• The KIPS Transactions:PartA
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• v.11A no.2
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• pp.195-202
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• 2004

This paper introduces the design or parallel Pipeline high-speed analog-to-digital converter(ADC) for the high-resolution video applications which require very precise sampling. The overall architecture of the ADC consists of 4-channel parallel time-interleaved 10-bit pipeline ADC structure a]lowing 200MSample/s sampling speed which corresponds to 4-times improvement in sampling speed per channel. Key building blocks are composed of the front-end sample-and-hold amplifier(SHA), the dynamic comparator and the 2-stage full differential operational amplifier. The 1-bit DAC, comparator and gain-2 amplifier are used internally in each stage and they were integrated into single switched capacitor architecture allowing high speed operation as well as low power consumption. In this work, the gain of operational amplifier was enhanced significantly using negative resistance element. In the ADC, a delay line Is designed for each stage using D-flip flops to align the bit signals and minimize the timing error in the conversion. The converter has the power dissipation of 280㎽ at 3.3V power supply. Measured performance includes DNL and INL of +0.7/-0.6LSB, +0.9/-0.3LSB.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.87-97
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• 2016

This work proposes a time-shared 10b DAC based on a two-step resistor string to minimize the effective area of a DAC channel for driving each AMOLED display column. The proposed DAC shows a lower effective DAC area per unit column driver and a faster conversion speed than the conventional DACs by employing a time-shared DEMUX and a ROM-based two-step decoder of 6b and 4b in the first and second resistor string. In the second-stage 4b floating resistor string, a simple current source rather than a unity-gain buffer decreases the loading effect and chip area of a DAC channel and eliminates offset mismatch between channels caused by buffer amplifiers. The proposed 1-to-24 DEMUX enables a single DAC channel to drive 24 columns sequentially with a single-phase clock and a 5b binary counter. A 0.9pF sampling capacitor and a small-sized source follower in the input stage of each column-driving buffer amplifier decrease the effect due to channel charge injection and improve the output settling accuracy of the buffer amplifier while using the top-plate sampling scheme in the proposed DAC. The proposed DAC in a $0.18{\mu}m$ CMOS shows a signal settling time of 62.5ns during code transitions from '$000_{16}$' to '$3FF_{16}$'. The prototype DAC occupies a unit channel area of $0.058mm^2$ and an effective unit channel area of $0.002mm^2$ while consuming 6.08mW with analog and digital power supplies of 3.3V and 1.8V, respectively.

• Proceedings of the Korean DIstribution Association Conference
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• 2003.02a
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• pp.123-166
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• 2003

Marketing channel is recognized as one of the society systems which have the character of functional organization. These organizations are related to each other for specialized and cooperative work. Channel members in distribution channel are striving to accomplish exchange through reciprocal action. Thus channel members exercise their power to take better position in exchange. There will be struggling between members about satisfaction and conflict during this power exercise. Now a days, buyers use more harsh power as large retail firms are increasing. This phenomenon is occurring in the distribution channel. However, there will be different phenomenon in case of agricultural products. Not like industrial product suppliers, agricultural product suppliers have various supply channels and many agricultural products are seasonal. It has also unstable amount supplies. There should be differentiated marketing in agricultural products. Relatively weaker powered suppliers have to strengthen comparative factors and also have to be technically specialized through assessed experience in order to establish strong product sales chain. Making a brand of agricultural product would be also a good idea to increase the product comparability. Channel members need to be recognized their specialized functions in order to make balanced distribution channel. There have to be conversion of concept of relation between suppliers and buyers from subordinate relationship to cooperative relationship.

• Proceedings of the Korean Ceranic Society Conference
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• 2005.06a
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• pp.1-112
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• 2005

This report is results of a research on recent R&D trends in electroceramics, mainly focusing on the papers submitted to the organizing committee of the International Conference on Electroceramics 2005 (ICE-2005) which was held at Seoul on 12-15 June 2005. About 380 electroceramics researchers attended at the ICE-2005 from 17 countries including Korea, presenting and discussing their recent results. Therefore, we can easily understand the recent research trends in the field of electroceramics by analyses of the subject and contents of the submitted papers. In addition to the analyses of the papers submitted to the ICE-2005, we also collected some informations about domestic and international research trends to help readers understand this report easily. We analysed the R&D trends on the basis of four main categories, that is, informatics electroceramics, energy and environment ceramics, processing and characterization of electroceramics, and emerging fields of electroceramics. Each main category has several sub-categories again. The informatics ceramics category includes integrated dielectrics and ferroelectrics, oxide and nitride semiconductors, photonic and optoelectronic devices, multilayer electronic ceramics and devices, microwave dielectrics and high frequency devices, and piezoelectric and MEMS applications. The energy and environment ceramics category has four sub-categories, that is, rechargable battery, hydrogen storage, fuel cells, and advanced energy conversion concepts. In the processing and characterization category, there exist domain, strain, and epitaxial dynamics and engineering sub-category, innovative processing and synthesis sub-category, nanostructured materials and nanotechnology sub- category, single crystal growth and characterization sub-category, theory and modeling sub-category. Nanocrystalline electroceramics, electroceramics for smart sensors, and bioceramics sub-categories are included to the emerging fields category. We hope that this report give an opportunity to understand the international research trend, not only to Korean ceramics researchers but also to science and technology policy researchers.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• 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.