• Nuclear Engineering and Technology
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• 제51권1호
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• 농업과학연구
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• 제42권1호
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• pp.53-61
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• 2015

Development of highly efficient and energy-saving tractors has been one of the issues in agricultural machinery. For design of such tractors, measurement and analysis of load on major power transmission parts of the tractors are the most important pre-requisite tasks. Objective of this study was to perform pre-processing procedures before effective analysis of load data of agricultural tractors (30, 75, and 82 kW) during major field operations such as plow tillage, rotary tillage, baling, bale wrapping, and to select the suitable pre-processing method for the analysis. A load measurement systems, equipped in the tractors, were consisted of strain-gauge, encoder, hydraulic pressure, and radar speed sensors to measure torque and rotational speed levels of transmission input shaft, PTO shaft, and driving axle shafts, pressure of the hydraulic inlet line, and travel speed, respectively. The entire sensor data were collected at a 200-Hz rate. Plow tillage, rotary tillage, baling, wrapping, and loader operations were selected as major field operations of agricultural tractors. Same or different farm works and driving levels were set differently for each of the load measuring experiment. Before load data analysis, pre-processing procedures such as outlier removal, low-pass filtering, and data division were performed. Data beyond the scope of the measuring range of the sensors and the operating range of the power transmission parts were removed. Considering engine and PTO rotational speeds, frequency components greater than 90, 60, and 60 Hz cut off frequencies were low-pass filtered for plow tillage, rotary tillage, and baler operations, respectively. Measured load data were divided into five parts: driving, working, implement up, implement down, and turning. Results of the study would provide useful information for load characteristics of tractors on major field operations.

• 지구물리와물리탐사
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• 제17권3호
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• pp.171-178
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• 2014

항공자력탐사의 전처리(pre-processing)는 육상탐사에 비해 손품이 많이 들어가는 복잡한 과정이 더 많아 그래픽 사용자 인터페이스 기반의 전용 처리도구를 이용하는 것이 가장 효율적이다. 본 글에서는 항공자력자료의 전처리 전용 소프트웨어, $KMagLevelling^{TM}$을 개발하고 그 주요 기능을 간략히 소개하였다. $KMagLevelling^{TM}$은 전처리 과정을 크게 세부분으로 구분하여 구현하였다. 세부기능별 사용자 인터페이스 중 편의성과 독창성 측면에서 주목할 만한 기능으로서는 (1) 방대한 양의 항공자력자료 D/B를 비행경로 형태로 시각화하여 표현하는 기법 (2) 취합자료 중 특정 영역의 필요한 자료만의 발췌 (3) 자력자료 내의 원치 않는 부분을 선택적으로 쉽게 제거하는 사용자 인터페이스의 세 가지로 요약된다.

• 한국멀티미디어학회논문지
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• 제21권3호
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• pp.391-399
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• 2018

In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.

• Industrial Engineering and Management Systems
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• 제11권1호
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• pp.82-86
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• 2012

In For the past few years, query log data has been collected to find user's behavior in using the site. Many researches have studied on the usage of query logs to extract user's preference, recommend personalization, improve caching and pre-fetching of Web objects, build better adaptive user interfaces, and also to improve Web search for a search engine application. A query log contain data such as the client's IP address, time and date of request, the resources or page requested, status of request HTTP method used and the type of browser and operating system. A query log can offer valuable insight into web site usage. A proper compilation and interpretation of query log can provide a baseline of statistics that indicate the usage levels of website and can be used as tool to assist decision making in management activities. In this paper we want to discuss on the tasks performed of query logs in pre-processing of web usage mining. We will use query logs from an online newspaper company. The query logs will undergo pre-processing stage, in which the clickstream data is cleaned and partitioned into a set of user interactions which will represent the activities of each user during their visits to the site. The query logs will undergo essential task in pre-processing which are data cleaning and user identification.

• 대한전자공학회논문지SD
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• 제39권5호
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• pp.74-86
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• 2002

본 논문은 결정 궤환 구조를 갖는 차동 위상 검출기의 고속 데이터 처리를 위한 VLSI 구조를 제안한다. 기존 차동 위상 검출 방식의 낮은 BER 성능을 극복하기 위해 DF-DPD, DPD-RGPR, DFDPD-SA 등의 다중 심볼 검출 방식이 제시되었다. 이러한 검출 방식들은 참조 위상으로 사용되는 이전 심볼에서의 잡음 효과를 작게 하기 위하여 검출된 위상을 궤환시키는 구조를 갖고 있다. 하지만, 검출된 위상을 궤환시키는 작용은 데이터 처리 속도를 기존의 차동 위상 검출기보다 느리게 한다. 본 논문에서는 결정 궤환 구조를 갖는 차동 위상 검출기가 기존의 차동 위상 검출 방식처럼 고속으로 데이터를 처리할 수 있는 VLSI 구조를 제안하였다. 제안된 구조는 'M-1' 번째 과정에서 'M' 번째 과정을 미리 계산하는 선계산(pre-calculation) 방식과 'M-1'번째 과정에서 예견 위상들을 궤환시키는 선결정 궤환(pre-decision feedback) 방식을 갖는다. 본 논문에서 제안된 구조는 VHDL(Very-high-speed-IC Hardware Description Language)를 사용하여 RTL(Register Transfer Level)로 구현되었다. 시뮬레이션 결과, 제안된 구조는 고속으로 데이터를 처리함을 확인하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.88-91
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• 2008

The COMS IMPS (Communication Ocean and Meteorological Satellite IMage Pre-processing Subsystem) is developed for image pre-processing of COMS. For a test of the COMS IMPS, 7 support software are developed in KARI GS using simulated MI/GOCI WB (Wide-Band) data; COMS Fill Adder, MI (Meteorological Imager) CADU generator, GOCI (Geostationary Ocean Colour Imager) CADU generator, COMS CADU combiner, MI SD (Sensor Data) analyzer, GOCI SD analyzer, and COMS DM (Decomposition Module) test harness. This paper explains functions of developed support software and the COMS IMPS test using those software.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.84-89
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• 2023

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.

• 동의생리병리학회지
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• 제23권4호
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• pp.765-771
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• 2009

The medical informatics for clustering Sasang types with collected clinical data is important for the personalized medicine, but it has not been thoroughly studied yet. The purpose of this study was to examine the usefulness of neural network data mining algorithm for traditional Korean medicine. We used Kohonen neural network, the Self-Organizing Map (SOM), for the analysis of biomedical information following data pre-processing and calculated the validity index as percentage correctly predicted and type-specific sensitivity. We can extract 12 data fields from 30 after data pre-processing with correlation analysis and latent functional relationship analysis. The profile of Myers-Briggs Type Inidcator and Bio-Impedance Analysis data which are clustered with SOM was similar to that of original measurements. The percentage correctly predicted was 56%, and sensitivity for So-Yang, Tae-Eum and So-Eum type were 56%, 48%, and 61%, respectively. This study showed that the neural network algorithm for clustering Sasang types based on clinical data is useful for the sasang type diagnostic test itself. We discussed the importance of data pre-processing and clustering algorithm for the validity of medical devices in traditional Korean medicine.

• 한국자동차공학회논문집
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• 제8권3호
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• pp.190-197
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• 2000

A graphic vehicle modeling pre-processing program and a visualization post-processing program have been developed for AutoDyn7, which is a special program for vehicle dynamics. The Rapid-App for GUI(Graphic User Interface) builder and the Open Inventor for 3D graphic library have been employed to develop these programs in Silicon Graphics workstation. A Graphic User Interface program integrates vehicle modeling pre-processor, AutoDyn7 analysis processor, and visualization post-processor. In vehicle modeling pre-processor, vehicle hard point data for a suspension model are automatically converted into multibody vehicle system data. An interactive graphics capabilities provides suspension modeling aides to verify user input data interactively. In visualization post-processor, vehicle virtual test simulation results are animated with virtual testing environments.