• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1283-1297
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• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• 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.

• Journal of Animal Science and Technology
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• v.54 no.1
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• pp.1-8
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• 2012

This study aimed to investigate the single nucleotide polymorphisms (SNPs) of the porcine MC4R gene and validate the effect of the MC4R genotype for marker assisted selection (MAS). Six amplicons were produced to analyze the entire base sequences of the porcine MC4R gene and six SNPs were detected (c.-780C>G, c.-135C>T, c.175C>T-Leu59Leu, c.707A>G-Arg236His, c.892A>G-Asp298Asn, and c.*430A>T). Linkage disequilibrium (LD) of the six SNPs was analyzed by performing haploid analysis. There was a perfect linkage disequilibrium in c.-780C>G, c.-135C>T, c.175C>T-Leu59Leu, c.707A>G-Arg236His, and c.*430A>T. Only the c.892A>G (Asp298Asn) SNP showed a very low LD with an $r^2$ value of 0.028 and the D' value of 0.348. As a result, the two SNPs-c.707A>G (Arg236His) and c.892A>G (Asp298Asn)-were selected to extract the genotype frequencies from the 5 pig breeds by using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) genotype analysis method. The SNP frequency of c.707A>G (Arg236His) indicated the presence of the A (His) allele only in Yorkshire, while the G allele was fixed in the KNP, Landrace, Berkshire, and Duroc. Association analysis was carried out in 484 pigs with the c.707A>G (Arg236His) SNP and the meat quality traits of four different pig cross populations: a significant association was noted in crude fat, sirloin moisture, meat color, and the degree of red and yellow coloration. The frequency of the c.892A>G(Asp298Asn) SNP genotype varied among the breeds; while Duroc showed the highest frequency of the A (Asn) allele, KNP showed the highest frequency of the G (Asp) allele. Association analysis was carried out in 1126 pigs with the c.892A>G (Asp298Asn) SNP and the meat quality traits of four pig populations: a highly significant linkage was noted in the back-fat thickness (P<0.002). It was found that the back-fat thickness was higher in individuals with the AA genotype than in those with the AG or GG genotype. Thus, in this study, we verified that the c.892A>G (Asp298Asn) SNP in the pig MC4R gene has a sufficient effect as a gene marker for MAS in Korean pork industry.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.33 no.3
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• pp.38-49
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• 2015

This study aims to contemplated spatial recognition in Soswaewon Garden through garden visitors poetry. It was content analysis in poetry and extract frequency from words based on relationship of author. The results were as follows. First, relationship of authors who wrote Soswaewon Garden poetry was formed in companionship. In the Yang, San-Bo(梁山甫), poetry was written by Song, Soon(宋純), Kim, Un-Geo(金彦据) and Kim, In-Hu(金麟厚) as the central figure. Especially Kim, In-Hu was playing an important role in Soswaewon Garden poetry. He was wrote many of poetry and keep friends with Yang, Ja-Jeong(梁子渟) too. In the Yang, Ja-Jung, relationship of previous generation was sustained. In addition, Ko, Gyeong-Myeong(高敬命) and Kim, Seong-Won and Jeong, Chul(鄭澈) is more closely related than others. Because blood relationship by marriage. In the Yang, Jin-Tae(梁晋泰), He formed a relationship with a celebrity and attend to international activity. Since then Yang, Jin-Tae periord, Yang, Gyeong-Ji(梁敬之) and Yang, Chae-Ji(梁采之) formed relationship of previous generation was sustained. And surrounding people was written poetry as hold a banquet. Second, plant and ornament is a popular object for writing poetry. Bamboo grove and Fine tree with a high frequency of plant element in poetry. Bamboo grove is a typical species of trees in Soswaewon Garden. It was enclosed the Soswaewon Garden. Fine tree was often used target of poetry as a single tree. Meanwhile, ornament of the wall has been used most frequently. Descendants wrote a poem to see it because Kim, In-Hu's poetry was left. This phenomenon is involves respect for the ancient sages with high frequency. In addition, behavior of viewing the landscape was mainly appeared. Third, spatial recognition of Soswaewon Garden can be divided into landscape cognition, behavior cognition and emotional cognition. In a aspect of landscape cognition, early Soswaewon Garden was recognized as a pavilion. That was used garden name to 'Soswaewon Garden' since Yang, Ja-Jung's period. That is to say, Soswaewon Garden expanded from pavilion area surrounded by trees into the whole appearance is equipped garden area. Behavior cognition was consisting drink and enjoys a landscape. In the Yang, San-Bo, authors enjoyed drinking and viewing a landscape besides walking, writing poetry, viewing the moon. But after Yang, San-Bo's period other than drinking and enjoy a landscape has appeared a low frequency. These results were changed from internal place to blood relationship into external place to companionship. In the Yang, San-Bo's emotional cognition was sorrow and yearning about leave to Soswaewon Garden with an idly atmosphere. Pleasant emotion was sustained all generation. And emotion of respect for the ancient sages was appeared since Yang, Cheon-un.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.355-380
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• 2004

I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in