• Structural Engineering and Mechanics
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• 제46권3호
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• pp.433-445
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• 2013

Vibration-based structural identification has become an important tool for structural health monitoring and safety evaluation. However, various kinds of uncertainties (e.g., observation noise) involved in the field test data obstruct automation system identification for accurate and fast structural safety evaluation. A practical way including a data preprocessing procedure and a vector backward auto-regressive (VBAR) method has been investigated for practical bridge identification. The data preprocessing procedure serves to improve the data quality, which consists of multi-level uncertainty mitigation techniques. The VBAR method provides a determinative way to automatically distinguish structural modes from extraneous modes arising from uncertainty. Ambient test data of a cantilever beam is investigated to demonstrate how the proposed method automatically interprets vibration data for structural modal estimation. Especially, structural identification of a truss bridge using field test data is also performed to study the effectiveness of the proposed method for real bridge identification.

• International Journal of Automotive Technology
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• 제7권5호
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• pp.637-644
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• 2006

The detection of engine misfire events is one of major concerns in engine control due to its negative effect on air pollution and engine performance. In this paper, a misfire detection system based on crankshaft angular speed fluctuation is developed. Synthetic variable method is adopted for the preprocessing of crankshaft angular speed. This method successfully estimates the work output of each cylinder by finding the effect of combustion energy on the crankshaft rotational speed or acceleration after virtually removing the effect of the internal inertia forces from the measured crankshaft speed signals. The detection system is developed using neural network with the revised synthetic angular acceleration as input which is derived from the preprocessing. Mathematical simulation is carried out for developing and verifying the misfire detection system. Finally, the reliability of the developed system is validated through an experiment.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1998년도 추계학술대회 논문집
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• pp.71-74
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• 1998

본 연구는 최소비용문제에 적용할 수 있는 사전처리 기법의 이론과 그 구현에 대해서 다룬다. 일반적으로 해법을 적용하여 문제를 풀기 이전에 최적해에서 유통량을 알 수 있는 호나 중복적인 호와 점을 제거하여 문제 크기를 줄이는 과정을 사전처리(preprocessing)라 한다. 또한 문제의 비가능성이나 입력된 문제의 정확성 등을 검사하는 과정도 사전처리에 포함하기도 한다 따라서 사전처리는 문제 축소와 입력된 문제의 정확성 검사 등을 통해 해법의 수행도와 안정성을 높이는 효과를 가져다준다. 본 연구에서는 최소비용문제의 사전처리로 비가능성 판정, 중개지에 대한 사전처리, 병렬호에 대한 사전처리, 호의 유통상한과 유통하한을 이용한 유통량고정에 대한 사전처리, 우회경로에 대한 사전처리 등을 연구하였다. 본 연구에서는 네트워크 단체법 프로그램에 최소비용문제에서의 사전처리기법을 각각 구현하여 이러한 사전처리를 하지 않았을 때와 비교하여 문제의 크기를 줄일 수 있었고 수행시간을 16%정도 줄일 수 있다는 것을 실험적으로 보였다.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권7호
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• pp.327-333
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• 2002

In this study, we developed a snoring detection algorithm that detects snores automatically. It consists of preprocessing and snoring detection part. The preprocessing part is composed of a noise removal part using spectrum subtraction, and segmentation part, and computation part of temporal and spectral features. And the snoring detection part decides whether detected blocks are snores with BPNN(Back-Propagation Neural Network). BPNN with one hidden layer and one output layer, is trained with data of 7 subjects and tested with data of 11 subjects of total 18 subjects. The proposed algorithm showed a Sensitivity of 90.41% and a Predictive Positive Value of 84.95%.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.666-668
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• 2000

In this paper, a fuzzy prediction method is proposed for time series data having uncertainty and non-stationary characteristics. Conventional methods, which use past data directly in prediction procedure, cannot properly handle non-stationary data whose long-term mean is floating. To cope with this problem, a data preprocessing technique utilizing the differences of original time series data is suggested. The difference sets are established from data. And the optimal difference set is selected for input of fuzzy predictor. The proposed method based the Takigi-Sugeno-Kang(TSK or TS) fuzzy rule. Computer simulations show improved results for various time series.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2001년도 The Seoul International Simulation Conference
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• pp.223-230
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• 2001

This paper proposes a new contour detection method and adaptive reconstruction scheme for the cylindrical organs, such as blood vessels or arteries. Furthermore, we present java-based navigation controller which has been built to examine the inside of cylindrical objects. Tn the preprocessing procedure, a few preprocessing image filters are applied in order to remove unwanted artifacts from the medical images and to estimate threshold values for the object of interest. We define a context-free grammar, which is proper fur properties of contours of cylindrical objects. In the next procedure, we extract contours using advanced radial gradient method and represent contours as context-free grammar derivation trees. We build polygons between two contours efficiently by traversing the derivations trees of the contours. We fly through the reconstructed virtual models using java-based navigation controller and VRML viewer.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1998년도 추계학술대회논문집:21세기에 대비한 지능형 통합항만관리
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• pp.137-141
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• 1998

Todays the efficient management of container has not been realized in container terminal, because of the excessive quantity of container transported and manual system. For the efficient and automated management of container in terminal, the automated container identifier recognition system in terminal is a significant problem. However, the identifier recognition rate is decreased owing to the difficulty of image preprocessing caused the refraction of container surface, the change of weather and the damaged identifier characters. Therefore, this paper proposes more accurate system for container identifier recognition as suggestion of Line-Scan Proper Region Detect for stronger preprocessing against external noisy element and Moment Back-Propagation Neural Network to recognize identifier.

• Journal of information and communication convergence engineering
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• 제7권4호
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• pp.442-448
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• 2009

This paper proposes a fingerprint image processing algorithm to accurately extract minutiae in the process of fingerprint recognition. We improved the matching accuracy of low quality fingerprint images by using effective ridge vector and ridge probability. The proposed algorithm improves the clarity of ridge structures and reduces undesired noise. We collected thumb print images from 10 individuals 5 separate times each, in total using 50 thumbprints. We registered one of the five thumbprint images from each individual to match the registered one with the other four thumbprint images, and alternated the registered thumbprint image. We matched thumbprints 20 times for each individual. In total, we conducted 200 matches for the thumbprints from the 10 individuals. We improved the verification accuracy and reliability compared to conventional methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.459-460
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• 2001

In this paper, we analyze algorithms for diagnosing of VEP(visual evoked potential) signal. We used wavelet transform for the preprocessing of VEP signal data and back propagation neural network for the pattern recognition. We used several wavelets to study their effects and efficiency in the preprocessing of VEP. The diagnosis system led to good results. We obtained the noise reduced and compressed signal with the wavelet transform of the training VEP signal. So it is possible to train the neural network faster and exact diagnosis processing is possible in the neural network. From the experimental results, we know that the discrimination ability of the neural network is changed by the type of basis vector and the proposed system is good to the diagnosis of VEP.