• Communications of Mathematical Education
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• v.34 no.3
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• pp.393-419
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• 2020

Trigonometry allows us to recognize the usefulness of mathematics through connection with real life and other disciplines, and lays the foundation for the concept of higher mathematics through connection with trigonometric functions. Since international comparisons on the trigonometry area of textbooks can give implications to trigonometry teaching and learning in Korea, this study attempted to compare trigonometry in textbooks in Korea, Australia and Finland. In this study, through the horizontal and vertical analysis presented by Charalambous et al.(2010), the objectives of the curriculum, content system, achievement standards, learning timing of trigonometry content, learning paths, and context of problems were analyzed. The order of learning in which the three countries expanded size of angle was similar, and there was a difference in the introduction of trigonometric functions and the continuity of grades dealing with trigonometry. In the learning path of textbooks on the definition method of trigonometric ratios, the unit circle method was developed from the triangle method to the trigonometric function. However, in Korea, after the explanation using the quadrant in middle school, the general angle and trigonometric functions were studied without expanding the angle. As a result of analyzing the context of the problem, the proportion of problems without context was the highest in all three countries, and the rate of camouflage context problem was twice as high in Korea as in Australia or Finland. Through this, the author suggest to include the unit circle method in the learning path in Korea, to present a problem that can emphasize the real-life context, to utilize technological tools, and to reconsider the ways and areas of the curriculum that deal with trigonometry.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1909-1916
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• 2017

Recently, deep learning algorithms have good performance in various fields, but they are not actively applied to sonar systems. In this study, we carried out experiments to classify active sonar returns into a metal object such as a mine and a rock using a convolutional neural network which is one of the deep learning algorithms. Data augmentation is applied on this paper to avoid overfitting and increase performance. And we analyzed performance variation depending on hyperparameter value and change of the number of training data through data augmentation. The experiments are performed with two training data; an aspect-angle independent and an aspect-angle dependent. As a result, the performances are 88.9% and 94.9% in aspect-angle independent and dependent, respectively. These are up to 4.5% point higher than the performance obtained by applying artificial neural network and support vector machine algorithm in the previous study.

• Journal of the Korean Society of Physical Medicine
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• v.4 no.2
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• pp.93-100
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• 2009

Purpose：The purpose of this study was to examine the effects of self-controlled knowledge of result (KR) versus the yoked KR on learning of knee joint proprioception. Methods：Forty volunteer subjects (20 men and 20 women) were randomly assigned to each four groups: 1) self-controlled KR in open kinematic chain, 2) yoked KR in open kinematic chain, 3) self controlled KR in close kinematic chain, and 4) yoked KR in close kinematic chain. The difference between the angle of position and reproduction angle was determined as a proprioception error and measured using an angle reproduction test. The subjects in self-controlled groups were provided with feedback whenever they requested it, whereas the subjects in yoked groups were not provided with feedback. The data were analyzed using a one-way ANOVA. Results：The proprioception errors in close kinematic chain groups decreased significantly compared with those in close kinematic chain groups(p<.05). The proprioception errors in the self-controlled group decreased significantly compared with those in yoked groups during acquisition and retention test(p<.05). Conclusion：Self-controlled knowledge of result during open kinematic chain movement is considered to be a good method on motor learning.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.58-66
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• 1992

This paper proposes a neuro-controller for position control of one-link flexible robot arm. Basically the controller consists of a multi-layer neural network and a conventional PD controller. Two controller are parallelly connected. Neural network is traind by the conventional error back propagation learning rules. During learning period, the weights of neural network are adjusted to minimize the position error between the desired hub angle and the actual one. Finally the effectiveness of the proposed approach will be demonstrated by computer simulation.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.144-150
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• 1999

n active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. The result of simulations shows that the crane is just controlled for a very large swing angle of 1 radian within nearly one cycle.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.96-104
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• 1995

A study for a lathe to machine workpiece with noncircular cross-section is presented. The noncircular cutting is accomplished by controlling radial tool position synchronized with revolution angle of the spindle according to the desired cross-sectional shape. A learning control algorithm is suggested for the tool positioning. The learning law of the algorithm is based on pole-zero cancellation, which guarantees the control stability. The control performances are analyzed and simulated on a numerical computer that the effectiveness of the control algorithm is convinced. The algorithm is tested on a conventional NC-lathe which shows some successful results.

• Korean Journal of Applied Biomechanics
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• v.18 no.4
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• pp.179-189
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• 2008

The purpose of this study was to investigate interjoint and intersegmental coordination 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 have never been experienced a taekwondo. We analyzed kinematic variables of Side Kick motion through videographical analysis. The conclusions 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 increases which can be assumed to be a result of the effective momentum transfer between adjacent segments. 2 This research showed a sequential transfer from trunk, to thigh, and then to shank as it gets closer to the end of learning at intersegment angular velocity, and it also showed pattern of throwlike motion and pushlike motion. 3. In three dimension of flexion-extension, adduction-abduction and internal-external rotation of the thigh and shank segment, the angle-angle diagram of knee joint and of hip joint showed that dynamic change was indicated at the beginning of learning but stable coordination pattern was indicated like skilled subject as novice subjects became skilled.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.39-49
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• 2006

This paper proposes a hand-posture recognition method using reinforcement learning for the performance improvement of vision-based hand-posture recognition. The difficulties in vision-based hand-posture recognition lie in viewing direction dependency and self-occlusion problem due to the high degree-of-freedom of human hand. General approaches to deal with these problems include multiple camera approach and methods of limiting the relative angle between cameras and the user's hand. In the case of using multiple cameras, however, fusion techniques to induce the final decision should be considered. Limiting the angle of user's hand restricts the user's freedom. The proposed method combines angular features and appearance features to describe hand-postures by a two-layered data structure and reinforcement learning. The validity of the proposed method is evaluated by appling it to the hand-posture recognition system using three cameras.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.1
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• pp.63-68
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• 2017

This paper reviews the learning model of a forward slip ratio in order to improve the mass-flow stability and strip qualities in the hot strip finishing mill process. Firstly, it is shown, from mathematical analysis, that the significant parameters of the forward slip ratio are the tension, looper angle, and roll velocity. Secondly, a discrete-time learning model of the forward slip ratio is proposed from these parameters, which is identified by an instrumental variable (IV) identification algorithm. Finally, it is shown from computer simulation that the proposed learning model is more effective than the existing learning model.