• 한국지구과학회지
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• 제28권5호
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• pp.572-584
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• 2007

The quantification of ammonia concentrations has received a lot of scientific attention. Numerous devices for the quantification of $NH_3$ in the ambient air have been developed to provide more technical possibilities for research in abating $NH_3$ emission from various source processes. For the proper quantification of $NH_3$, a number of sampling methods have been discussed by grouping them into different categories based on the principle of functioning. In general, active samplers employ pumps to draw air in, while passive samplers are exposed to air over a certain period of time to obtain integrated signature of $NH_3$. In case of the former, impingers and absorption flasks can be employed simultaneously with suitable absorbents to capture $NH_3$ passing through them. The methods of analysis include both in-situ and laboratory determination. In the laboratory, colorimetric or ion chromatographic methods are generally used for its quantification. In the field, a number of real time analyzers have been proven to be useful. These real time analyzers can be grouped according to their principle of operation. These analyzers may use the principle of spectroscopy (e.g. DOAS), photoacousticics (e.g. photoacoustic monitor) or Chemiluminescence ($NO_x$ analyzer). The automated annular denuder sampling system with on-line analyzer is also suitable for continuous monitoring of ammonia in air.

• International journal of advanced smart convergence
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• 제7권1호
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• pp.42-47
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• 2018

Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.

• Smart Structures and Systems
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• 제19권1호
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• pp.39-46
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• 2017

Finite element model updating is very effective procedure to determine the uncertainty parameters in structural model and minimize the differences between experimentally and numerically identified dynamic characteristics. This procedure can be practiced with manual and automatic model updating procedures. The manual model updating involves manual changes of geometry and analyses parameters by trial and error, guided by engineering judgement. Besides, the automated updating is performed by constructing a series of loops based on optimization procedures. This paper addresses the ambient vibration based finite element model updating of long span reinforced concrete highway bridges using manual model updating procedure. Birecik Highway Bridge located on the $81^{st}km$ of Şanliurfa-Gaziantep state highway over Firat River in Turkey is selected as a case study. The structural carrier system of the bridge consists of two main parts: Arch and Beam Compartments. In this part of the paper, the arch compartment is investigated. Three dimensional finite element model of the arch compartment of the bridge is constructed using SAP2000 software to determine the dynamic characteristics, numerically. Operational Modal Analysis method is used to extract dynamic characteristics using Enhanced Frequency Domain Decomposition method. Numerically and experimentally identified dynamic characteristics are compared with each other and finite element model of the arch compartment of the bridge is updated manually by changing some uncertain parameters such as section properties, damages, boundary conditions and material properties to reduce the difference between the results. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of long span highway bridges. Maximum differences between the natural frequencies are reduced averagely from %49.1 to %0.6 by model updating. Also, a good harmony is found between mode shapes after finite element model updating.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• 대한한의학회지
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• 제42권4호
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• pp.102-119
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• 2021

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, using a fully automated facial shape analysis system, we show that facial morphological features are associated with cold pattern. Methods: The facial morphological features calculated from 68 facial landmarks included the angles, areas, and distances between the landmark points of each part of the face. Cold pattern severity was determined using a questionnaire and the cold pattern scores (CPS) were used for analysis. The association between facial features and CPS was calculated using Pearson's correlation coefficient and partial correlation coefficients. Results: The upper chin width and the lower chin width were negatively associated with CPS. The distance from the center point to the middle jaw and the distance from the center point to the lower jaw were negatively associated with CPS. The angle of the face outline near the ear and the angle of the chin line were positively associated with CPS. The area of the upper part of the face and the area of the face except the sensory organs were negatively associated with CPS. The number of facial morphological features that exhibited a statistically significant correlation with CPS was 37 (unadjusted). Conclusions: In this study of a Korean population, subjects with a high CPS had a more pointed chin, longer face, more angular jaw, higher eyes, and more upward corners of the mouth, and their facial sensory organs were relatively widespread.

• 한국지반신소재학회논문집
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• 제22권3호
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• pp.27-35
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• 2023

굴착 공사 중 가시설의 안정성을 확보하는 것은 매우 중요하다. 설계 시 지반의 안정성을 수치해석을 통해 분석하고 있지만, 시공시에는 여건이 달라지기 때문에 계측으로 벽체 안정성을 분석하는 일은 필수불가결한 일이다. 공사현장에서의 계측비용은 매우 낮은 단가로 책정되어있으며 이를 통해 흙막이 벽체의 사고위험성은 예측되고 있다. 따라서 본 연구에서는 흙막이 벽체의 자동 혹은 무선 시스템 계측의 중요성을 가상의 사고사례 분석을 통해 공사기간 및 사고비용을 산정하고 이를 계측비용과 비교하여 무선 및 자동계측 업무의 중요성을 주장하였다. 굴착공사 중 중대형 파괴 시 사고처리 금액에 대하여 계측비용은 5% 미만으로 계측비용을 증가시켜 사고를 미연에 방지하는 것이 경제적일 수 있다.

• 대한한의학회지
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• 제44권4호
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• pp.136-149
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• 2023

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, we proposed a model to quantitatively classify cold sensitivity using a fully automated facial image analysis system. Methods: We investigated cold sensitivity in 452 subjects. Cold sensitivity was determined using a questionnaire and the Cold Pattern Score (CPS) was used for analysis. Subjects with a CPS score below the first quartile (low CPS group) belonged to the cold non-sensitivity group, and subjects with a CPS score above the third quartile (high CPS group) belonged to the cold sensitivity group. After splitting the facial images into train/validation/test sets, the train and validation set were input into a convolutional neural network to learn the model, and then the classification accuracy was calculated for the test set. Results: The classification accuracy of the low CPS group and high CPS group using facial images in all subjects was 76.17%. The classification accuracy by sex was 69.91% for female and 62.86% for male. It is presumed that the deep learning model used facial color or facial shape to classify the low CPS group and the high CPS group, but it is difficult to specifically determine which feature was more important. Conclusions: The experimental results of this study showed that the low CPS group and the high CPS group can be classified with a modest level of accuracy using only facial images. There was a need to develop more advanced models to increase classification accuracy.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2003년도 제27회 추계학술대회
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• pp.63-63
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• 2003

Purpose: Planning target volume (PTV) for tumors in abdomen or thorax includes enough margin for breathing-related movement of tumor volumes during treatment. We developed a simple and handy method, which can reduce PTV margins in patients with moving tumors, respiratory motion reduction device system (RMRDs). Materials and Methods: The patients clinical database was structured for moving tumor patients and patient setup error measurement and immobilization device effects were investigated. The system is composed of the respiratory motion reduction device utilized in prone position and abdominal presser (strip device) utilized in the supine position, moving phantom and the analysis program, which enables the analysis on patients setup reproducibility. It was tested for analyzing the diaphragm movement and CT volume differences from patients with RMRDs, the magnitude of PTV margin was determined and dose volume histogram (DVH) was computed using a treatment planning software. Dose to normal tissue between patients with RMRDs and without RMRDs was analyzed by comparing the fraction of the normal liver receiving to 50% of the isocenter dose(TD50). Results: In case of utilizing RMRDs, which was personally developed in our hospital, the value was reduced to $5pm1.4 mm$, and in case of which the belt immobilization device was utilized, the value was reduced to 3$pm$0.9 mm. Also in case of which the strip device was utilized, the value was proven to reduce to $4pm.3 mm$0. As a result of analyzing the TD50 is irradiated in DVH according to the radiation treatment planning, the usage of the respiratory motion reduction device can create the reduce of 30% to the maximum. Also by obtaining the digital image, the function of comparison between the standard image, automated external contour subtraction, and etc were utilized to develop patients setup reproducibility analysis program that can evaluate the change in the patients setup. Conclusion: Internal organ motion due to breathing can be reduced using RMRDs, which is simple and easy to use in clinical setting. It can reduce the organ motion-related PTV margin, thereby decrease volume of the irradiated normal tissue.

• 정보보호학회논문지
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• 제30권5호
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• pp.847-857
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• 2020

IoT 장비가 상용화되면서 IP카메라, 도어락, 자동차, TV 등 일반 생활기기에 블루투스나 유무선의 네트워크가 내재되어 출시되고 있다. IoT 장비는 네트워크를 통해 많은 정보들을 공유하며 개인적인 정보들을 수집하여 시스템을 가동하기 때문에 IoT 장비에 대한 보안은 더욱 중요해지고 있다. 또한, 현재 사이버 위협 중 웹 기반 공격과 애플리케이션 공격이 상당히 많은 비중을 차지하고 있고, 이를 보안하기 위해 보안 전문가들이 수동 분석을 통해 사이버 공격의 취약점들을 분석하고 있다. 그러나 수동 분석으로만 취약점을 분석하기에는 사실상 불가능하기 때문에 현재 시스템 보안을 연구하는 연구원들은 자동화된 취약점 탐지 시스템을 연구하고 있고, 최근 USENIX에서 발표된 Firm-AFL은 커버리지 기반의 퍼저를 사용하여 퍼징의 처리속도와 효율성에 대해 연구를 진행하여 시스템을 제안했다. 하지만, 기존 도구는 펌웨어의 퍼징 처리속도에 초점을 두고 연구를 진행하다 보니 다양한 경로에서 취약점을 발견하지 못했다. 본 논문에서는 기존 도구에서 찾지 못한 다양한 경로에서 취약점을 발견하고자 변이과정을 강화시켜 기존 도구가 찾은 경로보다 더 많은 경로를 찾고, 제약조건을 해결하며 더 많은 크래시를 발견하는 IoTFirmFuzz를 제안한다.

• 전기전자학회논문지
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• 제28권1호
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• pp.90-96
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• 2024

본 논문에서는 라즈베리파이 카메라와 함수 발생기를 활용하여 이미지에서 휘도를 분석하고 이를 바탕으로 전압을 인가하여 스마트 윈도우에 착색을 통해 광 투과를 조절할 수 있는 자동화 시스템을 제안한다. 기존 휘도 측정에 사용되는 휘도계는 가격대가 높고 사용자의 불필요한 움직임을 요구해 실생활에서 활용하기 어렵다. 그러나 사진 촬영 후 Python Open Source Computer Vision Library (OpenCV)를 활용한 이미지에서의 휘도 분석은 저렴하고 휴대가 간편하여 실생활에서 쉽게 응용할 수 있다. 이 시스템을 스마트 윈도우가 적용된 환경에 사용하여 창호의 휘도를 검출하였다. 이미지의 휘도를 바탕으로 스마트 윈도우의 착색 조절을 통해 창호의 휘도를 감소시켜 재실자는 쾌적한 시 환경을 구축할 수 있다.