• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.341-351
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• 2003

Digital image watermarking is the method that can protect the copyright of the image by embedding copyright information, which is called watermark. Watermarking must have robustness to intentional or unintentional data changing, called attack. The conventional watermarking schemes are robust to waveform attacks such as image compression, filtering etc. However, they are vulnerable to geometrical attacks such as rotation, scaling, translation, and cropping. Accordingly, this paper proposes new watermarking scheme that is robust to geometrical attacks by using invariant centroid. Invariant centroid is the gravity center of a central area in a gray scale image that remains unchanged even when the image is attacked by RST including cropping and proposed scheme uses invariant centroids of original and inverted image as the template. To make geometrically invariant domain, template and angle compensated Log -Polar Map(LPM) is used. Then Discrete Cosine Transform(DCT) is performed and the watermark is embedded into the DCT coefficients. Futhermore, to prevent a watermarked image from degrading due to interpolation during coordinate system conversion, only the image of the watermark signal is extracted and added to the original image. Experimental results show that the proposed scheme is especially robust to RST attacks including cropping.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.183-200
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• 2009

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content-based filtering. Content-based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well.known department stores in Korea, is used.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.151-166
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• 2020

A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.451-454
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• 2000

Invasive methode and Non-invasive methode are used in blood pressure measurement. The Invasive methode can Set the correct measured blood pressure but, it has patient feels uncomfortable. So most of cases use Non-invasive methode. The Oscillometric method is commonly apply to modem electric sphygmomanometer and using various algorithm. In this paper describe about a algorithm it control and to determinate the cuff pressure, and filtering that data for measure the blood pressure. The communicating with personal computer can pressure deflation is by Solenoid valve and it uses RS-232 system in packet communication. The main using algorithm for blood pressure measurements are maximum amplitude algorithm and oscillometric algorithm. MAA(maximum amplitude algorithm) has various measured oscillation it depend on patient's age, height, weight and arm circumference size. In this paper, 1 studied the various measured oscillation apply to characteristic ratio and can get the result of systolic blood pressure, diastolic blood pressure, mean blood pressure. It was not used same ratio to measuring oscillation. In the MAA(maximum amplitude algorithm), we hope for reduce the difference with the real blood pressure and the measured blood pressure, when it applied with various specific ratio.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.479-494
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• 2000

Although the proliferation of real-time multimedia services over the Internet might indicate its successfulness in dealing with heterogeneous environments, it is obvious, on the other hand, that the internet now has to cope with a flood of multimedia data which consumes most of network communication channels due to a great deal of video or audio streams. Therefore, for the purpose of an efficient and appropriate utilization of network resources, it requires to develop and deploy a new scalable transmission technique n consideration of respective network environment and individual clients computing power. Also, we can eliminate the waste effects of storage device and data transmission overhead in that the same video stream duplicated according to QoS. The purpose of this paper is to develop a technology that can adjust the amount of data transmitted as an MPEG video stream according to its given communication bandwidth, and technique that can reflect dynamic bandwidth while playing a video stream. For this purpose, we introduce a media scalable media decomposer working on server side, and a scalable media composer working o n a client side, and then propose a scalable transmission method and a media sender and a media receiver in consideration of dynamic QoS. Those methods proposed her can facilitate an effective use of network resources, and provide multimedia MPEG video services in real-time with respect to individual client computing environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.249-257
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• 2017

Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.525-531
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• 2018

This paper proposes a bi-component dye, including the unripe fruit of Diospyros kaki THUNB and Ecklonia cava, to substitute for traditional persimmon dyeing because fabrics dyed with persimmon juice become stiffer and natural persimmon is insufficient for dyeing. This study examined the color difference and fabric stiffness depending on the ratio of Ecklonia cava for in a one-bath dye solution with Diospyros kaki THUNB and showed that 6% of Ecklonia cava in the bi-component dye was the optimum for decreasing the fabric stiffness. Based on these results, a bi-component dye constituting of 94% Diospyros kaki THUNB and 6% Ecklonia cava was prepared. The particle size was found to be smaller than both single dyes and it maintained a similar amount of Catechin to Diospyros kaki THUNB dye. Finally, cotton fabric dyed with a bi-component dye was much improved in terms of the fabric hand and the surface color was similar to that of the traditional persimmon-dyed fabric. These results could help to develop the natural persimmon dyeing industry.

• Resources Recycling
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• v.22 no.2
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• pp.53-61
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• 2013

Since the 2000s, to start inducement of SCR(Selective Catalytic Reduction) denitrification facility by large scale companies which are emitted large amount of nitrogen oxides such as power plants, combined heat and power plant, incinerators and chemical plants due to take effect the regulation of stationary sources of nitrogen oxide(NOx), and the total amount of discharged pollutants, such as regulatory gradually emissions regulations are being strengthened and the expanded coverage due to the use of SCR denitrification catalyst is a growing trend. Since 2010 due to the new catalysts to replace the already installed power plants and incinerators due to inactive, and catalytic denitrification SCR waste catalyst waste as a resource rather than the development of technologies for recycling situation is urgently needed. In this study, analyzed paper and patent for recycling technologies of waste catalyst. The range of search was limited in the open patents of USA (US), European Union (EP), Japan (JP), Korea (KR) and SCI journals from 1975 to 2012. Patents and journals were collected using key-words searching and filtered by filtering criteria. The trends of the patents and journals was analyzed by the years, countries, companies, and technologies.