• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.67-71
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• 1988

We present an algorithm that uses trajectory following errors to improve a feedforward command to a robot in the iterative manner. It has been shown that when the manipulator handles an unknown object, the P-type learning algorithm can make the trajectory converge to a desired path and also that the proposed learning control algorithm performs better than the other type learning control algorithm. A numerical simulation of a three degree of freedom manipulator such as PUMA-560 ROBOT has been performed to illustrate the effectiveness of the proposed learning algorithm.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.11a
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• pp.123-123
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• 2007

• The Journal of Korean Association of Computer Education
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• v.17 no.5
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• pp.1-13
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• 2014

Programming education helps learners solve real world problems by applying the principles of computer science. In particular, in the case of object-oriented programming centered around objects or problems that are observed in the real world, learners are able to make the most use of their programming skills in real life. Therefore, in this study, we sought ways to have learners grasp object-oriented concepts in a learner-friendly manner. To this end we conducted an experiment with six students as participants. As a result we could derive two main points. First, the learning tools which can be easily used by learners are required. Second, it is necessary to think thoroughly to learn concepts before implementation of the programming. This study has significance in the sense that it provides a learning method which helps a novice learner to learn the object-oriented programming that he or she feels difficulty to understand.

• Toxicological Research
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• v.24 no.3
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• pp.183-188
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• 2008

The behavioral phenotypes of out-bred ICR mice were compared with those of in-bred C57BL/6 and BALB/c mice. In particular, this study examined the locomotor activity and two forms of hippocampus-dependent learning paradigms, passive avoidance and object recognition memory. The basal open-field activity of the ICR strain was greater than that of the C57BL/6 and BALB/c strains. In the passive avoidance task, all the mice showed a significant increase in the cross-over latency when tested 24 hours after training. The strength of memory retention in the ICR mice was relatively weak and measurable, as indicated by the shorter cross-over latency than the C57BL/6 and BALB/c mice. In the object recognition memory test, all strains had a significant preference for the novel object during testing. The index for the preference of a novel object was lower for the ICR and BALB/c mice. Nevertheless, the variance and the standard deviation in these strains were comparable. Overall, these results confirm the strain differences on locomotor activity and hippocampus-dependent learning and memory in mice.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1790-1796
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• 2021

A large amount of image data sets are required for image deep learning, and there are many differences in the method of obtaining images and constructing image data sets depending on the type of object. In this paper, we presented a method of constructing an image data set for deep learning and analyzed the performance that varies depending on the object to be tracked. We took a video by rotating the object, and then created a data set by segmenting the video using the proposed data set construction method. As a result of performance analysis, detection rate was more than 95%, and detection rate of objects with little change in shape was higher performance. It is considered that it is effective to use the data set construction method presented in this paper for a situation in which it is difficult to obtain image data and to track an object with little change in shape within a video.

• Smart Media Journal
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• v.12 no.5
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• pp.73-80
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• 2023

Recently, intelligent control systems are developing rapidly in various application fields, and methods for utilizing technologies such as deep learning, IoT, and cloud computing for intelligent control systems are being studied. An important technology in an intelligent control system is recognizing and tracking objects in images. However, existing multi-object tracking technology has problems in accuracy and speed. In this paper, a real-time intelligent control system was implemented using YOLO v5 and YOLO v6 based on a one-shot architecture that increases the accuracy of object tracking and enables fast and accurate tracking even when objects overlap each other or when there are many objects belonging to the same class. The experiment was evaluated by comparing YOLO v5 and YOLO v6. As a result of the experiment, the YOLO v6 model shows performance suitable for the intelligent control system.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.72-81
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• 2022

Visual Object Tracking is known as the most fundamental problem in the field of computer vision. Object tracking localize the region of target object with bounding box in the video. In this paper, a custom CNN is created to extract object feature that has strong and various information. This network was constructed as a Siamese network for use as a feature extractor. The input images are passed convolution block composed of a bottleneck layers, and features are emphasized. The feature map of the target object and the search area, extracted from the Siamese network, was input as a local proposal network. Estimate the object area using the feature map. The performance of the tracking algorithm was evaluated using the OTB2013 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.611 in Success Plot and 0.831 in Precision Plot were achieved.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.447-459
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• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.235-238
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• 2019

In real world object detection is an active research topic for understanding different objects from images. There are different models presented in past and had significant results. In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN). Both the front and rear view of the vehicles were used for training and testing the proposed method. Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images. Using perspective transformation, the top view images were transformed into front view images. This algorithm has higher detection rate as compared to raw images. Furthermore, YOLO model has better result as compare to F-RCNN model.