• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.189-196
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• 2024

Classification of drones and birds is challenging due to diverse flight patterns and limited data availability. Previous research has focused on identifying the flight patterns of unmanned aerial vehicles by emphasizing dynamic features such as speed and heading. However, this approach tends to neglect crucial spatial information, making accurate discrimination of unmanned aerial vehicle characteristics challenging. Furthermore, training methods for situations with imbalanced data among classes have not been proposed by traditional machine learning techniques. In this paper, we propose a data processing method that preserves angle information while maintaining positional details, enabling the deep learning model to better comprehend positional information of drones. Additionally, we introduce a training technique to address the issue of data imbalance.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.315-319
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• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.687-690
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• 2000

Lately, many studies have been progressed for the protection human's lives and property as holding in check accidents happened by human's carelessness or mistakes. One part of these is the development of an autonomouse vehicle. General control method of vision-based autonomous vehicle system is to determine the navigation direction by analyzing lane images from a camera, and to navigate using proper control algorithm. In this paper, characteristic points are abstracted from lane images using lane recognition algorithm with sobel operator. And then the vehicle is controlled using two proposed auto-steering algorithms. Two steering control algorithms are introduced in this paper. First method is to use the geometric relation of a camera. After transforming from an image coordinate to a vehicle coordinate, a steering angle is calculated using Ackermann angle. Second one is using a neural network algorithm. It doesn't need to use the geometric relation of a camera and is easy to apply a steering algorithm. In addition, It is a nearest algorithm for the driving style of human driver. Proposed controller is a multilayer neural network using Levenberg-Marquardt backpropagation learning algorithm which was estimated much better than other methods, i.e. Conjugate Gradient or Gradient Decent ones.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.38-44
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• 2018

In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.

• Journal of Elementary Mathematics Education in Korea
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• v.17 no.2
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• pp.245-263
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• 2013

Mathematical learning via projects, which enables the reconstruction of curriculum through integration and emphasizes the process of solving problems by posing questions, has attracted the attention of the department of mathematics. This research is aimed at exploring the link between mathematics and project learning by analyzing an example of student-oriented project 'the secrets of pyramid' focused on understanding 'triangle' specifically designed for forth graders. From 115-hour process of subject-oriented project, this study reinterpreted the mathematical meaning of only 24 hours directly related to mathematics, especially to figure exploration. Consequently, this problem solving involved a variety of geometric activities as a process, such as measuring an angle, constructing a triangle, etc. Thus students attempt to actively participate in the process, thereby allowing them to learn how to measure things more accurately. Moreover, project learning improved students' understanding on not only plane figures but solid figures. This indicates that by project learning, learning from given problems or contents can be extended to other mathematical areas.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.95-104
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• 2008

The purpose of this study was to investigate kinematic coordination and control of lower segments in skill process. For the investigation, we examined the difference of resultant linear velocity of segments and angle vs angle graph. Novice subjects were 9 male middle school students who has never been experienced a taekwondo and expert subjects were 7 university taekwondo players. We analyzed kinematic variables of Dollyochagi motion through videographical analysis and the conclusion were as follows. 1. Examining the graph of novice subjects' maximal resultant linear velocity of the thigh, shank, and foot segment, as it gets closer to the end of the training, the maximal resultant linear velocity in each segment increased. Statistical analysis showed the following results; thigh segment caused the increase of speed, using the trunk segment's momentum in the latter term of learning, while the shank segment utilized the momentum of the adjacent proximal segment at the beginning of learning, and the foot segment in the middle of learning. 2. Until the point where the knee joint angle is minimum, as the novice group learn the skill, the flexion of knee and hip joints has changed into the form of coordination pattern in phase. On the other hand, the expert group showed continual coordination pattern in phase that the movement sequences were smooth. From the knee joint maximal flexion to impact timing, all novice and expert groups showed coordination pattern out of phase. 3. From the knee joint maximal flexion to impact timing, the ankle joint was fixed and the knee joint was extended to all the novice stages and expert subjects.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.92-99
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• 2004

In this paper, the decoupled neural network reference compensation technique (DRCT) is applied to the control of a two degrees-of-freedom inverted pendulum mounted on an x-y table. Neural networks are used as auxiliary controllers for both the x axis and y axis of the PD controlled inverted pendulum. The DRCT method known to compensate for uncertainties at the trajectory level is used to control both the angle of a pendulum and the position of a cart simultaneously. Implementation of an on-line neural network learning algorithm has been implemented on the DSP board of the dSpace DSP system. Experimental studies have shown successful balancing of a pendulum on an x-y plane and good position control under external disturbances as well.

• Journal of the Korean Institute of Rural Architecture
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• v.14 no.1
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• pp.21-28
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• 2012

Campus facilities were recently remodeled to provide the substantial learning rights of handicapped students in many campus to embody the dignity and value as man. So this study aims to identify the exterior connection facilities for handicapped students of S and D campuses. The summaries of this research are as follows. Installations of even crossing area(1.5mx1.5m) per 50m and even rest area(1.5mx1.5m) per 30m in walking or access ramp. Improving in accordance with exterior connection facility repairing master plan in S campus. Bringing down an angle degrees of the inclined walking or access ramp in D campus. Installation of exterior braille guide sign for blind students. All handicapped students must be guaranteed the same learning rights as normal men to remove obstacles as the upper mentioned imperfections in using exterior campus facilities.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.244-248
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• 1996

Nowadays, most shop floors using industrial robots have many problems such as constructing robot workcell, generating robot arm moving trajectory, etc.. In the case of programming robot-arms for a specific task, shop operator commonly use the teach pendant to record the target position and determine the moving trajectory. However, such a teaching process may result in an inefficient trajectory in the sense of moving distance and joint angle fluctuation. Moreover, shop operators who have little knowledge about robot programming process need a lot of learning time and cost. The purpose of this paper is to propose a user friendly robot programming method to program robot-arms easily and efficiently for shop operator so that the programming time is reduced and a short and stable trajectory is obtained.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.3
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• pp.137-143
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• 2002

This paper presents the In-process compensation to control cutter runout and improve the machined surface quality. Cutter runout compensation system consists of the micro-positioning servo system with piezoelectric actuator which is embeded in the sliding table to manipulate radial depth of cut in real-time. Cutting force feedback control was proposed in the angle domain based upon repetitive learning control strategy to eliminate chip load variation in end milling process. Micro-positioning control due to adaptive actuation force response improves the machined surface quality by compensation runout effect induced cutting force variation. This result will provide lots of information to build-up the preciswion machining technology.