• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.651-658
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• 2021

Because bolts with anti-loosening coatings are used mainly for joining safety-related components in automobiles, accurate automatic screening of these coatings is essential to detect defects efficiently. The performance of the convolutional neural network (CNN) used in a previous study [Identification of bolt coating defects using CNN and Grad-CAM] increased with increasing number of data for the analysis of image patterns and characteristics. On the other hand, obtaining the necessary amount of data for coated bolts is difficult, making training time-consuming. In this paper, resorting to the same VGG16 model as in a previous study, transfer learning was applied to decrease the training time and achieve the same or better accuracy with fewer data. The classifier was trained, considering the number of training data for this study and its similarity with ImageNet data. In conjunction with the fully connected layer, the highest accuracy was achieved (95%). To enhance the performance further, the last convolution layer and the classifier were fine-tuned, which resulted in a 2% increase in accuracy (97%). This shows that the learning time can be reduced by transfer learning and fine-tuning while maintaining a high screening accuracy.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.4
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• pp.16-28
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• 2009

Stage art basically builds upon the essence of "seeing," and at the same time, possesses relativity in showing and seeing. Stage lighting uses artificial light to solve the essence of "seeing", which is the foundation of stage art, and coming into the modern age, its role has been enhanced to an important medium for visual expression in stage art, due to the lighting tools that developed at a rapid pace along with the discovery of electricity, as well as the development of optics. Therefore, not only does lighting use a medium known as light in a field of stage art that gives mental and emotional inspiration to the audience, and aesthetically expresses time and space. In other words, stage lighting is a complex function of light engineering (technology and science) and aesthetic sense (feeling and art). This study aims to do research on methods for the visualization of stage space through lighting, mainly focused on dancing. I have studied the basics of stage lighting, its relations with other fields of stage art, and the functions and characteristics of lighting. Results show that lighting could be used to maximize the visualization of dancing and emphasizing the artistic growth of lighting and its ability to aesthetically express and I came to the following conclusions. First, lighting uses the forms and directions of light that various tools are able to produce in order to visualize the space on stage, and can maximally express the image that the work seeks. Second, it is possible to use lighting, through the movement of light, as a visual representation of the configuration of space in dancing works. Third, through the expression of visual and spatial aspects created by light, the work's dramatic catharsis can bring out mental and emotional feelings form the audience. Fourth, lighting can be seen not as a supporting role, but as an original visual design. To conclude, in order for lighting to be freed form the simple function of "lighting up the stage," which a majority of people think is common knowledge, and grow as one area in art, lighting designers must understand the intentions of the choreographer and the work with creativity and artistry they must consider light and color as an aesthetic language in order to heighten the effects of the work and allow it to partake as one element of work creation, so that lighting will be treated as a form of art.

• Journal of Korean Home Economics Education Association
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• v.25 no.3
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• pp.19-38
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• 2013

This study focuses on the differences in general self-concept, academic self-concept, significant others self-concept and emotional-physical self-concept in relation to appearance management behavior. It goes on to show that appearance management behaviors such as styles in clothing, makeup, skin care, hair care, cosmetic surgery and body shaping, weight control management are strongly influenced by self-concept. Therefore, this study was carried out with the aim of providing basic understanding and information on the appearance management behavior of middle school students. It was also done in an effort to find ways of improving the self-concept of students through education as a part of the domestic science curriculum. The results obtained in this study are as follows: On average, the middle school students who took part in this study showed low self-concept and appearance management behavior which indicates a negative image of themselves. This suggests that efforts need to be made so that students can see themselves in a positive way and improve their self-concept through appearance management behavior. Middle school students with a positive self-concept try to present themselves by keeping their skin clean and their hair attractive. They express their self-esteem and personality through fashion and by keeping and maintaining their clothing, shoes and bags. They also tend to show a positive attitude towards their studies and are more likely to understand and get along with others. The students who showed positive attitudes towards their bodies and emotions have a higher interest in clothing and try to express the image that they want for themselves. They are also less likely to change their bodies unnaturally through cosmetic surgery and body shaping. Appropriate appearance management behavior can help middle school students see themselves in a more positive way.

• Cartoon and Animation Studies
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• s.45
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• pp.147-175
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• 2016

This thesis aims to research ways for the regional balanced development of cartoon creation & production environment being centered in metropolitan area in Korean cartoon industry which has rapidly changed since 2013. As a cartoon can not only be produced with relative lower production cost comparing to those of other cultural contents industries, but also can be produced only if the minimal requirements for cartoon production is prepared, so the cartoon is a field that the decentralization can be accomplished very easily. Currently, most cartoon-relevant companies and cartoon promotion institutions are located in Seoul an Bucheon, etc. However, cartoon artists live nationwide, and even cartoon artists producing their works abroad are reached to a significant number. In some regions like Daejeon, Busan, Suncheon and Gyeongbuk (Gyeongsangbuk-do), there have been appeared full-scale movement to construct regional cartoon creation & production centers since 2015. This thesis aimed to investigate each region's movement to construct cartoon creation & production center with oversea cases, and to check how such movement could be balanced and harmonized with each region's unique features. First of all, this thesis analyzed the status quo of government's policy nurturing the cartoon industry. Korean government's cartoon-promotion policy around the axis of the Cartoon Industry's Mid.Long-Term Development Plan has been developed around the Korea Creative Content Agency and the Korea Manwha(cartoon) Contents Agency in Bucheon, but as the webtoon industry has rapidly grown up, the necessity for building a cartoon promotion institution in each region has been raised since 2015. With the establishment of 4th Cartoon Industry Mid.Long-Term Development Plan to be executed from 2019, it seems that full-scaled support framework for cartoon regional balanced development should be occupied. For the case of foreign countries, cartoon promotion institutions and relevant events have been developed around regions from early times like San-Diego, USA(Comicon), Angouleme, France(National Image Center), Kyoto (Cartoon Museum), Sakaiminato(Misuki Sigeru Road), Japan gave a lot of implications. In the section of conclusion, this study aimed to suggest the importance of and necessity for establishing a cartoon creation & production center in each region appropriately for the region's identity and characteristics with specific plans. Based on that, this thesis aimed to suggest a vision for cartoon & webtoon industry that regional creation & production system can be settled almost only in the cultural contents industry.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1021-1028
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• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Korean Journal of Environmental Biology
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• v.39 no.1
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• pp.119-135
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• 2021

Korea has stepped up efforts to investigate and catalog its flora and fauna to conserve the biodiversity of the Korean Peninsula and secure biological resources since the ratification of the Convention on Biological Diversity (CBD) in 1992 and the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits (ABS) in 2010. Thus, after its establishment in 2007, the National Institute of Biological Resources (NIBR) of the Ministry of Environment of Korea initiated a project called the Korean Indigenous Species Investigation Project to investigate indigenous species on the Korean Peninsula. For 15 years since its beginning in 2006, this project has been carried out in five phases, Phase 1 from 2006-2008, Phase 2 from 2009-2011, Phase 3 from 2012-2014, Phase 4 from 2015-2017, and Phase 5 from 2018-2020. Before this project, in 2006, the number of indigenous species surveyed was 29,916. The figure was cumulatively aggregated at the end of each phase as 33,253 species for Phase 1 (2008), 38,011 species for Phase 2 (2011), 42,756 species for Phase 3 (2014), 49,027 species for Phase 4 (2017), and 54,428 species for Phase 5(2020). The number of indigenous species surveyed grew rapidly, showing an approximately 1.8-fold increase as the project progressed. These statistics showed an annual average of 2,320 newly recorded species during the project period. Among the recorded species, a total of 5,242 new species were reported in scientific publications, a great scientific achievement. During this project period, newly recorded species on the Korean Peninsula were identified using the recent taxonomic classifications as follows: 4,440 insect species (including 988 new species), 4,333 invertebrate species except for insects (including 1,492 new species), 98 vertebrate species (fish) (including nine new species), 309 plant species (including 176 vascular plant species, 133 bryophyte species, and 39 new species), 1,916 algae species (including 178 new species), 1,716 fungi and lichen species(including 309 new species), and 4,812 prokaryotic species (including 2,226 new species). The number of collected biological specimens in each phase was aggregated as follows: 247,226 for Phase 1 (2008), 207,827 for Phase 2 (2011), 287,133 for Phase 3 (2014), 244,920 for Phase 4(2017), and 144,333 for Phase 5(2020). A total of 1,131,439 specimens were obtained with an annual average of 75,429. More specifically, 281,054 insect specimens, 194,667 invertebrate specimens (except for insects), 40,100 fish specimens, 378,251 plant specimens, 140,490 algae specimens, 61,695 fungi specimens, and 35,182 prokaryotic specimens were collected. The cumulative number of researchers, which were nearly all professional taxonomists and graduate students majoring in taxonomy across the country, involved in this project was around 5,000, with an annual average of 395. The number of researchers/assistant researchers or mainly graduate students participating in Phase 1 was 597/268; 522/191 in Phase 2; 939/292 in Phase 3; 575/852 in Phase 4; and 601/1,097 in Phase 5. During this project period, 3,488 papers were published in major scientific journals. Of these, 2,320 papers were published in domestic journals and 1,168 papers were published in Science Citation Index(SCI) journals. During the project period, a total of 83.3 billion won (annual average of 5.5 billion won) or approximately US $75 million (annual average of US $5 million) was invested in investigating indigenous species and collecting specimens. This project was a large-scale research study led by the Korean government. It is considered to be a successful example of Korea's compressed development as it attracted almost all of the taxonomists in Korea and made remarkable achievements with a massive budget in a short time. The results from this project led to the National List of Species of Korea, where all species were organized by taxonomic classification. Information regarding the National List of Species of Korea is available to experts, students, and the general public (https://species.nibr.go.kr/index.do). The information, including descriptions, DNA sequences, habitats, distributions, ecological aspects, images, and multimedia, has been digitized, making contributions to scientific advancement in research fields such as phylogenetics and evolution. The species information also serves as a basis for projects aimed at species distribution and biological monitoring such as climate-sensitive biological indicator species. Moreover, the species information helps bio-industries search for useful biological resources. The most meaningful achievement of this project can be in providing support for nurturing young taxonomists like graduate students. This project has continued for the past 15 years and is still ongoing. Efforts to address issues, including species misidentification and invalid synonyms, still have to be made to enhance taxonomic research. Research needs to be conducted to investigate another 50,000 species out of the estimated 100,000 indigenous species on the Korean Peninsula.