• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.545-552
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• 2001

Nondestructive methods such as ultrasonic and magnetic resonance imaging systems have many advantages but still much expensive. And they do not give exact color information and may miss some details. If it is allowed to destruct a biological object to obtain interior and exterior informations, 3D image visualization model from a series of sliced sectional images gives more useful information with relatively low cost. In this paper, a PC based automatic 3D visualization system is presented. The system is composed of three modules. The first module is the handling and image acquisition module. The handling module feeds and slices a cylindrical shape paraffin, which holds a biological object inside the paraffin. And the paraffin is kept being solid by cooling while being handled. The image acquisition modulo captures the sectional image of the object merged into the paraffin consecutively. The second one is the system control and interface module, which controls actuators for feeding, slicing, and image capturing. And the last one is the image processing and visualization module, which processes a series of acquired sectional images and generates a 3D volumetric model. To verify the condition for the uniform slicing, normal directional forces of the cutting edge according to the various cutting angles were measured using a strain gauge and the amount of the sliced chips were weighed and analyzed. Once the 3D model was constructed on the computer, user could manipulate it with various transformation methods such as translation, rotation, and scaling including arbitrary sectional view.

• Journal of Bio-Environment Control
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• v.10 no.3
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• pp.172-180
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• 2001

A grape fruit is required for a lot of labor to harvest in time in Korea, since the fruit is cut and grabbed currently by hand. In foreign country, especially France, a grape harvester has been developed for processing to make wine out of a grape, not to eat a fresh grape fruit. However, a harvester which harvests to eat a fresh grape fruit has not been developed yet. Therefore, this study was designed and constructed to develope a image processing system for a fresh grape harvester. Its development involved the integration of a vision system along with an personal computer and two cameras. Grape recognition, which was able to found the accurate cutting position in three dimension by the end-effector, needed to find out the object from the background by using two different images from two cameras. Based on the results of this research the following conclusions were made: The model grape was located and measured within less than 1,100 mm from camera center, which means center between two cameras. The distance error of the calculated distance had the distance error within 5mm by using model image in the laboratory. The image processing system proved to be a reliable system for measuring the accurate distance between the camera center and the grape fruit. Also, difference between actual distance and calculated distance was found within 5 mm using stereo vision system in the field. Therefore, the image processing system would be mounted on a grape harvester to be founded to the position of the a grape fruit.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.318-324
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• 2000

Ultrasonic and magnetic resonance imaging systems are used to visualize the interior states of biological objects. These nondestructive methods have many advantages but too much expensive. And they do not give exact color information and may miss some details. If it is allowed to destruct some biological objects to get the interior and exterior information, constructing 3D image from the series of the sliced sectional images gives more useful information with relatively low cost. In this paper, PC based automatic 3D model generator was developed. The system was composed of three modules. One is the object handling and image acquisition module, which feeds and slices objects sequentially and maintains the paraffin cool to be in solid state and captures the sectional image consecutively. The second is the system control and interface module, which controls actuators for feeding, slicing, and image capturing. And the last is the image processing and visualization module, which processes a series of acquired sectional images and generates 3D graphic model. The handling module was composed of the gripper, which grasps and feeds the object and the cutting device, which cuts the object by moving cutting edge forward and backward. Sliced sectional images were acquired and saved in the form of bitmap file. The 3D model was generated to obtain the volumetric information using these 2D sectional image files after being segmented from the background paraffin. Once 3-D model was constructed on the computer, user could manipulate it with various transformation methods such as translation, rotation, scaling including arbitrary sectional view.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.3
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• pp.307-312
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• 2010

In this study, advanced computational technique for mechatronic analysis has been developed for the efficient design and test of typical machine tool models. Flexible multi-body dynamic (FMBD) analysis method combined with motion controller including control logics is used to simulate typical operation conditions. The present FMBD machine tool model is composed of flexible column structure, rigid body spindle, vertical motion guide (arm) and screw elements. Driving motor clement with rotating degree-of-freedom is interconnected and governed by the designed Matlab Simulink control logic, and then the position of the spindle is feedback into the control logic. It is practically shown from the results that the investigation of designed machine tools with controller can be effectively conducted and verified.

• Journal of Biosystems Engineering
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• v.27 no.3
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• pp.203-210
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• 2002

In this study, the leveling control system for a tractor has been developed. The experimental model showed that the implementation of the proposed hydraulic control system fur the prototype design of a slope land tractor was feasible. The front axle was designed as a center pin type and the rear axle was designed as a trailing arm type. The leveling control of the body on the slope land was accomplished by controlling the height of the right and left trailing arms using the electronic controlled hydraulic cylinder. The maximum leveling control angles were ${\pm}$15$^{\circ}$ for roll angle and 7$^{\circ}$far pitch angle. The front and rear wheel drives were transmitted by gears from the main shaft to the final drive. The adaptability of the hydraulic control system was tested and investigated by analyzing the system response in time and frequency domain. The hydraulic control system on a step input showed a linearly increasing trend without any overshoot state. The hydraulic control system on a frequency input showed a little phase differences and gain drops within the range of 0.3Hz.

• International Journal of Automotive Technology
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• v.7 no.5
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• pp.609-618
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• 2006

This paper presents the lateral control of an autonomous vehicle by using a look-ahead sensing system. In look-ahead sensing by an absolute positioning system, a reference lane, constructed by straight lanes or circular lanes, was switched by a segment switching algorithm. To cope with sensor noise and modeling uncertainty, a robust LMI-based $H_{\infty}$ lateral controller was designed by the feedback of lateral offset and yaw angle error at the vehicle look-ahead. In order to verify the safety and the performance of lateral control, a scaled-down vehicle was developed and the location of the vehicle was detected by using an ultrasonic local positioning system. In the mechatronic scaled-down vehicle, the lateral model and parameters are verified and estimated by a J-turn test. For the lane change and reference lane tracking, the lateral controllers are used experimentally. The experimental results show that the $H_{\infty}$ controller is robust and has better performance compared with look-down sensing.

• ETRI Journal
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• v.39 no.4
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• pp.592-604
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• 2017

Intrusion detection is very important for network situation awareness. While a few methods have been proposed to detect network intrusion, they cannot directly and effectively utilize semi-quantitative information consisting of expert knowledge and quantitative data. Hence, this paper proposes a new detection model based on a directed acyclic graph (DAG) and a belief rule base (BRB). In the proposed model, called DAG-BRB, the DAG is employed to construct a multi-layered BRB model that can avoid explosion of combinations of rule number because of a large number of types of intrusion. To obtain the optimal parameters of the DAG-BRB model, an improved constraint covariance matrix adaption evolution strategy (CMA-ES) is developed that can effectively solve the constraint problem in the BRB. A case study was used to test the efficiency of the proposed DAG-BRB. The results showed that compared with other detection models, the DAG-BRB model has a higher detection rate and can be used in real networks.

• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.337-344
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• 2022

Due to corrosion defects on the surface of the oil pipe, the sealing performance of the annular blowout preventer (BOP) decreases, and the leakage of toxic and harmful gases such as H₂S and SO₂ will threaten the safety of operators on the well. Therefore, this paper establishes the FE model for evaluating the sealing performance of BOP-oil pipe corrosion defects, which is based on the rubber large deformation theory and rubber core sealing mechanism, and designs the experiment of BOP sealing performance to verify the accuracy of the FE model. The sealing performance of BOP sealing oil pipe with corrosion defects is studied. The research results show that the sealing performance of BOP is more sensitive to the axial size of corrosion defects. With the increase of oil pipe outer diameter, the critical size of defects increases continuously. The sensitivity of radial and depth dimensions is low, When for 88.9 mm outer diameter oil pipe, the axial critical size of corrosion defect is 20 mm, the radial critical size is 16 mm and the critical depth is 2 mm. Fit the formula between the outer diameter of oil pipe and the piston increment. According to the formula, the operator can calculate the piston stroke increment required by the BOP to complete the sealing when the oil pipe is corroded.

• Smart Structures and Systems
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• v.19 no.6
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• pp.633-641
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• 2017

The complexity of macro-fiber composite (MFC) materials increasing the difficulty in simulation and analysis of MFC integrated structures. To give an accurate prediction of MFC bonded smart structures for the simulation of shape and vibration control, the paper develops a linear electro-mechanically coupled static and dynamic finite element (FE) models based on the first-order shear deformation (FOSD) hypothesis. Two different types of MFCs are modeled and analyzed, namely MFC-d31 and MFC-d33, in which the former one is dominated by the $d_{31}$ effect, while the latter one by the $d_{33}$ effect. The present model is first applied to an MFC-d33 bonded composite plate, and then is used to analyze both active shape and vibration control for MFC-d31/-d33 bonded plate with various piezoelectric fiber orientations.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.715-721
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• 2000

In this research, rule and neuro net based boundary extraction algorithm was developed. Extracting boundary of the interest, lean tissue, is essential for the quality evaluation of the beef based on color machine vision. Major quality features of the beef are size, marveling state of the lean tissue, color of the fat, and thickness of back fat. To evaluate the beef quality, extracting of loin parts from the sectional image of beef rib is crucial and the first step. Since its boundary is not clear and very difficult to trace, neural network model was developed to isolate loin parts from the entire image input. At the stage of training network, normalized color image data was used. Model reference of boundary was determined by binary feature extraction algorithm using R(red) channel. And 100 sub-images(selected from maximum extended boundary rectangle 11${\times}$11 masks) were used as training data set. Each mask has information on the curvature of boundary. The basic rule in boundary extraction is the adaptation of the known curvature of the boundary. The structured model reference and neural net based boundary extraction algorithm was developed and implemented to the beef image and results were analyzed.