• 한국생산제조학회지
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• 제7권1호
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• pp.69-74
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• 1998

In order to monitor the tool wear in milling operation, cutting force is measured as the tool wear increased. The digital signal processing methods are used to detect the tool wear . As AR parameter extract the feature of tool wear , it can be used as input parameter of pattern classifier. The FFT monitor the tool wear exactly , but it can not do real time signal processing. The band energy method can be used to real time monitoring of tool wear ,but int can degrade the exact monitoring.

• 한국정밀공학회지
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• 제31권7호
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• pp.585-590
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• 2014

Recently, monitoring systems, such as POP, take a core role in scheduling or planning of manufacturing facilities for production, maintenance, and so on. Such monitoring systems require functionalities for real-time parameter monitoring and controlling to maximize efficiency of facilities. However, vendors usually do not provide internal communication protocols or interface to access the machine controller. Therefore, the values of parameters related to machine operations and controls cannot be easily accessed from external devices. In this paper, we propose an interface methodology for a real-time monitoring and controlling of injection molding machine parameters such as user input parameters, embedded sensor data and injection molding status information.

• 비파괴검사학회지
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• 제31권2호
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• pp.165-173
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• 2011

음향방출기법은 대형 구조물의 구조건전성감시(SHM)를 위한 매우 효율적인 방법이지만, 롤러코스터 지지구조물처럼 승용물의 운행으로 인한 매우 큰 잡음이 일상적으로 존재하는 경우에는 균열 진전 신호만을 분류하여 실시간 감시를 수행하기가 쉽지 않다. 이와 같은 문제의 해결을 위해 본 연구에서는 실시간으로 음원의 분류가 가능한 파형 파라미터 필터링 기법을 제안하였다. 파형 파라미터 필터링 기법은 음향방출 신호의 파형 파라미터를 이용하여 음향방출 히트를 사전에 필터링함으로써 실시간으로 감시하고자 하는 대상 음원만을 분류해내는데 매우 유리한 점이 있다. 다양한 음원에 대해 음향방출 파형 파라미터를 측정 및 분석하여 제안한 기법의 타당성을 살펴보았다. 또한 파형 파라미터 필터가 내장된 음향 방출 시스템을 구축하고 이를 실제 롤러코스터 지지구조물에 적용하여 실시간 균열진전 감시를 위한 가능성을 타진하였다.

• 산업경영시스템학회지
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• 제45권4호
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• 한국항공운항학회지
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• 제19권2호
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• pp.16-22
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• 2011

It is recognized that the control surface fault is detected by monitoring the value of the coefficients due to the control surface deviation. It is found out the control surface stuck position by comparing the trim value with the reference value. To detect and isolate the fault, two mixed methods apply to the real-time parameter estimation and similarity measure. If the scatter of aerodynamic coefficients for the fault and normal are closing nearly, fault decision is difficult. Applying similarity measure to decide for fault or not, it makes a clear and easy distinction between fault and normal. Low power processor is applied to the real-time parameter estimator and computation of similarity measure.

• International Journal of Control, Automation, and Systems
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• 제5권4호
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• pp.410-418
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• 2007

For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.

• Journal of Astronomy and Space Sciences
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• 제18권2호
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• pp.129-136
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• 2001

GPS (Global Positioning System)를 이용한 정밀 응용분야에 있어 위성의 궤도력과 지구자전 상수 (Earth Orientation Parameter, EOP)의 정밀도는 매우 중요한 요소이다. 특히, GPS를 이용한 대기강시 등 신속한 정밀자료처리가 요구되는 응용분야는 실시간 또는 정밀하게 예측된 위성의 궤도력과 EOP를 필요로 한다. 이를 위해 IGS (International GPS Service)는 매일 3시, 15시 (UTC)에 IGU (lGS Ultra Rapid Product)를 생성하여 서비스하고 있다. IGU는 48시간의 정밀 궤도력과 EOP로 구성되어 있는데, 처음 24시간은 관측한 데이터를 처리하여 산출하고 다음 24시간은 예측을 통해서 산출한 값으로 이루어져 있다. 본 논문에서는 독자적인 URP (Ultra Rapid Product)를 산출하기 위한 프로세싱 전략을 수립하고 타당성을 검증하였다. 이를 위해 32개 IGS 관측소의 48시간 관측 자료를 처리하여 URP를 산출하고, 그 결과를 IGS에서 제공하는 여러 정밀 궤도력 및 EOP와 비교하였다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제11권1호
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• pp.1-5
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• 2011

To enter next process control, numerous approaches, including run-to-run (R2R) process control and fault detection and classification (FDC) have been suggested in semiconductor manufacturing industry as a facilitation of advanced process control. This paper introduces a novel type of optical plasma process monitoring system, called plasma eyes chromatic system (PECSTM) and presents its potential for the purpose of fault detection. Qualitatively comparison of optically acquired signal levels vs. process parameter modifications are successfully demonstrated, and we expect that PECSTM signal can be a useful indication of onset of process change in real-time for advanced process control (APC).

• 한국진공학회:학술대회논문집
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• 한국진공학회 2013년도 제44회 동계 정기학술대회 초록집
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• pp.342-343
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• 2013

AlSb is a promising material for optical devices, particularly for high-frequency and nonlinear-optical applications. And AlSb offers significant potential for devices such as quantum-well lasers, laser diodes, and heterojunction bipolar transistors. In this work we study molecular beam epitaxy (MBE) growth of an unstrained AISb film on a GaAs substrate and identify the real-time monitoring capabilities of in situ spectroscopic ellipsometry (SE). The samples were fabricated on semi-insulating (0 0 1) GaAs substrates using MBE system. A rotating sample stage ensured uniform film growth. The substrate was first heated to $620^{\circ}C$ under As2 to remove surface oxides. A GaAs buffer layer approximately 200 nm- thick was then grown at $580^{\circ}C$. During the temperature changing process from $580^{\circ}C$ to $530^{\circ}C$, As2 flux is maintained with the shutter for Ga being closed and the reflection high-energy electron diffraction (RHEED) pattern remaining at ($2{\times}4$). Upon reaching the preset temperature of $530^{\circ}C$, As shutter was promptly closed with Sb shutter open, resulting in the change of RHEED pattern from ($2{\times}4$) to ($1{\times}3$). This was followed by the growth of AlSb while using a rotating-compensator SE with a charge-coupled-device (CCD) detector to obtain real-time SE spectra from 0.74 to 6.48 eV. Fig. 1 shows the real time measured SE spectra of AlSb on GaAs in growth process. In the Fig. 1 (a), a change of ellipsometric parameter ${\Delta}$ is observed. The ${\Delta}$ is the parameter which contains thickness information of the sample, and it changes in a periodic from 0 to 180o with growth. The significant change of ${\Delta}$ at~0.4 min means that the growth of AlSb on GaAs has been started. Fig. 1b shows the changes of dielectric function with time over the range 0.74~6.48 eV. These changes mean phase transition from pseudodielectric function of GaAs to AlSb at~0.44 min. Fig. 2 shows the observed RHEED patterns in the growth process. The observed RHEED pattern of GaAs is ($2{\times}4$), and the pattern changes into ($1{\times}3$) with starting the growth of AlSb. This means that the RHEED pattern is in agreement with the result of SE measurements. These data show the importance and sensitivity of SE for real-time monitoring for materials growth by MBE. We performed the real-time monitoring of AlSb growth by using SE measurements, and it is good agreement with the results of RHEED pattern. This fact proves the importance and the sensitivity of SE technique for the real-time monitoring of film growth by using ellipsometry. We believe that these results will be useful in a number of contexts including more accurate optical properties for high speed device engineering.

• Smart Structures and Systems
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• 제23권4호
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.