• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.232-237
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• 2003

A new displacement-based transmitting boundary is developed for the transient analysis of dynamics interactions between flexible dam body and reservoir impounding compressible water The mechanical model is derived analytically in time domain from the kernel function, Bessel function, appearing in the convolution integral and corresponding mechanical model is developed that consists of mass, damping and stiffness matrices. The resulting system of, equations uses displacement degrees of freedom. Hence it can be coupled directly with the displacement-based solid finite element model of dam body, linear of nonlinear. The method was applied to the rigid and flexible dam models. The results showed very good agreement : with the semi-analytic frequency domain solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2191-2208
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• 2022

Motion blur in PET (Positron emission tomography) images induced by respiratory motion will reduce the quality of imaging. Although exiting methods have positive performance for respiratory motion correction in medical practice, there are still many aspects that can be improved. In this paper, an improved 3D unsupervised framework, Res-Voxel based on U-Net network was proposed for the motion correction. The Res-Voxel with multiple residual structure may improve the ability of predicting deformation field, and use a smaller convolution kernel to reduce the parameters of the model and decrease the amount of computation required. The proposed is tested on the simulated PET imaging data and the clinical data. Experimental results demonstrate that the proposed achieved Dice indices 93.81%, 81.75% and 75.10% on the simulated geometric phantom data, voxel phantom data and the clinical data respectively. It is demonstrated that the proposed method can improve the registration and correction performance of PET image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.205-208
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• 2022

Multi-exposure high dynamic range (HDR) image reconstruction, the task of reconstructing an HDR image from multiple low dynamic range (LDR) images in a dynamic scene, often produces ghosting artifacts caused by camera motion and moving objects and also cannot deal with washed-out regions due to over or under-exposures. While there has been many deep-learning-based methods with motion estimation to alleviate these problems, they still have limitations for severely moving scenes. They also require large parameter counts, especially in the case of state-of-the-art methods that employ attention modules. To address these issues, we propose a frequency domain approach based on the idea that the transform domain coefficients inherently involve the global information from whole image pixels to cope with large motions. Specifically we adopt Residual Fast Fourier Transform (RFFT) blocks, which allows for global interactions of pixels. Moreover, we also employ Depthwise Overparametrized convolution (DO-conv) blocks, a convolution in which each input channel is convolved with its own 2D kernel, for faster convergence and performance gains. We call this LFFNet (Lightweight Frequency Fusion Network), and experiments on the benchmarks show reduced ghosting artifacts and improved performance up to 0.6dB tonemapped PSNR compared to recent state-of-the-art methods. Our architecture also requires fewer parameters and converges faster in training.

• Progress in Medical Physics
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• v.18 no.2
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• pp.55-64
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• 2007

Our objective was to evaluate the clinical feasibility of spatial domain filtering as an alternative to additional image reconstruction using different kernels in CT. Kernels were grouped as H30 (head medium smooth), B30 (body medium smooth), S80 (special) and U95 (ultra sharp). Derived from thin coilimated source images, four sets of images were generated using phantom kernels. MTF (50%, 10%, 2%) measured with H30 (3.25, 5.68, 7.45 Ip/cm) B30 (3.84, 6.25, 7.72 Ip/cm), S80 (4.69, 9.49, 12.34 Ip/cm), and U95 (14.19, 20.31, 24.67 Ip/cm). Spatial resolution for the U95 kernel (0.6 mm) was 33.3% greater than that of the H30 and B30 (0.8 mm) kernels. Initially scanned kernels images were rated for subjective image qualify, using a five-point scale. Image scanned with a convolution kernel led to an increase in noise (U95), whereas the results for CT attenuation coefficient were comparable. CT images increase the diagnostic accuracy in head (H30), abdomen (B30), temporal bone and lung (U95) kernels may be controlled by adjusting CT various algorithms, which should be adjusted to take into account the kernels of the CT undergoing the examination.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.642-652
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• 2009

In order to improve the performance of the existing relation extraction approaches, we propose a method for combining two pivotal concepts which play an important role in classifying semantic relationships between entities in text. Having built a composite kernel-based relation extraction system, which incorporates both entity features and syntactic structured information of relation instances, we define nine classes of lexical features and synthetically apply them to the system. Evaluation on the ACE RDC corpus shows that our approach boosts the effectiveness of the existing composite kernels in relation extraction. It also confirms that by integrating the three important features (entity features, syntactic structures and contextual lexical features), we can improve the performance of a relation extraction process.

• Fibers and Polymers
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• v.2 no.3
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• pp.164-170
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• 2001

Authors studied on the applicability of image analysis technique using a scanner with a CCD (charged coupled deviced) to the evaluation of evenness of nonwovens because it has distinctive features to considerably save time and labor in the analysis compared with other classical methods. As specimens fur the experiment, two different types that are unpatterned and patterned ones were prepared. For the unpatterned specimen, webs were chemically bonded, while for the patterned specimen, webs being thermally calendered with engraved roller. Several webs having various areal densities were prepared and bonded. Coefficient of variation (CV%) was used as a parameter to evaluate the evenness. Scanning conditions could be suitably set up through comparing the total variance to the between-group variance and to the within-group variance, respectively, on the images scanned at the different conditions. The 2D convolution method with smoothing filter kernel was introduced to further filter the noises on the scanned images. After the filtering process, the increase of web areal densities gave an uniform decrease of the CV%. This showed that the scanned image analysis with proper filtering process could be successfully applicable to the evaluation of evenness in nonwovens.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.1
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• pp.23-30
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• 1980

The reconstruction problem in a low dose case requires some compromise of resolution and noise artifacts, and also some modification of filter kernels depending on the signal-to-noise ratio of projection data. In this paper, ail algorithm for the reconstruction of an image function from noisy projection data is suggested, based on minimum-mean-square error criterion. Modification of the falter kernel is made from information (statistics) obtained from the projection data. The simulation study Proves that this algorithm, based on the Wiener falter approach, provides substantially improved image with reduction of noise as well as improvement of the resolution. An approximate method was also studied which leads to the possible use of a recursive filter in the convolution process of image reconstruction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2425-2430
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• 2013

This paper presents an shifted linear interpolation method with an image-dependent parameter. The previous shifted linear interpolation proposed the optimal shift parameter of 0.21, which is calculated by spectrum analysis of the shifted linear interpolation kernel. However, the parameter can be different if we takes an input image spectrum into account. Thus, we introduce an image-dependent parameter. An experiment shows the best shift parameter is 0.19 in average for real images. Also, simulation results indicate the proposed method is superior to the existing shifted linear interpolation as well as conventional methods such as linear interpolation and cubic convolution interpolation in terms of the subjective and objective image quality.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.85-92
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• 2020

Deep learning shows outstanding performance in image and video analysis, such as object classification, object detection and semantic segmentation. In this paper, it is analyzed that the performances of deep learning models can be affected by characteristics of train dataset. It is proposed as a method for selecting activation function and optimization algorithm of deep learning to classify facial expression. Classification performances are compared and analyzed by applying various algorithms of each component of deep learning model for CK+, MMI, and KDEF datasets. As results of simulation, it is shown that genetic algorithm can be an effective solution for optimizing components of deep learning model.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.37-45
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• 2018

The current system upon which a variety of programs are in operation has continuously expanded its domain from conventional single-core and multi-core system to many-core and heterogeneous system. However, existing researches have focused mostly on parallelizing programs based CUDA framework and rarely on AMD based GCN-GPU optimization. In light of the aforementioned problems, our study focuses on the optimization techniques of the GCN architecture in a GPGPU environment and achieves a performance improvement. Specifically, by using performance techniques we propose, we have reduced more then 30% of the computation time of matrix multiplication and convolution algorithm in GPGPU. Also, we increase the kernel throughput by more then 40%.