• Title/Summary/Keyword: Measurement-based Model

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A Through-focus Scanning Optical Microscopy Dimensional Measurement Method based on a Deep-learning Regression Model (딥 러닝 회귀 모델 기반의 TSOM 계측)

  • Jeong, Jun Hee;Cho, Joong Hwee
    • Journal of the Semiconductor & Display Technology
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    • v.21 no.1
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    • pp.108-113
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    • 2022
  • The deep-learning-based measurement method with the through-focus scanning optical microscopy (TSOM) estimated the size of the object using the classification. However, the measurement performance of the method depends on the number of subdivided classes, and it is practically difficult to prepare data at regular intervals for training each class. We propose an approach to measure the size of an object in the TSOM image using the deep-learning regression model instead of using classification. We attempted our proposed method to estimate the top critical dimension (TCD) of through silicon via (TSV) holes with 2461 TSOM images and the results were compared with the existing method. As a result of our experiment, the average measurement error of our method was within 30 nm (1σ) which is 1/13.5 of the sampling distance of the applied microscope. Measurement errors decreased by 31% compared to the classification result. This result proves that the proposed method is more effective and practical than the classification method.

R&D performance measurement model - Quantitative value measurement of technology and Its capitalization - (연구개발투자의 성과측정 모형 - 기술의 정량적 가치추정과 자산화 방안 -)

  • 조현춘;박상덕
    • Proceedings of the Technology Innovation Conference
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    • 1999.12a
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    • pp.159-177
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    • 1999
  • Many companies still struggle with the issue of research and development(R&D) performance measurement, in particular, the nonfinancial performance measurement of R&D with coming of knowledge-based society, Of course, we would not deny the fact that financial measures play the central role in assessing the overall performance of R&D, The aim of this paper is to provide the new model to evaluate the quantitative value of technology (nonfinancial benefits). This new model is based on the technology stock(technology level) acquired in R&D process, That is, we take it for granted that the acquired technology below a certain level(<70% compare to the advanced country) can not be utilized in developing the new products or in proving the manufacturing processes, The evaluation model we create can explains the quantitative relation between the technology stock and the market value considering R&D expenditure to acquire the technology above certain level(>70%) and cost to prevent the technology obsolescence. The value of non-destructive testing technology, which is one of the electric Power technology, is measured quantitatively using our new model as a case study, We also discussed briefly the possibility of capitalization of the measured technology value.

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A Random Forest Model Based Pollution Severity Classification Scheme of High Voltage Transmission Line Insulators

  • Kannan, K.;Shivakumar, R.;Chandrasekar, S.
    • Journal of Electrical Engineering and Technology
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    • v.11 no.4
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    • pp.951-960
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    • 2016
  • Tower insulators in electric power transmission network play a crucial role in preserving the reliability of the system. Electrical utilities frequently face the problem of flashover of insulators due to pollution deposition on their surface. Several research works based on leakage current (LC) measurement has been already carried out in developing diagnostic techniques for these insulators. Since the LC signal is highly intermittent in nature, estimation of pollution severity based on LC signal measurement over a short period of time will not produce accurate results. Reports on the measurement and analysis of LC signals over a long period of time is scanty. This paper attempts to use Random Forest (RF) classifier, which produces accurate results on large data bases, to analyze the pollution severity of high voltage tower insulators. Leakage current characteristics over a long period of time were measured in the laboratory on porcelain insulator. Pollution experiments were conducted at 11 kV AC voltage. Time domain analysis and wavelet transform technique were used to extract both basic features and histogram features of the LC signal. RF model was trained and tested with a variety of LC signals measured over a lengthy period of time and it is noticed that the proposed RF model based pollution severity classifier is efficient and will be helpful to electrical utilities for real time implementation.

New Techniques for Impedance Characteristics Measurement of Islanded Microgrid based on Stability Analysis

  • Hou, Lixiang;Zhuo, Fang;Shi, Hongtao
    • Journal of Power Electronics
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    • v.16 no.3
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    • pp.1163-1175
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    • 2016
  • In recent years, microgrids have been the focus of considerable attention in distributed energy distribution. Microgrids contain a large number of power electronic devices that can potentially cause negative impedance instability. Harmonic impedance is an important tool to analyze stability and power quality of microgrids. Harmonic impedance can also be used in harmonic source localization. Precise measurement of microgrid impedance and analysis of system stability with impedances are essential to increase stability. In this study, we introduce a new square wave current injection method for impedance measurement and stability analysis. First, three stability criteria based on impedance parameters are presented. Then, we present a new impedance measurement method for microgrids based on square wave current injection. By injecting an unbalanced line-to-line current between two lines of the AC system, the method determines all impedance information in the traditional synchronous reference frame d-q model. Finally, the microgrid impedances of each part and the overall microgrid are calculated to verify the measurement results. In the experiments, a simulation model of a three-phase AC microgrid is developed using PSCAD, and the AC system harmonic impedance measuring device is developed.

Vocabulary Recognition Model using a convergence of Likelihood Principla Bayesian methode and Bhattacharyya Distance Measurement based on Vector Model (벡터모델 기반 바타챠랴 거리 측정 기법과 우도 원리 베이시안을 융합한 어휘 인식 모델)

  • Oh, Sang-Yeob
    • Journal of Digital Convergence
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    • v.13 no.11
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    • pp.165-170
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    • 2015
  • The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. The vector values of the existing system to the model created by configuring the database was used in the recognition vocabulary. The model to be formed during the search for the recognition vocabulary is recognizable because there is a disadvantage not configured with a database. In this paper, it induced to recognize the vector model is formed by the search and configuration using a Bayesian model recognizes the Bhattacharyya distance measurement based on the vector model, by applying the Wiener filter improves the recognition rate. The result of Convergence of two method's are improved reliability experiments for distance measurement. Using a proposed measurement are compared to the conventional method exhibited a performance of 98.2%.

Image-based structural dynamic displacement measurement using different multi-object tracking algorithms

  • Ye, X.W.;Dong, C.Z.;Liu, T.
    • Smart Structures and Systems
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    • v.17 no.6
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    • pp.935-956
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    • 2016
  • With the help of advanced image acquisition and processing technology, the vision-based measurement methods have been broadly applied to implement the structural monitoring and condition identification of civil engineering structures. Many noncontact approaches enabled by different digital image processing algorithms are developed to overcome the problems in conventional structural dynamic displacement measurement. This paper presents three kinds of image processing algorithms for structural dynamic displacement measurement, i.e., the grayscale pattern matching (GPM) algorithm, the color pattern matching (CPM) algorithm, and the mean shift tracking (MST) algorithm. A vision-based system programmed with the three image processing algorithms is developed for multi-point structural dynamic displacement measurement. The dynamic displacement time histories of multiple vision points are simultaneously measured by the vision-based system and the magnetostrictive displacement sensor (MDS) during the laboratory shaking table tests of a three-story steel frame model. The comparative analysis results indicate that the developed vision-based system exhibits excellent performance in structural dynamic displacement measurement by use of the three different image processing algorithms. The field application experiments are also carried out on an arch bridge for the measurement of displacement influence lines during the loading tests to validate the effectiveness of the vision-based system.

An Application of a Sunshine Duration Model Based on GIS Data to Suitability of Measurement Site around the Seonleung Park

  • Kim, Eun-Ryoung;Kim, Jae-Jin
    • Korean Journal of Remote Sensing
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    • v.31 no.4
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    • pp.331-336
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    • 2015
  • In this study, a numerical model developed for sunshine duration based on GIS data was used. This model considers blocking caused by topography and buildings and it is properly applicable to evaluation of sunshine duration environment in urban areas. The model reasonably well predicted the solar altitude and azimuth angels, compared to those provided by Korea Astronomy and Space Science Institute (KASI). The developed model was applied to evaluation of sunshine duration environment around the Seonleung Park located near a building-congested area in Seoul. The model well reproduced shadow caused by buildings and/or topography in the numerical domain at 09:00 on August 1, 2015. In addition, the model was applied to finding a suitable measurement sites for pyrheliometer around the Seonleung Park. The model was also usefully applied to finding a suitable site for pyrheliometer in an urban area.

Blind Measurement of Blocking Artifacts in Block-based DCT Image Coder (블록기반 DCT 영상 부호화기의 블록화 왜곡 블라인드 측정)

  • Chung, Tae-Yun;Park, Sung-Wook
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.1
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    • pp.39-45
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    • 2004
  • This paper proposes a new blind measurement model of blocking artifacts. This model plays an important role in the assessment and enhancement of image quality caused by block-based DCT coding system. The proposed model can measure blocking artifacts without reference to original images and consider the HVS based visual model such as frequency sensitivity and channel masking effect to detect and measure overall blocking artifacts quantitatively. The experimental results show that the proposed model is highly effective in measuring blocking artifacts.

A novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges

  • Wen-Qiang Liu;En-Ze Rui;Lei Yuan;Si-Yi Chen;You-Liang Zheng;Yi-Qing Ni
    • Smart Structures and Systems
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    • v.31 no.4
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    • pp.393-407
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    • 2023
  • To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BI-SMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage.

dynamic localization of a mobile robot using a rotating sonar and a map (회전 초음파 센서와 지도를 이용한 이동 로보트의 동적 절대 위치 추정)

  • 양해용;정학영;이장규
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.544-547
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    • 1997
  • In this paper, we propose a dynamic localization method using a rotating sonar and a map. The proposed method is implemented by using extended Kalman filter. The state equation is based on the encoder propagation model and the encoder error model, and the measurement equation is a map-based measurement equation using a rotating sonar sensor. By utilizing sonar beam characteristics, map-based measurements are updated while AMR is moving continuously. By modeling and estimating systematic errors of a differential encoder, the position is successfully estimated even the interval of the map-based measurement. Monte-Carlo simulation shows that the proposed global position estimator has the performance of a few millimeter order in position error and of a few tenth degrees in heading error and of compensating systematic errors of the differential encoder well.

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