• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1115-1126
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• 2003

In this paper, we propose modeling method for video data that represents structural and semantic contents of video data efficiently. Also, a browsing method that helps users easily understand and play the contents of video data is presented. The proposed modeling scheme consists of three layers such as raw data layer, content layer and key frame layer The content layer represents logical hierarchy and semantic contents of video data. We implement two kinds of browsers for playing video data and providing video contents. The playing browser plays video data and Presents the information of currently playing shot. The content browser allows users to browse raw data, structural information and semantic contents of video data.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.47-56
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• 2009

Effective detection and correction of outliers of raw traffic data collected from the field is of keen interest because reliable traffic information is highly dependent on the quality of raw data. Global positioning system (GPS) based traffic surveillance systems are capable of producing individual vehicle speeds that are invaluable for various traffic management and information strategies. This study proposed a locally weighted regression (LWR) based filtering method for individual vehicle speed data. An important feature of this study was to propose a technique to generate synthetic outliers for more systematic evaluation of the proposed method. It was identified by performance evaluations that the proposed LWR-based method outperformed an exponential smoothing. The proposed method is expected to be effectively utilized for filtering out raw individual vehicle speed data.

• Structural Engineering and Mechanics
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• v.91 no.1
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• pp.39-47
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• 2024

Bridge deterioration shows the change of bridge condition during its operation, and predicting bridge deterioration is important for implementing predictive protection and planning future maintenance. However, in practical application, the raw inspection data of bridges are not continuous, which has a greater impact on the accuracy of the prediction results. Therefore, two kinds of bridge deterioration models are established in this paper: one is based on the traditional regression theory, combined with the distribution fitting theory to preprocess the data, which solves the problem of irregular distribution and incomplete quantity of raw data. Secondly, based on the theory of Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), the network is trained using the raw inspection data, which can realize the prediction of the future deterioration of bridges through the historical data. And the inspection data of 60 prestressed concrete box girder bridges in Xiamen, China are used as an example for validation and comparative analysis, and the results show that both deterioration models can predict the deterioration of prestressed concrete box girder bridges. The regression model shows that the bridge deteriorates gradually, while the LSTM-RNN model shows that the bridge keeps great condition during the first 5 years and degrades rapidly from 5 years to 15 years. Based on the current inspection database, the LSTM-RNN model performs better than the regression model because it has smaller prediction error. With the continuous improvement of the database, the results of this study can be extended to other bridge types or other degradation factors can be introduced to improve the accuracy and usefulness of the deterioration model.

• Food Science of Animal Resources
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• v.38 no.1
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• pp.43-51
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• 2018

This study was conducted to analyze the microbiological contamination status of raw beef distributed in Korea, and evaluate the suitability of current aerobic plate count (APC) guidelines. We analyzed five years (2010-2014) of microbiological monitoring data obtained from the Ministry of Food and Drug Safety and investigated the microbiological status of raw beef collected from meat packing centers and meat shops in the Seoul/Gyeonggi, Gangwon, and Chungcheong regions in August 2015. From 2010-2014, most raw beef (>94%) displayed APC levels of < $1.0{\times}10^6CFU/g$. However, raw beef samples collected from all three regions in August 2015 had comparatively higher APC levels than those reported in previous years. To evaluate the relationship between the APC level and quality, changes in beef loin were evaluated during cold storage for 15 days at $4^{\circ}C$. On day 11, the mean APC level ($4.7{\times}10^6CFU/g$) conformed to current guidelines in Korea ($1.0{\times}10^7CFU/g$) and the pH value was 5.82. However, the sensory evaluation score for color and overall acceptability was under 3.0, meaning that the beef loin was not acceptable for eating. These results suggest that current APC guideline for raw beef should be lowered to $1.0{\times}10^6CFU/g$ to improve both the microbiological safety and palatability of raw beef.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2002.10a
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• pp.107-116
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• 2002

GLP regulations were initially “promulgated to address assuring the validity of data in the wake of investigations by EPA and FDA during the mid -1970's which revealed that some studies submitted to the agencies had not been conducted in accordance with acceptable laboratory practices.” [1] In the early 1970s, results of an investigation by the FDA in about 40 laboratories revealed many cases of badly managed studies, poor training of personnel and some cases of deliberate fraud. The general findings were that there were poorly trained study directors and study personnel, poorly designed protocols, protocols not followed, procedures not conducted as described, raw data badly collected, data not correctly identified, data without traceability, data not verified and approved by responsible persons, lack of standardised procedures, poor animal husbandry, inadequate characterisation of test items and test systems, inadequate resources, equipment poorly calibrated or otherwise qualified, reports not sufficiently verified, not an accurate account of the actual study, not a proper reflection of raw data and inadequate archiving of data. These problems are not just past history, since they resurface time and time again, even in quite recent times as the experience of GLP inspectors shows [1]. The GLPs specify minimum practices and procedures in order to ensure the quality and integrity of data submitted in accordance with a regulatory requirement

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2327-2334
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• 2007

This paper presents a method for improvement of communication speed and reduction of data storage space in gas turbine monitoring system to acquire, save, and display combustion vibration data. The proposed method implements FFT from sampled raw data. The FFT result data are encoded to be transferred to monitoring PC for storage. By this way, it can reduce data storage space. To display the received data, it needs inverse FFT to reconstruct original signal. To verify the validity and efficiency of the proposed scheme, computer-aided simulation are carried out. It includes the analyzed results the relationship between FFT's order and Gibb's Phenomenon. Finally, high-performance of the proposed method is proved by combustion experiment results using a prototype gas turbine.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.40-41
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• 2020

As the capacity of the 3d scanner developed, the reverse engineering using the 3d scanner is emphasized in the construction industry to obtain the 3d geometric representation of buildings. However, big size of the indoor point cloud data acquired by the 3d scanner restricts the efficient process in the reverse engineering. In order to solve this inefficiency, several pre-processing methods simplifying and denoising the raw point cloud data by the rough standard are developed, but these non-standard methods can cause the inaccurate recognition and removal the key-points. This paper analyzes the correlation between the accuracy of wall recognition and the density of the data, thus proposes the proper method for the raw point cloud data. The result of this study could improve the efficiency of the data processing phase in the reverse engineering for indoor point cloud data.

• ETRI Journal
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• v.35 no.1
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• pp.166-169
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• 2013

In this letter, we propose a data randomization scheme for endurance and interference mitigation of deeply-scaled multilevel flash memory. We address the relationships between data patterns and the raw bit error rate. An on-chip pseudorandom generator composed of an address-based seed location decoder is developed and evaluated with respect to uniformity. Experiments performed with 2x-nm and 4x-nm NAND flash memory devices illustrate the effectiveness of our scheme. The results show that the error rate is reduced up to 86% compared to that of a conventional cycling scheme. Accordingly, the endurance phenomenon can be mitigated through analysis of interference that causes tech shrinkage.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.235-236
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• 2015

전력계통의 오프라인 해석에 가장 보편적으로 사용되는 시뮬레이션 프로그램으로는 PSS/E와 DSATools가 있다. 두 개의 프로그램 모두 PSS/E Raw Data Format을 조류계산을 위한 기본 입력 Data로서 사용하고 있다. Raw Data는 모선 번호를 이용하여 계통의 토폴로지를 구성하고, 전력계통 해석을 수행한다. 오프라인 해석에서는 모선 번호를 이용한 해석이 가능하다. 하지만, 모선 개수가 변하는 실시간 환경에서는 모선 번호 기반의 해석이 어렵다. 본 논문에서는 실시간 환경에서 온라인 DSATools를 이용한 해석이 가능 하도록 설비명이라는 개념을 도입한다. 이 설비명 기반의 온라인 DSATools를 실시간 EMS와 연계를 위한 시스템 구성 및 데이터 종류에 대하여 알아보도록 한다.

• Proceedings of the KSMRM Conference
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• 2002.11a
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• pp.98-98
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• 2002

목적： RF Coil sensitivity 또는 MRI system의 여러 요인들로 인해서 생길 수 있는 영상의 Bias field 즉, 유난히 밝거나 어두운 부분을 raw data 의 low frequency 값들을 임의로 변화를 줌으로써 어느 정도 보정이 가능하다. 대상 및 방법： Bias field로 인해서 분석에 어려움이 있는 이미지의 k-space 데이터를 가지고 있으면 부위에 상관없이 모두 가능하다. k-space에서 얻어진 raw data를 Kx와 Ky의 2-D로 표현한 후에 DC 성분에 해당하는 영점을 그대로 놔둔 상태에서 영점 주변으로 일정 범위 안에 있는 low frequency 성분 값들을 FT(Fourier Transform)를 거치기 전에 0으로 바꾼 후에 image processing을 거치도록 한다.