• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.9
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• pp.2341-2348
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• 1996

This paper proposes an image compression algorithm that adopts projection scheme on mean-residual metod. Sub-blocks of an image are encoded using mean-residual method where mean value is predicted according to that of neighboring blocks. Projection scheme with 8 directions is applied to the compression of residual signals of blocks. Projection vectors are finite-state vector quantized according to the projection angle of nighboring blocks in order to exploit the correlation among them. Side information to represent the repetition of projection is run-length coded while the information for projection direction is compressed using entropy encoding. The proposed scheme apears to be better in PSNR performance when compared with conventional projection scheme as well as in subjective quality preserving the edges of images better than most tranform methods which usually require heavy computation load.

• Journal of Welding and Joining
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• v.28 no.3
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• pp.59-64
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• 2010

The purpose of this study is to identify the principal factor controlling transverse residual stress at the weldment for joining unit hull blocks. In order to do it, the comprehensive FE analyses were carried out to evaluate the effect of distance between fillet and butt weldments, stiffener span and in-plane restraint degree on the amount and distribution of transverse residual stress in way of the weldments between unit hull blocks. In accordance with FEA results, principal factor controlling the amount of transverse residual stress at the weldments was identified as in-plane restraint degree of butt weldment for unit blocks. The effect of other variables on the transverse residual stress was very small relatively.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.94-94
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• 2009

The purpose of this study is to identify the principal factor controlling transverse residual stress at the weldment for joining unit hull blocks. In order to do it, the comprehensive FE analyses were carried out to evaluate the effect of distance between fillet and butt weldments, in-plane and out-of-plane restraint degree on the amount and distribution of transverse residual stress in way of the weldments between unit hull blocks. In accordance with FEA results, principal factor controlling the amount of transverse residual stress at the weldments was identified as in-plane restraint degree of butt weldment for unit blocks. The effect of other variables on the transverse residual stress was very small relatively. Based on the results, it can be concluded that the proper distance between fillet weldment for stiffener and butt weldment for joining unit blocks should be determined in consideration of in-plane restraint intensity of butt weldments.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• Proceedings of the KIEE Conference
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• 1998.11c
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• pp.907-909
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• 1998

This paper describes the effect of impulse current on degradation of ZnO blocks. In this study, an impulse current generator which can produce 8/20 [${\mu}s$], 3 [kA] and 4/10 [${\mu}s$], 5 [kA] waveform is designed and fabricated to simulate the lightning impulse current. The residual voltage, reference voltage, and leakage current flowing to the ZnO blocks are observed. The experimental results show that the leakage current increases continuously with the number of applied impulse current, but no significant changes in residual voltage and in operating voltage are observed until the ZnO block is destroyed. Also, it is confirmed that the main factor on degradation of ZnO blocks is rather the total energy applied to ZnO blocks than the peak value of the impulse current.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.7
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• pp.550-555
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• 1999

This paper deals with the effect of lightning impulse current on electrical characteristics of ZnO blocks used in distribution lightning arrester. The electrical characteristics of ZnO blocks are degraded by overtime impulse current, and the degraded ZnO block is brought to a thermal runaway and finally destroyed. It is therefore important to estimate the change of electrical characteristics of ZnO blocks. In this study, an impulse current generator which can produce 8/20$[\mus]$, 3[㎄] and 4/10$[\mus]$, 5[㎄] waveform is designed and fabricated to simulate the lightning impulse current of power systems. Total energy applied to the ZnO blocks at each time is 739[J] in 8/20$[\mus]$, and 523[J] in 4/10$[\mus]$, impulse current, respectively. From the experimental results, the 3rd harmonic of the leakage current increases continuously with the number of applied impulse current, but no significant changes in residual voltage and in reference voltage are observed until the ZnO block is destroyed. Also, it is confirmed that the main factor on degradation of ZnO blocks is rather the total energy applied to ZnO blocks than the peak value of the impulse current.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.102-105
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• 2019

In the classical single-image super-resolution (SISR) reconstruction method using convolutional neural networks, the extracted features are not fully utilized, and the training time is too long. Aiming at the above problems, we proposed an improved SISR method based on a residual network. Our proposed method uses a feature fusion technology based on improved residual blocks. The advantage of this method is the ability to fully and effectively utilize the features extracted from the shallow layers. In addition, we can see that the feature fusion can adaptively preserve the information from current and previous residual blocks and stabilize the training for deeper network. And we use the global residual learning to make network training easier. The experimental results show that the proposed method gets better performance than classic reconstruction methods.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.4
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• pp.173-180
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• 2004

The tread pattern of a tire has an important effect on tire performances such as handling, wear, noise, hydroplaning and so on. However, a finite element analysis of a patterned tire with detailed tread blocks has been limited owing to the complexity of making meshes for tread blocks and the huge computation time. The computation time has been shortened due to the advance in the computer technology. The modeling of tread blocks usually requires creating a solid model using a CAD software. Therefore it is a very complicated and time-consuming job to generate meshes of a patterned tire using a CAD model. A new efficient and convenient method for generating meshes of a patterned tire has been developed. In this method, 3-D meshes of tread pattern are created by mapping 2-D meshes of tread geometry onto 3-D tread surfaces and extruding them through tread depth. Then, the tread pattern meshes are assembled with the tire body meshes by the tie contact constraint. Residual aligning torque and frictional energy are calculated by using a patterned tire model and compared to the experimental results. It is shown that the calculated results of a patterned tire model are in a good agreement with the experimental ones.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.323-326
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• 2016

High Efficiency Video Coding (HEVC) adopts intra transform skip mode, in which a residual block is directly quantized in the pixel domain without transforming the block into the frequency domain. Intra transform skip mode provides a significant coding gain for screen content. However, when intra-prediction errors are not transformed, the errors are often correlated along the intra-prediction direction. This paper introduces a residual differential pulse code modulation (DPCM) method for the intra-predicted and transform-skipped blocks to remove redundancy. The proposed method performs pixel-by-pixel residual prediction along the intra-prediction direction to reduce the dynamic range of intra-prediction errors. Experimental results show that the transform skip mode's Bjøntegaard delta rate (BD-rate) is improved by 12.8% for vertically intra-predicted blocks. Overall, the proposed method shows an average 1.2% reduction in BD-rate, relative to HEVC, with negligible computational complexity.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.105-110
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• 2022

Traffic sign recognition plays an important role in solving traffic-related problems. Traffic sign recognition and classification systems are key components for traffic safety, traffic monitoring, autonomous driving services, and autonomous vehicles. A lightweight model, applicable to portable devices, is an essential aspect of the design agenda. We suggest a lightweight convolutional neural network model with residual blocks for traffic sign recognition systems. The proposed model shows very competitive results on publicly available benchmark data.