• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.1
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• pp.127-130
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• 2010

This paper deals with the formability of DP590 steel considering the strain rate. The strain hardening coefficient, elongation and r-value were obtained from the static and dynamic tensile test. As strain rate increases from static to 100/s, the strain hardening coefficient and the uniform elongation decrease and the elongation at fracture and r-value decrease to 0.1/s and increase again to 100/s. The high speed forming limit tests with hemi-spherical punch were carried out using the high speed crash testing machine and high speed forming jig. The high speed forming limit of DP590(order of $10^2$/s) decreases compared to the static forming limit(order of $10^{-3}$/s) and the forming limit band in high speed forming test is narrower than that in the static forming test. This tendency may be due to the development of brittleness with increase of stain rate.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• Journal of Biosystems Engineering
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• v.31 no.6 s.119
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• pp.514-523
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• 2006

Production of green pepper has been increased due to customer's preference and a projected ten-year boom in the industry in Korea. This study was carried out to develop an automatic grading and sorting system for green pepper using machine vision. The system consisted of a feeding mechanism, segregation section, an image inspection chamber, image processing section, system control section, grading section, and discharging section. Green peppers were separated and transported using a bowl feeder with a vibrator and a belt conveyor, respectively. Images were taken using color CCD cameras and a color frame grabber. An on-line grading algorithm was developed using Visual C/C++. The green peppers could be graded into four classes by activating air nozzles located at the discharging section. Length and curvature of each green pepper were measured while removing a stem of it. The first derivative of thickness profile was used to remove a stem area of segmented image of the pepper. While pepper is moving at 0.45 m/s, the accuracy of grading sorting for large, medium and small pepper are 86.0%, 81.3% and 90.6% respectively. Sorting performance was 121 kg/hour, and about five times better than manual sorting. The developed system was also economically feasible to grade and sort green peppers showing the cost about 40% lower than that of manual operations.

• Journal of Biosystems Engineering
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• v.33 no.3
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• pp.186-195
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• 2008

In this paper, a prototype tele-operative system with a mobile base was developed in order to automate cultivation of house melon. A man-machine interactive hybrid decision-making system via tele-operative task interface was proposed to overcome limitations of computer image recognition. Identifying house melon including position data from the field image was critical to automate cultivation. And it was not simple especially when melon is covered partly by leaves and stems. The developed system was composed of 5 major modules: (a) main remote monitoring and task control module, (b) wireless remote image acquisition and data transmission module, (c) three-wheel mobile base mounted with a 4 dof articulated type robot manipulator (d) exchangeable modular type end tools, and (e) melon storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. Once task was selected from the task control and monitoring module, the analog signal of the color image of the field was captured and transmitted to the host computer using R.F. module by wireless. A sequence of algorithms to identify location and size of a melon was performed based on the local image processing. Laboratory experiment showed the developed prototype system showed the practical feasibility of automating various cultivating tasks of house melon.

• Steel and Composite Structures
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• v.49 no.3
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• pp.337-348
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• 2023

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.65-72
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• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

• Korean Journal of Materials Research
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• v.33 no.7
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• pp.273-278
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• 2023

In this study, based on the saturation magnetic flux density experimental values (B_s) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict B_s using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R² score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict B_s above the threshold value of B_s (1.4 T). After obtaining two ANN models optimized for the R² and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.6
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• pp.431-438
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• 2016

Recently, concerns about the environment are becoming more important because of global warming and the exhaustion of earth's resources. In the aviation and automobile industries, the application of light materials is increasingly important for eco-friendly and effective. Carbon Fiber Reinforced Plastics is a composite material which great formability and the high strength of carbon fiber. CFRP, which is both light and strong, is hard to manufacture. In addition, CFRP machining has a high chance of defects. This research discusses the development of a manufacturing system package for CFRP machining. It involving CFRP Drilling/Water-jet Manufacturing Machines, Inspection/Post-processing Systems, CNC platform for an EtherCAT servo Communication, Flexible Manufacturing Systems and CFRP machining Processes.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.1211-1214
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• 1992

AC servo motor drives, Fara DS series, proposed in this paper can be effectively used in robots, CNC machine tools, and FA system with AC servo motors as actuators. The inverter of the AC servo drive consists of IGBT (Insulated Gate Bipolar Transistor) which have high switching frequency. Noises and vibrations generated in variable speed control of AC servo motors can be greatly reduced due to their high switching frequencies. In the developed servo drive, maximum torque is always generated in the whole speed range by compensating phase shift, which results from the nonlinearies of the AC servo motor during abrupt acceleration and deceleration. Abundant protection functions are provided to prevent abnormal state of the servo motor, and furthermore diverse user options are considered provided for the effective application. The proposed AC servo motor drive is designed to minimize velocity variation with respect to external load, supply voltage, environmental temperature, and humidity, so can be widely used in the fields of factory automation including robots and CNC msachine tools.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.226-231
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• 1991

A graphic control language using function block diagram approach is designed and implemented, applicable to real-time control for continuous process automation system. The procedure implementing the control language is composed of three parts, editor, compiler, and executer. The editor generates the control algorithm file, which contains function block information in the text form, by menu-driven method on the color graphic screen. The compiler translates the contents of the control algorithm file to machine codes and their related data. Then, the executer generates a task that makes the machine codes executed at every sampling period in the target processor. The validity of the concept in its design and implementaion is assured by on-line simulation in the multi-function controller designed for continuous process automation.