• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.26-36
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• 2006

In this paper, we propose a cost-effective color filter may (CFA) demosaicing method for digital still cameras in which a single CCD or CMOS image sensor is used. Since a CFA is adopted, we must interpolate missing color values in the red, green and blue channels at each pixel location. While most state-of-the-art algorithms invest a great deal of computational effort in the enhancement of the reconstructed image to overcome the color artifacts, we focus on eliminating the color artifacts with low computational complexity. Using spatial correlation of the adjacent pixels, the edge-directional information of the neighbor pixels is used for determining the edge direction of the current pixel. We apply our method to the state-of-the-art algorithms which use edge-directed methods to interpolate the missing color channels. The experiment results show that the proposed method enhances the demosaiced image qualify from $0.09{\sim}0.47dB$ in PSNR depending on the basis algorithm by removing most of the color artifacts. The proposed method was implemented and verified successfully using verilog HDL and FPGA. It was synthesized to gate-level circuits using 0.25um CMOS standard cell library. The total logic gate count is 12K, and five line memories are used.

• Korean journal of communication and information
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• v.34
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• pp.275-305
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• 2006

The main research question of this paper is as follows: How can broadcasting both cause the crisis in democratic participation and yet also offer the solution? The contradiction in broadcast localism has never been adequately resolved in spite of regulation at all in practice, in that localism simply cannot account for the diversity of modern life and for the external forces that incorporate local communities into much larger economic and communications network. The concept of locality in local television, however, has been multiplied and enlarged in order to adjust to "time-space compression". Recently the local television have been "interface" combining and negotiating the globalization of media market and the decentralization of political power, the economies of scale and the activation of local democracy, consequently aiming at the horizontal-cooperative network instead of old vertical-dependent one.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.147-157
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• 2017

In recent years, Super-pixels have become very popular for use in computer vision applications. Super-pixel algorithm transforms pixels into perceptually feasible regions to reduce stiff features of grid pixel. In particular, super-pixels are useful to depth estimation, skeleton works, body labeling, and feature localization, etc. But, it is not easy to generate a good super-pixel partition for doing these tasks. Especially, super-pixels do not satisfy more meaningful features in view of the gestalt aspects such as non-sum, continuation, closure, perceptual constancy. In this paper, we suggest an advanced algorithm which combines simple linear iterative clustering with fuzzy clustering concepts. Simple linear iterative clustering technique has high adherence to image boundaries, speed, memory efficient than conventional methods. But, it does not suggest good compact and regular property to the super-pixel shapes in context of gestalt aspects. Fuzzy similarity measures provide a reasonable graph in view of bounded size and few neighbors. Thus, more compact and regular pixels are obtained, and can extract locally relevant features. Simulation shows that fuzzy similarity based super-pixel building represents natural features as the manner in which humans decompose images.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.373-373
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• 2022

전세계적으로 기후변화로 인해 3년 이상의 기간동안 지속되는 다년 가뭄의 빈도와 심도가 증가하고 있으며, 이로 인한 피해도 증가하고 있다. 본 연구에서는 이를 반영하여 전국 다목적댐 및 용수댐에서 모두 주요 가뭄 대응 대책으로 사용되고 있는 현행 용수공급 조정기준을 개선하는 방안을 제안하고자 한다. 가장 먼저, 장기 기억 반영이 가능한 시계열 모형인 ARFIMA(Autoregressive Fractional Integrated Moving Average) 모델을 사용하여 다양한 강도의 장기 기억을 가지고 있는 연간 유입량을 생성하였다. 이후, 연간 유입량을 k-최근접 이웃 방법 기반의 배분 도구를 사용하여 10일 단위 유입량으로 분배하였으며 이를 대체 용수공급 조정기준을 생성하기 위한 입력 변수로 사용하였다. 새로운 용수공급 조정기준은 매 시점마다 새롭게 업데이트되는 정보를 통해 현행 기준과 함께 적응형으로 저수지 운영에 사용되었다. 다년 가뭄이 반영된 유입량으로 적응형으로 저수지 운영을 관측 유입량 하에서 빈도와 크기의 측면에서 분석을 시행하였다. 그 결과, 심각한 실패(물 부족 비율 30% 이상)의 빈도의 경우 현행 기준 운영 시 6.14%에서 적응형 운영 시행 시 2.99%로 개선되었지만, 전체 기간 동안의 신뢰도는 적응형 운영보다(26.42%) 현행 운영 하에서 더욱 나은 결과를 보였다(41.19%). 위와 같은 분석 결과는 심각한 실패의 빈도와 크기를 줄이는 용수공급 조정기준을 시행하는 원론적인 목적과 일치하기에, 본 연구에서 제안하는 다년 가뭄에 대비한 적응형 운영 방안은 향후 길게 지속되는 가뭄 조건에서 저수지 운영 정책으로 활용될 수 있음을 확인하였다.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.529-542
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• 2000

Recent years, the use of multimedia information is rapidly increasing, and the video media is the most rising one than any others, and this field Integrates all the media into a single data stream. Though the availability of digital video is raised largely, it is very difficult for users to make the effective video access, due to its length and unstructured video format. Thus, the minimal interaction of users and the explicit definition of video structure is a key requirement in the lately developing image and video management systems. This paper defines the terms and hierarchical video structure, and presents the system, which construct the clustering-based video hierarchy, which facilitate users by browsing the summary and do a random access to the video content. Instead of using a single feature and domain-specific thresholds, we use multiple features that have complementary relationship for each other and clustering-based methods that use normalization so as to interact with users minimally. The stage of shot boundary detection extracts multiple features, performs the adaptive filtering process for each features to enhance the performance by eliminating the false factors, and does k-means clustering with two classes. The shot list of a result after the proposed procedure is represented as the video hierarchy by the intelligent unsupervised clustering technique. We experimented the static and the dynamic movie videos that represent characteristics of various video types. In the result of shot boundary detection, we had almost more than 95% good performance, and had also rood result in the video hierarchy.

• Journal of forensic and investigative science
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• v.2 no.1
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• pp.23-31
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• 2007

Apartments are built in small countries to accommodate densely populated cities and maximize urbanization. Many apartment complexes have been built in recent reconstruction or redevelopment projects. An increase in crime has resulted due to residents living in a narrow space. Larceny is the most commonly reported crime in apartment complexes. Apartments can be classified as stairway, hallway, or plane surface. This study compares and analyzes the frequency of theft by apartment type to assist in creating a safer residential space. In America and England, scholars studied to make a safer residential space, and have applied the theory of 'the Defensible Space' and 'Crime Prevention Through Environmental Design(CPTED)' since 1970s. Korean apartment design now reflects CPTED in new apartment construction. In this study, 12 apartment complexes were selected in Changwon city to conduct analysis of theft in selected complexes. The study will cover housing invasion theft, motorcycle and car theft and snatching. The most frequency larceny is motorcycle and car theft, the second is housing invasion theft, and the least frequent is snatching. More residents' motorcycles and cars are damaged in a hallway style apartment. More frequently inhabitants have their possessions snatched on a stairway form. 1) When we build new apartment complexes, we must plan to improve territoriality and enhance a natural surveillance by reinforcing dwellers' relationship. Through planning we can prevention the larceny in apartments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.3-6
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• 2012

Due to recent rapid growth of mobile market, the modern people increasing make use of mobile contents as a means to obtain the desired information quickly by overcoming various restraints of a computer. The wide range of recommended contents, however, takes much time in selection of contents. To resolve such issues, a system that predicts the contents desired by the user and makes an accurate recommendation is necessary. In this paper, in order to provide the desired contents in line with the user demands, a method to increase select the number of recommendation using cooperative filtering is proposed. In the first step, the categories are formulated with super-classes and the similarity between the target customer and users is found, and the nearest-neighbors are constituted to find the preference predictions between super-classes, and the super-class with the highest resulting value is recommended to the target customer. In the second step, the preference predictions between sub-classes are found and the sub-class with the highest value is recommended to the target customer. In the experiment, mobile contents are recommended through super-class-based cooperative filtering, and then the mobile contents are recommended through sub-class-based cooperative filtering, and sub-class collaborative filtering method to select a high number of verification.

• Journal of agricultural medicine and community health
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• v.37 no.1
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• pp.1-11
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• 2012

Objectives: To survey the awareness of patient to scrub typhus to provide data for education and communication concerning scrub typhus. Methods: Patients with scrub typhus (case group, n=299) and people without scrub typhus within the previous 2 years (control group, n=598) were matched for age (within 5 years), gender, and occupation (farmer or non-farmer). The participants were recruited from 15 study areas between October and December 2006. Results: The awareness rate of scrub typhus was 75.1%, and was significantly higher than in the case group (79.4% vs. 66.6%, respectively; p<0.01). The major routes of awareness were from 'past history of scrub typhus in family members or neighbors' (54.9%), 'television' (28.3%), and their past history of scrub typhus (5.5%). The average correct rate of scrub typhus was 48.4%, and the correct response rate of cases was significantly higher than controls (p<0.01). Especially, the correct rate of etiology, incubation period, route of transmission, and acquired immunity was <40%. Through conditional logistic regression test, the factor significantly associated with awareness in case group was age (odds ratio [OR], 0.96; 95% confidence interval [CI], 0.94-0.98). And the factors associated with awareness in control group were female (OR, 1.56; 95% CI, 1.03-2.36) age (OR, 0.98; 95% CI, 0.96-0.99), family history of scrub typhus (OR, 10.18; 95% CI, 1.37-75.99), history of receiving prevention education (OR, 8.47; 95% CI, 1.14-63.00). Conclusions: The rate of awareness was relatively low in study population. Thus, effective working guidelines and educational program to prevent scrub typhus must be developed, and publicity activities about the prevention of scrub typhus are needed for high-risk groups.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.