• Journal of the Korean Wood Science and Technology
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• 제46권4호
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• pp.309-314
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• 2018

Size reduction is an important pre-processing operation for utilizing biomass as a sustainable resource in industrial-scale energy production and as a raw material for other industries. This work investigates the size reduction characteristics of air-dried Miscanthus sacchariflorus Goedae-Uksae 1 (Amur silver grass) via image processing and identifies the morphological characteristics of comminuted and screened M. sacchariflorus. At chopping lengths of 18, 40, 80, and 160 mm, 81%, 77%, 78%, and 76% of the particles, respectively, passed through a 4-mm sieve. Even a knife mill with a very small screen aperture (>1 mm) admitted over 10% of the particles. The average circularity and aspect ratio of the particles were <0.30 and >10, respectively. These results confirm that in all preparation modes, most M. sacchariflorus particles were needle-like in shape, irrespective of the type of preparation.

• 한국인공지능학회지
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• 제11권1호
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• pp.17-24
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• 2023

With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.

• 한국실내디자인학회논문집
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• 제27권3호
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• pp.136-144
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• 2018

The purpose of this study is to examined the unconscious response of the pupil in the observation toward the image in the eye-tracking experiments that target on a large complex cultural space. Twenty-five men participated in this experiment and the observation data were analyzed in seconds per minute on watching time. It could examine the unconscious response of information searching in the change of pupil size in the process of observing the space. The results could be defined as following several points. First, it was possible to outline the unconscious response characteristics of pupil by analyzing sudden changes in pupil size as total, cumulative, and individual. The response characteristics using frequency and time can be utilized as the analytical method to examine the degree of interest of spatial components according to the purpose of analysis in the future. Second, according to the over ${\pm}5%$ of cumulative variation rate on the pupil size change, during in the 60 seconds, the continuous pupil dilation was used 25.2 seconds in 8.8 rounds and the pupil reduction was used 18.0 seconds in 7.0 rounds. Third, when the variation rate of ${\pm}5%$ or more was regarded as the sudden changes on pupil size by individual variation, the pupil dilation was 7.2 rounds of 8.6 seconds and pupil reduction was 6.0 seconds in 5.0 rounds. This means that the pupil increases 9.3% in one expansion and decreases -8.5% in the reduction process. As regarding pupil changes as cumulative rate, it appeared high change rate on pupil reduction but it became higher on pupil dilation in individual.

• 대한방사선기술학회지:방사선기술과학
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• 제46권6호
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• pp.475-484
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• 2023

This study aimed to compare filters for reducing speckle noise in ultrasound images using clinical liver images. We acquired the clinical liver ultrasound images, and noisy images were obtained by adding 0.01, 0.05, 0.10, and 0.50 intensity levels of speckle noise to the liver images. The Wiener filter, median modified Wiener filter, gamma filter, and Lee filter were designed for the noisy images by setting window sizes at 3×3, 5×5, and 7×7. The coefficient of variation (COV) and contrast to noise ratio (CNR) were calculated to evaluate noise reduction and various filters. Moreover, the filter with the highest image quality was selected and quantitatively compared to a noisy image. As a result, COV and CNR showed the noise improved result when the Lee filter was applied. Furthermore, the Lee filter image with a window size of 7×7 was noted to possess approximately a minimum of 1.28 to a maximum of 3.38 times better COV and a minimum of 2.18 to a maximum of 5.50 times better CNR than the noisy image. In conclusion, we confirmed that the Lee filter was effective in reducing speckle noise and proved that an appropriate window size needs to be set considering blurring.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.275-280
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• 2021

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

• 한국멀티미디어학회논문지
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• 제25권5호
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• pp.676-684
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• 2022

As realistic media are widespread in various image processing areas, image or video compression is one of the key technologies to enable real-time applications with limited network bandwidth. Generally, image or video compression cause the unnecessary compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a Deep Residual Channel-attention Network, so called DRCAN, which consists of an input layer, a feature extractor and an output layer. Experimental results showed that the proposed DRCAN can reduced the total memory size and the inference time by as low as 47% and 59%, respectively. In addition, DRCAN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed images compared to the previous methods.

• 생물정신의학
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• 제19권4호
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• pp.211-218
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• 2012

Objectives Body image distortion is found in eating disorder and obesity and there are some evidence that schizophrenia is associated with body image distortion. This study sought to find whether schizophrenic patients report more body image distortion than healthy individuals and whether it is related with symptomatology. Methods A total of 88 inpatients with schizophrenia and 88 healthy controls were recruited. Weight, height, and body image accuracy were assessed in all participants, and assessment of mood, psychotic symptom severity and self-esteem, and personal and social performance scale were conducted. Results The patients with schizophrenia had higher Body Mass Index (p < 0. 001) and underestimated their body size more than controls (26.14% vs. 5.13%, p < 0.001). Multiple regression analysis showed that lower depressive symptoms and higher scores of general psychopathology predicted underestimation of body size. Conclusion Weight gain and metabolic syndrome are common adverse events of pharmacological treatment of schizophrenia. Thus, underestimation of body size among patients with schizophrenia may interfere with effort to lose weight or seek weight reduction programs. Clinicians need to consider possible unterestimation of underestimation of body size in patients whose general symptomatology is severe.

• 전자공학회논문지B
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• 제28B권5호
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• pp.318-326
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• 1991

A Quantizer reconstruction level control method for block artifact reduction in DCT image coding is described. In our scheme, quantizer reconstruction level control is obtained by adding quantization level step size to the optimum quantization level in the direction of reducing the block artifact by minimizing the mean square error(MSE) and error difference(EDF) distribution in boundary without the other additional bits. In simulation results, although the performance in terms of signal to noise ratio is degraded by a little amount, mean square of error difference at block boundary and mean square error having relation block artifact is greatly reduced. Subjective image qualities are improved compared with other block artifact reduction method such as postprocessing by filtering and trasform coding by block overlapping. But the addition calculations of 1-dimensional DCT become to be more necessary to coding process for determining the reconstruction level.

• 방송공학회논문지
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• 제21권2호
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• pp.252-260
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• 2016

사람 검출의 성능은 카메라의 위치 및 각도 등에 큰 영향을 받는다. 이로 인해 획득한 2D 영상에서 사람은 위치에 따라 각기 다른 크기를 갖는 형태로 나타난다. 이렇게 다양한 크기를 갖는 사람들을 모두 검출하는 것은 실시간 시스템의 구현을 어렵게 만드는 요인이 된다. 그러나 만일 영상의 특정 위치의 사람의 크기를 예측할 수 있다면, 해당 위치의 사람 검출을 위한 연산량이 크게 감소될 수 있을 것이다. 본 논문에서는 실내 공간의 구조를 깊이맵으로 구성하고, 실내 공간에 존재하는 사람의 영상을 3D 공간에 재구성함으로써 크기를 예측하는 기법을 제안한다. 3D 공간에서는 어느 위치에서든지 사람의 크기가 일관되므로 이를 2D 영상으로 투영하게 되면 2D 영상의 좌표에 따른 정확한 사람의 크기를 추정할 수 있다. 실험 결과로부터 제안 방법이 효과적으로 사람의 크기를 예측할 수 있고, 기존이 기계학습 기반 사람 검출 방법들의 처리속도가 감소됨을 증명하였다.

• Earthquakes and Structures
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• 제8권4호
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• pp.821-841
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• 2015

This paper proposes a two-stage imaging approach for quantitative inspection of damages in metallic plates using the fundamental anti-symmetric mode of ($A_0$) Lamb wave. The proposed approach employs a number of transducers to transmit and receive $A_0$ Lamb wave pulses, and hence, to sequentially scan the plate structures before and after the presence of damage. The approach is applied to image the corrosion damages, which are simplified as a reduction of plate thickness in this study. In stage-one of the proposed approach a damage location image is reconstructed by analyzing the cross-correlation of the wavelet coefficient calculated from the excitation pulse and scattered wave signals for each transducer pairs to determine the damage location. In stage-two the Lamb wave diffraction tomography is then used to reconstruct a thickness reduction image for evaluating the size and depth of the damage. Finite element simulations are carried out to provide a comprehensive verification of the proposed imaging approach. A number of numerical case studies considering a circular transducer network with eight transducers are used to identify the damages with different locations, sizes and thicknesses. The results show that the proposed methodology is able to accurately identify the damage locations with inaccuracy of the order of few millimeters of a circular inspection area of $100mm^2$ and provide a reasonable estimation of the size and depth of the damages.