• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.230-237
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• 2023

Self-driving is a key technology of the fourth industry and can be applied to various places such as cars, drones, cars, and robots. Among them, localiztion is one of the key technologies for implementing autonomous driving as a technology that identifies the location of objects or users using GPS, sensors, and maps. Locilization can be made using GPS or LIDAR, but it is very expensive and heavy equipment must be mounted, and precise location estimation is difficult for places with radio interference such as underground or tunnels. In this paper, to compensate for this, we proposes an image retrieval using attention module and image segmentation maps using color images acquired with low-cost vision cameras as an input.

• Kyungpook Mathematical Journal
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• v.55 no.2
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• pp.259-266
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• 2015

Let G be a group with identity e. Let R be a G-graded commutative ring and M a graded R-module. In this paper, we introduce the concept of graded quasi-prime submodules and give some basic results about graded quasi-prime submodules of graded modules. Special attention has been paid, when graded modules are graded multiplication, to find extra properties of these submodules. Furthermore, a topology related to graded quasi-prime submodules is introduced.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.14-25
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• 2021

Change detection, which finds changes in remote sensing images of the same location captured at different times, is very important because it is used in various applications. However, registration errors, building displacement errors, and shadow errors cause false positives. To solve these problems, we propose a novle deep convolutional network called CADNet (Change Attention Dense Siamese Network). CADNet uses FPN (Feature Pyramid Network) to detect multi-scale changes, applies a Change Attention Module that attends to the changes, and uses DenseNet as a feature extractor to use feature maps that contain both low-level and high-level features for change detection. CADNet performance measured from the Precision, Recall, F1 side is 98.44%, 98.47%, 98.46% for WHU datasets and 90.72%, 91.89%, 91.30% for LEVIR-CD datasets. The results of this experiment show that CADNet can offer better performance than any other traditional change detection method.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.538-547
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• 2022

Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

• The Journal of Korean Academy of Sensory Integration
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• v.13 no.2
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• pp.63-73
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• 2015

Objective : To report the effects of a specific intervention, the Interactive Metronome$^{(R)}$ (IM), on timing, attention and motor function of a children with ADHD. Methods : The study is case reports about two boys with ADHD. One boy who is born 2008 is attending general elementary school as a first year student (case 1), and another boy who is born 2001 is attending general elementary school as a second year student (case 2). For each case subject, IM training was provided during 3 weeks, from January 2015 to Febrary 2015. Evaluations were performed pre- and post-intervention in order to exam timing, attention and motor skills. The measurements uses in this study are Long Form Assessment (LFA) for the timing, RehaCom screening module for the attention, and Bruininks-Oseretsky Test of Morot Proficiency, second version (BOT-2) for the motor function. Results : The timing function was improved in both cases since both showed reduced response time for all motor tasks of LFA. In terms of attention, case 1 showed improvement of visual attention division, neglect and response Inhibition, and case 2 showed improvement of sustained attention. Lastly, in the BOT-2, case 1 showed improved the percentile rank of short (from 42%ile to 96%ile), and case 2 also showed similar improvement (from 21%ile to 66%ile). Conclusion : This study provides positive evidence that the Interactive Metronome$^{(R)}$ training has positive power to facilitate several body functions such as timing, attention and motor control of children with ADHD, through two case studies.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.501-512
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• 2014

Due to smart phones introduced since 2007, mobile contents production become activated and with this trend, attention among different age groups to production of mobile games has been growing. Whereas in the past, development of games was the domain of developers equipped with technical knowledge, provision of editor-type engines in recent times is lowering the high barrier of entry to game production. This paper proposes an event-based module design method from the perspective of general users, aimed at producing games by oneself with a Unity 3D, one of editor-type game engines. This is to plan behaviors and roles in the unit of modules in the whole process of a game to be in line with the perspective of game production by one person. Each module includes diverse events that express game characteristics. In addition, the script function provided by the Unity 3D is appropriate to embody the proposed module structure and utilizes the Unity 3D. Lastly, this study produce a simple 3D mobile casual game in order to verify whether effective game production from the planning to the development is possible through the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.19-25
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• 2011

With the issue of a Green IT Technology, studies on a environment-friendly luminous source that can reduce Carbon discharge and increase energy efficiency are actively progressed all over the world. Especially, with the problems of high oil price and environmental pollution, LED has made a great attention as a new luminous source that can replace the existing incandescent bulbs and fluorescent lights. In this paper, the proposed streetlight system becomes more intellectual by combining the low power consuming, high efficient, and high luminous LED module with a complex sensor module with temperature, humidity, illumination and motion sensors. Then, we design and implement the Ad-Hoc based LED-IT-Sensor integrated streetlight system that can maximize the energy savings efficiently with central monitoring system and selective remote dimming control by connecting them to the wireless ubiquitous sensor network(USN) using a Zigbee module.

• Convergence Security Journal
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• v.23 no.5
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• pp.135-142
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• 2023

In addressing the prominent issues of climate change, resource scarcity, and environmental pollution associated with household waste, extensive research has been conducted on intelligent waste classification methods. These efforts range from traditional classification algorithms to machine learning and neural networks. However, challenges persist in effectively classifying waste in diverse environments and conditions due to insufficient datasets, increased complexity in neural network architectures, and performance limitations for real-world applications. Therefore, this paper proposes i-YOLOX as a solution for rapid classification and improved accuracy. The proposed model is evaluated based on network parameters, detection speed, and accuracy. To achieve this, a dataset comprising 10,000 samples of household waste, spanning 17 waste categories, is created. The i-YOLOX architecture is constructed by introducing the Involution channel convolution operator and the Convolution Branch Attention Module (CBAM) into the YOLOX structure. A comparative analysis is conducted with the performance of the existing YOLO architecture. Experimental results demonstrate that i-YOLOX enhances the detection speed and accuracy of waste objects in complex scenes compared to conventional neural networks. This confirms the effectiveness of the proposed i-YOLOX architecture in the detection and classification of multiple household waste objects.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.83-90
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• 2023

Blurred image is an important factor in lowering image recognition rates in Computer vision. This mainly occurs when the camera is unstablely out of focus or the object in the scene moves quickly during the exposure time. Blurred images greatly degrade visual quality, weakening visibility, and this phenomenon occurs frequently despite the continuous development digital camera technology. In this paper, it replace the modified building module based on the Deep multi-patch neural network designed with convolution neural networks to capture details of input images and Attention techniques to focus on objects in blurred images in many ways and strengthen the image. It measures and assigns each weight at different scales to differentiate the blurring of change and restores from rough to fine levels of the image to adjust both global and local region sequentially. Through this method, it show excellent results that recover degraded image quality, extract efficient object detection and features, and complement color constancy.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.166-173
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• 2024

This study explores lip-sync detection, focusing on the synchronization between lip movements and voices in videos. Typically, lip-sync detection techniques involve cropping the facial area of a given video, utilizing the lower half of the cropped box as input for the visual encoder to extract visual features. To enhance the emphasis on the articulatory region of lips for more accurate lip-sync detection, we propose utilizing a pre-trained visual attention-based encoder. The Visual Transformer Pooling (VTP) module is employed as the visual encoder, originally designed for the lip-reading task, predicting the script based solely on visual information without audio. Our experimental results demonstrate that, despite having fewer learning parameters, our proposed method outperforms the latest model, VocaList, on the LRS2 dataset, achieving a lip-sync detection accuracy of 94.5% based on five context frames. Moreover, our approach exhibits an approximately 8% superiority over VocaList in lip-sync detection accuracy, even on an untrained dataset, Acappella.