• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1234-1245
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• 2018

The risk of multi-unit nuclear power plants (NPPs) at a site has received considerable critical attention recently. However, current probabilistic safety assessment (PSA) procedures and computer code do not support multi-unit PSA because the traditional PSA structure is mostly used for the quantification of single-unit NPP risk. In this study, the main purpose is to develop a multi-unit Level 2 PSA method and apply it to full-power operating six-unit OPR1000. Multi-unit Level 2 PSA method consists of three steps: (1) development of single-unit Level 2 PSA; (2) extracting the mapping data from plant damage state to source term category; and (3) combining multi-unit Level 1 PSA results and mapping fractions. By applying developed multi-unit Level 2 PSA method into six-unit OPR1000, site containment failure probabilities in case of loss of ultimate heat sink, loss of off-site power, tsunami, and seismic event were quantified.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.104-110
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• 2007

As the exploration of the seabed is extended ever further, automated recognition and classification of sonar images become increasingly important. However, most of the methods ignore the directional information and its effect on the image textures produced. To deal with this problem, we apply 2D Gabor filters to extract the features of sonar images. The filters are designed with constrained parameters to reduce the complexity and to improve the calculation efficiency. Meanwhile, at each orientation, the optimal Gabor filter parameters will be selected with the help of bandwidth parameters based on the Fisher criterion. This method can overcome some disadvantages of the traditional approaches of extracting texture features, and improve the recognition rate effectively.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.1-7
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• 2020

Clustering methods essentially take a two-step approach; extracting feature vectors for dimensionality reduction and then employing clustering algorithm on the extracted feature vectors. However, for clustering images, the traditional clustering methods such as stacked auto-encoder based k-means are not effective since they tend to ignore the local information. In this paper, we propose a method first to effectively reduce data dimensionality using convolutional auto-encoder to capture and reflect the local information and then to accurately cluster similar data samples by using a hierarchical clustering approach. The experimental results confirm that the clustering results are improved by using the proposed model in terms of clustering accuracy and normalized mutual information.

• Journal of the Korean Society of Safety
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• v.9 no.2
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• pp.26-32
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• 1994

It is quite similar in the current automated works likewise in the past manual works that single trivial human error and/or unsafe acts may lead to serious industrial accidents. Though the traditional approach for accident prevention focused on the serious injuries or losses, that was misleaded by failure of accident perception. As Heinrich pointed out, there are still enormous numbers of unsafe acts or near-misses before a real accident happen. Thus, for industrial accident prevention, a research on unsafe acts was committed. With accident data occurred during the last decade, statistics were analyzed for extracting behavioral characteristics. After that, a practical method Integrating AHP and statistics which shows possible accident factors and their priority at an individual factory was suggested. A computer program was developed also.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1559-1563
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• 2014

Recently, the prevention of crime using CCTV draws special in accordance with the higher crime incidence rate. Therefore security systems like a CCTV with audio capability are developing for giving an instant alarm. This paper proposes a scream detecting engine from various ambient noises in real environment for surveillance systems. The proposed engine detects scream signals among the various ambient noises using the features extracted in time/frequency domain. The experimental result shows the performance of our engine is very promising in comparison with the traditional engines using the model based features like LPC, LPCC and MFCC. The proposed method has a low computational complexity by using FFT and cross correlation coefficients instead of extracting complex features like LPC, LPCC and MFCC. Therefore the proposed engine can be efficient for audio-based surveillance systems with low SNRs in real field.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.35-41
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• 2014

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.286-290
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• 2009

In recent years, LiDAR technology has been becoming more popular and important. Its applications are completely replacing the traditional remote sensing technique. One of these applications is creating Digital City Models in urban areas, which is essential for many others such as disaster management, cartographic mapping, simulation of new buildings, updating and keeping cadastral data. In most of these cases the building outlines is the primary feature of interest. In this paper, a method of extracting building outlines from LiDAR data will be performed.

• Journal of the Korean Society of Food Culture
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• v.7 no.1
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• pp.19-23
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• 1992

Jindo Hongju is a traditional liquor in Jindo island of Korea. The characteristics of Hongju are its unique flavour by fermetation and red color of gromwell(Lithospermum erythrorhizon) root. However, the evaluation of red pigment is different from one manufactures to other manufactures and from place to place, also the standard method is not established. An attempt has made to compare the quality of gromwell root from different places and to standardize the extracting method. The results obtained from this study are summerized as follow, The chemical properties and composition of gromwell root from Jindo and other areas were compared. There were no difference among the samples in moisture content, content of naphtoquinone derivatives and absoption spectra. These results indicate that the pigments from Jindo and other region products seems to be the same quality. For efficient extraction of gromwell pigment, more than 40% ethanol as solvent and at least 10 hours extraction time was required. According to the visual test for Hongju pigment, the most preferable color was that it shows absorbance of 1.0 (contents of shikonin was 3.90 mg/45% EtOH 20 ml). From this visual test it can be proposed that the may be applied absorbance at 1.0 for the quality control of pigment.

• Korean Institute of Interior Design Journal
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• v.20 no.4
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• pp.74-82
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• 2011

All the phenomena and subjects of nature and society are within correlation interconnection, and they are inseparably connected one another. The elements of this interaction can be found out through the concept of transparency in the space composition of Korean traditional architecture. This study is focusing on the access space, in other words, a gate-house that is a buffer zone playing a process role up to the main space among successive spaces. It was chosen to be the subject of the study since it strengthens convergence into the main building and with the effect connecting spaces, it could show well the spatial possibility of transparency. Besides, the subject of the study is limited to the Moonru Multi roofs that improves the functionality of spaces between gate-houses, and it is intended to progress contents by comparative analysis of two kinds such as Seo Won gate-house and temple gate-house. Korean traditional architecture places emphasis on harmony within the whole spaces. There are intimate relations between surrounding environment, external spaces and internal spaces, and it is important understand the spatial relations according to the shape appearing through interactions of parts in the whole spaces. In conclusion, the Moonru Multi roofs is analyzed with the method of extracting the concept that is contained in the frame of analysis and through ecological views through a visible and structural method. It can be understood what kinds of method for communication were used for ancestors to recognize and use spaces with the deduced concept through the analysis of the Moonru Multi roofs with different character.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.365-372
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• 2022

The accurate object recognition is important for the precise and accurate manipulation. To enhance the recognition performance, we can use various types of sensors. In general, acquired data from sensors have a high sampling rate. So, in the past, the RNN-based model is commonly used to handle and analyze the time-series sensor data. However, the RNN-based model has limitations of excessive parameters. CNN-based model also can be used to analyze time-series input data. However, CNN-based model also has limitations of the small receptive field in early layers. For this reason, when we use a CNN-based model, model architecture should be deeper and heavier to extract useful global features. Thus, traditional methods like RN N -based and CN N -based model needs huge amount of learning parameters. Recently studied result shows that Fast Fourier Convolution (FFC) can overcome the limitations of traditional methods. This operator can extract global features from the first hidden layer, so it can be effectively used for feature extracting of sensor data that have a high sampling rate. In this paper, we propose the algorithm to recognize objects using tactile sensor data and the FFC model. The data was acquired from 11 types of objects to verify our posed model. We collected pressure, current, position data when the gripper grasps the objects by random force. As a result, the accuracy is enhanced from 84.66% to 91.43% when we use the proposed FFC-based model instead of the traditional model.