• Architectural research
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• v.13 no.1
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• pp.11-16
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• 2011

This article illuminates the relationship between the human being and the surrounding things by referring to the philosophy of Martin Heidegger and Maurice Merleau-Ponty. Criticizing our habitual approaches to apprehending 'what a thing is,' the two thinkers elucidate how 'what a thing is' can be understood only in conjunction with situations in the everyday and how humanity is joined with the qualities of the thing. In addition to the situated-ness of a thing, this article demonstrates the situated-ness of the human being, too, by referring to the notion of the horizon in the tradition of phenomenology. The last part of the paper discusses the basic premises of sustainability in reference to the situated-ness of both things and human beings. Framing natural things such as light as the alternative sources of energy propagandized in sustainability seems progressive. However, this attitude maintains fundamentally the same instrumental attitude we had towards nature, an attitude that has caused the current ecological crisis. By pointing this out, this article seeks to shape a ground for a broad spectrum of sustainability that embraces non-instrumental dimensions such as the practical, the ethical and the spiritual. This article also points out the limits of some of the currently available versions of ecology such as Shallow Ecology and Deep Ecology. In so doing, it seeks to lay out the parameters that any future version of sustainability and ecology needs to address.

• Korean Journal of Human Ecology
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• v.22 no.3
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• pp.485-492
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• 2013

The temperature changes of dumpling(mandu) during cooking process were examined and the effects of time-temperature and/or time-size interactions on internal temperature were studied. Mandu was purchased from local markets and classified by its weight(small, medium, and large). Boiling, steaming, pan frying, and deep fat frying were adopted. Internal temperature was measured with a food thermometer in every one minute. The internal temperature of mandu increased over time in every cooking process(p<0.05). After three minutes the internal temperature of mandu in boiling, pan frying, and deep fat frying reached over at $74^{\circ}C$, which is high enough temperature to kill the harmful bacteria, but not in steaming. The internal temperature of mandu was significantly affected by cooking time, size, and both in boiling, steaming, and deep fat frying(p<0.05). There were significant differences between the internal and surface temperatures of mandu in the cooking processes except pan frying in three minutes(p<0.05). The results of this study indicate three minutes' cooking of the mandu by boiling, pan frying, and deep fat frying is safe enough to eat. However, longer steaming time is needed in order to reach safe temperature. This study also indicates the cooking time and size of mandu appear to be major factors in determining the internal temperature achieved at $74^{\circ}C$. More research is needed to check time to reach a safe temperature in the cooking process of mandu by steaming.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Korean Journal of Human Ecology
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• v.2 no.1
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• pp.142-148
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• 1999

Tencel fabrics were treated with cellulase after mechanical prefibrillation treatment. SEM analysis was carried out to study morphological change of the treated fabric. The cellulase-treated Tencel fabrics were evaluated for weight loss and tensile strength. X-ray diffraction method, moisture regain, and K/S value were used to elucidate crystalline structural changes occurred by cellulase treatment. Degree of polymerization and copper number of the cellulase-treated fabrics were also measured to estimate effect of hydrolysis. SEM analysis indicated that with treatment of prefibrillation and cellulase, fibrils were produced and damage occurred deep into the fiber. Increases in concentration and time of cellulase treatment increased weight loss and decreased tensile strength retention of the treated fabrics. As cellulase hydrolysis progressed, degree of crystallinity, moisture regain and K/S value were not much changed. (Korean J Human Ecology 2(1) : 142∼148, 1999)

• Journal of Environmental Science International
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• v.19 no.5
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• pp.655-663
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• 2010

Today, the importance of environmental education is a matter we are all concerned about. The environment surrounding us, such as the air we breathe, the water we drink, and the soil plants grow in, is critical for our survival. Currently there is a lot of interest in environmental education at the elementary, middle, and high school levels. This is a result of the deep recognition of the importance of the environment. However the environmental education being conducted in schools is not yet at a satisfactory level. The practical issues, including an entrance exam-oriented atmosphere, student' and parents' lack of understanding, and teachers' lack of expertise in environmental education, interfere with the stability of environmental curriculum in the schools. Accordingly, we need to devise an alternative environmental curriculum due to the fact that it hasn't been included as a regular subject in the curriculum of many national schools. This study, carried out from April to December 2009, was an examination of the effect of volunteer work at the place of ecology experience on the environmental sensitivity & state-trait anxiety of the 61 Gifted Students. The students were divided into two groups. The test group consisted of 30 gifted students who did volunteer work at the place of ecology experience run by Ulsan Science High School, in Ulsan Metropolitan City. The control group consisted of the rest of the students. The following are the study results: First, the volunteer work at the place of ecology experience was influential in increasing the environmental sensitivity of the gifted students. Second, the volunteer work at the place of ecology experience was influential in decreasing the state anxiety of in gifted students. Third, the volunteer work at the place of ecology experience was influential in decreasing the trait anxiety of in gifted students. Fourth, the volunteer work at the place of ecology experience positively influenced not only the gifted students' view of environmental education, but also their overall character.

• Korean Journal of Ichthyology
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• v.24 no.1
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• pp.27-34
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• 2012

Population ecology of $Acheilognathus$ $macropterus$ was investigated at the part of the Bulgapcheon stream in Korea from March to November in 2006 and April in 2008. It mainly inhabited in slow water with sand and mud bottoms. The standard length of this population indicated that below 48 mm group is one year old, 48~58mm group is two years old, 58~64mm group is three years old and longer than 66 mm group is regarded over four years old. The sex ratio of the female to the male was 1:0.99. Spawning season from April to June with the water temperature in $15{\sim}20^{\circ}C$. The average number of eggs in ovary was $680{\pm}209$. The matured egg size was $1.92{\times}1.60mm$. Stomach contents were mainly phytoplanktons such as the genera Navicular, Cymbella, Fragilaria.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.41 no.2
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• pp.119-124
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• 2008

Reproductive ecology of the snow crab Chionoecetes opilio was investigated in terms of the reproductive organs, abdominal flap, fecundity, and maturity. Specimens were collected with gill nets and trawls from June 2002 to May 2003 in the eastern waters of Korea. The female and male C. opilio are distinguished only by the shape of the abdominal flap, which is triangular in males and circular in females. The gonads of female and male crabs are generally X-shaped. The male gonad is white, while the female gonad appears milk-white after spawning and then turns from light orange to dark orange with maturation. The female gonads matured from June, and mature and immature groups could be distinguished from December to February or March. Brooding eggs changed from bright orange to dark brown with formation of the compound eye immediately before hatching. Accordingly, the main spawning season is February and March. The minimum maturity carapace width of female crabs was 61.1 mm, and the mean fecundity is about 108,300 eggs.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.165-181
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• 2022

The fashion industry is creating innovative business models using artificial intelligence. To efficiently utilize artificial intelligence (AI), fashion data must be classified. Until now, such data have been classified focusing only on the objective properties of fashion products. Their subjective attributes, such as fashion brand sensibilities, are holistic and heuristic intuitions created by a combination of design elements. This study aims to improve the performance of collaborative filtering in the fashion industry by extracting fashion brand sensibility using computer vision technology. The image data set of fashion brand sensibility consists of high-end fashion brand photos that share sensibilities and communicate well in fashion. About 26,000 fashion photos of 11 high-end fashion brand sensibility labels have been collected from the 16FW to 21SS runway and 50 years of US Vogue magazines beginning from 1971. We use EfficientNet-B1 to establish the main architecture and fine-tune the network with ImageNet-ILSVRC. After training fashion brand sensibilities through deep learning, the proposed model achieved an F-1 score of 74% on accuracy tests. Furthermore, as a result of comparing AI machine and human experts, the proposed model is expected to be expanded to mass fashion brands.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.