• Title/Summary/Keyword: Error Inspection Algorithm

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Methods and Systems for High-temperature Strain Measurement of the Main Steam Pipe of a Boiler of a Power Plant While in Service

  • Guang, Chen;Qibo, Feng;Keqin, Ding
    • Journal of the Optical Society of Korea
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    • v.20 no.6
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    • pp.770-777
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    • 2016
  • It has been a challenge for researchers to accurately measure high temperature creep strain online without damaging the mechanical properties of the pipe surface. To this end, a noncontact method for measuring high temperature strain of a main steam pipe based on digital image correlation was proposed, and a system for monitoring of high temperature strain was designed and developed. Wavelet thresholding was used for denoising measurement data. The sub-pixel displacement search algorithm with curved surface fitting was improved to increase measurement accuracy. A field test was carried out to investigate the designed monitoring system of high temperature strain. The measuring error was less than $0.4ppm/^{\circ}C$, which meets actual measurement requirements for engineering. Our findings provide a new way to monitor creep damage of the main steam pipe of a boiler of an ultra-supercritical power plant in service.

Vision Inspection and Correction for DDI Protective Film Attachment

  • Kang, Jin-Su;Kim, Sung-Soo;Lee, Yong-Hwan;Kim, Young-Hyung
    • Journal of Advanced Information Technology and Convergence
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    • v.10 no.2
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    • pp.153-166
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    • 2020
  • DDI(Display Driver IC) are used to drive numerous pixels that make up display. For stable driving of DDI, it is necessary to attach a protective film to shield electromagnetic waves. When the protective film is attached, defects often occur if the film is inclined or the center point is not aligned. In order to minimize such defects, an algorithm for correcting the center point and the inclined angle using camera image information is required. This technology detects the corner coordinates of the protective film by image processing in order to correct the positional defects where the protective film is attached. Corner point coordinates are detected using an algorithm, and center point position finds and correction values are calculated using the detected coordinates. LUT (Lookup Table) is used to quickly find out whether the angle is inclined or not. These algorithms were described by Verilog HDL. The method using the existing software requires a memory to store the entire image after processing one image. Since the method proposed in this paper is a method of scanning by adding a line buffer in one scan, it is possible to scan even if only a part of the image is saved after processing one image. Compared to those written in software language, the execution time is shortened, the speed is very fast, and the error is relatively small.

A Single Order Assignment Algorithm Based on Multi-Attribute for Warehouse Order Picking (물류창고 오더피킹에 있어서 다 속성 기반의 싱글오더 할당 알고리즘)

  • Kim, Daebeom
    • Journal of the Korea Society for Simulation
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    • v.28 no.1
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    • pp.1-9
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    • 2019
  • Recently, as the importance of warehouses has increased, much efforts are being made to improve the picking process in order to cope with a small amount of high frequency and fast delivery. This study proposes an algorithm to assign orders to pickers in the situation where Single Order Picking policy is used. This algorithm utilizes five attributes related to picking such as picking processing time, elapsed time after receipt of order, inspection/packing workstation situation, picker error, customer importance. A measure of urgency is introduced so that the units of measure for each attribute are the same. The higher the urgency, the higher the allocation priority. In the proposed algorithm, the allocation policy can be flexibly adjusted according to the operational goal of the picking system by changing the weight of each attribute. Simulation experiments were performed on a hypothetical small logistics warehouse. The results showed excellent performance in terms of system throughput and flow time.

Identification of Track Irregularity by Frequency-Domain Transfer Function (주파수영역 전달함수를 이용한 궤도틀림 식별)

  • Kim, Jae-Cheon;Kwon, Soon-Jung;Yin, Jing-Lin;Lee, Hyeung-Jin;Kim, Man-Cheol;Shin, Soo-Bong
    • Journal of the Korean Society for Railway
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    • v.12 no.4
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    • pp.506-511
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    • 2009
  • An algorithm for identifying track irregularities along the railway is presented. A baseline frequency-domain transfer function based on the equivalent SlSO(Single Input Single Output) model is defined at the intact condition between the measured track geometry of the ground displacement and the acceleration measured at a location in a train. The pre-defined transfer function at the intact condition is used inversely to predict track geometry in time with the currently measured acceleration at the same location in a train. The predicted track geometry is compared in time with that of the baseline values at the intact condition. The difference between them is calculated as an error in time and used to identify the track irregularities. An irregularity index is proposed as the ratio between the moving variance of the error at the current inspection and that at the intact condition. A 3D numerical simulation study has been carried out with a train model to verify the validity of the presented algorithm. In the analysis for the simulation, the track geometry has been considered as the displacement boundary condition varying in time.

Development of a Laser-Generated Ultrasonic Inspection System by Using Adaptive Error Correction and Dynamic Stabilizer (적응적 에러 보정과 다이나믹 안정기를 이용한 레이저 유도 초음파 검사 시스템 개발)

  • Park, Seung-Kyu;Baik, Sung-Hoon;Park, Moon-Cheol;Lim, Chang-Hwan;Ra, Sung-Woong
    • Journal of the Korean Society for Nondestructive Testing
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    • v.25 no.5
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    • pp.391-399
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    • 2005
  • Laser-generated ultrasonic inspection system is a non-contact scanning inspection device with high spatial resolution and wide bandwidth. The amplitude of laser-generated ultrasound is varied according to the energy of pulse laser and the surface conditions of an object where the CW measuring laser beam is pointing. In this paper, we correct the generating errors by measuring the energy of pulse laser beam and correct the measuring errors by extracting the gain information of laser interferometer at each time. h dynamic stabilizer is developed to stably scan on the surface of an object for an laser-generated ultrasonic inspection system. The developed system generates ultrasound after adaptively finding the maximum gain time of an laser interferometer and processes the signal in real time after digitization with high speed. In this paper, we describe hardware configuration and control algorithm to build a stable laser-generated ultrasonic inspection system. Also, we confirmed through experiments that the proposed correction method for the generating errors and measuring errors is effective to improve the performance of a system.

Adaptive Force Ripple Compensation and Precision Tracking Control of High Precision Linear Motor System (초정밀 선형 모터 시스템의 적응형 힘리플 보상과 정밀 트랙킹 제어)

  • Choi Young-Man;Gweon Dae-Gab;Lee Moon G.
    • Journal of the Korean Society for Precision Engineering
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    • v.22 no.12 s.177
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    • pp.51-60
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    • 2005
  • This paper describes a robust control scheme for high-speed and long stroke scanning motion of high precision linear motor system consisting of linear motor, air bearing guide and position measurement system using heterodyne interferometer. Nowadays, semiconductor process and inspection of wafer or LCD need high speed and long travel length for their high throughput and extremely small velocity fluctuations or tracking errors. In order to satisfy these conditions, linear motor system are widely used because they have large thrust force and do not need motion conversion mechanisms such as ball screw, rack & pinion or capstan with which the system are burdened. However linear motors have a problem called force ripple. Force ripple deteriorates the tracking performances and makes periodic position errors. So, force ripple must be compensated. To maximize the tracking performance of linear motor system, we propose the control scheme which is composed of a robust control method, Time Delay Controller (TDC) and a feedforward control method, Zero Phase Error Tracking Control (ZPETC) for accurate tracking a given trajectory and an adaptive force ripple compensation (AFC) algorithm fur estimating and compensating force ripple. The adaptive ripple compensation is continuously refined on the basis of tracking error. Computer simulation results based on modeled parameters verify the effectiveness of the proposed control scheme for high-speed, long stroke and high precision scanning motion and show that the proposed control scheme can achieve a sup error tracking performance in comparison to conventional TDC control.

Study on Prediction of Compressive Strength of Concrete based on Aggregate Shape Features and Artificial Neural Network (골재의 형상 특성과 인공신경망에 기반한 콘크리트 압축강도 예측 연구)

  • Jeon, Jun-Seo;Kim, Hong-Seop;Kim, Chang-Hyuk
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.25 no.5
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    • pp.135-140
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    • 2021
  • In this study, the concrete aggregate shape features were extracted from the cross-section of a normal concrete strength cylinder, and the compressive strength of the cylinder was predicted using artificial neural networks and image processing technology. The distance-angle features of aggregates, along with general aggregate shape features such as area, perimeter, major/minor axis lengths, etc., were numerically expressed and utilized for the compressive strength prediction. The results showed that compressive strength can be predicted using only the aggregate shape features of the cross-section without using major variables. The artificial neural network algorithm was able to predict concrete compressive strength within a range of 4.43% relative error between the predicted strength and test results. This experimental study indicates that various material properties such as rheology, and tensile strength of concrete can be predicted by utilizing aggregate shape features.

Correlation Extraction from KOSHA to enable the Development of Computer Vision based Risks Recognition System

  • Khan, Numan;Kim, Youjin;Lee, Doyeop;Tran, Si Van-Tien;Park, Chansik
    • International conference on construction engineering and project management
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    • 2020.12a
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    • pp.87-95
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    • 2020
  • Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

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Evaluation on the Lost Prestressing Force of an External Tendon Using the Combination of FEM and HGA: II. Experimental Verification and Field Applications (FEM과 HGA의 조합을 이용한 외부 긴장재의 손실 긴장력 평가: II. 실험적 검증 및 현장적용)

  • Jang, Hang-Teak;Noh, Myung-Hyun;Park, Kyu-Sik;Park, Taehyo
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.13 no.5 s.57
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    • pp.121-132
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    • 2009
  • This paper introduces an experimental verification and a field application of the proposed technique using the combination of FEM and HGA about the loss prestressing force of an exteranl tendon by above same authors. The vibration tests have been conducted by using a laboratory models and the externally prestressed tendon at the field and the natural frequencies are extracted from the vibration tests. The proposed technique based on the extracted natural frequencies is applied. It is seen that the errors in the tension and lost prestressing force by proposed technique are about 4% from a laboratory model test. For the model verification at field, exact modeling has beem made with Rayleigh damping. It is seen that the error in the tension by proposed technique is less than 1% and the estimated lost prestressing force converges less than the exact value.

Development of Bond Strength Model for FRP Plates Using Back-Propagation Algorithm (역전파 학습 알고리즘을 이용한 콘크리트와 부착된 FRP 판의 부착강도 모델 개발)

  • Park, Do-Kyong
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.10 no.2
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    • pp.133-144
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    • 2006
  • In order to catch out such Bond Strength, the preceding researchers had ever examined the Bond Strength of FRP Plate through their experimentations by setting up of various fluent. However, since the experiment for research on such Bond Strength takes much of expenditure for equipment structure and time-consuming, also difficult to carry out, it is conducting limitedly. This Study purposes to develop the most suitable Artificial Neural Network Model by application of various Neural Network Model and Algorithm to the adhering experiment data of the preceding researchers. Output Layer of Artificial Neural Network Model, and Input Layer of Bond Strength were performed the learning by selection as the variable of the thickness, width, adhered length, the modulus of elasticity, tensile strength, and the compressive strength of concrete, tensile strength, width, respectively. The developed Artificial Neural Network Model has applied Back-Propagation, and its error was learnt to be converged within the range of 0.001. Besides, the process for generalization has dissolved the problem of Over-Fitting in the way of more generalized method by introduction of Bayesian Technique. The verification on the developed Model was executed by comparison with the resulted value of Bond Strength made by the other preceding researchers which was never been utilized to the learning as yet.