• Progress in Medical Physics
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• v.23 no.4
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• pp.309-316
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• 2012

Multimodal-imaging technique has been rapidly developed for improvement of diagnosis and evaluation of therapeutic effects. In despite of integrated hardware, registration accuracy was decreased due to a discrepancy between multimodal image and insufficiency of count in accordance with different acquisition method of each modality. The purpose of this study was to improve the PET image by event data resampling through analysis of data format, noise and statistical properties of small animal PET list data. Inveon PET listmode data was acquired as static data for 10 min after 60 min of 37 MBq/0.1 ml $^{18}F$-FDG injection via tail vein. Listmode data format was consist of packet containing 48 bit in which divided 8 bit header and 40 bit payload space. Realigned sinogram was generated from resampled event data of original listmode by using adjustment of LOR location, simple event magnification and nonparametric bootstrap. Sinogram was reconstructed for imaging using OSEM 2D algorithm with 16 subset and 4 iterations. Prompt coincidence was 13,940,707 count measured from PET data header and 13,936,687 count measured from analysis of list-event data. In simple event magnification of PET data, maximum was improved from 1.336 to 1.743, but noise was also increased. Resampling efficiency of PET data was assessed from de-noised and improved image by shift operation of payload value of sequential packet. Bootstrap resampling technique provides the PET image which noise and statistical properties was improved. List-event data resampling method would be aid to improve registration accuracy and early diagnosis efficiency.

• Journal of the Korean earth science society
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• v.39 no.5
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• pp.419-435
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• 2018

The purpose of this study is to understand meteorological and climatological factors that have influence on the garlic product in Seosan and Taean, and to analyze the economic value according to the use of climatical information data for garlic farmers. The climatological characteristics and trends in this area are analyzed using the meteorological data at the Seosan local meteorological agency from 1984 to 2013, the national statistical data for the product of garlic from 1989 to 2013, and the scenario data for climate change (RCP 4.5 and 8.5) for the period from 2001 to 2100. The results are as follows. First, the condition of lower temperature for garlic growth in winter season is satisfied with the mean air temperature. The wind speed are lower and stronger in Seosan and Taean than other garlic area. The suitable condition for the growth of northern type of garlic shows the decreasing trend in the accumulated precipitation in May. However, the area of growing the northern type garlic in the future is likely diminished because mean air temperature, accumulated precipitation, and mean wind speed are strong in the harvest time of garlic. Second, the seedtime of the northern and southern type of garlic using climate change scenarios (RCP 4.5, 8.5) in Seosan and Taean is getting late as time passes. and the harvest time gets faster, which indicates s that the period of garlic cultivation becomes shorter from 50 days to around 90 in the next 100 years. Third, the beginning days of white rot and delia platura of garlic are estimated by applying to the meteorological algorithm using mean air temperature and soil humidity. Especially, the beginning day of white rot garlic is shown to be faster according to the scenarios of RCP 4.5 and RCP 8.5. Fourth, the product of garlic (kg/10a) shows a high correlation with the minimum air temperature of a wintering time, the mean wind speed of a wintering time, the accumulated precipitation of a corpulent time, and the mean relative humidity of corpulent time of garlic. On the other hand, the analysis of garlic product when using the meteorological information data in cultivating garlic in Seosan and Taean reveals that the economic value increases up to 9% in total.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.6
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• pp.427-446
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• 2008

A review on the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering in 2006 has been accomplished. Focus has been put on current status of research in the aspect of heating, cooling, ventilation, sanitation and building environments. The conclusions are as follows. (1) The research trends of fluid engineering have been surveyed as groups of general fluid flow, fluid machinery and piping, etc. New research topics include micro heat exchanger and siphon cooling device using nano-fluid. Traditional CFD and flow visualization methods were still popular and widely used in research and development. Studies about diffusers and compressors were performed in fluid machinery. Characteristics of flow and heat transfer and piping optimization were studied in piping systems. (2) The papers on heat transfer have been categorized into heat transfer characteristics, heat exchangers, heat pipes, and two-phase heat transfer. The topics on heat transfer characteristics in general include thermal transport in a cryo-chamber, a LCD panel, a dryer, and heat generating electronics. Heat exchangers investigated include pin-tube type, plate type, ventilation air-to-air type, and heat transfer enhancing tubes. The research on a reversible loop heat pipe, the influence of NCG charging mass on heat transport capacity, and the chilling start-up characteristics in a heat pipe were reported. In two-phase heat transfer area, the studies on frost growth, ice slurry formation and liquid spray cooling were presented. The studies on the boiling of R-290 and the application of carbon nanotubes to enhance boiling were noticeable in this research area. (3) Many studies on refrigeration and air conditioning systems were presented on the practical issues of the performance and reliability enhancement. The air conditioning system with multi indoor units caught attention in several research works. The issues on the refrigerant charge and the control algorithm were treated. The systems with alternative refrigerants were also studied. Carbon dioxide, hydrocarbons and their mixtures were considered and the heat transfer correlations were proposed. (4) Due to high oil prices, energy consumption have been attentioned in mechanical building systems. Research works have been reviewed in this field by grouping into the research on heat and cold sources, air conditioning and cleaning research, ventilation and fire research including tunnel ventilation, and piping system research. The papers involve the promotion of efficient or effective use of energy, which helps to save energy and results in reduced environmental pollution and operating cost. (5) Studies on indoor air quality took a great portion in the field of building environments. Various other subjects such as indoor thermal comfort were also investigated through computer simulation, case study, and field experiment. Studies on energy include not only optimization study and economic analysis of building equipments but also usability of renewable energy in geothermal and solar systems.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.347-358
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• 2019

Lambertian cloud model (Lambertian Cloud Model) is the simplified cloud model which is used to effectively retrieve the vertical ozone distribution of the atmosphere where the clouds exist. By using the Lambertian cloud model, the optical characteristics of clouds required for radiative transfer simulation are parametrized by Optical Centroid Cloud Pressure (OCCP) and Effective Cloud Fraction (ECF), and the accuracy of each parameter greatly affects the radiation simulation accuracy. However, it is very difficult to generalize the vertical ozone error due to the OCCP error because it varies depending on the radiation environment and algorithm setting. In addition, it is also difficult to analyze the effect of OCCP error because it is mixed with other errors that occur in the vertical ozone calculation process. This study analyzed the ozone retrieval error due to OCCP error using two methods. First, we simulated the impact of OCCP error on ozone retrieval based on Optimal Estimation. Using LIDORT radiation model, the radiation error due to the OCCP error is calculated. In order to convert the radiation error to the ozone calculation error, the radiation error is assigned to the conversion equation of the optimal estimation method. The results show that when the OCCP error occurs by 100 hPa, the total ozone is overestimated by 2.7%. Second, a case analysis is carried out to find the ozone retrieval error due to OCCP error. For the case analysis, the ozone retrieval error is simulated assuming OCCP error and compared with the ozone error in the case of PROFOZ 2005-2006, an OMI ozone profile product. In order to define the ozone error in the case, we assumed an ideal assumption. Considering albedo, and the horizontal change of ozone for satisfying the assumption, the 49 cases are selected. As a result, 27 out of 49 cases(about 55%)showed a correlation of 0.5 or more. This result show that the error of OCCP has a significant influence on the accuracy of ozone profile calculation.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1509-1521
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• 2020

Experiments for validation of surface reflectance produced by Korea Multi-Purpose Satellite (KOMPSAT-3A) were conducted using Chinese Baotou (BTCN) data among four sites of the Radical Calibration Network (RadCalNet), a portal that provides spectrophotometric reflectance measurements. The atmosphere reflectance and surface reflectance products were generated using an extension program of an open-source Orfeo ToolBox (OTB), which was redesigned and implemented to extract those reflectance products in batches. Three image data sets of 2016, 2017, and 2018 were taken into account of the two sensor model variability, ver. 1.4 released in 2017 and ver. 1.5 in 2019, such as gain and offset applied to the absolute atmospheric correction. The results of applying these sensor model variables showed that the reflectance products by ver. 1.4 were relatively well-matched with RadCalNet BTCN data, compared to ones by ver. 1.5. On the other hand, the reflectance products obtained from the Landsat-8 by the USGS LaSRC algorithm and Sentinel-2B images using the SNAP Sen2Cor program were used to quantitatively verify the differences in those of KOMPSAT-3A. Based on the RadCalNet BTCN data, the differences between the surface reflectance of KOMPSAT-3A image were shown to be highly consistent with B band as -0.031 to 0.034, G band as -0.001 to 0.055, R band as -0.072 to 0.037, and NIR band as -0.060 to 0.022. The surface reflectance of KOMPSAT-3A also indicated the accuracy level for further applications, compared to those of Landsat-8 and Sentinel-2B images. The results of this study are meaningful in confirming the applicability of Analysis Ready Data (ARD) to the surface reflectance on high-resolution satellites.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.331-343
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• 2022

Purpose To systematically compare the performance of liver imaging reporting and data system treatment response (LR-TR) with the modified Response Evaluation Criteria in Solid Tumors (mRECIST) for diagnosing viable hepatocellular carcinoma (HCC) treated with locoregional therapy (LRT). Materials and Methods Original studies of intra-individual comparisons between the diagnostic performance of LR-TR and mRECIST using dynamic contrast-enhanced CT or MRI were searched in MEDLINE and EMBASE, up to August 25, 2021. The reference standard for tumor viability was surgical pathology. The meta-analytic pooled sensitivity and specificity of the viable category using each criterion were calculated using a bivariate random-effects model and compared using bivariate meta-regression. Results For five eligible studies (430 patients with 631 treated observations), the pooled per-lesion sensitivities and specificities were 58% (95% confidence interval [CI], 45%-70%) and 93% (95% CI, 88%-96%) for the LR-TR viable category and 56% (95% CI, 42%-69%) and 86% (95% CI, 72%-94%) for the mRECIST viable category, respectively. The LR-TR viable category provided significantly higher pooled specificity (p < 0.01) than the mRECIST but comparable pooled sensitivity (p = 0.53). Conclusion The LR-TR algorithm demonstrated better specificity than mRECIST, without a significant difference in sensitivity for the diagnosis of pathologically viable HCC after LRT.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.145-156
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• 2015

Purpose : The purpose of this study is to evaluate the efficiency of Split VMAT planning(Contouring rectum divided into an upper and a lower for reduce rectum dose) compare to Conventional VMAT planning(Contouring whole rectum) for prostate cancer radiotherapy involving pelvic lymph nodes. Materials and Methods : A total of 9 cases were enrolled. Each case received radiotherapy with Split VMAT planning to the prostate involving pelvic lymph nodes. Treatment was delivered using TrueBeam STX(Varian Medical Systems, USA) and planned on Eclipse(Ver. 10.0.42, Varian, USA), PRO3(Progressive Resolution Optimizer 10.0.28), AAA(Anisotropic Analytic Algorithm Ver. 10.0.28). Lower rectum contour was defined as starting 1cm superior and ending 1cm inferior to the prostate PTV, upper rectum is a part, except lower rectum from the whole rectum. Split VMAT plan parameters consisted of 10MV coplanar $360^{\circ}$ arcs. Each arc had $30^{\circ}$ and $30^{\circ}$ collimator angle, respectively. An SIB(Simultaneous Integrated Boost) treatment prescription was employed delivering 50.4Gy to pelvic lymph nodes and 63~70Gy to the prostate in 28 fractions. $D_{mean}$ of whole rectum on Split VMAT plan was applied for DVC(Dose Volume Constraint) of the whole rectum for Conventional VMAT plan. In addition, all parameters were set to be the same of existing treatment plans. To minimize the dose difference that shows up randomly on optimizing, all plans were optimized and calculated twice respectively using a 0.2cm grid. All plans were normalized to the prostate $PTV_{100%}$ = 90% or 95%. A comparison of $D_{mean}$ of whole rectum, upperr ectum, lower rectum, and bladder, $V_{50%}$ of upper rectum, total MU and H.I.(Homogeneity Index) and C.I.(Conformity Index) of the PTV was used for technique evaluation. All Split VMAT plans were verified by gamma test with portal dosimetry using EPID. Results : Using DVH analysis, a difference between the Conventional and the Split VMAT plans was demonstrated. The Split VMAT plan demonstrated better in the $D_{mean}$ of whole rectum, Up to 134.4 cGy, at least 43.5 cGy, the average difference was 75.6 cGy and in the $D_{mean}$ of upper rectum, Up to 1113.5 cGy, at least 87.2 cGy, the average difference was 550.5 cGy and in the $D_{mean}$ of lower rectum, Up to 100.5 cGy, at least -34.6 cGy, the average difference was 34.3 cGy and in the $D_{mean}$ of bladder, Up to 271 cGy, at least -55.5 cGy, the average difference was 117.8 cGy and in $V_{50%}$ of upper rectum, Up to 63.4%, at least 3.2%, the average difference was 23.2%. There was no significant difference on H.I., and C.I. of the PTV among two plans. The Split VMAT plan is average 77 MU more than another. All IMRT verification gamma test results for the Split VMAT plan passed over 90.0% at 2 mm / 2%. Conclusion : As a result, the Split VMAT plan appeared to be more favorable in most cases than the Conventional VMAT plan for prostate cancer radiotherapy involving pelvic lymph nodes. By using the split VMAT planning technique it was possible to reduce the upper rectum dose, thus reducing whole rectal dose when compared to conventional VMAT planning. Also using the split VMAT planning technique increase the treatment efficiency.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.