• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.49-51
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• 2006

휴대폰의 사용영역이 넓어지면서 휴대폰에 저장되는 컨텍스트 정보 활용에 관심이 높아지고 있다. 하지만 정보의 양이 방대하기 때문에 개인이 정보를 분석하여 자신에게 필요한 정보로 바꾸기 위해서는 많은 노력이 필요하다. 본 논문에서는 휴대폰으로부터 컨텍스트 정보를 수집하여 활용할 수 있는 방법으로 개인이 하루 동안 경험한 일에 대한 정보를 한 눈에 알아볼 수 있도록 도와주는 계층적 이야기 구성 모델을 제안한다. 계층적 이야기 구성 모델은 3단계로 구성된다. 우선 각각의 로그를 분석하여 관련 있는 것들을 그룹으로 분류하고 분류된 그룹 내에서 설정된 경로에 대한 가중치를 계산하여 해당 그룹의 가중치로 저장한다. 마지막으로 그룹간의 경로에 대한 가중치를 계산하여 가장 높은 가중치를 갖는 경로를 한아 이야기 구성 모델로 설정한다. 계층적으로 이야기 경로를 선택한 경우와 그룹으로 분류하지 않고 경로를 계산한 경우의 시간 복잡도를 비교 평가하여 성능을 측정하였다. 이야기 구성모델을 계층적으로 분류했을 때의 성능이 분류하지 않은 경우보다 경로를 선정할 때 더 높은 성능을 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3237-3256
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• 2012

In the context of effective usage of a scarce spectrum resource, emerging wireless communication standards will demand spectrum sharing with existing systems as well as multiple access with higher spectral efficiency. We mathematically analyze the sum throughput of a spectrum sharing space-division multiple access (SDMA) system, which forms a transmit null in the direction of other coexisting systems while satisfying orthogonal beamforming constraints. For a large number of users N, the SDMA throughput scales as log N at high signal-to-noise ratio (SNR) ((J-1) loglog N at normal SNR), where J is the number of transmit antennas. This indicates that multiplexing gain of the spectrum sharing SDMA is $\frac{J-1}{J}$ times less than that of the non-spectrum sharing SDMA only using orthogonal beamforming, whereas no loss in multiuser diversity gain. Although the spectrum sharing SDMA always has lower throughput compared to the non-spectrum sharing SDMA in the non-coexistence scenario, it offers an intriguing opportunity to reuse spectrum already allocated to other coexisting systems.

• American Studies
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• v.42 no.1
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• pp.133-159
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• 2019

On the website r/NYYankees, a sub-forum ("subreddit") of Reddit is devoted to the Major League Baseball team New York Yankees, with its predominantly male users showing their appreciation for baseball heroes by expressing erotic desires towards the players. When a player performs well, the subreddit is filled with admiration of desires to become the player's intimate lover-explicitly expressed by "male" fans. This paper explains the phenomenon of young male fans' desire for the now-lost model of traditional masculinity of domination and control, displayed in the context of baseball players' dominant performances. The discrepancy between a fan's non-homosexual real-world self and his homoerotic language on the subreddit is explained using the "performative fandom" theory, developed by Osborne and Coombs borrowing Butler's notion of performativity. This paper suggests how this desire for traditional masculinity serves as recognition to the collapse of masculinity in the modern American society.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.2
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• pp.209-228
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• 2011

Web search behavior has evolved. People now search using many diverse information devices in various situations. To monitor these scattered and shifting search patterns, an improved way of learning and analysis are needed. Traditional web search studies relied on the server transaction logs and single query instance analysis. Since people use multiple smart devices and their searching occurs intermittently through a day, a bundled query research could look at the whole context as well as penetrating search needs. To observe and analyze bundled queries, we developed a proprietary research software set including a log catcher, query bundling tool, and bundle monitoring tool. In this system, users' daily search logs are sent to our analytic server, every night the users need to log on our bundling tool to package his/her queries, a built in web survey collects additional data, and our researcher performs deep interviews on a weekly basis. Out of 90 participants in the study, it was found that a normal user generates on average 4.75 query bundles a day, and each bundle contains 2.75 queries. Query bundles were categorized by; Query refinement vs. Topic refinement and 9 different sub-categories.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.73-83
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• 2009

In this paper we propose an object similarity matching method based on shape characteristics of an object in an image. The proposed method extracts edge points from edges of objects and generates a log polar histogram with respect to each edge point to represent the relative placement of extracted points. It performs the matching in such a way that it compares polar histograms of two edge points sequentially along with edges of objects, and uses a well-known k-NN(nearest neighbor) approach to retrieve similar objects from a database. To verify the proposed method, we've compared it to an existing Shape-Context method. Experimental results reveal that our method is more accurate in object matching than the existing method, showing that when k=5, the precision of our method is 0.75-0.90 while that of the existing one is 0.37, and when k=10, the precision of our method is 0.61-0.80 while that of the existing one is 0.31. In the experiment of rotational transformation, our method is also more robust compared to the existing one, showing that the precision of our method is 0.69 while that of the existing one is 0.30.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.497-503
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• 2011

In this paper, we proposed the MCE as a GMM training method to improve the performance of environmental sounds recognition. We model the environmental sounds data with newly defined misclassification function using the log likelihood of the corresponding class and the log likelihood of the rest classes for discriminative training. The model parameters are estimated with the loss function using GPD(generalized probabilistic descent). For recognition performance comparison, we extracted the 12 degrees features using preprocessing and MFCC(mel-frequency cepstral coefficients) of the 9 kinds of environmental sounds and carry out GMM classification experiments. According to the experimental results, MCE training method showed the best performance by an average of 87.06% with 19 mixtures. This result confirmed us that MCE training method could be effectively used as a GMM training method in environmental sounds recognition.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.747-756
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• 2010

With the recent recession, studies on the economy are actively being conducted throughout the industry. Based on the Small Business data registered in the Credit Guarantee Fund, we estimated the survival probability in the context of the survival analysis. We also analyzed the survival time for the construction and the other industries which are distinguished depending on the types of business and assets in the Small Business. The survival probability was estimated by using the life-table and the difference between the survival probabilities for the different types of business was described via the method of the Log-rank test and the Wilcoxon test. We found that the small business with over one billion asset has the highest survival probability and that with less than 1000 million asset showed the similar survival probability. In terms of types of business Wholesale and Retail trade industry and Services were relatively high in the survival probability than Light, Heavy, and the construction industries. Especially the construction industry showed the lowest survival probability. Most of the Small Business tend to increase in the hazard rate over time.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.341-341
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• 2017

Abiotic stress adversely affects crop growth worldwide. Drought of the major abiotic stresses have the most significant impact on all of the crop. The main objective of this study was to assess the effects of drought stress on photochemical performance and vitality of maize (Zea mays L.). The photochemical characteristics were analyzed in the context of period of drought stress during the maize growth. Drought experiment was carried out for four weeks, thereafter, the drought treated maize was re-watered. The polyphasic OJIP fluorescence transient was used to evaluate the behavior of photosystem II (PSII) and photosystem I (PSI) during the entire experiment period. In drought stress, the performance Index (PI) level was reached earlier when compared to the controls. For the screening of drought stress tolerance the drought factor index (DFI) of each variety was calculated as follow DFI= log(A) + 2log(B). All the fourteen cultivars show DFI ranged from -0.69 to 0.30, meaning less useful in selection of drought tolerant cultivars. PI and electron transport flux values of fourteen cultivars were to indicate reduction of photosynthetic performance during the early vegetative stage under drought stress. In conclusion, DFI and energy flux parameters can be used as photochemical and physiological index.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1785-1790
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• 2016

There is exhibit a degree of pain in the occurrence and course of treatment levels or oral, pain rating scale actions, such as illness, for example, the discomfort scale because of the pain "annoying", "unpleasant" "annoyed am", "painful" represents a pain scale of the order of "painful", "hard to bear", "very difficult to bear". Depression is recognized based on the premise of the situation, because it is difficult to recognize themselves. In this paper we define the components of the depression can be seen lifestyle which can lead to depression or through a biological signal. The depression index was derived from the ontology modeling to understand the state of depression. Depression ontology components and depression index will be aware of the situation based information services for depression. Combined with the situational awareness based devices and can be synchronized to verify the results of the depression index. It will be applied to improve lifestyle factors that of depression.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.615-620
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• 2013

With the development of smartphones, the number of applications for smartphone increases sharply. As a result, users need to try several times to find their favorite apps. In order to solve this problem, we propose a recommendation system to provide an appropriate app list based on the user's log information including time stamp, location, application list, and so on. The proposed approach learns three recommendation models including Naive-Bayesian model, SVM model, and Most-Frequent Usage model using temporal and spatial attributes. In order to figure out the best model, we compared the performance of these models with variant features, and suggest an hybrid method to improve the performance of single models.