• Membrane Journal
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• 제15권3호
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• pp.224-232
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• 2005

The use of the lung assist device (LAD) would be well suited for acute respiratory failure (ARF) patients, combining the simplicity of mechanical ventilation with the ability of extracoporeal membrane oxygenators (ECMO) to provide temporary relief for the natural lungs. This study's specific attention was focused on the effect of membrane vibration in the LAD. Quantitative experimental measurements were performed to evaluate the performance of the device, and to identify membrane vibration dependence on blood hemolysis. We tried to decide upon excited frequency band of limit hemolysis when blood hemolysis came to through a membrane vibration action. The excited frequency of the module type 5, consisted of 675 hollow fiber membranes, showed the maximum gas transfer rate. We concluded that the maximum oxygen transfer rate seemed to be caused by the occurrence of maximum amplitude and the transfer of vibration to hollow fiber membranes. It was excited up to $25{\pm}5$ Hz at each blood flow rate of module type 5. We found that this frequency became the 2nd mode resonance riequency of the flexible in blood flow. Blood hemolysis was low at the excited frequency of $25{\pm}5$ Hz. Therefore, we decided that limit hemolysis frequency of this LAD was $25{\pm}5$ Hz.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제11권7호
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• pp.2586-2594
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• 2010

A pure tone audiometer should be able to produce both pure tone and masking noise with exact sound pressure level and frequency. For such purpose, it is unavoidable to periodically calibrates pure tone audiometer. However, manual acoustic calibration requires not only attention but also long time. It is possible to execute automatically calibration using software if it is PC-based pure tone audiometer. In this paper, we describe auto-calibration software for PC-based pure tone audiometer and dedicated sound level meter which has been implemented upon PC by us. To verify auto-calibration module, we examine whether output of PC-based audiometer calibrated through auto-calibration of this paper satisfies RETSPL of IEC or not.

• Journal of Digital Convergence
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• 제19권9호
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• pp.189-199
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• 2021

In order to increase the self-reliance rate of new and renewable energy in order to respond to the mandatory domestic zero-energy buildings, the taller the building, the more limited the site area, and installing PV modules on the roof is not enough. Therefore, BIPV (Building integrated photovoltaic, hereinafter BIPV) is the industry receiving the most attention as a core energy source that can realize zero-energy buildings. Therefore, this study conducted a survey on the problems of the BIPV industry in a self-discussing method for experts with more than 10 years of experience of designers, builders, product manufacturers, and maintainers in order to suggest the right direction and revitalize the BIPV industry. Industrial problems of BIPV adjustment are drawn extention range of standard and certification for products, range improvement for current small condition of various kind productions, need to revise standards for capable of accomodating roof-type, color-module and louver-module, necessary of barrier in flow of foreign modules into korea through domestic certification mandatory, difficulty in obtaining BIPV information, request to prevent confusion among participants by exact guidelime about architectural application part of BIPV, and lack of the BIPV definition clearness, support policy, etc. Based on the improvements needed for the elements, giving change and competitiveness impacts aims to present and propose counter measures and direction.

• KIPS Transactions on Software and Data Engineering
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• 제12권1호
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• KIPS Transactions on Software and Data Engineering
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• 제12권12호
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• Journal of Broadcast Engineering
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• 제27권2호
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• pp.228-239
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• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.

• Korean Journal of Remote Sensing
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• 제39권2호
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2012년도 춘계학술대회
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• pp.743-744
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• 2012

LED digital control convergence technology is receiving attention. It enables to analyze lighting and living environments by recognizing user information and situations through a signal process system composed of a multi-functional composite sensor's module. LED lighting is higly efficient, long-lived, environmentally, and is possible to converge with communication, and receiving as a next-generation general lighting that will replace a florescent light including the light bulb. The proposed system is an intelligent LED control system that uses solar light. This study is about a lighting control technology associated with user-estimated information/situation and related low-cost technology. Also, this study aims to embody emotional lighting by appropriately lighting 10% of the discharge current with supplementary colored LED according to the surrounding environment.

• Journal of Korean Academy of Nursing
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• 제45권4호
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• pp.604-612
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• 2015

Purpose: The purpose of this study was to investigate the levels of physical symptoms, anxiety, depression, and quality of life (QOL) during chemotherapy for colorectal cancer patients in South Korea and to identify factors influencing their QOL. Methods: Data were collected from 144 colorectal cancer patients receiving chemotherapy during 2012 at one general hospital located in Seoul. Physical symptoms were measured by the M. D. Anderson Symptom Inventory-Gastrointestinal Cancer Module, and anxiety and depression were measured by the Hospital Anxiety Depression Scale. QOL was measured by the Functional Assessment of Cancer Therapy-Colorectal. Data were analyzed using descriptive statistics, t-test, one-way ANOVA, $Scheff{\acute{e}}$ post hoc test, Pearson correlation and stepwise multiple regression. Results: Mean age of the participants was 56.6 and most of them were not employed. In terms of cancer stage, 38.2% were in stage 3, followed by stage 4 (34.7%). The most frequent symptom was lack of appetite, followed by sleep disturbance and fatigue. The mean score for anxiety was 5.40 with a prevalence of 23% and that of depression 8.85 with a prevalence of 64.6%. The mean score for quality of life was 81.93 out of 136 and 75.3% of the variance in QOL was explained by depression, symptoms, anxiety, treatment place, and occupational status. Depression was the strongest predictive factor. Conclusion: Oncology professionals need to pay special attention to relieving depression as well as physical symptoms to improve QOL during chemotherapy for colorectal cancer patients.

• Journal of Satellite, Information and Communications
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• 제11권4호
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• pp.81-88
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• 2016

In this paper, we propose an adaptive multi-target estimation algorithm using the characteristics of signals in the IR-UWB(Impulse-Radio Ultra Wideband) radar system, which is attracting attention because it has good transparency, robustness to the indoor environment, and high precision positioning of tens of centimeters. We proposed an algorithm that estimates multiple peaks with the characteristic that the signal reflected by the target has a peak. To verify the performance of these algorithms, multiple targets were placed in front of the radar and the existing technique and the multi - target estimation algorithm were compared. The location of the targets is estimated in real time with one transmitting antenna and one receiving antenna. The number of estimates can be increased compared with the existing peak signal derivation method, and multiple targets can be derived. The conventional technique estimates only one target, which results in a mean square error of 1 while a multi - target estimation algorithm yields a result of about 0.05. The proposed method is expected to be able to apply multiple targets to the estimation and application in one IR-UWB module environment.