• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.358-367
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• 2016

SSL (Sound Source Localization) has been applied to several applications such as man-machine interface, video conference system, smart car and so on. But in the process of sound source localization, angle estimation error is occurred mainly due to the non-linear characteristics of the sine inverse function. So an approach was proposed to decrease the effect of this non-linear characteristics, which divides the microphone's covering space into narrow regions. In this paper, we proposed an optimal space dividing way according to the pattern of microphone array. In addition, sound source's 2-dimensional position is estimated in order to evaluate the performance of this dividing method. In the experiment, GCC-PHAT (Generalized Cross Correlation PHAse Transform) method that is known to be robust with noisy environments is adopted and triangular pattern of 3 microphones and rectangular pattern of 4 microphones are tested with 100 speech data respectively. The experimental results show that triangular pattern can't estimate the correct position due to the lower space area resolution, but performance of rectangular pattern is dramatically improved with correct estimation rate of 67 %.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.505-510
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• 2009

In recent years the space where a robot works has been expanding to the human space unlike traditional industrial robots that work only at fixed positions apart from humans. A human in the recent situation may be the owner of a robot or the target in a robotic application. This paper deals with the latter case; when a robot vision system is employed to monitor humans for a surveillance application, each person in a scene needs to be identified. Humans, however, often move together, and occlusions between them occur frequently. Although this problem has not been seriously tackled in relevant literature, it brings difficulty into later image analysis steps such as tracking and scene understanding. In this paper, a probabilistic neural network is employed to learn the patterns of the best dividing position along the top pixels of an image region of partly occlude people. As this method uses only shape information from an image, it is simple and can be implemented in real time.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.277-283
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• 2014

Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.

• Transactions on Electrical and Electronic Materials
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• v.13 no.1
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• pp.10-15
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• 2012

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the $C_bC_r$ color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.111-114
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• 1997

Generally, fuzzy models have the capability of dividing input space into several subspaces. compared to liner ones. But hitherto suggested fuzzy modeling algorithms not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem. this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently than conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space. the method of principal component is used. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.58-66
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• 1998

In many situations, it is very important to identify a certain unkown system, it from its input-output data. For this purpose, several system modeling algorithms have been suggested heretofore, and studies regarding the fuzzy modeling based on its nonlinearity get underway as well. Generatlly, fuzzy models have the capability of dividing input space into several subspaces, compared to linear ones. But hitherto subggested fuzzy modeling algorithms do not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem, this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently that conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space, the method of principal component is ued. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• Korean Institute of Interior Design Journal
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• no.39
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• pp.124-131
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• 2003

The purpose of this study is furnishing applicable data, in deviation from the conventional study of architectural planning, for funeral ceremony hall actually built as a various form. For that, this study performed analysis of condolent space, through dividing space into three phase which is planning unit, organizing unit and making layout as a general method of architectural planning steps. The results are as follows. Establishing spaces for bereaved family's rest in funeral space is also applicable, through the renovation, in case of A type u nit that is more than 25$m^2$. If wish to organize funeral space unit by monolithic space type at planning, than should be controle d size of unit so as not to excessive and if wish to organize by connection style and 1:1 separation style, than area of room where coffin is placed should be considered not overly lacking than condoler's waiting space. On arranging of middle corridor type should be controled scale of funeral space so that is not lacking than room where coffin is placed. In case of hall type, there are necessity to make area of condoler's waiting space do not excess than area of room where coffin is placed.

• KIEAE Journal
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• v.12 no.1
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• pp.35-43
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• 2012

Algorithm-based architecture helps specifically dividing the environmental variables of a target space into individual factors to build object-oriented programming, classifying them into an individual object according to the environmental variables of each planning circumstance, and distributing each structure into small structures. In addition, each itemized matter of construction is set as a condition, and thus it is possible to respond to the space condition of various circumstances which occur in the architectural planning process. This study is intended for predicting that a sketch role in the design process can be replaced by a design method utilizing an algorithm, through the external solar radiation and the illuminance value of indoor lighting device among the environmental variables of a target space, and for seeking a way to create a design alternative and improve the design quality by using computer-based algorithm design.

• Smart Structures and Systems
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• v.1 no.3
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• pp.283-308
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• 2005

This paper features about the modeling and design of a fast output sampling feedback controller for a smart Timoshenko beam system for a SISO case by considering the first 3 vibratory modes. The beam structure is modeled in state space form using FEM technique and the Timoshenko beam theory by dividing the beam into 4 finite elements and placing the piezoelectric sensor/actuator at one location as a collocated pair, i.e., as surface mounted sensor/actuator, say, at FE position 2. State space models are developed for various aspect ratios by considering the shear effects and the axial displacements. The effects of changing the aspect ratio on the master structure is observed and the performance of the designed FOS controller on the beam system is evaluated for vibration control.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.43-48
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• 2018

In this paper, we propose an indoor-space recognition using DNN and super-pixel. In order to recognize the indoor space from the image, segmentation process is required for dividing an image Super-pixel is performed algorithm which can be divided into appropriate sizes. In order to recognize each segment, features are extracted using a proposed method. Extracted features are learned using DNN, and each segment is recognized using the DNN model. Experimental results show the performance comparison between the proposed method and existing algorithms.