• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.4
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• pp.6-13
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• 2011

Due to its robustness to illumination change, normalized cross-correlation based similarity measurement is widely used in many machine vision applications. However, its inefficient computation structure is not adequate for real-time embedded vision system. In this paper, we present an efficient hardware architecture based on a normalized cross correlation (NCC) for fast image similarity measure. The proposed architecture simplifies window-sum process of the NCC using the integral-image. Relieving the overhead to constructing integral image, we make it possible to process integral image construction at the same time that pixel sequences are inputted. Also the proposed segmented integral image method can reduce the buffer size for storing integral image data.

• Korean Chemical Engineering Research
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• v.55 no.2
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• pp.180-189
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• 2017

Pharmaceutical co-crystals primarily to improve the solubility as well as stability of insoluble drug are to be investigated more intensively for IMDs as US FDA has reclassified co-crystal as a special case of solvates in August this year. In this study, we proposed a mechanism of indomethacin-saccharin co-crystal formation and the creation of transient indomethacin meta-stable form using in-line monitoring tools with the addition rate of anti-solvent as a critical process parameter. Among various instruments, we combined PVM (particle vision measurement) and FBRM (focused beam reflectance measurement) for the in-line monitoring of anti-solvent co-crystallization process. The off-line characterization of resulting powders was carried out employing the PXRD (powder x-ray diffraction) and DSC (differential scanning calorimeter). It was observed that the pathway to the final IMC-SAC co-crystal was significantly dependent upon the anti-solvent addition rate. The process conditions to obtain high quality co-crystal powder effectively were established. Consequently, we concluded that in-line monitoring combing the PVM and FBRM should be useful for the in-line monitoring of pharmaceutical co-crystallization processes.

• Food Science of Animal Resources
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• v.38 no.5
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• pp.1109-1119
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• 2018

In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop the prediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation - developed to estimate LMP in whole carcasses based on six variables - was characterized by a coefficient of determination ($R^2_v$) value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited $R^2_v$ values${\geq}0.8$ (0.73 for loin parts) with low RMSEV values. However, lower accuracy ($R^2_v=0.67$) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.594-603
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• 2018

Recently, according to the development of measurement techniques, it has become possible to take complicated and time-consuming field measurements in a simple and convenient manner. In this background, this study estimated the tension of cables under ambient vibration using minimized measurement and signal processing. The VBDM using video-only by low-cost equipment was used as a minimized measurement. An estimation of the natural frequency using the mirror frequency concept was also proposed to solve the shortage of frequency band in this case. Furthermore, the FDD method was adopted for a natural frequency estimation in the ambient vibration related to field application. Experimental studies using a cable-stayed bridge model were carried out to examine the properties of the mirror frequency and the applicability of FDD with the proposed minimized system. The results showed that FDD for ambient vibration also works properly in an estimation of the natural frequency using the minimized system. In addition, the mirror frequency concept can allow a high natural frequency estimation even in a distorted signal by low-speed recording, which can overcome the limit of the minimized system. Overall, the proposed minimized system can be effective for the tension estimations of a cable under ambient vibration.

• Proceedings of the Materials Research Society of Korea Conference
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• 2009.05a
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• pp.15.2-15.2
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• 2009

Manufacturing of printed electronics using printing technology has begun to get into the hot issue in many ways due to the low cost effectiveness to existing semi-conductor process. This technology with both low cost and high productivity, can be applied in the production of organic thin film transistor (OTFT), solar cell, radio frequency identification (RFID) tag, printed battery, E-paper, touch screen panel, black matrix for liquid crystal display (LCD), flexible display, and so forth. The emerging technology to manufacture the products in mass production is roll-to-roll printing technology which is a manufacturing method by printings of multi-layered patterns composed of semi-conductive, dielectric and conductive layers. In contrary to the conventional printing machines in which printing precision is about $50~100{\mu}m$, the printing machines for printed electronics should have a precision under $30{\mu}m$. In general, in order to implement printed electronics, narrow width and gap printing, register of multi-layer printing by several printing units, and printing accuracy of under $30{\mu}m$ are all required. We developed the roll-to-roll printing equipment used for printed electronics, which is composed of un-winder, re-winder, tension measurement system, feeding units, dancer systems, guide unit, printing unit, vision system, dryer units, and various auxiliary devices. The equipment is designed based on cantilever type in which all rollers except printing ones have cantilever types, which could give more accurate machine precision as well as convenience for changing rollers and observing the process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.702-707
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• 2004

This article is concerned with container pose estimation using CCD a camera and a range sensor. In particular, the issues of characteristic point extraction and image noise reduction are described. The Euler-Lagrange equation for gaussian and random noise reduction is introduced. The alternating direction implicit(ADI) method for solving Euler-Lagrange equation based on partial differential equation(PDE) is applied. The vertex points as characteristic points of a container and a spreader are founded using k order curvature calculation algorithm since the golden and the bisection section algorithm can't solve the local minimum and maximum problems. The proposed algorithm in image preprocess is effective in image denoise. Furthermore, this proposed system using a camera and a range sensor is very low price since the previous system can be used without reconstruction.

• Transactions of Materials Processing
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• v.14 no.6 s.78
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• pp.514-519
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• 2005

A tube hydroformability testing system was designed and fabricated enabling to apply the forming condition along arbitrarily pre-programmed internal pressure-axial feed path. The free-bulging and T-forming tests were carried out on the extruded aluminum (A6063) tube specimens with 40.6 mm outer diameter and 2.25 mm thickness. Nine different combinations of internal pressure and axial feed, yielding different strain paths from one another, were taken into consideration in order to induce bursting at various deformation modes. Major and minor strains were automatically measured from deformed grids around the fracture using a stereo-vision-based surface strain measurement system, named ASIAS. The forming limit diagram of the A6063 tube material was successfully obtained. Most of the data points acquired from free bulging and T-forming tests appeared in the range of negative minor strain on the FLD and are mostly located near the strain paths calculated from explicit finite element simulations. The forming limit obtained from tests after pre-tension was considerably lower than that from tests without pre-tension, which showed the strain path-dependency of the forming limit as well known in the sheet forming fold.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.2
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• pp.77-83
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• 2014

This paper describes a new technology to develop the character recognition technology based on pattern recognition for non-contacting inspection optical lens slant or precision parts, and including external form state of lens or electronic parts for the performance verification, this development can achieve badness finding. And, establish to existing reflex data because inputting surface badness degree of scratch's standard specification condition directly, and error designed to distinguish from product more than schedule error to badness product by normalcy product within schedule extent after calculate the error comparing actuality measurement reflex data and standard reflex data mutually. Developed system to smallest 1 pixel unit though measuring is possible 1 pixel as $37{\mu}m{\times}37{\mu}m$ ($0.1369{\times}10-4mm^2$) the accuracy to $1.5{\times}10-4mm$ minutely measuring is possible performance verification and trust ability through an experiment prove.

• Journal of Korean Society of Transportation
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• v.14 no.4
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• pp.107-129
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• 1996

Recently, surveillance system designed to collect various trsffic information are becoming new areas of development . Among these, the image detector is a ayatem which can measure the travel time and speed in realtime and this is emerging as the most effcient tool to be available in future related areas. But in measuring wide-area information in realtime, the image detector are yet full of problem in its accuracy. The aim of this ahesis is to develop an algorithms which can collect wide-area information such as travel time and travel speed in urban networks and freeways in realtime. The information on wide-area such as travel time and travel speed is important in accomplishing strategic function in traffic control. The algorithm developed from this study is based on the image tracking model which tracks a moving vehicle form image datas collected continuously, and is constructed to perform realtime measurement. To evaluate the performance of the developed algorithms, 600 ind vidual vehicles in total were used as data for the study, and this evaluation was carried out with the differenciation of day and night condition at the access roads in front of AJou University, In the statistical analysis results, the error rate was recorded as 5.69% and it has proved to be applicable on the field in both day and noght conditions.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.