• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.12
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• pp.3235-3245
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• 1996

A DDFS(Direct Digital Frequency Synthesizer) used in spread spectrum communication systems must need fast switching speed, high resolution(the step size of the synthesizer), small size and low power. The chip has been designed with four parallel sine look-up table to achieve four times throughput of a single DDFS. To achieve a high processing speed DDFS chip, a 24-bit pipelined CMOS technique has been applied to the phase accumulator design. To reduce the size of the ROM, each sine ROM of the DDFS is stored 0-.pi./2 sine wave data by taking advantage of the fact that only one quadrant of the sine needs to be stored, since the sine the sine has symmetric property. And the 8 bit of phase accumulator's output are used as ROM addresses, and the 2 MSBs control the quadrants to synthesis the sine wave. To compensate the spectrum purity ty phase truncation, the DDFS use a noise shaper that structure like a phase accumlator. The system input clock is divided clock, 1/2*clock, and 1/4*clock. and the system use a low frequency(1/4*clock) except MUX block, so reduce the power consumption. A 107MHz DDFS(Direct Digital Frequency Synthesizer) implemented using 0.8.mu.m CMOS gate array technologies is presented. The synthesizer covers a bandwidth from DC to 26.5MHz in steps of 1.48Hz with a switching speed of 0.5.mu.s and a turing latency of 55 clock cycles. The DDFS synthesizes 10 bit sine waveforms with a spectral purity of -65dBc. Power consumption is 276.5mW at 40MHz and 5V.

• Applied Chemistry for Engineering
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• v.7 no.3
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• pp.543-553
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• 1996

Oxygen reduction in an alkaline fuel cell was studied by using perovskite type oxides as an oxygen electrode catalyst. The high surface area catalysts were prepared by malic acid method and had a formula of $La_{0.6}Sr_{0.4}Co_{1-x}Fe_xO_3$(x=0.00, 0.01, 0.10, 0.20, 0.35 and 0.50). From the result of XRD pattern and specific surface area due to the amount of Fe substitution and the consumption of ammonia-water, the complex formation of Fe ion with $NH_3$ was the main factor for both the phase stability of perovskite and the increase of specific surface area. Multi-step calcination was necessary to give a single phase of perovskite in catalyst precursor. The crystal structure of the catalysts was simple cubic perovskite, which was verified from the XRD patterns of the catalysts. The activity of oxygen reduction was monitored by the techniques of cyclic voltammetry, static voltage-current method, and current interruption method. The activity(current density) of oxygen reduction showed its minimum at x=0.01 and its maximum between 0.20 and 0.35 of x-value in $La_{0.6}Sr_{0.4}Co_{1-x}Fe_xO_3$. This tendency was independent of the change of surface area.

• Korean Journal of Microbiology
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• v.44 no.4
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• pp.282-288
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• 2008

Tissue ischemia resulting from the constriction or obstruction of blood vessels leads to an illness that may affect many organs including the heart, brain, and legs. In recent years, considerable progress has been made in the field of therapeutic angiogenesis and the new approaches are expected to cure those "no-option patients" who are unsuited to conventional therapies. Although single angiogenic growth factor may be successful in inducing angiogenesis, combination of multiple growth factors is increasingly sought these days to augment the therapeutic responses. This trend is proper in light of the fact that blood vessel formation is a complex and multi-step process that requires the actions of many different factors. To meet the growing need for functionally significant blood flow recovery in the ischemic tissues, a novel strategy that can provide concerted actions of multiple factors is required. One way to achieve such a goal is to use a transcription factor that can orchestrate the expression of multiple target genes in the ischemic region and thus induce significant level of angiogenesis. Here, a putative transcription factor, cardiac ankyrin repeat protein (CARP), was evaluated in adenoviral vector context for angiogenic activity in human umbilical vein endothelial cells. The results indicated significant increase in proliferation, capillary-like structure formation, and induction of vascular endothelial growth factor, a typical angiogenic gene. Taken together, these results suggest that CARP represents itself as a novel target for therapeutic angiogenesis and warrants further investigation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.12
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• pp.1327-1336
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• 2012

In this paper, the dual-faced monopole antenna, which is arranged by numerous rectangular ring patches in sequence for the multi-bands is proposed. The ring type structure of the patch can be increased the bandwidth. Therefore the bandwidth and beam width are improved by using multiple arrayed patches. When the ring type patches are inserted serially, the resonance frequencies are occurred by the current flow from the first ring patch. It is possible because the gap between the patches is very narrow. In addition, if the patches are composed on the same plane as the feed-line, fabrication could be very difficult because the gap between the patches is extremely narrow. The thickness and permittivity of the antenna, moreover, are very important parameters because both sides of the substrate are used. We finally found the optimal thickness and permittivity to generate the coupling effect by simulation. All patches are consisted of 4-steps which the patch size was decreased 85 % by each step. In conclusion, the resonant frequency bands are 1.75~2.6 GHz(850 MHz), 3.24~3.46 GHz(220 MHz), 3.8~4.0 GHz(200 MHz), and 4.4~4.9 GHz(500 MHz).

• Animal Bioscience
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• v.35 no.11
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• pp.1787-1799
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• 2022

Objective: Choline deficiency, one main trigger for nonalcoholic fatty liver disease (NAFLD), is closely related to lipid metabolism disorder. Previous study in a choline-deficient model has largely focused on gene expression rather than gene structure, especially sparse are studies regarding to alternative splicing (AS). In modern life science research, primary hepatocytes culture technology facilitates such studies, which can accurately imitate liver activity in vitro and show unique superiority. Whereas limitations to traditional hepatocytes culture technology exist in terms of efficiency and operability. This study pursued an optimization culture method for duck primary hepatocytes to explore AS in choline-deficient model. Methods: We performed an optimization culture method for duck primary hepatocytes with multi-step digestion procedure from Pekin duck embryos. Subsequently a NAFLD model was constructed with choline-free medium. RNA-seq and further analysis by rMATS were performed to identify AS events alterations in choline-deficency duck primary hepatocytes. Results: The results showed E13 (embryonic day 13) to E15 is suitable to obtain hepatocytes, and the viability reached over 95% by trypan blue exclusion assay. Primary hepatocyte retained their biological function as well identified by Periodic Acid-Schiff staining method and Glucose-6-phosphate dehydrogenase activity assay, respectively. Meanwhile, genes of alb and afp and specific protein of albumin were detected to verify cultured hepatocytes. Immunofluorescence was used to evaluate purity of hepatocytes, presenting up to 90%. On this base, choline-deficient model was constructed and displayed significantly increase of intracellular triglyceride and cholesterol as reported previously. Intriguingly, our data suggested that AS events in choline-deficient model were implicated in pivotal biological processes as an aberrant transcriptional regulator, of which 16 genes were involved in lipid metabolism and highly enriched in glycerophospholipid metabolism. Conclusion: An effective and rapid protocol for obtaining duck primary hepatocytes was established, by which our findings manifested choline deficiency could induce the accumulation of lipid and result in aberrant AS events in hepatocytes, providing a novel insight into various AS in the metabolism role of choline.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.191-204
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• 2010

Recently, the CO2 emission and energy consumption have become critical global issues to decide the future of nations. Especially, the spread of IT products and the increased use of internet and web applications result in the energy consumption and CO2 emission of IT industry though information technologies drive global economic growth. EU, the United States, Japan and other developed countries are using IT related environmental regulations such as WEEE(Waste Electrical and Electronic Equipment), RoHS(Restriction of the use of Certain Hazardous Substance), REACH(Registration, Evaluation, Authorization and Restriction of CHemicals) and EuP(Energy using Product), and have established systematic green business/IT strategies to enhance the competitiveness of IT industry. For example, the Japan government proposed the "Green IT initiative" for being compatible with economic growth and environmental protection. Not only energy saving technologies but energy saving systems have been developed for accomplishing sustainable development. Korea's CO2 emission and energy consumption continuously have grown at comparatively high rates. They are related to its industrial structure depending on high energy-consuming industries such as iron and steel Industry, automotive industry, shipbuilding industry, semiconductor industry, and so on. In particular, export proportion of IT manufacturing is quite high in Korea. For example, the global market share of the semiconductor such as DRAM was about 80% in 2008. Accordingly, Korea needs to establish a systematic strategy to respond to the global environmental regulations and to maintain competitiveness in the IT industry. However, green competitiveness of Korea ranked 11th among 15 major countries and R&D budget for green technology is not large enough to develop energy-saving technologies for infrastructure and value chain of low-carbon society though that grows at high rates. Moreover, there are no concrete action plans in Korea. This research aims to deduce the priorities of the Korean green business/IT strategies to use multi attribute weighted average method. We selected a panel of 19 experts who work at the green business related firms such as HP, IBM, Fujitsu and so on, and selected six assessment indices such as the urgency of the technology development, the technology gap between Korea and the developed countries, the effect of import substitution, the spillover effect of technology, the market growth, and the export potential of the package or stand-alone products by existing literature review. We submitted questionnaires at approximately weekly intervals to them for priorities of the green business/IT strategies. The strategies broadly classify as follows. The first strategy which consists of the green business/IT policy and standardization, process and performance management and IT industry and legislative alignment relates to government's role in the green economy. The second strategy relates to IT to support environment sustainability such as the travel and ways of working management, printer output and recycling, intelligent building, printer rationalization and collaboration and connectivity. The last strategy relates to green IT systems, services and usage such as the data center consolidation and energy management, hardware recycle decommission, server and storage virtualization, device power management, and service supplier management. All the questionnaires were assessed via a five-point Likert scale ranging from "very little" to "very large." Our findings show that the IT to support environment sustainability is prior to the other strategies. In detail, the green business /IT policy and standardization is the most important in the government's role. The strategies of intelligent building and the travel and ways of working management are prior to the others for supporting environment sustainability. Finally, the strategies for the data center consolidation and energy management and server and storage virtualization have the huge influence for green IT systems, services and usage This research results the following implications. The amount of energy consumption and CO2 emissions of IT equipment including electrical business equipment will need to be clearly indicated in order to manage the effect of green business/IT strategy. And it is necessary to develop tools that measure the performance of green business/IT by each step. Additionally, intelligent building could grow up in energy-saving, growth of low carbon and related industries together. It is necessary to expand the affect of virtualization though adjusting and controlling the relationship between the management teams.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.