• Journal of Korean Society on Water Environment
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• v.34 no.2
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• pp.183-201
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• 2018

The tolerance of Korean benthic macroinvertebrates to organic pollution has been analyzed since the early 1990s. However, considering the fact that there have been related studies carried out in some European countries since the early 20th century, the history of the research in Korea is very short and there is still much knowledge to supplement. We revised the saprobic valency, the saprobic value and the indicator weight value of 190 benthic macroinvertebrates taxa through the data of water quality and individual abundance collected from 7,086 sampling units in Korea from 2008 to 2014. The individual abundance of Uracanthella (Ephemeroptera) as a representative, one of the most common and abundant taxa in Korea, showed a typical lognormal distribution to 5-day biochemical oxygen demand (BOD5) concentration, and a normal distribution to the class interval of BOD5 concentration according to saprobic series. The value combining the mean individual abundance and the relative frequency of occurrence was a more efficient indicator value than that of each property alone. Benthic Macroinertebrates Index (BMI) was newly proposed as a modification of the saprobic index of Zelinka and Marvan (1961). BMI showed extremely significant correlation (determination coefficient $r^2$ > 0.6, n = 569 sites) with the concentration of BOD5, and the coefficient was a little higher than those of the previous indices. Until now, there has been very little research on the assessment of biological integrity of benthic macroinvertebrates community in Korea. While continuing researches into improve the reliability of BMI, it is necessary to develop multimetric indices for evaluating the integrity, including the composition of species and functional guilds, and the richness and diversity of the community.

• Korean Journal of Environment and Ecology
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• v.31 no.2
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• pp.230-251
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• 2017

According to the survey on higher fungi from 2009 to 2011 and also in 2015 in Byeonsanbando National Park, a total of 2 division, 6 class, 18 orders, 61 families, 157 genera and 323 species were observed. In case of Agaricales, there were 23 families, 67 genera and 153 species; Boletales, there were 6 families, 27 genera and 45 species; Russulales, there were 3 family, 4 genera and 40 species; Polyporales, there were 6 family, 21 genera, 28 species. Thus, most of them belonged to the following 4 orders: Agaricales, Russulales, Boletales and Polyporales. Dominant species belonged to Boletaceae (37 species), Russulaceae (36 species), Agaricaceae (28 species) and Amamtaceae (25 species). For the habitat environment, the ectomycorrhizal mushrooms were 40.2% (poisonous mushrooms, 46 species; edible & medicinal mushrooms, 51 species; unknown edible & poisonous mushrooms, 26 species), litter decomposing and wood rotting fungi 35.3%(poisonous mushrooms, 10 species; edible & medicinal mushrooms, 52 species; unknown edible & poisonous mushrooms, 46species), grounding Fungi 22.3%(poisonous mushrooms, 8 species; edible & medicinal mushrooms, 31 species; unknown edible & poisonous mushrooms, 29 species). Monthly, most of poisonous mushrooms, edible & medicinal mushrooms and unknown edible & poisonous mushrooms were found in July and August. In terms of altitude, the most species were observed at 1~99m and the populations dropped by a significant level at an altitude of 200m or higher. It seemed that the most diversified poisonous mushrooms, edible & medicinal mushrooms and unknown edible & poisonous mushrooms occurred at climate conditions with a mean air temperature at $24.0{\sim}25.9^{\circ}C$, the highest air temperature at $28.0{\sim}29.9^{\circ}C$, the lowest air temperature at $20.0{\sim}21.9^{\circ}C$, a relative humidity at 77.0~79.9% and a rainfall of 300.0~499.9mm.

• Journal of Distribution Science
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• v.9 no.3
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• pp.73-82
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• 2011

This study presents an effective and distinctive marketing strategy through the implementation of the aesthetic art marketing communication technique of storytelling. The reason applying art to marketing is effective is that it gives "class" and aesthetic beauty to the brand's image, which will lead to an increase in revenue and loyalty of consumers. The story stands in for the brand's subject of "desire." Luxury brand customers not only consume high-quality products, require the utmost in service, and value of the brand, they also appreciate the story the brand is telling. The story, combined with art, is called art marketing communication; it makes the brand more unique through its enhanced visual elements. The study discusses art collaboration, art exhibition, a transforming architecture project, art advertisement, a flagship store, and a human resource training center. Based on the "desire," I adopted the element and principle of storytelling. By visualizing the brand with a symbol, the company is able to relate to consumers' sentimentality. Through storytelling art marketing communication, and the strategy using relevance of brand and artist's popularity, the research shows efficient art marketing influences to the brand's image. The results of the research indicate that by using adequate art marketing communication that best reflects the identity and story of the luxury brand, it produces great results; the research also demonstrated, in various ways, that art marketing will succeed. The case showed the following outcomes. First, consumers have a tendency to choose a brand that is associated with an empathizing story. World renowned brands see through the market's "desires" for unique stories, and they also provide the ability to amuse consumers. The story in a product will become an important competitive element in future markets. Second, the art marketing communication applying a story rendered a brand with distinction. The most effective art marketing communications are art collaboration, art exhibition, locomotive architecture project, and others that are adopted as various means. Third, the brand's products were considered as an art piece, which led to not only strengthening the loyalty of consumers but also an increase in sales. In addition, the company could sustain a premium price for the goods sold. By adapting art to a brand's tradition, an innovative and creative new product provides consumer satisfaction, and producing goods in limited editions creates enthusiastic collectors. Fourth, this study suggests an abridged report, implication, limitation of the study, and directions for further research. Referring to the case for the adaptation of luxury brands, efficient art marketing strategies considering Korean company brand and efficiently study preceding Korean company brand art marketing strategy could be proposed.

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.167-178
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• 2024

In this study, we investigated the perceptions of science-related careers and career education among middle school science teachers. Sixty-four science teachers experienced in teaching unit 7 in the first year of middle school participated. The results of the study revealed that not only careers in science but also careers with science were found to be quite high when teachers were asked to provide examples of science-related careers. Jobs related to research/engineering, which are careers in science, comprised the highest proportion of teachers' answers, followed by jobs related to education/law/social welfare/police/firefighting/military, and health/medical, which are careers with science. However, the proportion of jobs mentioned related to installation/maintenance/production was extremely low. The skills required for science-related careers were mainly perceived to consist of tools for working and ways of working. The number of skills classified under living in the world was perceived to be extremely low across most careers, irrespective of career type. Most teachers only taught unit 7 for two to four sessions and devoted little time to science-related career education, even in general science classes. In the free semester system, a significant number of teachers responded that they provide science-related career education for more than 8 hours. Teachers mainly utilize lecture, discussion/debate, and self-study activities. Meanwhile, in the free semester system, the resource-based learning method was utilized at a high proportion compared to other class situations. Teachers generally made much use of media materials, with the use of textbooks and teacher guides found to be lower than expected. There were also cases of using materials supported by science museums or the Ministry of Education. Teachers preferred to implementing student-centered classes and utilizing various teaching and learning methods. Based on the above research results, discussions were proposed to improve teachers' perceptions of science-related careers and career education.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.47-66
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• 2023

The purpose of this study was to develop an educational smart app for classes by reconstructing some of the teaching-learning contents of the clothing preparation planning within the 'clothing preparation planning and selection' curriculum unit. To this end, a teaching-learning process plan was planned for the classes, a smart app was developed for classes, and feedback from home economics teachers and app development experts was received for the developed app. The main composition of the developed app consists of five steps. The first step is to set up a profile using a real photo, ZEPETO or Galaxy emoji, or iPhone Memoji. In the second step, students make a list of clothes by figuring out the types, quantities and conditions of their exisitng wardrobe items. Each piece of clothing is assigned an individual registration number, and stduents can take pictures of the front and back, along with describing key attributes such as type, color, season-appropriateness, purchase date, and current status. Step three guides students in deciding which garments to retain and which to discard. Building on the clothing inventory from the previous step, students classify items to keep and items to dispose of. In Step 4, Deciding How to Arrange Clothing, students decide how to arrange clothing by filling out an alternative scorecard. Through this process, students can learn in advance the subsection of resource management and self-reliance, laying the foundationa for future learning in 'Practice of Rational Consumption Life'. Lastly, in the fifth stage of determining the disposal method, this stage is to develop practical problem-oriented classes on how to dispose of the clothes to be discarded in the thirrd stage by exploring various disposal methods, engaging in group discussions, and sharing opinions. This study is meaningful as a case study as an attempt to develop a smart app for education by an instructor to align teaching plans and educational content with achievement standards for the class. In the future, upgrades will have to be made through user application.

• 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.