• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.2
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• pp.14-25
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• 2009

We live in an age which is exponentially growing as the knowledge paradigm is changing. New sites are subject to contemporary landscape architecture function as "fields" in which this hybrid aspect is both actively practiced and becoming a catalyst for change in the area of landscape architecture. With this as its background, this study attempts to deal with how the aspect of integration in environmental design is manifested. For this purpose, the tendencies for the discussion of integration in various fields of practice were examined: planning theories, urban theories, architecture, public environment, engineering, and landscape architecture. As yet, the discussions of interdisciplinary integration, which occur in practice in these respective fields, mainly tend to be oriented toward the effective implementation of the merits of other related fields. Seen from these examples of practice, integrated design approaches can be found in the following three aspects: design objects, respective professional areas, and methodologies of approaches and design. In terms of design objects, the positions of individual design subjects present themselves as most obvious, and integration or combination of the physical targets that come to exist through design can be easily seen. Most examples of integration turn out to be this, in almost every case of which the theme and the target of expression are integrated via a small number of certain methods. In terms of professional areas, what can be mainly evidenced is how the individual subject acts when the subject designs. The strong points of professionals from each field seem to create synergy, achieving through integration optimum results. In terms of methodologies of approaches and design, there are attempts to create integrated approaches as ways of effective decision-making, in which case the integration of all of the interest parties is of primary concern. As yet, few instances have been found in which integrated design has had enough strength to be seen as a concrete design methodology based on practical examples. However, it is encouraging that theoretical approaches and the necessity for integrated design have been identified from multiple perspectives, and that a practical movement such as landscape urbanism has come into active being. The authors of this study find this point in time to be ripe for discussions on integrated practices in terms of environmental design, on the basis of the synthetic approaches mentioned above.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.175-182
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• 2018

Knowledge of the optimum cultivation period for Chinese cabbage would help growers especially in spring in Korea. Growth and yield of Chinese cabbage in a temperature gradient chamber was evaluated for the growing periods of 64 days from three set of transplanting dates including March 6, March 20, and April 3 in 2017. Air temperature in the chamber was elevated step-by-step, by $2^{\circ}C$ above the ambient temperature. This increment was divided into three phases; i.e. low (ambient+$2^{\circ}C$, A), medium (ambient+$4^{\circ}C$, B), and high temperature (ambient+$6^{\circ}C$, C). The fresh weight of Chinese cabbage was greater under B and C conditions in the first period and A in the second period, which indicated that GDDs affected the fresh weight considerably. However, leaf growth (number, area, length, and width) did not differ by GDDs. Bolting appeared under A condition in the first period, which was caused by low temperature in the early growth stage. Soft rot was developed under C condition in the second period and all temperature conditions in the third period, which resulted from high temperature in the late stage. Fresh weight increased when GDDs ranged from 587 to 729. However, it decreased when GDDs > 729. The maximum expected yield (16.3 MT/10a) was attained for the growing period of 64 days from transplanting date during which GDDs reached 601. The GDDs for optimum cultivation ranged from 478-724 under which the yield was about 95% (15.5 MT/10a) of maximum fresh weight. Such an optimum condition for GDDs was validated at five main cultivation regions including Jindo, Haenam, Naju, Seosan, and Pyeongtaek in Korea. In these regions, GDDs ranged from 619-719. This suggested that the optimum GDDs for Chinese cabbage cultivation would range from 478-724, which would give the useful information to expect the cultivation periods for ensuring maximum yield.

• 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 Science Education
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• v.46 no.1
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• pp.93-108
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• 2022

The purpose of this study is to develop a STEAM program that can be used in the high school credit system to be fully implemented in 2025, and to examine its validity and effectiveness. The STEAM program analyzed the 2015 revised curriculum centering on science, technology, and engineering through the 2015 revised curriculum analysis, and then selected the five latest issues: hydrogen fuel, climate crisis, data science, appropriate technology, and barista. In accordance with this self-developed program development format (frame), it was developed for seven months through a process of group deliberation. The draft of the STEAM program for 29 sessions of five types, developed to indirectly experience the career path and occupation of high school students, was verified through consultation with 2 STEAM education experts. It was applied at five different high schools for a pilot implementation. As a result of the pilot application, it was confirmed that the students' STEAM attitude significantly improved in the post-test than the pre-test, and the students' high satisfaction with the program was confirmed. In addition, through an interview with the pilot application teacher, it was positively evaluated that 'the content and level of the program are suitable and through experience solving real-life problems, you can apply the content knowledge of related subjects and have an opportunity to experience careers.' Based on the results of the pilot application, the high school credit system STEAM program for students and teachers was finally completed in 29 lessons of five types. Through this study, the development and operation of the next-generation STEAM program that can be applied in the high school credit system should be actively developed, and a plan to improve teachers' professionalism so that the high school credit system can be established and operated properly for blended classes triggered by COVID-19. The necessity of design was suggested. This study is expected to be used as basic data for the development and operation of STEAM programs in the high school credit system, which will be fully implemented in 2025.

• 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.