• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.19-25
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• 2017

In this paper, we designed and implemented IoT sensors based badminton motion analysis and training system that can be readily used by badminton players with PC. Unlike the traditional badminton training system which uses signals of the flags by coach, the proposed electronic training system used IoT sensors to automatically detect and analysis the motions for badminton players. The proposed badminton motion analysis and training system has the advantage with low power, because it communicates with the program through BLE communication. The badminton motion analysis system automatically measures the training time according to the player's movement, so it is possible to collect objective result data with less errors than the conventional flag signal based method by coach. In this paper, training data of 5 athletes were collected and it provides the feedback function through the visualization of each section of the training results by the players which can enable the effective training. For the weakness section of each player, the coach and the player can selectively and repeatedly perform the training function with the proposed training system. Based on this, it is possible to perform the repeated training on weakness sections and they can improve the response speed for these sections. Continuous research is expected to be able to compare more various players' agility and physical fitness.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.53-60
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• 2017

Since processors are handling inputs and outputs independently on distributed memory computers, different file input/output methods are used. In this paper, we implemented and compared various file I/O methods to show their efficiency on distributed memory parallel computers. The implemented I/O systems are as following: (i) parallel I/O using NFS, (ii) sequential I/O on the host processor and domain decomposition, (iii) MPI-IO. For performance analysis, we used a separated file server and multiple processors on one or two computational servers. The results show the file I/O with NFS for inputs and sequential output with domain composition for outputs are best efficient respectively. The MPI-IO result shows unexpectedly the lowest performance.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.183-188
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• 2019

As social perceptions of pets change, cultural attitudes toward pets are becoming more friendly. In particular, dogs have been living familiarly and closely with humans for a long time. In the changing times, various services are being used to improve the understanding of dogs and to prevent companion dogs and increase awareness of respect for life. Therefore, in this paper, we implemented a smart lead line in which IoT service and application technology are linked to the walking dog's automatic lead line. To do this, we developed a smart dog lead line by designing and implementing an integrated module in connection with heterogeneous sensors and linking it with a dog lead line. Finally, a smart dog lead line was used to collect the dog's biological signals in real time, identify the location of the dog, and provide a notification system. Through this, we believe that the culture of dog culture can be further grown.

• Journal of radiological science and technology
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• v.47 no.1
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• pp.29-37
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• 2024

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.85-89
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• 2017

This paper proposes an antenna incorporating a bandstop filter to miniaturize the rectenna used for wireless power transmission with the emerging interest these days. To suppress the harmonics that can be re-radiated, this paper proposes a microstrip patch antenna that can suppress the harmonics while maintaining the size of the antenna by inserting a U-slot, which acts as a bandstop filter, on the ground plane of the antenna. As a result, S11 of the second harmonic(4.6GHz) was reduced from -5.61dB to -0.338dB and the efficiency was suppressed significantly from 29.76% to 1.5%. In addition, the maximum gain was reduced to -12dBi from 2.89dBi. On the other hand, at the fundamental frequency (2.45GHz), the S11 value was reduced from -18 dB to -15 dB, and the efficiency was reduced slightly from 68.2% to 60%. In the case of applying a microstrip antenna combined with the proposed bandstop filter to a rectenna, it is believed that the harmonics that degrade the performance of the rectenna can be removed effectively while reducing the large area occupied by harmonic suppression.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• 한국디지털정책학회:학술대회논문집
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• 2007.06a
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• pp.325-332
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• 2007

The purpose of this study is to get a politic advanced idea to operate a pro-environment based u-city through the overcome problems on the construction policy for the even areal distribution, and the development for the model structure of the high level. Advanced pro-environmental ubiquitous urban construction becomes an example of the practical level linked by the national agenda as uKorea policy. The Idea of the national land informal ion systems transforms to enhance or to guide the national strategic industry to implement balanced development as grand objectives of the national land due to the factor that 'the economic development 5 years plan' altered to 'the national land 5 years plan'. Therefore, ubiquitous echo city construct ion becomes realized as static spatial informal ion construct ion and dynamic mobile based ubiquitous lives operable by the information infrastructure and IT839 policy items operation. For the synergy effects through this task, it requires a strong empowerment of the information industries and a new growing core engine of the national economy through the policy of the mutual satisfaction on the spat io-temporal information and the pro-environment information systems.

• Smart Structures and Systems
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• v.29 no.1
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

• Smart Media Journal
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• v.11 no.7
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• pp.94-103
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• 2022

Weeds bring disadvantages to crops since they can damage them, and a clean treatment with less pollution and contamination should be developed. Artificial intelligence gives new hope to agriculture to achieve smart farming. This study delivers an automated weeds growth point detection using deep learning. This study proposes a combination of semantic graphics for generating data annotation and U-Net with pre-trained deep learning as a backbone for locating the growth point of the weeds on the given field scene. The dataset was collected from an actual field. We measured the intersection over union, f1-score, precision, and recall to evaluate our method. Moreover, Mobilenet V2 was chosen as the backbone and compared with Resnet 34. The results showed that the proposed method was accurate enough to detect the growth point and handle the brightness variation. The best performance was achieved by Mobilenet V2 as a backbone with IoU 96.81%, precision 97.77%, recall 98.97%, and f1-score 97.30%.