• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.103-110
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• 2012

The population growth along with the urbanization, has caused more problems in many public areas, such as subway airport terminals, hospital, etc. Many surveillance systems have been installed in the public areas, but not all of those can be monitored in real-time, because the operators that observe the monitors are very small compared with the number of the monitors. For example, the observer can miss some crucial accidents or detect after considerable delays. Thus, intelligent surveillance system for preventing the accidents are needed, such as Intelligent Surveillance Systems. in this paper, we propose a new crowd density estimation method which aims at estimating moving crowd using images from surveillance cameras situated in outdoor locations. The moving crowd is estimated from the area where using optical flow. The edge information is also used as feature to measure the crowd density, so we improve the accuracy of estimation of crowd density. A multilayer neural network is designed to classify crowd density into 5 classes. Finally the proposed method is experimented with PETS 2009 images.

• Journal of Platform Technology
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• v.6 no.3
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• pp.17-22
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• 2018

Smart home is a technology for monitoring and controlling all the information about a house by integrating various home applications like cooling, heating, lighting, kitchen and security systems into a network. Although home appliances have become more convenient to use due to the development of smart home technology, they are also more vulnerable to information security hazards. Unauthorized visitors may have access to any of home appliance to arbitrarily control it or acquire information. This causes serious privacy and security problems, which should be solved to further smart home technology. This present paper proposed a dynamic user access control system for privacy protection in smart homes. The proposed system defines the role of a user of smart home services by automatically identifying the status information of the user and dynamically controls the access range for the service. In this way, the privacy of a user can be protected and the inter-smart device service is effectively provided. Consequently, the proposed dynamic user access control for smart home will improve the security service for protecting privacy in smart home devices.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.47-55
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• 2018

In this paper, we propose a novel face recognition(FR) method that takes advantage of combining weighted deep local features extracted from multiple Deep Convolutional Neural Networks(DCNNs) learned with a set of facial local regions. In the proposed method, the so-called weighed deep local features are generated from multiple DCNNs each trained with a particular face local region and the corresponding weight represents the importance of local region in terms of improving FR performance. Our weighted deep local features are applied to Joint Bayesian metric learning in conjunction with Nearest Neighbor(NN) Classifier for the purpose of FR. Systematic and comparative experiments show that our proposed method is robust to variations in pose, illumination, and expression. Also, experimental results demonstrate that our method is feasible for improving face recognition performance.

• Journal of the Korean Geographical Society
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• v.43 no.2
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• pp.220-234
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• 2008

In many security monitoring contexts, the performance or efficiency of surveillance sensors/networks based on a single sensor type may be limited by environmental conditions, like illumination change. It is well known that different modes of sensors can be complementary, compensating for failures or limitations of individual sensor types. From a location analysis and modeling perspective, a challenge is how to locate different modes of sensors to support security monitoring. A coverage-based optimization model is proposed as a way to simultaneously site k different sensor types. This model considers common coverage among different sensor types as well as overlapping coverage for individual sensor types. The developed model is used to site sensors in an urban area. Computational results show that common and overlapping coverage can be modeled simultaneously, and a rich set of solutions exists reflecting the tradeoff between common and overlapping coverage.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.286-291
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• 2008

Intelligence of collaborative environments starts from a trial to provide optimal services to with users based on real-time information about participants and environments of the collaboration itself. Up to now, we can collect information such as temperature, light, time, each participant's gestures, faces, voices, and locations by adopting ubiquitous computing technologies. However, since social relationship is intrinsic to collaborative activities, the relationships and roles among participants should be fully considered to provide optimal services. To do so, we have to integrate collected data from various sensors and extracted data about relationships and roles among participants as unified one context. Thus, this paper designs collaborative services filtered, by using the integrated data as a context, and introduces an implemented example, context-aware based interactive display service, called as smart meeting system (SMeet system).

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.923-932
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• 2017

In this paper, we propose a GRNN(: Generalized Regression Neural Network) algorithms for new eyes and face recognition identification system to solve the points that need corrective action in accordance with the existing problems of facial movements gaze upon it difficult to identify the user and. Using a Kalman filter structural information elements of a face feature to determine the authenticity of the face was estimated future location using the location information of the current head and the treatment time is relatively fast horizontal and vertical elements of the face using a histogram analysis the detected. And the light obtained by configuring the infrared illuminator pupil effects in real-time detection of the pupil, the pupil tracking was to extract the text print vector. The abstract is to be in fully-justified italicized text as it is here, below the author information.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1188-1193
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• 2006

본 논문에서는 동적 객체의 3 차원 정보를 표현하는 깊이 영상의 노이즈 필터링 방법을 제안한다. 실제 객체의 동적인 3 차원 정보는 적외선 깊이 센서가 장착된 깊이 비디오 카메라를 이용하여 실시간으로 획득되며, 일련의 깊이 영상, 즉 깊이 비디오(depth video)로 표현될 수 있다. 하지만 측정환경의 조명조건, 객체의 반사속성, 카메라의 시스템 오차 등으로 인해 깊이 영상에는 고주파 성분의 노이즈가 발생하게 된다. 이를 효과적으로 제거하기 위해 깊이 영상기반의 모델링 기법(depth image-based modeling)을 이용한 3 차원 메쉬 모델링을 수행한다. 생성된 3 차원 메쉬 모델은 깊이 영상의 노이즈로 인해 경계 영역과 형상 내부 영역에 심각한 형상 오차를 가진다. 경계 영역의 오차를 제거하기 위해 깊이 영상으로부터 경계 영역을 추출하고, 가까운 순서로 정렬한 후 angular deviation 을 이용하여 불필요하게 중복된 점들을 제거한다. 그리고 나서 2 차원 가우시안 스무딩 기법을 적용하여 부드러운 경계영역을 생성한다. 형상 내부에 대해서는 경계영역에 제약조건을 주고 3 차원 가우시안 스무딩 기법을 적용하여 전체적으로 부드러운 형상을 생성한다. 최종적으로 스무딩된 3 차원 메쉬모델을 렌더링할 때, 깊이 버퍼에 있는 정규화된 깊이 값들을 추출하여 원래 깊이 영상과 동일한 깊이 영역을 가지도록 저장함으로서 전역적으로 연속적이면서 부드러운 깊이 영상을 생성할 수 있다. 제안된 방법에 의해 노이즈가 제거된 깊이 영상을 이용하여 고품질의 영상기반 렌더링이나 깊이 비디오 기반의 햅틱 렌더링에 적용할 수 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.805-814
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• 2013

This paper proposes a robust hand segmentation method using view-invariant characteristic of a wrist-mounted camera, and deals with a hand shape recognition system based on segmented hand information. We actively utilize the advantage of the proposed camera device that provides view-invariant images physically, and segment hand region using a Bayesian rule based on adaptive histograms. We construct HSV histograms from RGB histograms, and update HSV histograms using hand region information from a current image. We also propose a user adaptation method by which hand models gradually approach user-dependent models from user-independent models as the user uses the system. The proposed method was evaluated using 16 Korean manual alphabet, and we obtained increases of 27.91% in recognition success rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.92-101
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• 2005

It is difficult to detect the accurate detection which leads the camera it moves follows in change of the noise or illumination and Also, it could be recognized with backgound if the object doesn't move during hours. In this paper, the proposed method is updating changed background image as much as N＊M pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect moving by computing fixed distance pixel instead of operate all pixel. Also, set up minimum area of object to use boundary point of object abstracted through checking image pixel and motion detect of object. Therefore motion detection is available as is fast and correct without doing checking image pixel every Dame. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

• Journal of Energy Engineering
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• v.27 no.2
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• pp.32-48
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• 2018

Monitoring of energy use should be a priority in order to efficiently manage building energy use. Energy use in buildings can be managed by dividing them into energy sources, uses, and ZONE. By energy source, electricity, gas, fuel, and district heating are supplied to run the building's facilities. The purpose can be divided into five main applications, including cooling, heating, lighting, hot water and ventilation, but not many elevators and electric heaters that are difficult to include in the five applications are classified. ZONE Star refers to the comparison or separate management of areas for which the purpose of the building is similar or different. In addition, energy efficiency management requires control of the temperature, humidity, and people who will be measuring energy in the building, and the recent problem of fine dust should directly affect the ventilation of the building.