• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.162-165
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• 2018

In this paper, we propose a pre-processing procedure to obtain scene change information using spatial down-scaled input image for efficient encoding of super-high resolution image and propose a reconstruction of reference picture list in inter-picture prediction using this information. The experimental results show that the proposed method improves the BD-Rate by 0.44% and reduces encoding time by 12.46% when compared to HM 16.12.

• Journal of Welding and Joining
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• v.34 no.2
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• pp.11-15
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• 2016

FE analyses for weldment of ship structure are required for various reasons such as stress concentration for bead tow, residual stress and distortion after welding, and hydrogen diffusion for prediction of low temperature crack. These analyses should be done by solid element modeling, but most of ship structures are modeled by shell element. If we are able to make solid element in the shell element FE modeling it is easily to solve the requirement for solid elements in weld analysis of large ship structures. As the nodes of solid element cannot take moments from nodes of shell element, these two kinds of element cannot be used in one model by conventional modeling. The PSCM (Perpendicular shell coupling method) can connect shell to solid. This method uses dummy perpendicular shell element for transferring moment from shell to solid. The target of this study is to develop a FE pre-processing system applicable at welding at ship structure by using PSCM. We also suggested glue-contact technique for controlling element numbers and element qualities and applied it between PSCM and solid element in automatic pre-processing system. The FE weldment modeling through developed pre-processing system will have rational stiffness of adjacent regions. Then FE results can be more reliable when turn-over of ship-block with semi-welded state or ECA (Engineering critical assessment) of weldment in a ship-block are analyzed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1164-1170
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• 2011

In this paper, we propose accurate lattice extraction of elemental image array and pre-processing methods in computational integral imaging. Pre-processing methods remove distortions and noises of the image. Such distortions occurred in pickup systems are rotational errors. Distortions will degrade the resolution of reconstructed images. To overcome this problem, we propose our methods for extraction of elemental image array and pre-processing methods. Also, we describe that distortions affect the high quality reconstruction. Optical and computational experiments indicate that reconstructed images applied our methods is better than reconstructed images unapplied our methods.

• The Journal of Information Technology
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• v.7 no.2
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• pp.43-54
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• 2004

Since Dr. John J. Hopfield has proposed the HOpfield network, it has been widely applied to the pattern recognition and the routing optimization. The method of Jian-Hua Li improved efficiency of Hopfield network which input pattern's weights are regenerated by SVD(singluar value decomposition). This paper deals with Li's Hopfield Network by linear pre-processing. Linear pre-processing is used for increasing orthogonality of input pattern set. Two methods of pre-processing are used, Hadamard method and random method. In manner of success rate, radom method improves maximum 30 percent than the original and hadamard method improves maximum 15 percent. In manner of success time, random method decreases maximum 5 iterations and hadamard method decreases maximum 2.5 iterations.

• Journal of Sensor Science and Technology
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• v.17 no.6
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• pp.406-413
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• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.86-91
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• 1991

A capability of performing the optical logic and arithmetic operations is followed by an effective encoding technique. In this paper, we proposed the color light encoding technique. By using this encoding technique, the space bandwidth product(SBP) is minimized in the output plane. In addition, we proposed the pre-operation pro-carry processing method that performs faster than the same time operation and carry processing method in optical computing. We proposed that the color liquid crystal device(CLCD) is used as the encoded color light input source.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1994.10a
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• pp.17-24
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• 1994

The pre- and post-processor for finite element structural analysis considering the user-friendly device are developed by using GUI. These can be used on WINDOWS' environment which is realized the multi-tasking and the concurrency by object-oriented paradigm. They are designed to control integratedly the pre-processing, execution and the post-processing of the finite element structural analysis program on multiple windows. These object-oriented modeling approach can be used for complex integrated engineering systems.

• Proceedings of the Korean Society of Dyers and Finishers Conference
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• 2009.03a
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• pp.43-44
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• 2009

Fabrics for Digital Textile Printing (DTP) are different from it of general textile printing. It is necessary to pre-treatment of chemical agents for desired quality. But this process does not correspond with simplification of DTP processing. In this research, we pre-treated of cotto is necessary to pre-treatment of chemical agents for desired quality. But this process does not correspond with simplification of DTP processing. In this research, we pre-treated of cotton fabric for DTP by atmosphere plasma treatment and we understood that pre-treatment of fabric by atmosphere plasma treatment was more simple DTP process.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.482-490
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• 2017

The radar signal processing procedure is divided into the pre-processing such as frequency down converting, down sampling, pulse compression, and etc, and the post-processing such as doppler filtering, extracting target information, detecting, tracking, and etc. The former is generally designed using FPGA because the procedure is relatively simple even though there are large amounts of ADC data to organize very quickly. On the other hand, in general, the latter is parallel processed by multiple DSPs because of complexity, flexibility and real-time processing. This paper presents the radar signal processor design using FPGA which includes not only the pre-processing but also the post-processing such as doppler filtering, bore-sight error, NCI(Non-Coherent Integration), CFAR(Constant False Alarm Rate) and etc.