• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.836-842
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• 2015

In this paper, the target size and model based target size estimator (TMBE) algorithm is presented for iimaging infrared (IIR) seeker. At the imaging seeker, target size information is important factor for accurate tracking. The model based target size estimator filter (MBEF) algorithm was proposed to estimate target size at imaging infrared seeker. But, the model based target size estimator filter algorithm need to know relative distance from the target. In order to overcome the problem, we propose target size and model based target size estimator filter (TMBEF) algorithm which based on the target size. The performance of proposed algorithm is tested at target intercept scenario. The experiment results show that the proposed algorithm has the accurate target size estimating performance.

• Speech Sciences
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• v.15 no.4
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• pp.97-105
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• 2008

Nowadays an HMM-based text-to-speech system (HTS) has been very widely studied because it needs less memory and low computation complexity and is suitable for embedded systems in comparison with a corpus-based unit concatenation text-to-speech one. It also has the advantage that voice characteristics and the speaking rate of the synthetic speech can be converted easily by modifying HMM parameters appropriately. We implemented an HMM-based Korean text-to-speech system using a small size Korean speech DB and proposes a method to increase the naturalness of the synthetic speech by controlling duration model parameters in the HMM-based Korean text-to speech system. We performed a paired comparison test to verify that theses techniques are effective. The test result with the preference scores of 73.8% has shown the improvement of the naturalness of the synthetic speech through controlling the duration model parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4379-4397
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• 2017

Three different improvements of the Velocity-based model were proposed in a minimal velocity-based pedestrian model. The improvements of the models are based on the different agent forms. The different representations of the agent lead to different results, in this paper, we simulated the pedestrian movements in some typical scenes by using different agent forms, and the agent forms included the circles with different radiuses, the ellipse and the multi-circle stand for one pedestrian. We have proposed a novel model of pedestrian dynamics to optimize the simulation. Our model specifies the pedestrian behavior using a dynamic ellipse, which is parameterized by their velocity and can improve the simulaton accuracy. We found a representation of the pedestrian much closer to the reality. The phenomena of the self-organization can be observable in the improved models.

• The Journal of Korean Association of Computer Education
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• v.15 no.2
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• pp.29-36
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• 2012

This thesis focuses on developing the appropriate model for teaching Internet ethics based on problem-based learning. In the new learning model, participation of students such as discussion and writing methods is considered important. The research applied the new method at class and analyzed the change in Internet ethics. The result of research shows that internet ethics of learner improved in the voluntariness, respect, participation, responsibility area. Especially, the voluntariness and responsibility areas showed statistically meaningful improvement.

• IE interfaces
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• v.16 no.spc
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• pp.87-92
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• 2003

Progressive mesh representation and generation have become one of the most important issues in network-based computer graphics. However, current researches are mostly focused on triangular mesh models. On the other hand, solid models are widely used in industry and are applied to advanced applications such as product design and virtual assembly. Moreover, as the demand to share and transmit these solid models over the network is emerging, the generation and the transmission of progressive solid models depending on specific engineering needs and purpose are essential. In this paper, we present a Cellular Topology-based approach to generating and transmitting progressive solid models from a feature-based solid model for internet-based design and collaboration. The proposed approach introduces a new scheme for storing and transmitting solid models over the network. The Cellular Topology (CT) approach makes it possible to effectively generate progressive solid models and to efficiently transmit the models over the network with compact model size.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1179-1185
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• 2007

This paper presents a new approach to automotive embedded systems development and validation. Recently automotive embedded systems become even more complex and the product life cycle is getting reduced. To overcome these problems AUTOSAR, a standardized software platform and component based approach, was introduced. Model-based approach has been widely applied in the development of embedded systems and has strong benefits such as early validation and automated testing. In this paper cooperative development and validation of AUTOSAR and model-based approach are introduced and automated testing techniques are proposed. With the proposed techniques we can improve complexity management through increased reuse and exchangeability of software module and automated testing is realized.

• Asia Marketing Journal
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• v.24 no.3
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• pp.121-130
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• 2022

To achieve the widespread use of Mobility as a Service (MaaS), a novel transportation platform, it is important to increase consumers' intention to use MaaS. Therefore, this study clarifies the determinants of consumers' intention to use MaaS based on the UTAUT2 model. The research model is tested using structural equation modeling based on data from a web-based questionnaire survey of Japanese consumers. The results show that performance expectancy, social influence, hedonic motivation, and price value have significant effects on the intention to use MaaS. Moreover, the relationship between the intention to use MaaS and independent variables is moderated by old age. Theoretical and practical implications are discussed based on the findings.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.289-296
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• 2022

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.81-87
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• 2023

With the development of BCI devices, it is now possible to use EEG control technology to move the robot's arms or legs to help with daily life. In this paper, we propose a customized vehicle control model based on BCI. This is a model that collects BCI-based driver EEG signals, determines information according to EEG signal analysis, and then controls the direction of the vehicle based on the determinated information through EEG signal analysis. In this case, in the process of analyzing noisy EEG signals, controlling direction is supplemented by using a camera-based eye tracking method to increase the accuracy of recognized direction . By synthesizing the EEG signal that recognized the direction to be controlled and the result of eye tracking, the vehicle was controlled in five directions: left turn, right turn, forward, backward, and stop. In experimental result, the accuracy of direction recognition of our proposed model is about 75% or higher.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.128-132
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• 2023

Recently, with the development of deep learning technology, research on pedestrian attribute recognition technology using deep neural networks has been actively conducted. Existing pedestrian attribute recognition techniques can be obtained in such a way as global-based, regional-area-based, visual attention-based, sequential prediction-based, and newly designed loss function-based, depending on how pedestrian attributes are detected. It is known that the performance of these pedestrian attribute recognition technologies varies greatly depending on the type of backbone network that constitutes the deep neural networks model. Therefore, in this paper, several backbone networks are applied to the baseline pedestrian attribute recognition model and the performance changes of the model are analyzed. In this paper, the analysis is conducted using Resnet34, Resnet50, Resnet101, Swin-tiny, and Swinv2-tiny, which are representative backbone networks used in the fields of image classification, object detection, etc. Furthermore, this paper analyzes the change in time complexity when inferencing each backbone network using a CPU and a GPU.