• Journal of Environmental Science International
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• v.7 no.5
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• pp.679-685
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• 1998

The Soysauce wastewater removal characteristics of submerged biofilters filled with two filter media respectively were experimentally examined with constant temperature, pH value and variable BOD loading and recirculation ratio. The decreasing order of BOD removal is Netring(random plastic media), cubic wire meshes(plastic module). This is mainly due to the media characteristics such as void ratio, specific surface area and media shapes. The BOD removal ratio decreases with increasing $BOD_5$/ volumetric loading rate, and the loading rate for the BOD removal over 85% is lower than 1.5kg$BOD_5$ 5/$m^3$d for the plastic media of Netring and cubic wire meshes. The $BOD_5$ removal rate increases with the recirculation ratio, but the rate of increase become smaller as the recirculation ratio increases over 20. When $BOD_5$ volumetric loading is 1.5kg$BOD_5$/$m^3$d, the required recirculation ratio to obtain 85% BOD$_{5}$ removal is about 20 for Netring and it was about 30 for cubic wire meshes.s.

• Smart Media Journal
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• v.4 no.4
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• pp.18-23
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• 2015

In this paper, we propose a method for detecting the number of clusters. This method can improve the performance of a gaussian mixture model function in conventional markov random field method by using the tensor voting. The key point of the proposed method is that extracts the number of the center through the continuity of saliency map of the input data of the tensor voting token. At first, we separate the foreground and background region candidate in a given natural images. After that, we extract the appropriate cluster number for each separate candidate regions by applying the tensor voting. We can make accurate modeling a gaussian mixture model by using a detected number of cluster. We can return the result of natural binary text image by calculating the unary term and the pairwise term of markov random field. After the experiment, we can confirm that the proposed method returns the optimal cluster number and text binarization results are improved.

• Smart Media Journal
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• v.5 no.1
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• pp.38-43
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• 2016

As smart environment is spread out in our living environments, the needs of an approach related to Human Computer Interaction(HCI) is increases. One of them is head pose estimation. it related to gaze direction estimation, since head has a close relationship to eyes by the body structure. It's a key factor in identifying person's intention or the target of interest, hence it is an essential research in HCI. In this paper, we propose an approach for head pose estimation with pre-defined several directions by random forest classifier. We use canny edge detector to extract feature of the different facial image which is obtained between input image and averaged frontal facial image for extraction of rotation information of input image. From that, we obtain the binary edge image, and make two accumulated histograms which are obtained by counting the number of pixel which has non-zero value along each of the axes. This two accumulated histograms are used to feature of the facial image. We use CAS-PEAL-R1 Dataset for training and testing to random forest classifier, and obtained 80.6% accuracy.

• Broadcasting and Media Magazine
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• v.5 no.1
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• pp.59-71
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• 2000

This paper presents a survey of the CMOs-based image sensor and its applications to various real field digital camera. CMOS image sensor, called active pixel sensor (APS), has many interesting properties such ash I회 sensitivity, high speed readout, random access and lower power consumption when it is compared with CCd. this paper also addresses the state-of-the-art of CMOS image sensor, and gives some examples of its application to digital camera and special-purpose cameras. with the advancement of semiconductor technology, CMOS image sensor is a future technology for imaging system, and will be widely used in the filed of image capturing for consumer electronics and scientific measurements.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.231-240
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• 2019

The paper aims to investigate the relationship between firm size and organizational actions on adopting social media for corporate reputation management. The sample group of 198 companies is selected with a simple random sample method from the New York Stock Exchange (NYSE) listings: Sixty nine companies were from the Fortune 500 listings, seventy one companies from the NYSE midsize capitalization and fifty eight companies from the NYSE small capitalization listings. This study employs cross tabulations and Chi-square analysis, and the Kruskal-Wallis that enables the comparison of three samples that are independent. The results of the study show that (1) large firms have more social media ownership than small firms, (2) large firms respond to social media posts at a greater frequency and quickly than small firms, and (3) firm size is less likely associated with response styles to social media for online reputation management. The results show that reply time and response styles of organizations to social media customers in the 2015 survey has no significant change compared to that of 2011. There appears to be a pervasive lack strategic framework as most firms in the study were found not to be adequately monitoring or leveraging social media communication for their reputation management.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.133-135
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• 2017

본 논문에서는 스테레오 영상을 이용하여 외삽 시점 영상 생성 시 발생하는 홀을 채우는 방법을 제안한다. 스테레오 영상에 3D 워핑을 이용하여 다수의 시점을 생성할 수 있다. 하지만 이 방법은 보이지 않는 시점에서의 영역을 완벽히 복원할 수 없기 때문에 필연적으로 홀이 발생한다. 홀을 채우기 위해 먼저 홀 영역의 경계를 Random Walks 알고리즘을 이용하여 전경과 배경으로 구분한다. 그리고 홀을 배경 성분에 해당하는 영역만을 이용하여 채우게 된다. 홀 채움 과정에서는 패치 내의 홀의 비율과 컬러와 깊이 영상의 텍스처에 대한 복잡도를 정의하고 패치 별로 우선순위를 계산하여 높은 순위의 패치로 홀을 채우게 된다. 실험 결과 제안하는 기법이 홀을 효과적으로 채우는 것을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.63-66
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• 2009

Automatic segmentation of foreground from background in video sequences has attracted lots of attention in computer vision. This paper proposes a novel framework for the background subtraction that the foreground is segmented from the background by directly subtracting a background image from each frame. Most previous works focus on the extraction of more reliable seeds with threshold, because the errors are occurred by noise, weak color difference and so on. Our method has good segmentations from the approximate seeds by using the Random Walks with Restart (RWR). Experimental results with live videos demonstrate the relevance and accuracy of our algorithm.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.335-344
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• 2001

Texture is extensively studied in a variety of image processing applications such as image segmentation and classification because it is an important property to perceive regions and surfaces. This paper focused on the analysis and synthesis of textured single and multiband images using Markov Random Field model considering the existent spatial correlation. Especially, for multiband images, the cross-channel correlation existing between bands as well as the spatial correlation within band should be considered in the model. Although a local interaction is assumed between the specified neighboring pixels in MRF models, during the maximization process, short-term correlations among neighboring pixels develop into long-term correlations. This result in exhibiting phase transition. In this research, the role of temperature to obtain the most probable state during the sampling procedure in discrete Markov Random Fields and the stopping rule were also studied.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.660-661
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• 2020

본 논문에서는 기존 인공 신경망 기반 시계열 학습 기법인 Recurrent Neural Network (RNN)의 많은 연산량 및 고 사양 시스템 요구를 개선하기 위해 랜덤 포레스트 (Random Forest)기반의 새로운 시계열 학습 기법을 제안한다. 기존의 RNN 기반 방법들은 복잡한 연산을 통해 높은 성능을 달성하는 데 집중하고 있다. 이러한 방법들은 학습에 많은 파라미터가 필요할 뿐만 아니라 대규모의 연산을 요구하므로 실시간 시스템에 적용하는데 어려움이 있다. 따라서 본 논문에서는, 효율적이면서 빠르게 동작할 수 있는 시계열 다층 랜덤 포레스트(Time series Multilayered Random Forest)를 제안하고 산불 조기 탐지에 적용해 기존 RNN 계열의 방법들과 성능을 비교하였다. 다양한 산불화재 실험데이터에 알고리즘을 적용해본 결과 GPU 상에서 방대한 연산을 수행하는 RNN 기반 방법들과 비교해 성능적인 한계가 존재했지만 CPU 에서도 빠르게 동작 가능하므로 성능의 개선을 통해 다양한 임베디드 시스템에 적용 가능하다.

• Asian Journal of Business Environment
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• v.8 no.4
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• pp.5-15
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• 2018

Purpose - The research aimed to reveal real decisional behavioral of management institutes in India for social media marketing usage, and analyses of empirical elements of social media consumption pattern. Research design, data, and methodology - The investigation was based around a research methodology using quantitative analysis with appropriate statistical techniques on random surveys of consumers, detailed exploratory and confirmatory factor analyses are applied to assess the empirical validity of the model and multiple regression employed using R studio edition to validate the reliability of the developed models. Results - A new conceptual framework is proposed - the management institutions decision model, providing a tool for effective and more focused decision-making strategies for developing better utilization techniques for social media. Management institutions have different requirements based upon objectives and resources available. The evidence suggests that the administrators need to be more aware of consumer indicators when targeting and designing social media marketing strategy. Conclusions - The research was based on samples and not the entire population of target consumers, providing limitations. As an inferential statistical method was chosen, the results might be susceptible to inaccuracy. The model developed from different age users, thereby providing rich perspectives into social media usage pattern.