• Electronics and Telecommunications Trends
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• v.29 no.6
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• pp.71-81
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• 2014

5G 무선통신시스템은 동일한 영역에서 스펙트럼 사용 효율성을 개선하기 위해 매크로셀과 소형셀이 공존하는 이종 네트워크(HetNet: Heterogeneous Network) 형태로 진화하고 있으며, 급증하는 모바일 트래픽을 효율적으로 처리하기 위해 소형셀들을 고밀도 네트워크(High dense network)로 구축하는 방안이 연구되고 있다. 매크로셀과 고밀도 소형셀들이 중첩되어 구축되는 HetNet 기반 셀룰러 네트워크에서 소형셀 시스템의 구성과 파라미터 최적화를 통한 성능 유지를 운영자가 수동으로 조정하는 것은 한계가 있으므로 네트워크 환경변화에 따라 시스템에서 자율적으로 파라미터를 조정하여 시스템 성능을 유지하는 기술이 요구되고 있다. 본고에서는 시스템 운용 중 자율적인 최적화를 통해 시스템 성능을 최적으로 유지하고 유지비용을 최소화하는 3GPP 자율적 네트워크 최적화 기술을 소개한다.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2341-2347
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• 2021

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a^99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.15-17
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• 2018

VVC(Versatile Video Coding)는 입력된 영상을 CTU(Coding Tree Unit) 단위로 분할하여 코딩하며, 이를 다시 QTBTT(Quadtree plus binary tree and triple tree)로 분할하고, TU(Transform Unit)도 이와 같은 단위로 분할된다. 따라서 TU의 크기는 $4{\times}4$, $4{\times}8$, $4{\times}16$, $4{\times}32$, $8{\times}4$, $16{\times}4$, $32{\times}4$, $8{\times}8$, $8{\times}16$, $8{\times}32$, $16{\times}8$, $32{\times}8$, $16{\times}16$, $16{\times}32$, $32{\times}16$, $32{\times}32$, $64{\times}64$의 17가지 종류가 있다. 기존의 VVC 참조 Software인 VTM에서는 디블록킹필터와 SAO(Sample Adaptive Offset)로 이루어진 인루프필터를 이용하여 에러를 복원하는데, 본 논문은 TU 크기에 따라서 원본블록과 복원블록의 차이(에러)가 통계적으로 다름을 이용하여 서로 다른 CNN(Convolution Neural Network)을 구축하고 에러를 복원하는 방법으로 VTM의 인루프 필터를 대체한다. 복원영상의 에러를 감소시키기 위하여 TU 블록크기에 따라 DenseNet의 Dense Block기반 CNN을 구성하고, Hyper Parameter와 복잡도의 감소를 위해 네트워크 간에 일부 가중치를 공유하는 모양의 Network를 구성하였다.

• Journal of Information Technology Services
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• v.19 no.1
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• pp.1-16
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• 2020

Suncheon City Regeneration Project is known as the concept of cultural residents. Through the previous projects, the residents' capabilities have been improved, and the projects have been carried out according to their strategies. For this reason, participants in urban regeneration projects are important. The purpose of this study is to actually identify the 'rescue center' and 'direct relationship' with the analysis utilizing the characteristics of social networks NetMiner solution of the participants, who led the project, Suncheon. Surveys and interviews were conducted for participants, and the characteristics of social networks were analyzed in time series to quantify and visualize the results. As a result of the analysis, social networks were changed among the participants before and after the urban regeneration project. Initially, loose networks were denser over time, and initially networks formed only around participants were expanded over time. Network analysis has revealed that the system is strengthening with urban regeneration projects in the form of public and public-private cooperation. This highlights the need for a city-centered urban regeneration strategy centered on people and shows that a dense network of participants can be a success factor.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.133-138
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• 2020

This paper introduces a method for improving accuracy using the first convolution layer, which is not used in most modified CNN(: Convolution Neural Networks). In CNN, such as GoogLeNet and DenseNet, the first convolution layer uses only the traditional methods(3×3 convolutional computation, batch normalization, and activation functions), replacing this with RGB-csb. In addition to the results of preceding studies that can improve accuracy by applying RGB values to feature maps, the accuracy is compared with existing CNN using a limited number of images. The method proposed in this paper shows that the smaller the number of images, the greater the learning accuracy deviation, the more unstable, but the higher the accuracy on average compared to the existing CNN. As the number of images increases, the difference in accuracy between the existing CNN and the proposed method decreases, and the proposed method does not seem to have a significant effect.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1007-1014
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• 2022

Compact Advanced Satellite 500 (CAS500) can be used for various purposes, including vegetation, forestry, and agriculture fields. It is expected that it will be possible to acquire satellite images of various areas quickly. In order to use satellite images acquired through CAS500 in the agricultural field, it is necessary to develop a satellite image-based extraction technique for crop-cultivated areas.In particular, as research in the field of deep learning has become active in recent years, research on developing a deep learning model for extracting crop cultivation areas and generating training data is necessary. This manuscript classified the onion and garlic cultivation areas in Hapcheon-gun using PlanetScope satellite images and farm maps. In particular, for effective model learning, the model performance was analyzed according to the proportion of crop-cultivated areas. For the deep learning model used in the experiment, Fully Convolutional Densely Connected Convolutional Network (FC-DenseNet) was reconstructed to fit the purpose of crop cultivation area classification and utilized. As a result of the experiment, the ratio of crop cultivation areas in the training data affected the performance of the deep learning model.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.133-141
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• 2022

This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.197-202
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• 2021

Biometric technology using finger veins is receiving a lot of attention due to its high security, convenience and accuracy. And the recent development of deep learning technology has improved the processing speed and accuracy for authentication. However, the training data is a subset of real data not in a certain order or method and the results are not constant. so the amount of data and the complexity of the artificial neural network must be considered. In this paper, the deep learning model of Inception-Resnet-v2 was used to improve the high accuracy of the finger vein recognizer and the performance of the authentication system, We compared and analyzed the performance of the deep learning model of DenseNet-201. The simulations used data from MMCBNU_6000 of Jeonbuk National University and finger vein images taken directly. There is no preprocessing for the image in the finger vein authentication system, and the results are checked through EER.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.