• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.757-770
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• 2022

Palmprint recognition has drawn increasingly attentions in the past decade due to its uniqueness and reliability. Traditional palmprint recognition methods usually use high-resolution images as the identification basis so that they can achieve relatively high precision. However, high-resolution images mean more computation cost in the recognition process, which usually cannot be guaranteed in mobile computing. Therefore, this paper proposes an improved low-resolution palmprint image recognition method based on residual networks. The main contributions include: 1) We introduce a channel attention mechanism to refactor the extracted feature maps, which can pay more attention to the informative feature maps and suppress the useless ones. 2) The ResStage group structure proposed by us divides the original residual block into three stages, and we stabilize the signal characteristics before each stage by means of BN normalization operation to enhance the feature channel. Comparison experiments are conducted on a public dataset provided by the Hong Kong Polytechnic University. Experimental results show that the proposed method achieve a rank-1 accuracy of 98.17% when tested on low-resolution images with the size of 12dpi, which outperforms all the compared methods obviously.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.403-410
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• 2019

Image de-snowing aims at eliminating the negative influence by snow particles and improving scene understanding in images. In this paper, a perceptual generative adversarial network based a single image snow removal method is proposed. The residual U-Net is designed as a generator to generate the snow free image. In order to handle various sizes of snow particles, the inception module with different filter kernels is adopted to extract multiple resolution features of the input snow image. Except the adversarial loss, the perceptual loss and total variation loss are employed to improve the quality of the resulted image. Experimental results indicate that our method can obtain excellent performance both on synthetic and realistic snow images in terms of visual observation and commonly used visual quality indices.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.49-52
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• 2005

The purpose of this study is to investigate the relation between color change and residual compressive strength in concrete exposed to high temperature. In order to study the color image analysis, the specimens have been tested with variables of concrete strengths (20Mpa, 40Mpa, 60Mpa) in transient heating conditions($800^{\circ}C$ heating and 4 hour preservation). The results show that the residual strength of specimens are coincident with the full development of the pink/red color and the method may be used to define the distance from a heated surface where strength degradation has occurred.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.151-164
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• 2005

The regularized structured total least norm (RSTLN) method finds an approximate solution x and error matrix E to the overdetermined linear system (H + E)x $\approx$ b, preserving structure of H. A new separation scheme by parts of variables for the regularized structured total least norm on blind deconvolution problem is suggested. A method combining the regularized structured total least norm method with a separation by parts of variables can be obtain a better approximated solution and a smaller residual. Computational results for the practical problem with Block Toeplitz with Toeplitz Block structure show the new method ensures more efficiency on image restoration.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.2
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• pp.55-61
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• 2002

In this paper, 'lifetime-temperature rise model' based on the 'lifetime-resistance model' is theoretically Proposed, in order to find out the evaluation method of degradation and the residual lifetime by use of infrared image camera for electric connections/contacts. Two assumptions have been builded up for the 'lifetime-temperature rise model': one is associated with the linear relationship between the temperature ism ΔK and contact resistance, and the other the functional relationship between the temperature of electric connections/contacts and the operating time presenting in the 'lifetime-resistance model'. To prove the proposed model, experiments have been performed for various electric connections/contacts. From the experimental results, measured values were quite similar to the calculated values, which proved the above-mentioned two assumptions. Therefore, by use of 'lifetime-temperature rise model', it is possible to estimate the trend of degradation and the residual lifetime for electric connections/contacts through the temperature measurements .

• Current Optics and Photonics
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• v.7 no.5
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• pp.511-517
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• 2023

Pipelines play an important role in urban water supply and drainage, oil and gas transmission, etc. This paper presents a technique for pattern recognition of fiber optic vibration signals collected by a distributed vibration sensing (DVS) system using a deep learning residual network (ResNet). The optical fiber is laid on the pipeline, and the signal is collected by the DVS system and converted into a 64 × 64 single-channel grayscale image. The grayscale image is input into the ResNet to extract features, and finally the K-nearest-neighbors (KNN) algorithm is used to achieve the classification and recognition of pipeline damage.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.119-121
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• 2019

부가적 백색 잡음 모델(additive white Gaussian noise, AWGN에서 학습된 깊은 신경만 (deep neural networks)을 이용한 잡음 제거기는 제거하려는 잡음이 AWGN인 경우에는 뛰어난 성능을 보이지만 실제 카메라 잡음에 대해서 잡음 제거를 시도하였을 때는 성능이 크게 저하된다. 본 논문은 U-Net 구조의 깊은 인공신경망 모델에 residual block을 결합함으로서 실제 카메라 영상에서 기존 알고리즘보다 뛰어난 성능을 지니는 신경망을 제안하다. 제안한 방법을 통해 Darmstadt Noise Dataset에서 PSNR과 SSIM 모두 CBDNet 대비 향상됨을 확인하였다.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.621-637
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• 2005

A hybrid method solving regularized structured linear total least norm (RSTLN) problems, which have highly ill-conditioned coefficient matrix with special structures, is suggested and analyzed. This scheme combining RSTLN algorithm and separation by parts guarantees the convergence of parameters and has an advantages in reducing the residual norm and relative error of solutions. Computational tests for problems arisen in signal processing and image formation process confirm that the presenting method is effective for more accurate solutions to (R)STLN problem than the (R)STLN algorithm.

• Journal of Information Display
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• v.9 no.4
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• pp.39-44
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• 2008

This paper investigates the effects of the existing sealing methods, such as the conventional atmospheric-pressure sealing method and vacuum sealing, on temporal bright-image sticking. To produce a residual image caused by temporal brightimage sticking, the entire region of a 42-in panel with an Xe-(11%)-He(35%) gas mixture was abruptly changed to a full-white background image after displaying a square-type image at peak luminance for about 60s. From the monitoring of the difference in the display luminance, infrared emission, color temperature, and disappearing time between the cells with and without temporal bright-image sticking, it was observed that the vacuum sealing method contributes to the reduction of temporal bright-image sticking.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.338-339
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• 2021

In this paper, we proposed a multiclass image scene classification method based on transform learning. The method using the Residual Network (ResNet) model which pre-trained on the large image dataset ImageNet for image classification. Compared with the image classification method of the CNN model, it can greatly improve the classification accuracy and efficiency