• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.251-258
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• 2020

This study investigated the effect of Zardaverine supplementation in freezing extender, on kinetic characteristics of post-thawed boar sperm. Cryopreservation of boar sperm is an important technique of assisted reproductive technology and genetic resource banking. Although this technique is particularly useful, freeze-thaw cycles associated with sperm cryopreservation significantly reduce sperm quality. Semen from mature Duroc boars were collected and cryopreserved in freezing extenders (LEY) treated with varying concentrations of Zardaverine (0, 20, 50, 75, 100 𝜇M). The time-dependent kinetic characteristics of post-thawed spermatozoa were determined after thawing by applying computer-assisted sperm analysis (CASA). We observed that the motility immediately after thawing was significantly higher in 20 𝜇M stocks than in control (0 𝜇M) and the other treatments (p<0.05). Curvilinear velocity (VCL) in 0 𝜇M and 20 𝜇M stocks were significantly higher than the other treatment groups, except 75 𝜇M (p<0.05). Higher average path velocity (VAP) was obtained at 20 𝜇M as compared to 100 𝜇M, whereas amplitude of head lateral displacement (ALH) was significantly higher at 20 𝜇M than 50 𝜇M and 100 𝜇M (p<0.05). No differences were obtained for Straight-line velocity (VSL) and Linearity (LIN). In conclusion, our results indicate that Zardaverine improves the motility, VCL, VAP, and ALH of post-thawed boar sperm.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.151.2-151.2
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• 2010

우리나라는 "제 3차 신재생에너지 기술개발 및 이용 보급 기본계획"을 통해 2030년까지 111.5조 원을 투자하여 전체 에너지의 11%를 신재생에너지로 공급한다는 목표를 설정하였다. 그러나 신재생에너지는 기존의 원자력이나 화석에너지에 비하여 생산비용이 높아 보조나 융자 등의 정부지원에 의존하여 보급이 이루어져왔다. 신재생에너지 보급확대 및 산업발전을 위한 보급정책의 일환으로 정부는 2012년부터 RPS(Renewable Portfolio Standard, 신재생에너지 공급의 무화제도)를 도입키로 확정하였다. RPS의 도입은 일정규모 신재생에너지 시장수요를 창출함과 동시에 신재생에너지원간 가격경쟁 구도 형성의 유인이 될 수 있다. 이는 전력가격 일괄상승 및 녹색가격제도(Green Pricing) 등의 정책적 논의로 이어질 수 있다. 따라서 소비자 측면에서 신재생에너지 전력의 가치를 어떻게 평가하는지를 분석하여 RPS 시행제반의 정책적 논의의 기초자료를 마련할 필요가 있다. 특히 RPS는 신재생에너지원 간의 경쟁을 가능하게 하므로 개별 신재생에너지원에 따라 소비자 선호의 차이가 어떻게 나타나는지 연구되어야 한다. 이에 본 연구는 환경재 혹은 비시장재화의 가치추정에 가장 널리 활용되고 있는 조건부가치평가법(Contingent Valuation Method; CVM)을 적용하여 풍력, 태양광, 수력으로 생산한 전력에 대한 소비자의 지불의사액(Willingness to Pay; WTP)을 분석하는 모형을 수립하였다. 이를 위해 Zografakis et al.(2010), Yoo and Kwak(2009), 이창훈 황석준(2009), Nomura and Akai(2004), Bately et al.(2001) 등의 선행연구를 참조하여 신재생에너지 전력 가치의 설문에서 고려되어야 하는 요인들을 선정하였다. 이를 토대로 설문 시나리오를 작성하여 각 요인들이 신재생에너지 전력에 대한 지불의사액 결정에 어떠한 영향을 미치는지 분석가능한 모형을 설정하였다. 뿐만 아니라 기존 연구들이 신재생에너지로 생산된 일반적인 전력에 대한 지불의사액을 질문하는데 그쳤다면, 본 연구에서는 각 원별로 지불의사액의 차이를 도출할 수 있는 설문모형을 구축하는데 중점을 두었다. 본 연구결과는 향후 설문수행을 통하여 신재생에너지원별로 소비자의 선호를 분석할 수 있는 연구로 발전될 수 있으며, 이는 RPS 도입으로 인한 전력가격 정책 수립의 기초 연구자료로 활용될 수 있다 하겠다.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.34-40
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• 2012

The fuel cell is one of the renewable energy sources. And it is a new source of energy that can be applied to various fuels and continuously supported by the excellent city-gas infrastructure. It is important to improve performances and reliabilities, and reduce the cost of fuel cell systems for commercialization. And, some safety performances of blower domestically produced are evaluated and some improvements are researched to save the cost of fuel cell systems. In this paper, the performance and worst stress condition of blowers are evaluated in operating environment similar to the fuel cell systems. Actually, the correlation of flow, leakage and thermal behavior are evaluated in the worst stress condition at $70^{\circ}C$ and, some major factors of blower degradation such as a motor deterioration, material and structures of the outlet are examined.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.1
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• pp.329-336
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• 2013

The infrastructure of flexible pavement is composed of aggregate subbase, anti-frost layer, and subgrade. In particular, the subgrade performance is affected by climates such as frost action and precipitation. The method of TDR(Time Domain Reflectometry) sensors to measure moisture contents in subgrade layer has been used in the research. Due to the TDR method using dielectric permitivity of soil and water, the sensors can be affected by the low subgrade temperatures. The air temperatures frequently drops below $-20^{\circ}C$ in the winter in Korea. As a result, it is necessary to estimate the accuracy of the TDR moisture sensors in the range of below zero temperatures. In this study, the subgrade temperatures of lower than $-2^{\circ}C$ were extended to evaluate temperature sensitivity of the TDR moisture sensors. The test results revealed that the moisture contents around the sensors were reduced while those of the upper part of specimen showed a tendency to increase as the specimen surface temperature drops below zero under the volumetric moisture contents(VMC) of 20% and 30%. However, the impact of temperature on the function of the sensor at lower water contents was found to be negligible if any.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.47-57
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• 2012

This study was conducted to investigate the pre-service teacher's perception of the possibility of using smartphones in the classroom, moreover, to confirm the policy related to using smartphones in schools. For the objectives, this study, firstly, investigated the pre-service teacher's awareness of having cellphones in the classroom, secondly, analyzed the pre-service teacher's opinion of using smartphones for educational objectives and elements for those investigated objectives, finally, investigated the school policy for educational objectives of using smartphones. The participants of this study were 146 pre-service teachers among three universities in Seoul. The results showed that the pre-service teachers opposed using cellphones in the classroom. Next, it was found that most of them had smartphones and they knew how to use them effectively. For the aspects of educational use of smartphones, they recognized that smartphones could be used as a smart educational tool, an efficient teaching and learning tool, and an assistant tool for teaching and learning. In order to use smartphones for the investigated educational tools, the learning contents, the ways of teaching and learning, and the technical support of the school should be prepared. Finally, the pre-service teachers thought that the school policy should be changed in order to use smartphones for educational objectives, and the school policy with regard to using smartphones in the classroom should be decided by the teachers. Most of all, for the educational use of smartphones, the pre-service teachers believed that the change of the students' perception was the most significant.

• Journal of Agricultural Extension & Community Development
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• v.20 no.4
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• pp.953-987
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• 2013

This study aimed at identifying level of self-sufficiency, and support need for it in rural multicultural families. Frist, the level of self-sufficiency in rural multicultural families was the lowest in a information sub-area, whereas it was the highest in a socio-psychological relation sub-area. Second, the chi-square test showed that the level of assistant request for a cost-of-living allowance was high in the multicultural family group received the medical social security(MSS) or not prepared the expenditure for children education or the golden years. Whereas the level of assistant request for the education of marketing or agricultural technology was high in the multicultural family group not received MSS or prepared the expenditure for children education or the golden years. Third, rate of PC ownership in the rural multicultural families was lower than that of national whole. and difference of it according to the living characteristics uch as MSS was not statistically significant. Fourth, difference in level of assistant request for children education and social dimension according to the living characteristics such as MSS was not statistically significant. It means that assistant request for children education and social dimension have universality without distinction the living characteristics such as MSS. And to conclude, support for self-sufficiency in rural multicultural families should be selective approach with discriminative or integrational viewpoint according to the living characteristics such as MSS or area of self-sufficiency. Findings of this study may be used as a basic material to establish the policy supporting self-sufficiency in rural multicultural families.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.