• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.87-94
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• 2006

For realizing an ubiquitous home network(uHome-net), various sensors should be able to be connected to an integrated wire/wireless sensor network. This paper describes an application case of applying LonWorks technology being widely used in control network to wire/wireless sensor network in uHome-net and the design and application of LonRF device that consists of a neuron chip including LonTalk protocol, a 433.92MHz RF transceiver, a sensor, and application programs. As an application example of the LonRF device, the LonRF smart badge that can measure the 3D location of objects in indoor environment and interwork with the uHome-net was developed. LonRF device based home network services were realized on the uHome-net testbed such as indoor positioning service, remote surveillance service and remote metering service were realized. This research shows that LonWorks technology based sensor network could be applicable to the control network in an ubiquitous home network and the LonRF device can be used as a wireless node in various sensor networks.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.219-228
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• 2008

For real-time social networking service, mobile interaction in a usable-unified-ubiquitous (U3) web service was studied. Both as a convenient mobile HCI for real-time social networks and as indexing keys to metadata information in ubiquitous web service, the multi-lingual single-character domain names (e.g. 김.net, 이.net, 가.net, ㄱ.net, ㄴ.net, ㅎ.net, ㅏ.net, ㅔ.net, ㄱ.com, ㅎ.com) are convenient mobile interfaces when searching for social information and registering information. We introduce the sketched design goals and experience of mobile interaction in Korea, Japan and China, with the implementation of real-time social networking service as an example of U3 Web service. We also introduce the possibility of extending the application to the metadata directory service in IP-USN (IP-based Ubiquitous Sensor Network) for a unified information management in the service of social networking and sensor networking.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.9-16
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• 2020

In the Image processing and computer vision, the problem of reconstructing from one image to another or generating a new image has been steadily drawing attention as hardware advances. However, the problem of computer-generated images also continues to emerge when viewed with human eyes because it is not natural. Due to the recent active research in deep learning, image generating and improvement problem using it are also actively being studied, and among them, the network called Generative Adversarial Network(GAN) is doing well in the image generating. Various models of GAN have been presented since the proposed GAN, allowing for the generation of more natural images compared to the results of research in the image generating. Among them, pix2pix is a conditional GAN model, which is a general-purpose network that shows good performance in various datasets. pix2pix is based on U-Net, but there are many networks that show better performance among U-Net based networks. Therefore, in this study, images are generated by applying various networks to U-Net of pix2pix, and the results are compared and evaluated. The images generated through each network confirm that the pix2pix model with Attention, R2, and Attention-R2 networks shows better performance than the existing pix2pix model using U-Net, and check the limitations of the most powerful network. It is suggested as a future study.

• Journal of IKEEE
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• v.27 no.4
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• pp.456-463
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• 2023

In this paper, we introduce the SSResUNet network model, which integrates the SPADE structure with the U-Net network model for accurate land cover classification using high-resolution satellite imagery without requiring user intervention. The proposed network possesses the advantage of preserving the spatial characteristics inherent in satellite imagery, rendering it a robust classification model even in intricate environments. Experimental results, obtained through training on KOMPSAT-3A satellite images, exhibit superior performance compared to conventional U-Net and U-Net++ models, showcasing an average Intersection over Union (IoU) of 76.10 and a Dice coefficient of 86.22.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.93-99
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• 2024

The digital medical imaging, especially, computed tomography (CT), should necessarily be considered in terms of noise distribution caused by converting to X-ray photon to digital imaging signal. Recently, the denoising technique based on deep learning architecture is increasingly used in the medical imaging field. Here, we evaluated noise reduction effect according to various noise types based on the U-net deep learning model in the lung CT images. The input data for deep learning was generated by applying Gaussian noise, Poisson noise, salt and pepper noise and speckle noise from the ground truth (GT) image. In particular, two types of Gaussian noise input data were applied with standard deviation values of 30 and 50. There are applied hyper-parameters, which were Adam as optimizer function, 100 as epochs, and 0.0001 as learning rate, respectively. To analyze the quantitative values, the mean square error (MSE), the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. According to the results, it was confirmed that the U-net model was effective for noise reduction all of the set conditions in this study. Especially, it showed the best performance in Gaussian noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.81-83
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• 2021

In this paper, we propose a method to segment a plant from a plant image using U-Net. The network is an end-to-end fully convolutional network that is mainly used for image segmentation. When training the network, we used a binary image that is acquired by the manual segmentation of a plant from the background. Experimental results show that the U-Net based segmentation network can extract a plant from a digital image accurately.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.313-330
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• 2021

In an autonomous driving system equipped with a camera and lidar, depth completion techniques enable dense depth estimation. In particular, using self-supervised learning it is possible to train the depth completion network even without ground truth. In actual autonomous driving, such depth completion should have very short latency as it is the input of other algorithms. So, rather than complicate the network structure to increase the accuracy like previous studies, this paper focuses on network latency. We design a U-Net type network with RegNet encoders optimized for GPU computation. Instead, this paper presents several techniques that can increase accuracy during the process of self-supervised learning. The proposed techniques increase the robustness to unreliable lidar inputs. Also, they improve the depth quality for edge and sky regions based on the semantic information extracted in advance. Our experiments confirm that our model is very lightweight (2.42 ms at 1280x480) but resistant to noise and has qualities close to the latest studies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.67-68
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• 2021

기존 객체탐지는 경계 상자 회귀방식을 적용하였지만, 문자는 왜곡과 변형이 심한 특성을 가진 객체로 U-net 구조의 이미지 분할 방식을 사용하는 경우가 많다. 따라서 최근 문자 탐지는 통계적 모델에 비해 높은 정확도를 보이는 심층 신경망 기반의 모델 연구가 많이 진행되고 있다. 본 연구에서는 이미지 분할을 통한 양방향 특징 결합 기법을 사용한 문자 탐지 모델을 제안한다. 이미지 분할 방식은 메모리의 효율이 떨어지기 때문에 이를 극복하고자 특징 추출 단계에서 경량화된 네트워크를 적용하였다. 또한, 객체 탐지에서 큰 성과를 보인 양방향 특징 결합 모듈을 U-net 구조에 추가하여 추출된 특징이 효과적으로 결합 되는 결과를 얻었다. 제안하는 모델의 문자 탐지 성능은 합성 문자 데이터셋을 이용한 실험을 통해 기존의 U-net 구조의 이미지 분할 방식보다 향상되었음을 확인하였다.

• Informatization Policy
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• v.25 no.1
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• pp.3-29
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• 2018

The Federal Communications Commission voted to repeal net neutrality protections on December 14, 2017. This is the very opposite decision of the net neutrality rule that the Obama administration has consistently maintained. The ensuing storm from the repeal of net neutrality protections has an extensively effect enough on individuals and businesses to cover the entire spectrum, and the impact is hard to assess in the U. S. content industry, which dominates the worldwide Internet content and platform market. On the other hand, Korea's net neutrality protections have been firmly pursued, and there is no sign of change even after the decision happened in the U. S. Net neutrality is not a simple theme that is associated with the Constitution, such as freedom of expression, as well as the issue of network enhancement to prepare for 5G. Accordingly, this study examines how the net neutrality has been carried out in the U. S. and Korea over the years, and provides the issues of Internet enhancement, perspectives of ISP and ICP, and implications for the Constitution, market economy, fair competition and zero rating. This research delivers future direction and implications of domestic net neutrality policies.