• Proceedings of the Korean Printing Society Conference
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• 2000.12b
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• pp.5-10
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• 2000

In case of outputting the image with color printer, image is occurred color distortion by characteristics of paper, effect by overlap between neighbor dots and the mechanical characteristics if printer. Color calibration is needed to reduce this color distrotion. To color calibration, we select the color sample in printer color gamut. The accuracy of color calibration is determined by the number of sample, distribution, and calibration method. Generally, color space is selected the color sample dividing equal interval. In this case, the range of gamut of printed color patches is reduced due to the effect of inks overlap. Therefore, error is occurred when color transformation relatively. In this paper, we have the color sampling based on equi-visual perception and then reproduce the color using the Neural-Network and interpolation by LUT.

• IE interfaces
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• v.15 no.1
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• pp.20-25
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• 2002

In this research, a nameplate engraving system for images and texts downloaded through Internet on nameplate is developed. The system consists of two subsystems: thinning algorithm and NC code generation module. In the thinning algorithm, the concept of connectivity is used and center lines of images and texts, which will be used as NC tool paths, can be obtained successfully. Because the center lines are composed of a lot of pixels, NC code would be too long. In the NC code generation module, many useless pixel data are removed and linear interpolation algorithm is applied to only the remaining pixels. By performing actual experiments, the thinning algorithm and the NC code generation module are verified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2211-2220
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• 1998

This paper presetns an adaptive critic self-learning control system with cerebellar model articulation controller (CMAC)-based decoder integrated with the associative search element (ASE) and adatpive critic element(ACE)- based scheme. The tast of the system is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. The problem is that error feedback information is limited. This problem can be sloved when some adaptive control devices are involved. The ASE incorporates prediction information for reinforrcement from a critic to produce evaluative information for the plant. The CMAC-based decoder interprets one state to a set of patways into the ASE/ACE. These signals correspond to te current state and its possible preceding action states. The CMAC's information interpolation improves the learning speed. And design inverted pendulum hardware system to show control capability with neural network.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2276-2279
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• 2002

For detecting of Energized Insulator, a new Lateral Information Propagation Networks(LIPN) has been proposed. Faulty insulator is reduced the rate of insolation extremely, and taken the results dirty and injured. It is necessary to be actions that detect the faulty insulator and exchange the new one. And thus, we have designed the LIPN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through the results of simulation experiments, we define the ability of real-time detecting the faulty insulators.

• Journal of the Korean Society of Safety
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• v.24 no.6
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• pp.1-6
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• 2009

This study describes a construction procedure of 3D virtual space using the NGIS data and its application to simulation. 3D space topography is modeled by using DEM consisted with triangular regular network. The elevations of nodal points of DEM are calculated through the interpolation with contour line and elevation points from the NGIS. Also, data for 2D roads and their environments, such as trees, lamps, and traffic signals, were extracted from the NGIS and projected on the DEM surfaces to get 3D virtual space. To give a reality to 3D virtual space and accelerate its graphic speed, data were converted into the directX format. It is believed that the virtual space constructed in this work can be applicable to the ubiqutous because DEM data can be converted to the AutoCAD format and ASCII code.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05c
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• pp.25-29
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• 2004

For detecting of Energized Insulator, a new Lateral Information Propagation Networks(LIPN) has been proposed. Faulty insulator is reduced the rate of insulation extremely, and taken the results dirty and injured. It is necessary to be actions that detect the faulty insulator and exchange the new one. And thus, we have designed the LIPN to be detected that insulators by the real time computation method through the inter-node diffusion. 1n the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through the results of simulation experiments, we difine the ability of real-time detecting the faulty insulators.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.536-546
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• 2003

We propose an object extraction technique adequate for the radial shape's radar signal structure for the purpose of implementing ARPA(Automatic Radar Plotting Aid) installed in the vessel. The radar signal data are processed by interpolation and accumulation to acquire a qualified image. The objects of the radar image have characteristics of having different shape and size as it gets far from the center, and it is not adequate for clustering generally. Therefore, this study designs a new vigilance distance model of elliptical shape and adopts this model in the ART2 neural network. We prove that the proposed clustering method makes it possible to extract objects adaptively and to separate the connected objects effectively.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.105-112
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• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.236-238
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• 2012

Ad-hoc 네트워크에서 각 노드는 분산 및 협동을 통해 자체적으로 이웃노드와 무선네트워크를 구축하고 주고받게 된다. 그러나 Ad-hoc 네트워크에서 하위노드의 제한적인 저장/통신/계산 능력, 상호인증의 어려움 등으로 기존의 보안대책을 그대로 적용할 수 없어 Ad-hoc 네트워크 특성에 맞는 새로운 보안대책이 필요하다. 이를 위해 비밀분산기법 중의 일종인 (t,n) 임계치 기법을 통해 노드를 인증하는 방식이 제안되기도 하였으나, 이는 고정된 t개 노드의 분산정보가 모여야만 원래의 비밀을 복원할 수 있는 것으로 주로 적대적 환경에 배치되어야 하는 전술 Ad-hoc 네트워크의 요구사항과는 부합하지 않는다. 따라서, 본 논문에서는 기존의 (t,n) 임계치 기법에 Newton의 보간 다항식을 최초로 적용하여 임계값 t를 동적으로 변경할 수 있는 공개키 인증방식을 제안하고, 그 유효성을 증명하고자 한다.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.