• Journal of the Institute of Convergence Signal Processing
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• v.18 no.1
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• pp.6-12
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• 2017

In this paper, we proposed IoT based Smart Health Service using Motion Recognition for Human UX/UI. Until now, sensor networks using M2M-based u-healthcare are using non-IP protocol instead of TCP / IP protocol. However, in order to increase the service utilization and facilitate the management of the IoT-based sensor network, many sensors are required to be connected to the Internet. Therefore, IoT-based smart health service is designed considering network mobility because it is necessary to communicate not only the data measured by sensors but also the Internet. In addition, IoT-based smart health service developed smart health service for motion detection as well as bio information unlike existing healthcare platform. WBAN communications used in u-healthcare typically consist of many networked devices and gateways. The method proposed in this paper can easily cope with dynamic changes even in a wireless environment by using a technology supporting mobility between WBAN sensor nodes, and systematic management is performed through detection of a user's motion.

• Information and Communications Magazine
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• v.30 no.8
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• pp.46-52
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• 2013

피처폰에서 스마트폰으로의 모바일 혁명과 All IP기반의 Network는 IT 생태계 변화에 있어 통신산업을 비롯하여 모든 산업분야의 변화를 촉구해 왔다. 하룻동안 구글의 Play store에 등록되는 앱의 수와 개통되는 안드로이드 기그들은 수십 만개에 달하고 스마트폰은 이제 단순한 사람과 사람과의 통신의 수단이 아닌 나와 연결된 모든 사물과 통신하기 위한 수단이 되고 있다. 이러한 폭발적인 모바일 성장과 함께 통신회사들도 자신들의 강점인 유무선 통합을 기반으로 사업을 확대하거나 신성장 동력을 찾으려는 많은 시도를 해왔다. LG 유플러스도 이러한 변화의 소용돌이를 극복하기 위해 과거 LG텔레콤, LG데이콤, LG파워콤의 3사 합병의 조직 변화를 시도했고, 유무선 네트워크 기반의 인프라를 중심으로 다양한 서비스 플랫폼을 구축하고 있으며, 이를 기반으로 다양한 서비스의 기획에서 상용화까지 경쟁력 있는 서비스를 쉽고 빠르게 제공할 수 있도록 하고 있다. 또한 플랫폼과 서비스, 플랫폼과 플랫폼간의 유기적인 연동을 통해 보다 차별화된 융합서비스와 개방형 API으로 누구나 쉽게 자사의 플랫폼과 서비스 기능을 사용할 수 있도록 생태계 조성에 힘을 쓰고 있다. 본 고에서는 LG U+의 플랫폼과 서비스 중 M2M분야에 대해 알아보고, M2M/IoT에서의 다양한 활동을 통해 LG U+가 바라보는 M2M/IoT의 미래를 조명해 보고자 한다.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.17-23
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• 2022

NB-IoT and LTE Cat.M1 based on LPWA(Low Power Wide Area) are commercialized and serviced by mobile carriers. As the demand for IoT devices is increased, the number of subscribers to these services is also increasing. In the beginning of service, there was no issue that eNB capacity for NB-IoT and LTE Cat.M1. However, as the number of subscribers increases, there is an issue that the eNB capacity for these service is insufficient. Active UE capacity issue may cause overload by continuous increase and temporary increase. In this paper, we propose a solution to solve the problem of LTE RRC(Radio Resource Control) Active UE capacity shortage and base station overload caused by the increase of NB-IoT and LTE Cat.M1 UE in same eNB. The proposed solution can increase a cell capacity without cell division and additional eNB, and can also improve the service quality of these UEs.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.595-598
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• 2020

IoU (Intersection over Union) is the most commonly used index in target detection. The core requirement of target detection is what is in the image and where. Based on these two problems, classification training and positional regression training are needed. However, in the process of position regression, the most commonly used method is to obtain the IoU of the predicted bounding box and ground-truth bounding box. Calculating bounding box regression losses should take into account three important geometric measures, namely the overlap area, the distance, and the aspect ratio. Although GIoU (Generalized Intersection over Union) improves the calculation function of image overlap degree, it still can't represent the distance and aspect ratio of the graph well. As a result of technological progress, Bounding-Box is no longer represented by coordinates x,y,w and h of four positions. Therefore, the IoU can be further optimized with the center point and aspect ratio of Bounding-Box.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1109-1124
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• 2022

This study proposes a marine debris monitoring methods using satellite and drone multispectral images. A multi-layer perceptron (MLP) model was applied to detect marine debris using Sentinel-2 satellite image. And for the detection of marine debris using drone multispectral images, performance evaluation and comparison of U-Net, DeepLabv3+ (ResNet50) and DeepLabv3+ (Inceptionv3) among deep learning models were performed (mIoU 0.68). As a result of marine debris detection using satellite image, the F1-Score was 0.97. Marine debris detection using drone multispectral images was performed on vegetative debris and plastics. As a result of detection, when DeepLabv3+ (Inceptionv3) was used, the most model accuracy, mean intersection over union (mIoU), was 0.68. Vegetative debris showed an F1-Score of 0.93 and IoU of 0.86, while plastics showed low performance with an F1-Score of 0.5 and IoU of 0.33. However, the F1-Score of the spectral index applied to generate plastic mask images was 0.81, which was higher than the plastics detection performance of DeepLabv3+ (Inceptionv3), and it was confirmed that plastics monitoring using the spectral index was possible. The marine debris monitoring technique proposed in this study can be used to establish a plan for marine debris collection and treatment as well as to provide quantitative data on marine debris generation.

• Information and Communications Magazine
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• v.30 no.8
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• pp.11-19
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• 2013

본고에서는 M2M/IoT 통신의 실현을 위한 무선 통신망 기술을 알아보고 IoT 통신을 위한 요구사항과 이를 해결하기 위한 연구 동향을 살펴본다. 특히, 많은 수의 IoT 디바이스가 싱크 노드를 이용하여 IP 망에 접속하는 Wireless Sensor network(WSN)에서의 문제와, LTE-A와 같은 cellular 망을 이용하여 접속하는 IoT 서비스로 나누어 논의한다. WSN관점에서는 에너지에 대한 제약이 심한 환경을 고려하여 발생할 수 문제점들을 분류하고 이에 대한 다양한 해결책을 제시하며, Cellular 망에서는 현재의 LTE-A 망에 많은 수의 IoT 디바이스가 연결될 경우 발생할 수 있는 문제점들을 논하고 기존의 통신에 영향을 최소화 하며 IoT 서비스를 공존할 수 있는 연구 동향을 논한다.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.289-303
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• 2022

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.