• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.109-114
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• 2012

Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.433-438
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• 2016

This paper proposes a grade prediction method to measure meat quality in Hanwoo (Korean Native Cattle) using classification and feature extraction algorithms. The applied classification algorithm is an AdaBoost and the texture features of the given ultrasound images are extracted using SFTA. In this paper, as an initial phase, we selected ultrasound images of Hanwoo for verifying experimental results; however, we ultimately aimed to develop a diagnostic decision support system for human body scan using ultrasound images. The advantages of using ultrasound images of Hanwoo are: accurate grade prediction without butchery, optimizing shipping and feeding schedule and economic benefits. Researches on grade prediction using biometric data such as ultrasound images have been studied in countries like USA, Japan, and Korea. Studies have been based on accurate prediction method of different images obtained from different machines. However, the prediction accuracy is low. Therefore, we proposed a prediction method of meat quality. From the experimental results compared with that of the real grades, the experimental results demonstrated that the proposed method is superior to the other methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.37-45
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• 2015

In modern electronic warfare systems, a demand on the more accurate estimation method based on TDOA and FDOA has been increased. TDOA/FDOA localization consists of two-stage procedures: the extraction of information from signals and the estimation of emitter location. Various algorithms based on CAF(complex ambiguity function), which is known as a basic method, has been presented in the area of extractions. When we extract TDOA and FDOA information using a conventional method based on the CAF algorithm from communication signals, considerably long integration time is required for the accurate position estimation of an unknown emitter far from sensors more than 300 km. Such long integration time yields huge amount of transmission data from sensors to a central processing unit, resulting in heavy computiational complexity. Therefore, we theoretically analyze the integration time for TDOA/FDOA information using CRLB and propose a two-stage global optimization algorithm which can minimize the transmission time and a computational complexity. The proposed method is compared with the conventional CAF-based algorithms in terms of a computational complexity and the CRLB to verify the estimation performance.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.5
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• pp.727-733
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• 2011

A new Korean standard for the determination of Cr(VI) in soils has been officially published as ES 07408.1 in 2009. This analytical method is based on the hot alkaline digestion and colorimetric detection prescribed by U.S. EPA method 3060A and 7196A. The hot alkaline digestion accomplished using 0.28 M $Na_2CO_3$ and 0.5 M NaOH solution (pH 13.4) at $90{\sim}95^{\circ}C$ determines total Cr(VI) in soils extracting all forms of Cr(VI), including water-soluble, adsorbed, precipitated, and mineral-bound chromates. This aggressive alkaline digestion, however, proved to be problematic for certain soils which contain large amounts of soluble humic substances or active manganese oxides. Cr(III) could be oxidized to Cr(VI) by manganese oxides during the strong alkaline extraction, resulting in overestimation (positive error) of Cr(VI). In contrast, Cr(VI) reduction by dissolved humic matter or Fe(II) could occur during the neutralization and acidic colorimetric detection procedure, resulting in underestimation (negative error) of Cr(VI). Futhermore, dissolved humic matter hampered the colorimetric detection of Cr(VI) using UV/Vis spectrophotometer due to the strong coloration of the filtrate, resulting in overestimation (positive error) of Cr(VI). Without understanding the mechanisms of Cr(VI) and Cr(III) transformation during the analysis it could be difficult to operate the experiment in laboratory and to evaluate the Cr(VI) results. For this reason, in this paper we described the theoretical principles and limitations of Cr(VI) analysis and provided useful guidelines for laboratory work and Cr(VI) data analysis.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.279-286
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• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.303-312
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• 2018

In Ethiopia, climate change and deforestation are major issues hindering sustainable development. Local Ethiopian communities commonly perceive an increase in temperature and a decrease in rainfall. Meteorological data shows that rainfall has declined in southern Ethiopia, and spring droughts have occurred more frequently during the last 10-15 years. The frequently occurring droughts have seriously affected the agriculture-dominated Ethiopian economy. Forests can play an important role in coping with climate change. However, deforestation is alarmingly high in Ethiopia, and this is attributed mainly to agricultural expansion and fuel wood extraction. Deforestation has led to a decrease in various benefits from forest ecosystem services, and increased ecological and environmental problems including loss of biodiversity. To resolve the issues effectively, it is crucial to enhance climate change resilience through reforestation and various international collaborations are urgently needed. To continue collaboration activities for resolving these issues, it is first necessary to address fundamental questions on the nature of collaboration: does collaboration aim for a support-benefit or a mutual benefit situation; dividing the workload or sharing the workload; an advanced technology or an appropriate technology; and short-term and intensive or long-term and extensive?. Potential collaboration activities were identified by sectors: in the governmental sector, advancing governmental structure and policy, enhancing international collaborations and negotiations, and capacity building for forest restoration and management; in the research and education sector, identifying and filling gaps in forestry and climate change education, capacity building for reforestation and climate change resilience research, and developing bioenergy and feed stocks; and in the business and industry sector, supporting conservation based forestry businesses and industries, while promoting collaboration with the research and education sectors. It is envisaged that international collaboration for enhancing climate change resilience through reforestation will provide a strong platform for resolving climate change and deforestation issues, and achieving sustainable development in Ethiopia.

• The Korean Journal of Blood Transfusion
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• v.29 no.3
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• pp.310-319
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• 2018

Background: Research on next-generation sequencing (NGS)-based HLA typing is active. To resolve the phase ambiguity and long turn-around-time of conventional high resolution HLA typing, this study developed a NGS-based high resolution HLA typing method that can handle large-scale samples within an efficient testing time. Methods: For HLA NGS, the condition of nucleic acid extraction, library construction, PCR mechanism, and HLA typing with bioinformatics were developed. To confirm the accuracy of the NGS-based HLA typing method, the results of 192 samples HLA typed by SSOP and 28 samples typed by SBT compared to NGS-based HLA-A, -B and -DR typing. Results: DNA library construction through two-step PCR, NGS sequencing with MiSeq (Illumina Inc., San Diego, USA), and the data analysis platform were established. NGS-based HLA typing results were compatible with known HLA types from 220 blood samples. Conclusion: The NSG-based HLA typing method could handle large volume samples with high-throughput. Therefore, it would be useful for HLA typing of bone marrow donation volunteers.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.103-113
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• 2019

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.28-36
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• 2019

The APR1400 (Advanced Power Reactor 1400MW) nuclear power plant is a large-scale national infrastructure facility with a total project cost of 8.6 trillion won and a project period of 10 years or more. The total project area is about 2.17 million square meters and consists of more than 20 buildings and structures. And the total number of drawings required for construction is about 65,000. In order to design such a large facility, it is important to establish a design standard that reflects the design intent and can increase conformity between documents (drawings). To this end, a design bases document (DBD) reflecting the design bases that extracted in regulatory requirements (e.g. 10CFR50, Korean Law, etc.) is created. However, although the design bases are important concepts that are a big framework for the whole design of the nuclear power plant, they are managed in 2-dimensional by the experts in each field fragmentarily. Therefore, in order to improve the usability of building information, we developed BIM(Building Information Model) based 3-dimensional design bases management system. For this purpose, the concept of design bases information layer (DBIL) was introduced. Through the simulation of developed system, design bases attribute and element data extraction for each DBIL was confirmed, and walls, floors, doors, and penetrations with DBIL were successfully extracted.