• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• 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%.

• Proceedings of the Korean Nuclear Society Conference
• /
• 1977.06a
• /
• pp.109.5-110
• /
• 1977

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1091-1100
• /
• 2022

As deep learning technology advances and various high-resolution remote sensing images are available, interest in using deep learning technology and remote sensing big data to detect buildings and change in urban areas is increasing significantly. In this paper, for semantic building segmentation of high-resolution remote sensing images, we propose a new building segmentation model, Convolutional Block Attention Module (CBAM)-DRUNet that uses the DeepResUNet model, which has excellent performance in building segmentation, as the basic structure, improves the residual learning unit and combines a CBAM with the basic structure. In the performance evaluation using WHU dataset and INRIA dataset, the proposed building segmentation model showed excellent performance in terms of F1 score, accuracy and recall compared to ResUNet and DeepResUNet including UNet.

• Smart Media Journal
• /
• v.9 no.2
• /
• pp.92-98
• /
• 2020

This paper proposes a vehicle license plate recognition system using light weight deep learning models without high-end server. The proposed license plate recognition system consists of 3 steps: [license plate detection]-[character area segmentation]-[character recognition]. SSD-Mobilenet was used for license plate detection, ResNet with localization was used for character area segmentation, ResNet was used for character recognition. Experiemnts using Samsung Galaxy S7 and LG Q9, accuracy showed 85.3% accuracy and around 1.1 second running time.

• Korean Journal of Breeding Science
• /
• v.40 no.4
• /
• 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.

• Journal of Korea Multimedia Society
• /
• v.22 no.6
• /
• 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.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 1990.10a
• /
• pp.54-55
• /
• 1990

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• 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.

• Proceedings of the Korean Society of Medical Physics Conference
• /
• 1999.11a
• /
• pp.86-87
• /
• 1999

• Journal of Electrical Engineering and Technology
• /
• v.2 no.3
• /
• 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.