of Q. acuta showed simultaneity between stands, it is judged that Quercus acuta Thunb. had a clear cycle of fruitfulness and fruitiness between forest objects. September showed the biggest amount of fallen acorns and largest damage from insect pests, indicating that preventing early fall of acorns could increase the fruiting period and enable mass production of sound acorns. There was no significant difference between annual average acorn length in each region. In the case of the acorn diameter and weight, the average values of acorns from Haenam were significantly higher than those from Wando and Jindo. There was no significant difference in the average annual acorn characteristics by month, and the average annual acorn length, diameter, and weight in November were 19.72mm, 12.23mm, and 1.64g, respectively, the highest between August and November.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.253-261
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• 2022

The purpose of this study is to investigate the relationship between transformative leadership, team cohesion, and team performance of Chinese university taekwondo leaders. Specifically, it is to investigate the effect of transformative leadership on team cohesion and team performance and to verify the mediating effect of team cohesion in the relationship between transformative leadership and team performance. In order to achieve the research purpose, a total of 350 people were sampled after setting taekwondo leaders at Chinese universities as a population. The measurement tool used in this study was a questionnaire consisting of 5 items on demographic characteristics, a total of 19 questions on transformational leadership, 10 questions on team cohesion, and 4 questions on team performance. The validity of the questionnaire was verified through exploratory factor analysis, and the reliability was verified through reliability analysis. The reliability Cronbach's α of the questionnaire was found to be α=0.755-0.799 for transformative leadership, α=0.848, and α=0.740 for team performance. As the data processing method, exploratory factor analysis and reliability analysis, one-way analysis (one-way ANOVA), and multiple regression analysis were used using SPSS WIN. The conclusions derived through the above research methods and procedures are as follows. First, the transformative leadership of Taekwondo leaders at Chinese universities influenced team cohesion. Second, the transformative leadership of Taekwondo leaders at Chinese universities influenced team performance. Third, the team cohesiveness of Taekwondo leaders at Chinese universities influenced team performance. Fourth, the transformative leadership of Taekwondo leaders at Chinese universities not only directly affects team performance, but also indirectly affects team cohesion. Therefore, it is believed that Chinese Taekwondo players will help improve their performance by affecting team cohesion and team performance for the best games through the leader's variable leadership.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.107-124
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• 2023

As getting in 4th Industrial Revolution Times, Continuing Education Enrolled Students(CEES) trying to find loophole for jepordized current life and need job transfer have surged their interest significantly on starting new business to bring up their post career after retirement through self-improvement. Government and university have actively initiated diverse policies of promoting startup for CEES in kicking off entrepreneurship courses and programs. However, relevant main policy, 'The 2nd University Startup Education Five-Year Plan (draft)' have too chiefly focused on theoretical start-up education rather than practical courses, causing the problem of inappropriate support for implementing real startup and business (Ministry of Education, 2018). This study is brought to empirically investigate the effect of self-efficacy as perspective of the impact of entrepreneurship education on entrepreneurial intention and behavior to come up with problem of poor entrepreneurial environment and entrepreneurship education to CEES. As to empirical research, this paper deliver on-line survey to CEES from September to October 2022, collect 207 effective feedbacks, In order to verify the reliability of the scale, the Cronbach's Alpha Coefficient (Cronbach's α) was calculated, analyzed, and measured. For hypothesis test, this paper utilize the multiple regression analysis statistical analysis method and use the SPSS 22.0 statistical processing program. Empirical results show, first, it was found that self-efficacy had a significant effect on start-up education. Second, start-up education had a significant effect on the intention to start a business of adult learners. Third, start-up education had a significant effect on the start-up behavior of adult learners. Fourth, self-efficacy had a significant effect on the intention of adult learners to start a business. Fifth, self-efficacy had a significant effect on the start-up behavior of adult learners. Sixth, self-efficacy had a mediating effect in the relationship between entrepreneurship education and adult learners' intention to start a business. Seventh, self-efficacy had a complete mediating effect in the relationship between start-up education and adult learners' start-up behavior. This paper is brought three significant implications. First, main consideration developing entrepreneurship education tools for CEES need to falls on defining potential needs of CEES as segmenting as to coming up with diversity of CEES's characteristics such as gender, age, experience, education, and occupation. Second, as to design specific entrepreneurship education program, both practical training program of utilizing CEES's career field experience benchmarking best practice startup and venture cases from domestic and global, and professional startup program of CEES initiating directly startup from ideation to develop business plan with pitching and discussing. Third, entrepreneurship education for CEES should be designed to incubate self-efficacy to enhance entrepreneurial intention of implementing entrepreneurial behavior as a real, eventually leading solid support system of self-improvement for CEES' Retirement life planning.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• 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.