• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.834-837
• /
• 2005

In this paper, we describe the method of non-invasive blood pressure measurement using pulse wave transit time(PWTT). PWTT is a new parameter involved with a vascular that can indicate the change of BP. PWTT is measured by continuous monitoring of ECG and pulse wave. No additional sensors or modules are required. In many cases, the change of PWTT correlates with the change of BP. We measure pulse wave using the photo plethysmograph(PPG) sensor in an earlobe and we measure ECG using the ECG monitoring device our made in the chest. The measurement device for detecting pulse wave consists of infrared LED for transmitted light illumination, pin photodiode as light detector, amplifier and filter. We composed 0.5Hz high pass, 60Hz notch and 10Hz low pass filter. ECG measurement device consists of multiplexer, amplifier, filter, micro-controller and RF module. After amplification and filtering, ECG signal and pulse wave is fed through micro-controller. We performed the initial work towards the development of ambulatory BP monitoring system using PWTT. An earlobe is suitable place to measure PPG signal without the restraint in daily work. From the results, we can know that the dependence of PWTT on BP is almost linear and it is possible to monitoring an individual BP continuously after the individual calibration.

• Smart Structures and Systems
• /
• v.17 no.3
• /
• pp.413-429
• /
• 2016

The recent research on proper orthogonal decomposition (POD) has revealed the linkage between proper orthogonal modes and linear normal modes. This paper presents an investigation into the modal identifiability of an instrumented cable-stayed bridge using an adapted POD technique with a band-pass filtering scheme. The band-pass POD method is applied to the datasets available for this benchmark study, aiming to identify the vibration modes of the bridge and find out the so-called deficient modes which are unidentifiable under normal excitation conditions. It turns out that the second mode of the bridge cannot be stably identified under weak wind conditions and is therefore regarded as a deficient mode. To judge if the deficient mode is due to its low contribution to the structural response under weak wind conditions, modal coordinates are derived for different modes by the band-pass POD technique and an energy participation factor is defined to evaluate the energy participation of each vibration mode under different wind excitation conditions. From the non-blind datasets, it is found that the vibration modes can be reliably identified only when the energy participation factor exceeds a certain threshold value. With the identified threshold value, modal identifiability in use of the blind datasets from the same structure is examined.

• Proceedings of the KWS Conference
• /
• 2009.11a
• /
• pp.10-10
• /
• 2009

파이프 용접은 중력의 영향으로 인하여 위치에 따라 같은 용접변수라도 비드 형상이 매우 달라 지게 된다. 또한 지금까지 많은 용접 기술자들이 위험하고 까다로운 환경에서 수작업으로 용접을 실행하였다. 따라서 이러한 이유로 용접 자동화 공정이 반드시 필요하게 된다. 본 연구에서는 FCAW를 사용하여 파이프 모재 대신 필릿 평판을 아래보기, 위보기 자세를 포함하여 9개 자세에서 실행하였다. 용접 자세를 비롯한 용접 변수와 비드 형상 변수간의 관계를 비선형 회귀 분석과 구간적 3차 에르미트 보간법을 이용하여 주어진 용접 변수에서의 비드 단면의 형상을 예측하고, 비드의 결함 유무를 파악하였다. 이러한 방법을 통하여 자세에 따라서 용접 결함이 없는 용접 변수를 구할 수 있었다. 시각센서를 이용하여 용접 후 비드 형상에 대해 모니터링을 실시하였다. 모니터링의 알고리즘은 영상획득, 이진화, 세선화, 적응형 미디언 필터링, 적응형 허프 변환, 용접 결함 검출의 순서로 구성되어 있으며, 본 연구에서는 보다 빠른 영상처리를 위하여 적응형 미디언 필터링을 제시하였다. 모니터링을 통하여 2차원 비드 단면뿐만 아니라, 디루니 삼각법을 적용하여 3차원으로 비드 표면을 표현할 수 있다. 보간법을 사용하여 얻은 비드 형상과 시각 센서를 통하여 얻은 비드 형상간의 비교를 통하여 본 연구의 적합성 여부를 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.11
• /
• pp.5419-5435
• /
• 2017

The construction of panoramic images on smartphones and low-powered devices is a challenging task. In this paper, we propose a new approach for smoothly stitching images on mobile phones in the presence of moving objects in the scene. Our main contributions include handling moving object problems, reducing processing time, and generating rectangular panoramic images. First, unique and robust feature points are extracted using fast ORB method and a feature matching technique is applied to match the extracted feature points. After obtaining good matched feature points, we employ the non-deterministic RANSAC algorithm to discard wrong matches, and the hommography transformation matrix parameters are estimated with the algorithm. Afterward, we determine precise overlap regions of neighboring images and calculate their absolute differences. Then, thresholding operation and noise removal filtering are applied to create a mask of possible moving object regions. Sequentially, an optimal seam is estimated using dynamic programming algorithm, and a combination of linear blending with the mask information is applied to avoid seam transition and ghosting artifacts. Finally, image-cropping operation is utilized to obtain a rectangular boundary image from the stitched image. Experiments demonstrate that our method is able to produce panoramic images quickly despite the existence of moving objects.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.49 no.3
• /
• pp.82-89
• /
• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• The KIPS Transactions:PartA
• /
• v.13A no.5 s.102
• /
• pp.429-434
• /
• 2006

A CCTV system reduces some number of cameras if we use convex-mirror. In this case, convex-mirror Image distorted, we need transformation to flat images. In the center of mirror images, a transformed image has no distortion, but at near boundary image has plentiful distortion. This distortion is caused by occlusion of angled ray and diffraction. We know that the linear filtering approach cannot separate noise from signal where their Fourier spectra overlap. But using a non-linear discretization method, we shall reduce blurred noise. In this paper, we introduce the backward solution of nonlinear wave equation for reducing blurred noise and biased expansion of equilibrium contour. We propose, after applying the introduced method, and calculate with discretization method. To analysis the experimental result, we investigate to PSNR and get about 4dB better than current method.

• Journal of the Institute of Convergence Signal Processing
• /
• v.10 no.1
• /
• pp.49-59
• /
• 2009

In this paper a watermarking algorithm is proposed to optimize the capacity of the digital watermark insertion in an experimental threshold using the characteristics of human visual system(HVS), adaptive scale factors, and weight functions based on discrete wavelet transform. After the original image is decomposed by a 3-level discrete wavelet transform, the watermarks for capacity optimization are inserted into all subbands except the baseband, by applying the important coefficients from the experimental threshold in the wavelet region. The adaptive scale factors and weight functions based on HVS are considered for the capacity optimization of the digital watermark insertion in order to enhance the robustness and invisibility. The watermarks are consisted of gaussian random sequences and detected by correlation. The experimental results showed that this algorithm can preserve a fine image quality against various attacks such as the JPEG lossy compression, noise addition, cropping, blurring, sharpening, linear and non-linear filtering, etc.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.22 no.5
• /
• pp.465-473
• /
• 2002

Images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in radiography is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in reading. We have proposed adaptive weighted median(AWM) filters based on local statistics. We show two ways of realizing the AWM filters. One is a simple type of AWM filter, whose weights are given by a simple non-linear function of three local characteristics. The other is the AWM filter which is constructed by homogeneous factor(HF). Homogeneous factor(HF) from the quantum noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the detection systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by visual C++ language on a IBM-PC Pentium 550 for testing purposes, the effects and results of the noise filtering were proposed by comparing with images of the other existing filtering methods.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.1A
• /
• pp.31-43
• /
• 2009

In this study, the numerical method is presented, which can consider the various train types and can solve the equations of motion for a vehicle-bridge interaction analysis by non-iteration procedure through formulating the coupled equations of motion. The coupled equations of motion for the vehicle-bridge interaction are solved by the Newmark ${\beta}$ of a direct integration method, and by composing the effective stiffness matrix and the effective force vector according to a analysis step, those can be solved with the same manner of the solving procedure of equilibrium equations in static analysis. Also, the effective stiffness matrix is reconstructed by the Skyline method for increasing the analysis effectiveness. The Cholesky's matrix decomposition scheme is applied to the analysis procedure for minimizing the numerical errors that can be generated in directly calculating the inverse matrix. The equations of motion for the conventional trains are derived, and the numerical models of the conventional trains are idealized by a set of linear springs and dashpots with 16 degrees of freedom. The bridge models are simplified by the 3 dimensional space frame element which is based on the Euler-Bernoulli theory. The rail irregularities of vertical and lateral directions are generated by the PSD functions of the Federal Railroad Administration (FRA). The results of the vehicle-bridge interaction analysis are verified by the experimental results for the railway plate girder bridges of a span length with 12 m, 18 m, and the experimental and analytical data are applied to the low pass filtering scheme, and the basis frequency of the filtering is a 2 times of the 1st fundamental frequency of a bridge bending.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.12
• /
• pp.1295-1304
• /
• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.