• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.159-168
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• 2018

In recent, mobile augmented reality game which has been attracting high attention is considered to be an good approach to increase immersion. In conventional augmented reality-based games that recognize target objects using a mobile camera and show the matching game characters, touch-based interaction is mainly used. In this paper, we propose an intuitive interaction method which manipulates a deformable game object by moving a target image of augmented reality in order to enhacne the immersion of the game. In the proposed method, the deformable object is intuitively manipulated by calculating the distance and direction between the target images and by adjusting the external force applied to the deformable object using them. In this paper, we focus on the cloth deformable object which is widely used for natural object animation in game contents and implement natural cloth simulation interacting with game objects represented by wind and rigid objects. In the experiments, we compare the previous commercial cloth model with the proposed method and show the proposed method can represent cloth animation more realistically.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.441-448
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• 2014

The application of spatial information has drawn significant attention from a wide range of industries. Railway spatial information facilitates the cooperation among related parties and improves the efficiency of asset management and operations. This study proposes the structure of Railway Object IDentifier(ROID) on railway spatial information. Current facility management and train operation are based on relative positioning system. Despite many advantages, relative positioning system causes serious problems such as the entire reconfiguration of positioning in the case of line change. Another major concern is the interface compatibility between individual facilities with different relative positioning. ROID based on absolute positioning addresses these issues, allowing the information exchange and convergence between independent parties. This study proposes ROID based on OID standard with object IDentifier and service object-oriented reference system. Our ROID employs the absolute positioning and the unique identifier, maintaining the compatibility with existing management system.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.544-551
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• 2023

Speech enhancement used to improve the perceptual quality and intelligibility of noise speech has been studied as a method using a complex-valued spectrum that can improve both magnitude and phase in a method using a magnitude spectrum. In this paper, a study was conducted on how to apply attention mechanism to complex-valued spectrum-based speech enhancement systems to further improve the intelligibility and quality of noise speech. The attention is performed based on additive attention and allows the attention weight to be calculated in consideration of the complex-valued spectrum. In addition, the global average pooling was used to consider the importance of the feature map. Complex-valued spectrum-based speech enhancement was performed based on the Deep Complex U-Net (DCUNET) model, and additive attention was conducted based on the proposed method in the Attention U-Net model. The results of the experiments on noise speech in a living room environment showed that the proposed method is improved performance over the baseline model according to evaluation metrics such as Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short Time Object Intelligence (STOI), and consistently improved performance across various background noise environments and low Signal-to-Noise Ratio (SNR) conditions. Through this, the proposed speech enhancement system demonstrated its effectiveness in improving the intelligibility and quality of noisy speech.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.47-55
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• 2009

As the wireless internet technology such as mobile phone, WIBRO, HSDPA develops, customized location-based services for traffic, tourism, shopping, and emergency relief has lately attracted attention. For giving customized services. we should consider dynamic characteristics of moving object which continuously change their location. In this paper, we define the context trigger type of moving object and design triggering method for processing context generated by moving object. Also we propose location-based push service platform including context trigger of moving object for supporting location-based information to user. The proposed system gathers moving object stream from the terminal based on MS-assisted or Stand-alone positioning mode of embedded GPS in terminal extract user context by user device agent, and send context information to server.

• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.967-980
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• 2007

Video objects of interest play an important role in representing the video content and are useful for improving the performance of video retrieval and compression. The objects of interest may be a main object in describing contents of a video shot or a core object that a video producer wants to represent in the video shot. We know that any object attracting one's eye much in the video shot may not be an object of interest and a non-moving object may be an object of interest as well as a moving one. However it is not easy to define an object of interest clearly, because procedural description of human interest is difficult. In this paper, a set of four filtering conditions for extracting moving objects of interest is suggested, which is defined by considering variation of location, size, and moving pattern of moving objects in a video shot. Non-moving objects of interest are also defined as another set of four extracting conditions that are related to saliency of color/texture, location, size, and occurrence frequency of static objects in a video shot. On a test with 50 video shots, the segmentation method based on the two sets of conditions could extract the moving and non-moving objects of interest chosen manually on accuracy of 84%.

• Journal of Information Technology Applications and Management
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• v.14 no.3
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• pp.49-77
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• 2007

Role-based access control (RBAC) models are recently receiving considerable attention as a generalized approach to access control. In line with the increase in applications that deal with spatial data. an advanced RBAC model whose entities and constraints depend on the characteristics of spatial data is required. Even if some approaches have been proposed for geographic information systems. most studies focus on the location of users instead of the characteristics of spatial data. In this paper. we extend the traditional RBAC model in order to deal with the characteristics of spatial data and propose new spatial constraints. We use the object-oriented modeling based on open GIS consortium geometric model to formalize spatial objects and spatial relations such as hierarchy relation and topology relation. As a result of the formalization for spatial relations. we present spatial constraints classified according to the characteristics of each relation. We demonstrate our extended-RBAC model called OOGIS-RBAC and spatial constraints through case studies. Finally. we compare our OOGIS-RBAC model and the DAC model in the management of access control to prove the efficiency of our model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.687-689
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• 2018

The current EEG device can determine the concentration, but can not determine the concentration of the state. Therefore, we distinguish attitude based on Mindwave Attention data and additionally Pitch data to distinguish whether or not we are looking at a video object, and suggest a method to obtain better performance. Attention data were measured in the state where the images were viewed and concentrated. In the case of the Pitch data, Sit was measured when sitting on a desk and Lie when lying down. Attention value was 38 or more. When the value of the Pitch is smaller than -48, it is judged that it is in a prone state. When the concentration and sitting state were satisfied with this threshold value, it was judged that they focused on watching the actual video.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1323-1331
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• 2017

Despite the fact that the copyright has grown into a large-scale business, there are many constant problems especially in image copyright. In this study, we propose an automatic object extraction and classification system for the scanned educational book image by combining document image processing and intelligent information technology like deep learning. First, the proposed technology removes noise component and then performs a visual attention assessment-based region separation. Then we carry out grouping operation based on extracted block areas and categorize each block as a picture or a character area. Finally, the caption area is extracted by searching around the classified picture area. As a result of the performance evaluation, it can be seen an average accuracy of 83% in the extraction of the image and caption area. For only image region detection, up-to 97% of accuracy is verified.