• Journal of Digital Contents Society
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• v.9 no.3
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• pp.499-507
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• 2008

This paper aims at studying the new SCORM based e-Learning system and self-course design method. To construct this aims, we have researched the merits, shortcomings and characteristics of the previous LMS(Learning Management System) and we have researched the merits, shortcomings and characteristics of SCORM(Sharable Content Object Reference Model). SCORM was suggested ADL (Advanced Distributed Learning) to elevate the reusability of learning contents. Also we have researched the related researches of SCORM, SCORM based LMS and case studies. This paper suggests the level based self learning and course design and the system based on SCORM. This system has elevated the effectiveness and satisfaction of the learners.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.536-546
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• 2022

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.70-74
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• 1998

This paper describes a method for recognition of two-dimensional position of an object by use of aerial ultra-sonic sensor and signal processing technique, which would become a help for blind person or self-mobile robot. First, we have developed a method for measuring the time difference between the transmitted and the received burst wave by use of one ultrasonic transmitter and three receivers. Secondly, a new method is developed for measuring the distance to an object by use of M-sequence correlation method. Thirdly, a measurement method to obtain the position of an object is described by use of phase-arrayed ultrasonic sensor, which gives us a wide-range position determination in a short time.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2008.05a
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• pp.46-49
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• 2008

This research does Complexity form, Interior epidermis cell re-organization, Object discovery that have correct numerical value concept by purpose. Research applied by Grid re-organization in form generation, Parameter variation of cell unit (morphor, tweener), Symbol, pattern of variation, self-organization cell substitution order. Representation through 3d digital modeler of polygon, Nurbs and street-sheet program(x,y,z coordinates & Network way of points) etc. of main work. Investigator specified numbers of U profiles*30, V point-20 that is 600 Paramaters individual in volume, and define circle radius of lighting in object, Projection size variously and tried difference. Transposition cell to point and Heightened brightness of color using pointillism of painting. Led lighting cell object is expressed being decoded by digital code.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.349-352
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• 1994

An object recognition and restoration using ultrasound sensors and neural networks are presented. The planar arrangement of the sensor is used to reduce the interference effects between sensors. The SOFM(Self-Organizing Feature Map) Neural Network and SCL(Simple Competitive Learning) method are learned with the acquired data. Lab experiments were performed that the object can be recognized ed the resolutions of the object can be enhanced by using the small number of the ultrasound array and neural networks.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.23-30
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• 2020

Recently, self-driving research has been actively studied in various institutions. Accurate recognition is important because information about surrounding objects is needed for safe autonomous driving. This study mainly deals with the signal processing of LiDAR among sensors for object recognition. LiDAR is a sensor that is widely used for high recognition accuracy. First, we clustered and tracked objects by predicting relative position and speed of objects. The characteristic points of all objects were extracted using point cloud data of each objects through proposed algorithm. The Classification between vehicle and pedestrians is estimated using number of characteristic points and distances among characteristic points. The algorithm for classifying cars and pedestrians was implemented and verified using test vehicle equipped with LiDAR sensors. The accuracy of proposed object classification algorithm was about 97%. The classification accuracy was improved by about 13.5% compared with deep learning based algorithm.

• ETRI Journal
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• v.45 no.5
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• pp.811-821
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• 2023

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.

• Korean Journal of Health Education and Promotion
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• v.26 no.2
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• pp.15-23
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• 2009

Objectives: This study was conducted to investigate the relationship between physical activity function and stages of self-change for exercise in the aged of a farming village. The object of this research was to make with the basic data for the exercise program for the aged of rural area. Methods: This study was a volunteer sample of 612 persons, 60 years and above, who were living at the 2 farming villages, in 2005 July. This instruments were analyzed using frequency analysis and descriptive statistics, multiple regression analysis. Results: The distribution of stages of self-change of the research object person showed that the pre-contemplation stage was most with 57.2%, and the contemplation stage : 8.1%, the preparation stage : 2.2%, the action stage : 22.5%, the maintenance stage : 10.0%. The person who having good physical function state and advanced stages of self-change of exercise were higher in the ratio of the educational level and the income level. Factors for physical function were effected by the aging and the woman negatively. Conclusion: Physical function scores were highly correlated with stages of self-change for exercise. So it will be helpful that the program which designed by one's physical function and stage of self-change for exercise would applied the one.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.397-401
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• 1997

In this paper, we propose a neural-network that detects moving objects in an image using a diffusion neural network. The proposed neural network is improved by adding a self loop to diffusion layer to remove the noise in an image and to reduce the detection of phantom edge. Computer simulation with real images show that the proposed neural network can extract edges of moving object efficiently.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.105-109
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• 2001

This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.