• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.17-25
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• 2016

In this study, the air quality of underground and overground railway stations was evaluated focusing on the degree of influence of the outside air quality. The measured components were particulate matter ($PM_{10}$), carbon dioxide ($CO_2$), carbon monoxide (CO), nitrogen dioxide ($NO_2$), formaldehyde (HCHO), ozone ($O_3$), total airborne bacteria (TAB), total volatile organic carbon (TVOC), and Radon (Rn), which are included in the maintenance standards and recommended standards of the Indoor Air Quality Management Act. Also, the indoor/outdoor concentration ratios of $PM_{10}$, $NO_2$, and $O_3$ were calculated to estimate the influence of the outdoor air quality. The concentrations of $PM_{10}$ HCHO, TVOC, $NO_2$, and Rn in the underground stations were found to be higher than those in the overground stations. These results indicate that the (present) generation of contaminants are caused by the indoor source of the underground station. The ozone concentration of the overground stations was higher than that of the underground stations, which indicates that the outdoor ozone concentration influenced that of the overground stations directly. Thus, methods of improving the IAQ should take into consideration the types of contamination.

• Journal of Food Hygiene and Safety
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• v.30 no.1
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• pp.28-34
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• 2015

This study is to investigate microbiological hazards which can be used as fundamental data to adequately control leeks hazards and develop leeks GAP model for those who want to get GAP system. The microbiological investigations on cultivation environments (soil and water), crops (leeks), personal hygiene (workers' hands, clothes and gloves) and working tools (boxes) have been conducted for one year, so the period was classified under non-cultivation, cultivation, and post harvest. Total bacteria was detected from soil (4.0~6.7 log CFU/g), leeks (4.6~5.1 log CFU/g), hands (ND~3.3 log CFU/hand) and gloves ($ND{\sim}5.4\;log\;CFU/cm^2$) while nothing was detected from the other samples. The coliform contamination of leeks (4.8~5.0 log CFU/g) was more high than that of soil (3.9~4.2 log CFU/g). In case of foodborne pathogens, only B. cereus was detected at the level of 0.5~4.6 log CFU/g (or hand, $100cm^2$). Fungi was observed at the level of 2.1~3.8 log CFU/g (or hand, $100cm^2$) excepting water and some working tools. These results demonstrate that the contamination of leeks is comparatively higher than that of soil sample. The reason may be the cross-contamination by biological hazards presenting on soil. Therefore, it is necessary to properly control soil and fertilizer for safety against biological hazards.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.245-254
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• 2002

IC package marking inspection system using machine vision locates and identifies the target elements from input image, and decides the quality of marking by comparing the extracted target elements with the standard patterns. This paper proposes an adaptive multi-level thresholding (AMLT) method which is suitable for a series of operations such as locating the target IC package, extracting the characters, and detecting the Pinl dimple. It also proposes a dynamic matching unit selection (DMUS) method which is robust to noises as well as effective to catch out the local marking errors. The main idea of the AMLT method is to restrict the inputs of Otsu's thresholding algorithm within a specified area and a partial range of gray values. Doing so, it can adapt to the specific domain. The DMUS method dynamically selects the matching unit according to the result of character extraction and layout analysis. Therefore, in spite of the various erroneous situation occurred in the process of character extraction and layout analysis, it can select minimal matching unit in any environment. In an experiment with 280 IC package images of eight types, the correct extracting rate of IC package and Pinl dimple was 100% and the correct decision rate of marking quality was 98.8%. This result shows that the proposed methods are effective to IC package marking inspection.

• Journal of Animal Science and Technology
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• v.53 no.1
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• pp.67-74
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• 2011

The aim of this study was to understand the management level of pathogenic bacteria in HACCP system implemented animal farms. Microbial samples were collected from manure, floor, compost depot, manure on belt, low milk tank, dust in laying house and egg collector in HACCP system implemented Korean beef cattle, dairy cattle, swine, and laying Hens farms. O157, O111 and O26 strains of E. coli were not detected in HACCP system implemented Korean beef cattle farm. The detection rate of E. coli from manure and floor in HACCP system implemented cattle farms (Korean beef cattle and dairy farm) was lower than those of non-HACCP system implemented cattle farm. Salmonella spp. was detected in HACCP system implemented cattle farms (Korean beef cattle and dairy farm). Compared with pervious studies, lower detection rate of Salmonella spp. at floor and compost depot in HACCP system implemented swine and commercial layer farms were indicated. In conclusion, implementation of HACCP system in animal farms would enhance the management level of biological hazard compare to normal animal farms.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.96-101
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• 2011

In recent days, the demand for the remote ECG monitoring system has been increasing and the automation of the monitoring system is becoming quite of a concern. Automatic detection of the abnormal ECG beats must be a necessity for the successful commercialization of these real time remote ECG monitoring system. From these viewpoints, in this paper, we proposed an automatic detection algorithm for the abnormal ECG beats using QRS width and RR interval patterns. In the previous research, many efforts have been done to classify the ECG beats into detailed categories. But, these approaches have disadvantages such that they produce lots of misclassification errors and variabilities in the classification performance. Also, they require large amount of training data for the accurate classification and heavy computation during the classification process. But, we think that the detection of abnormality from the ECG beats is more important that the detailed classification for the automatic ECG monitoring system. In this paper, we tried to detect the VEB which is most frequently occurring among the abnormal ECG beats and we could achieve satisfactory detection performance when applied the proposed algorithm to the MIT/BIH database.

• Journal of Food Hygiene and Safety
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• v.25 no.4
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• pp.303-309
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• 2010

This study was planned to monitor the change in the content of sulfur dioxide during the drying process of fresh vegetables. The analysis of sulfur dioxide was conducted by the Optimized Monier-Williams Method based on the Korea Food Code. The samples were kinds of vegetables which consisted of naturally-originated sulfur compounds (green onion, onion, cabbage, garlic, radish leaves, radish). Fresh vegetables (n = 182) and dried vegetables (n = 41) purchased from different local areas were investigated for the content of sulfur dioxide. The fresh vegetables were dried at 50~$60^{\circ}C$ using hot-air dryer. The moisture contents of dried samples were adjusted to keep 10 percents. The contents of sulfur dioxide in self-dried vegetables were 104.6 mg/kg in green onion, 75.4 mg/kg in onion, 129.1 mg/kg in cabbage, 197.6 mg/kg in garlic, 23.0 mg/kg in radish leaves and 52.5 mg/kg in radish, respectively. The increase of sulfur dioxide content according to the moisture content reduction was different from the expected. It means that the contents of sulfur dioxide can be altered by other factors except moisture contents. This results can be utilized as materials for the safe management of sulfites of dried vegetables.

• Tunnel and Underground Space
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• v.25 no.1
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• pp.56-67
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• 2015

In this study, a source locating technique applicable to transversely isotropic media was developed. Wave velocity anisotropy was considered based on the partition approximation method, which simply enabled AE source locating. Sets of P wave arrival time were decided by the two-step AIC algorithm and they were later used to locate the AE sources when having the least error compared with the partitioned elements. In order to validate the technique, pencil lead break test on artificial transversely isotropic mortar specimen was carried out. Defining the absolute error as the distance between the pencil lead break point and the located point, 1.60 mm ~ 14.46 mm of range and 8.57 mm of average were estimated therefore it was regarded as thought to be 'acceptable' considering the size of the specimen and the AE sensors. Comparing each absolute error under different threshold levels, results showed small discrepancies therefore this technique was hardly affected by background noise. Absolute error could be decomposed into each coordinate axis error and through it, effect of AE sensor position could be understood so if optimum sensor position was able to be decided, one could get more precise outcome.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.40-47
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• 2013

In case of wild fire, early detection of wild fire is the most important factor in minimizing the damages. In this paper, we suggest an effective system that detects wild fire using a panoramic image from a single camera with PAN/TILT head. This enables the system to detect the size and the location of the fire in the early stages. After converting RGB image input to color YCrCb image, the differential image is used to detect changes in movement of the smoke to determine the regions which may be prone to forest fire. Histogram analysis of fire flame is used to determine the possibility of fire in the predetermined regions. In addition, image matching and SURF were used to create the panoramic image. There are many advantages in this system. First of all, it is very economical because this system needs only a single camera and a monitor. Second, it shows the live image of wide view through panoramic image. Third, this system can reduce the quantity of saved data by storing panoramic images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2A
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• pp.132-138
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• 2009

Cognitive radio is considered as a promising solution to scarce spectrum problem. The primary object of cognitive radio is to increase spectral efficiency, while causing limited interference to primary users who are using the spectrum. Hence, an essential part of cognitive radio systems is spectrum sensing which determines whether a particular spectrum is occupied or not by a primary user at a particular time. However, sensing decision of each individual secondary user alone may not be reliable enough due to shadowing and multipath fading of wireless channels. The so called hidden terminal problem makes the problem even worse, possibly yielding undesired interference to the primary users. Recently, cooperative spectrum sensing is emerging as a remedy to these problems of individual sensing. Cooperative sensing allows a group of secondary users to share local sensing information to extract a global decision with high fidelity. In this paper, we investigate a cooperative spectrum sensing algorithm based on hard decisions of local sensing outcomes. Specifically, we propose an effective scheme for combining local decisions by introducing weighting factors that reflect reliability of the corresponding secondary user. Through computer simulations, the performance of the proposed combining scheme is compared with that of the conventional scheme without weighting factors in various environments.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.81-84
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• 2022

제조 산업에서의 이상치 검출은 생산품의 품질과 운영비용을 절감하기 위한 중요한 요소로 최근 딥러닝을 사용하여 자동화되고 있다. 이상치 검출을 위한 딥러닝 기법에는 CNN이 있으며, CNN을 계층적으로 구성할 경우 단일 CNN 모델에 비해 상대적으로 성능의 향상을 보일 수 있다는 것이 많은 선행 연구에서 나타났다. 이에 MVTec-AD 데이터셋을 이용하여 계층 CNN이 다중 클래스 이상치 판별 문제에 대해 효과적인지를 탐구하고자 하였다. 실험 결과 단일 CNN의 정확도는 0.7715, 계층 CNN의 정확도는 0.7838로 다중 클래스 이상치 판별 문제에 있어 계층 CNN 방식 접근이 다중 클래스 이상치 탐지 문제에서 알고리즘의 성능을 향상할 수 있음을 확인할 수 있었다. 계층 CNN은 모델과 파라미터의 개수와 리소스의 사용이 단일 CNN에 비하여 기하급수적으로 증가한다는 단점이 존재한다. 이에 계층 CNN의 장점을 유지하며 사용 리소스를 절약하고자 하였고 K-means, GMM, 계층적 클러스터링 알고리즘을 통해 제작한 새로운 클래스를 이용해 계층 CNN을 구성하여 각각 정확도 0.7930, 0.7891, 0.7936의 결과를 얻을 수 있었다. 이를 통해 Clustering 알고리즘을 사용하여 적절히 물체를 분류할 경우 물체에 따른 개별 상태 판단 모델을 제작하는 것과 비슷하거나 더 좋은 성능을 내며 리소스 사용을 줄일 수 있음을 확인할 수 있었다.