• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.295-302
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• 1997

An ultimate purpose of this study was to develop an automatic system for brown rice quality inspection using image processing technique. In this study emphasis was put on developing an algorithm for discriminating the brown rice kernels depending on their external quality with a color image processing system equipped with an adaptor magnifying the input image and optical fiber for oblique lightening. Primarily, geometical and optical features of images were analyzed with paddy and the various brown rice kernel samples such as a sound, cracked, peen-transparent, green-opaque, colored, white-opaque and brokens. Secondary, geometrical and optical parameters significant for identifying each rice kernels were screened by a statistical analysis(STEPWISE and DISCRIM procedure, SAS wer. 6) and an algorithm fur on- line discrimination of the rice kernels in static state were developed, and finally its performance was evaluated. The results are summarized as follows. 1) It was ascertained that the cracked kernels can be detected when e incident angle of the oblique light is less than 2$0^{\circ}C$ but detectivity was significantly affected by the angle between the direction of the oblique light and the longitudinal axis of the rice kernel and also by the location of the embryo with respect to the oblique light. 2) The most significant Parameters which can discriminate brown rice kernels are area, length and R, B and r values among the several geometrical and optical parameters. 3) Discrimination accuracies of the algorithm were ranged from 90% to 96% for a sound, cracked, colored, broken and unhulled, about 81 % for green-transparent and white-opaque and 75 % for green-opaque, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.649-652
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• 2005

The camera cellular phone has a large portion of cellular phone market in recent year. The variety of a customer demand makes a fast model change and the spatial resolution is changed from VGA to multi-mega pixel. The 1.3 mega pixel (MP) camera cellular phone was first released into the Korean market in October 2003. The major cellular phone companies released a 2MP camera cellular phone that supports zoom function and a 2MP camera cellular phone is settled down with the Korea cellular phone market. It makes a keen competition in price and demands automation for phone camera module. There is an increasing requirement for the automatic assembly to correspond to a fast model change. The hard automation techniques that rely on dedicated manufacturing system are too inflexible to meet this requirement. Therefore in this study, this system is designed with the flexibility concept in order to cope with phone camera module change. The system has a same platform that has X-Y-Z motion or X-Z motion with ${\mu}m$order accuracy. It has a special gripper according to the type of a component to be put together. If the camera model changes, the gripper may be updated to fit for the camera module. The controller of this system acquires the data sets that have the information about the assembly part by the tray. This information is obtained ahead of an inspection step. The controller excludes an inferior part to be assembled by using this information to diminish the inferior goods. The assembly jig used in this system has a function of self adjustment that reduces the tact time and also diminish the inferior goods. Finally, the intelligent assembly system for phone camera module will be designed to get a flexibility to meet model change and a high productivity with a high reliability.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.87-98
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• 2020

As the construction industry accounts for 28.5% of industrial accidents in Korea and 29.6% of industrial accident deaths in 2017, it is necessary to search for a priority disaster reduction scheme for the construction industry in order to reduce the industrial accident rate. Therefore, this research suggested improvement direction for construction accidents reduction based on factors affecting construction accidents occurring in the construction site by DEMATEL analysis. As a result of the analysis, the 4M factors with the highest Prominence and Relation is 'Management', and the detailed analysis of the 4M factors were Personal characteristics of workers, Defects such as machinery and equipment, Inadequate inspection of machinery and equipment, Insufficient safety management plan, Inappropriate work orders from supervisors and field managers. The analysis results of this research can be used as a basic data for establishing direction of reduction and improvement of construction accidents.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.6
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• pp.661-667
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• 2011

Kinematic contact trigger probes are widely used for feature inspection and measurement on coordinate measurement machines (CMMs) and computer numerically controlled (CNC) machine tools. Recently, the probing accuracy has become one of the most important factors in the improvement of product quality, as the accuracy of such machining centers and measuring machines is increasing. Although high-accuracy probes using strain gauge can achieve this requirement, in this paper we study the universal economic kinematic contact probe to prove its probing mechanism and errors, and to try to make the best use of its performance. Stylus-ball-radius and center-alignment errors are proved, and the probing error mechanism on the 3D measuring coordinate is analyzed using numerical expressions. Macro algorithms are developed for the compensation of these errors, and actual tests and verifications are performed with a kinematic contact trigger probe and reference sphere on a CNC machine tool.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.315-326
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• 2020

In this study, a real-time crop recognition system was developed for an unmanned farm machine for upland farming. The crop recognition system was developed based on a stereo camera, and an image processing framework was proposed that consists of disparity matching, localization of crop area, and estimation of crop height with coordinate transformations. The performance was evaluated by attaching the crop recognition system to a tractor for five representative crops (cabbage, potato, sesame, radish, and soybean). The test condition was set at 3 levels of distances to the crop (100, 150, and 200 cm) and 5 levels of camera height (42, 44, 46, 48, and 50 cm). The mean relative error (MRE) was used to compare the height between the measured and estimated results. As a result, the MRE of Chinese cabbage was the lowest at 1.70%, and the MRE of soybean was the highest at 4.97%. It is considered that the MRE of the crop which has more similar distribution lower. the results showed that all crop height was estimated with less than 5% MRE. The developed crop recognition system can be applied to various agricultural machinery which enhances the accuracy of crop detection and its performance in various illumination conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4509-4514
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• 2010

In this thesis, the effect of switch maintenance improvement is confirmed after testing and operating the switch monitoring system that were researched and developed originally in order to improve method of electric switch maintenance. However, as in an automatic interlocking station where a ground crew was not placed, repair and inspection could not be carried out until the maintenance person comes in case of switch problems or maintenance. In order to improve this issue, control module was installed in a monitoring system which can communicate through a data radio to a remote computer. Thus, the monitoring device can receive control information which a remote computer commands during the operation of switches. Afterward, it shows information on the real-time status of swith, in particular, anomaly situation through user interface after the switch is operated. By improving performance of the monitoring system in this way which can be managed and controled at a remote place, the prompt countermeasure system in case of disruption will be built and as a result, efficiency and convenience of maintenance improvement will be expected to increase.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.41-55
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• 2021

To detect thunderstorms occurring in Korea, National Meteorological Satellite Center (NMSC) also introduced the rapid-development thunderstorm (RDT) algorithm developed by EUMETSAT. At NMCS, the H-RDT (HR) based on the Himawari-8 satellite and the K-RDT (KR) which combines the GK2A convection initiation output with the RDT were developed. In this study, we optimized the KR (KU) to improve the detection level of thunderstorms occurring in Korea. For this, we used all available data, such as GK2A/AMI, RADAR, lightning, and numerical model data from the recent two years (2019-2020). The machine learning of logistic regression and stepwise variable selection was used to optimize the KU algorithms. For considering the developing stages and duration time of thunderstorms, and data availability of GK2A/AMI, a total of 72 types of detection algorithms were developed. The level of detection of the KR, HR, and KU was evaluated qualitatively and quantitatively using lightning and RADAR data. Visual inspection using the lightning and RADAR data showed that all three algorithms detect thunderstorms that occurred in Korea well. However, the level of detection differs according to the lightning frequency and day/night, and the higher the frequency of lightning, the higher the detection level is. And the level of detection is generally higher at night than day. The quantitative verification of KU using lightning (RADAR) data showed that POD and FAR are 0.70 (0.34) and 0.57 (0.04), respectively. The verification results showed that the detection level of KU is slightly better than that of KR and HR.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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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.

• Tunnel and Underground Space
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• v.34 no.2
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• pp.154-168
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• 2024

The Tunnel Boring Machine (TBM) disc cutter is subjected to wear and damage during the rock excavation process, and the worn disc cutter should be replaced on time. The manual inspection by workers is generally required to determine the disc cutter replacement. In this case, the workers are exposed to dangerous environments, and the measurements are sometimes inaccurate. In this study, we developed a technology that measures the disc cutter wear in real time. From a series of laboratory tests, a magnetic sensor was selected as the wear sensor, and the real-time disc cutter measurement system was developed integrating wireless communication modules, power supply and data processing board. In addition, the measurement system was verified in actual TBM excavation circumstances. As a result, it was confirmed that the accuracy and stability of the system.