• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.27-34
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• 2013

Due to the increased interest in global warming, interest in forest resources aimed towards reducing greenhouse gases have subsequently increased. Thus far, data related to forest resources have been obtained, through the employment of aerial photographs or satellite images, by means of plotting. However, the use of imaging data is disadvantageous; merely, due to the fact that recorded measurements such as the height of trees, in dense forest areas, lack accuracy. Within such context, the authors of this study have presented a method of data processing in which an individual tree is isolated within forested areas through the use of LIDAR data and ortho-images. Such isolation resulted in the provision of more efficient and accurate data in regards to the height of trees. As for the data processing of LIDAR, the authors have generated a normalized digital surface model to extract tree points via local maxima filtering, and have additionally, with motives to extract forest areas, applied object oriented image classifications to the processing of data using ortho-images. The final tree point was then given a figure derived from the combination of LIDAR and ortho-images results. Based from an experiment conducted in the Yongin area, the authors have analyzed the merits and demerits of methods that either employ LIDAR data or ortho-images and have thereby obtained information of individual trees within forested areas by combining the two data; thus verifying the efficiency of the above presented method.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.154-159
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• 2015

Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.

• Proceedings of the Korean Vacuum Society Conference
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• 2000.02a
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• pp.186-186
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• 2000

After LeComber et al. reported the first amorphous hydrogenated silicon (a-Si: H) TFT, many laboratories started the development of an active matrix LCDs using a-Si:H TFTs formed on glass substrate. With increasing the display area and pixel density of TFT-LCD, however, high mobility TFTs are required for pixel driver of TF-LCD in order to shorten the charging time of the pixel electrodes. The most important of these drawbacks is a-Si's electron mobiliy, which is the speed at which electrons can move through each transistor. The problem of low carier mobility for the a-Si:H TFTs can be overcome by introducing polycrystalline silicon (poly-Si) thin film instead of a-Si:H as a semiconductor layer of TFTs. Therefore, poly-Si has gained increasing interest and has been investigated by many researchers. Recnetly, fabrication of such poly-Si TFT-LCD panels with VGA pixel size and monolithic drivers has been reported, . Especially, fabricating poly-Si TFTs at a temperature mach lower than the strain point of glass is needed in order to have high mobility TFTs on large-size glass substrate, and the monolithic drivers will reduce the cost of TFT-LCDs. The conventional methods to fabricate poly-Si films are low pressure chemical vapor deposition (LPCVD0 as well as solid phase crystallization (SPC), pulsed rapid thermal annealing(PRTA), and eximer laser annealing (ELA). However, these methods have some disadvantages such as high deposition temperature over $600^{\circ}C$, small grain size (<50nm), poor crystallinity, and high grain boundary states. Therefore the low temperature and large area processes using a cheap glass substrate are impossible because of high temperature process. In this study, therefore, we have deposited poly-Si thin films on si(100) and glass substrates at growth temperature of below 40$0^{\circ}C$ using newly developed high rate magnetron sputtering method. To improve the sputtering yield and the growth rate, a high power (10~30 W/cm2) sputtering source with unbalanced magnetron and Si ion extraction grid was designed and constructed based on the results of computer simulation. The maximum deposition rate could be reached to be 0.35$\mu$m/min due to a high ion bombardment. This is 5 times higher than that of conventional sputtering method, and the sputtering yield was also increased up to 80%. The best film was obtained on Si(100) using Si ion extraction grid under 9.0$\times$10-3Torr of working pressure and 11 W/cm2 of the target power density. The electron mobility of the poly-si film grown on Si(100) at 40$0^{\circ}C$ with ion extraction grid shows 96 cm2/V sec. During sputtering, moreover, the characteristics of si source were also analyzed with in situ Langmuir probe method and optical emission spectroscopy.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1245-1255
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• 2010

In current Web 2.0 environment, one of the most core technology is social bookmarking which users put tags and bookmarks to their interesting Web pages. The main purpose of social bookmarking is an effective information service by use of retrieval, grouping and share based on user's bookmark information and tagging result of their interesting Web pages. But, current social bookmarking system uses the number of bookmarks and tag information separately in information retrieval, where the number of bookmarks stand for user's degree of interest on Web contents, information retrieval, and classification serve the purpose of tag information. Because of above reason, social bookmarking system does not utilize effectively the bookmark information and tagging result. This paper proposes a Web contents ranking algorithm combining bookmarks and tag information, based on preceding research on associative tag extraction by tag clustering. Moreover, we conduct a performance evaluation comparing with existing retrieval methodology for efficiency analysis of our proposed algorithm. As the result, social bookmarking system utilizing bookmark with tag, key point of our research, deduces a effective retrieval results compare with existing systems.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.567-576
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• 2011

In current Web 2.0 environment, one of the most core technology is social bookmarking which users put tags and bookmarks to their interesting Web pages. The main purpose of social bookmarking is an effective information service by use of retrieval, grouping and share based on user's bookmark information and tagging result of their interesting Web pages. But, current social bookmarking system uses the number of bookmarks and tag information separately in information retrieval, where the number of bookmarks stand for user's degree of interest on Web contents, information retrieval, and classification serve the purpose of tag information. Because of above reason, social bookmarking system does not utilize effectively the bookmark information and tagging result. This paper proposes a Web contents ranking algorithm combining bookmarks and tag information, based on preceding research on associative tag extraction by tag clustering. Moreover, we conduct a performance evaluation comparing with existing retrieval methodology for efficiency analysis of our proposed algorithm. As the result, social bookmarking system utilizing bookmark with tag, key point of our research, deduces a effective retrieval results compare with existing systems.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.718-729
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• 2006

There has been increasing interest in automatic term recognition (ATR), which recognizes technical terms for given domain specific texts. ATR is composed of 'term extraction', which extracts candidates of technical terms and 'term selection' which decides whether terms in a term list derived from 'term extraction' are technical terms or not. 'term selection' is a process to rank a term list depending on features of technical term and to find the boundary between technical term and general term. The previous works just use statistical features of terms for 'term selection'. However, there are limitations on effectively selecting technical terms among a term list using the statistical feature. The objective of this paper is to find effective features for 'term selection' by considering various aspects of technical terms. In order to solve the ranking problem, we derive various features of technical terms and combine the features using machine-learning algorithms. For solving the boundary finding problem, we define it as a binary classification problem which classifies a term in a term list into technical term and general term. Experiments show that our method records 78-86% precision and 87%-90% recall in boundary finding, and 89%-92% 11-point precision in ranking. Moreover, our method shows higher performance than the previous work's about 26% in maximum.

• The Journal of Industrial Distribution & Business
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• v.10 no.9
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• pp.37-43
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• 2019

Purpose - The self quality inspection which is mandatory in Republic of Korea can play an important role in promoting food safety, but the cost for self quality inspection is also a big burden especially for small businesses. In addition, excessive or duplicated inspection costs resulting from the standard gaps of various ministries and agencies ultimately effect negative social utility. Thus, this paper aims to suggest ways to improve the analysis of self quality inspection for the Ready-to-Eat Foods in terms of dried laver, in order to reduce costs caused by overinclusion. Research design, data, and methodology - The focus of interest is seaweed in Kimbab, which is to be exempted from the Vibrio parahaemolyticus test on the three grounds. First, existing literature provides little support for the necessity of performing a microbiological test. Second, laver products do not require the V.parahaemolyticus test by international standards such as the CODEX. Third, no case was found for seaweed products on the list of information on failed food items over the 2015~16 period. Results - Other types of seaweed such as exportable and seasoning seaweed are not subject to the test. Hence, exclusion of the V.pahaemolyticus test on seaweed is a valid point, bringing about large expected cost savings to many small businesses. Conclusions - Based on a complete survey of 75 food-testing agencies from March 20th to 30th, 2018, this paper finds that the proposed revision of the Kimbab test is likely to save an average of 130,000 won per business per year. Especially, in the case of the testing fee of Salmonella spp.(n,c,m,M), the cost difference by agencies was found to be up to five times. The regional gap in testing fees can be considered an unfair barrier. Accordingly, it is necessary to examine whether the testing fee is set at an appropriate level through the cost-extraction program proposed by the Ministry Food and Drug Safety. Hence, the survey results also point out that harmonization of testing fees charged by different food-testing agencies is appropriate with respect to transparency and efficiency.

• Applied Chemistry for Engineering
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• v.27 no.4
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• pp.343-352
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• 2016

It has been estimated that the Earth has nearly 1.688 trillion barrels of crude oil, which will last 53.3 years at current extraction rates. The organization of petroleum exporting countries (OPEC) group forecasted that the oil prices will not jump to triple-digit territory within a decade, but it can quickly increase as the political issue for reducing oil production appears. With the potential of serious shortage of conventional hydrocarbon resources, the heavy oil, one of unconventional hydrocarbon resources including oil sand and natural bitumen has attracted worldwide interest. The heavy oil contains heavy hydrocarbon compounds, commonly called as resins and asphaltenes, with long carbon chains more than sixty carbon atoms. The high content of heavier fraction corresponds with the high molecular weight, viscosity, and boiling point. Physicochemical properties of residues from vacuum distillation of conventional oil, referred to as vacuum residues (VR) were similar to those of heavy oil. For the development of heavy oil reserves, reducing the heavy oil viscosity is the most important. In this article, commercially employed aquathermolysis processes and their application to VR upgrading are discussed. VR contains transition metals such as Ni and V, but these metals should be eliminated in advance for further refining. Recent studies on demetallization technologies for VR are also reviewed.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.321-328
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• 2014

In this paper, we present a MapReduce-based mobility pattern mining system which can predict efficiently the next place of mobile users. It learns the mobility pattern model of each user, represented by Hidden Markov Models(HMM), from a large-scale trajectory dataset, and then predicts the next place for the user to visit by applying the learned models to the current trajectory. Our system consists of two parts: the back-end part, in which the mobility pattern models are learned for individual users, and the front-end part, where the next place for a certain user to visit is predicted based on the mobility pattern models. While the back-end part comprises of three distinct MapReduce modules for POI extraction, trajectory transformation, and mobility pattern model learning, the front-end part has two different modules for candidate route generation and next place prediction. Map and reduce functions of each module in our system were designed to utilize the underlying Hadoop infrastructure enough to maximize the parallel processing. We performed experiments to evaluate the performance of the proposed system by using a large-scale open benchmark dataset, GeoLife, and then could make sure of high performance of our system as results of the experiments.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.99-106
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• 2008

A synthetic aperture radar (SAR) system is of great interest in many fields of civil and military applications because of all-weather and luminance free imaging capability. SAR image quality parameters such as spatial resolution, peak to sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) can be normally estimated by modeling of impulse response function (IRF) which is obtained from various system design parameters such as altitude, operational frequency, PRF, etc. In modeling of IRF, however, background clutter environment surrounding the IRF is generally neglected. In this paper, analysis method for SAR mage quality is proposed in the real background clutter environment. First of all, SAR raw data of a point scatterer is generated based on various system parameters. Secondly, the generated raw data can be focused to ideal IRF by range Doppler algorithm (RDA). Finally, background clutter obtained from image of currently operating SAR system is applied to IRF. In addition, image quality is precisely analyzed by zooming and interpolation method for effective extraction of IRF, and then the effect of proposed methodology is presented with several simulation results under the assumption of estimation error of Doppler rate.