• International Journal of Computer Science & Network Security
• /
• 제21권2호
• /
• pp.198-204
• /
• 2021

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

• 한국원자력학회:학술대회논문집
• /
• 한국원자력학회 1977년도 제9회 학술발표회 초록
• /
• pp.109.5-110
• /
• 1977

• 대한원격탐사학회지
• /
• 제38권6_1호
• /
• pp.1091-1100
• /
• 2022

딥러닝 기술의 진보와 함께 다양한 국내외 고해상도 원격탐사 영상의 활용이 가능함에 따라 딥러닝 기술과 원격탐사 빅데이터를 활용하여 도심 지역 건물 검출과 변화탐지에 활용하고자 하는 관심이 크게 증가하고 있다. 본 논문에서는 고해상도 원격탐사 영상의 의미론적 건물 분할을 위해서 건물 분할에 우수한 성능을 보이는 DeepResUNet 모델을 기본 구조로 하고 잔차 학습 단위를 개선하고 Convolutional Block Attention Module(CBAM)을 결합한 새로운 건물 분할 모델인 CBAM-DRUNet을 제안한다. 제안한 건물 분할 모델은 WHU 데이터셋과 INRIA 데이터셋을 이용한 성능 평가에서 UNet을 비롯하여 ResUNet, DeepResUNet 대비 F1 score, 정확도, 재현율 측면에서 모두 우수한 성능을 보였다.

• 스마트미디어저널
• /
• 제9권2호
• /
• pp.92-98
• /
• 2020

본 논문은 고성능의 서버 없이 안드로이드 스마트폰 단독으로 동작할 수 있도록 경량화 딥러닝 모델을 사용하여 구현한 자동차 번호판 인식 시스템을 제안한다. 자동차 번호판 인식시스템은 [번호판검출]-[문자영역 분할]-[문자인식]으로 3단계의 과정으로 구성되며, 번호판검출은 SSD-Mobilenet, 문자영역 분할은 ResNet에 localization을 추가하여 사용하였고 문자인식은 ResNet을 이용하여 구현하였다. 테스트한 기기는 삼성 갤럭시 S7, LG Q9이며 정확도는 약 85.3%, 실행속도는 약 1.1초가 소요된다.

• 한국육종학회지
• /
• 제40권4호
• /
• pp.439-442
• /
• 2008

A new spray chrysanthemum cultivar, 'Cherry Blossom' was released by National Horticultural Research Institute (NHRI), Rural Development Administration (RDA), Korea in 2007. A cross was made in 2002 between 'Relance', a spray cultivar with red petals and resistant to white rust and 'Yeonja', a spray cultivar with pink petals. Trials were conducted from 2005 to 2007 for the evaluation and selection of this cultivar, including shading culture in summer and retarding culture in autumn. The natural flowering time of "Cherry Blossom" is late October, but year-round flowering is possible by shading and lighting treatment. This cultivar is single type flowers with dark pink petals and green flower center and resistant to white rust. It is very stable color of petals when the variety is cultivated under high temperature conditions in summer season. The diameter of flower is 55.0 mm. The number of flowers per stem is 10.5 and the number of petals per flower is 24.0. The days to flowering under the short day treatment is about 45 in spring season.

• 한국멀티미디어학회논문지
• /
• 제22권6호
• /
• pp.665-675
• /
• 2019

In this paper, we compare and analyze the classification performance of deep learning algorithm Convolutional Neural Network(CNN) ac cording to ensemble generation and combining techniques. We used several CNN models(VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, GoogLeNet) to create 10 ensemble generation combinations and applied 6 combine techniques(average, weighted average, maximum, minimum, median, product) to the optimal combination. Experimental results, DenseNet169-VGG16-GoogLeNet combination in ensemble generation, and the product rule in ensemble combination showed the best performance. Based on this, it was concluded that ensemble in different models of high benchmarking scores is another way to get good results.

• 한국작물학회:학술대회논문집
• /
• 한국작물학회 1990년도 추계 학술대회지
• /
• pp.54-55
• /
• 1990

• International Journal of Computer Science & Network Security
• /
• 제22권7호
• /
• pp.301-307
• /
• 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.

• 한국의학물리학회:학술대회논문집
• /
• 한국의학물리학회 1999년도 Japanese Journal of Medical Physics
• /
• pp.86-87
• /
• 1999

• Journal of Electrical Engineering and Technology
• /
• 제2권3호
• /
• pp.366-372
• /
• 2007

Joining thin aluminum alloy is difficult using most welding techniques. Many of the problems are associated with bum-through by the high heat input. Common welding techniques are TIG (Tungsten Inert Gas), MIG (Metal Inert Gas), and PULSE-MIG welding. The method provides more control of the heat balance in the welding arc by taking advantage of the different arc characteristics obtained with each of the two polarities. In this paper, we proposed a new welding method by control DSP 320C32, and the characteristic and experiment result-voltage, current, welding bead, and penetrations by this method are presented.