• Horticultural Science & Technology
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• v.34 no.5
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• pp.799-806
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• 2016

A standard rose cultivar, 'To Dios'(Rosa hybrida) was selected for use as a cut flower from the progeny of a cross between 'Vanilla perfume' and 'Marcia' at the research and development division of the Goyang-si Agricultural Technology Center in 2013. 'Vanilla perfume', an orange-colored (RHS Orange Group 27C) standard rose cultivar with 48 petals, was used as the female plant. 'Marcia', a white-colored (RHS Green white Group 157B) standard rose cultivar with 96 petals was used as the male parent. A cross was made in 2009 and seedlings were produced. Selections were made between 2010 and 2013, and a plant with good cut flower traits was finally selected and named 'To Dios'. 'To Dios' is a standard rose with large flowers of 11.2 cm in diameter and 128 peach-colored (RHS Red Group 36B) petals per flower. Vase life of this cultivar is up to 15 days. It takes 47 days from pruning to blooming and cut flower productivity is approximately $160stems/m^2$ per year. 'To Dios' was registered as a new cultivar No. 4875 with the Korea Seed & Variety Service on March 19, 2014.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.10
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• pp.18-25
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• 2000

In this paper, we analyze TCP congestion control algorithm over ATM-UBR network. TCP congestion control algorithm consists of slow start, congestion avoidance, fast recovery, fast retransmit. We analyze the ATM-UBR network service using the BSD 4.3 TCP Reno, Vanilla. However we found the fact that the characteristic of fast retransmit, recovery algorithm makes a serious degradation of Performance in multiple losses of packets. We propose new fast retransmit, recovery algorithm to improve the problem. The results of performance analysis improve the multiple losses of packets using a proposed fast retransmit, recovery algorithm.

• Food Science of Animal Resources
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• v.34 no.6
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• pp.757-762
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• 2014

The object of this study was to investigate the difference in physical and sensory properties of various premium ice creams. The physical properties of the various ice creams were compared by manufacturing brand. The water contents of the samples differed, with BR having the highest value at 60.5%, followed by NT and CS at 57.8% and 56.9%, respectively. The higher the water content, the lower Brix and milk fat contents in all samples. The density of the samples showed almost similar values in all samples (p>0.05). The viscosity of each ice cream had no effect on the water content in any of the brands. Before melting of the ice cream, the total color difference was dependent on the lightness, especially in the vanilla ice cream, owing to the reflection of light on the surface of the ice crystals. The CS product melted the fastest. In the sensory test, CS obtained a significantly higher sweetness intensity score but a lower score for color intensity, probably due to the smaller difference in total color, by which consumers might consider the color of CS as less intense. From this study, the cold chain system for ice cream distribution might be important to decide the physical properties although the concentration of milk fat is key factor in premium ice cream.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1023-1025
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• 2019

Coal and Natural gas are two biggest contributors to a generation of energy throughout the world. Most of these resources create environmental pollution while making energy affecting the natural habitat. Many approaches have been proposed as alternatives to these sources. One of the leading alternatives is Solar Energy which is usually harnessed using solar farms. In artificial intelligence, the most researched area in recent times is machine learning. With machine learning, many tasks which were previously thought to be only humanly doable are done by machine. Neural networks have two major subtypes i.e. Convolutional neural networks (CNN) which are used primarily for classification and Recurrent neural networks which are utilized for time-series predictions. In this paper, we predict energy generated by solar fields and optimal angles for solar panels in these farms for the upcoming seven days using environmental and historical data. We experiment with multiple configurations of RNN using Vanilla and LSTM (Long Short-Term Memory) RNN. We are able to achieve RSME of 0.20739 using LSTMs.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.53-60
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• 2023

Recently, the demand and traffic volume for various multimedia contents are rapidly increasing through real-time streaming platforms. In this paper, we predict real-time streaming traffic to improve the quality of service (QoS). Statistical models have been used to predict network traffic. However, since real-time streaming traffic changes dynamically, we used recurrent neural network-based deep learning models rather than a statistical model. Therefore, after the collection and preprocessing for real-time streaming data, we exploit vanilla RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU models to predict real-time streaming traffic. In evaluation, the training time and accuracy of each model are measured and compared.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.559-561
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• 2022

인공지능 시대에 들어서면서 개인 맞춤형 환경을 제공하기 위하여 사람의 감정을 인식하고 교감하는 기술이 많이 발전되고 있다. 사람의 감정을 인식하는 방법으로는 얼굴, 음성, 신체 동작, 생체 신호 등이 있지만 이 중 가장 직관적이면서도 쉽게 접할 수 있는 것은 표정이다. 따라서, 본 논문에서는 정확도 높은 얼굴 감정 식별을 위해서 Convolution Block Attention Module(CBAM)의 각 Gate와 Residual Block, Skip Connection을 이용한 Identity- CBAM Module을 제안한다. CBAM의 각 Gate와 Residual Block을 이용하여 각각의 표정에 대한 핵심 특징 정보들을 강조하여 Context 한 모델로 변화시켜주는 효과를 가지게 하였으며 Skip-Connection을 이용하여 기울기 소실 및 폭발에 강인하게 해주는 모듈을 제안한다. AI-HUB의 한국인 감정 인식을 위한 복합 영상 데이터 세트를 이용하여 총 6개의 클래스로 구분하였으며, F1-Score, Accuracy 기준으로 Identity-CBAM 모듈을 적용하였을 때 Vanilla ResNet50, ResNet101 대비 F1-Score 0.4~2.7%, Accuracy 0.18~2.03%의 성능 향상을 달성하였다. 또한, Guided Backpropagation과 Guided GradCam을 통해 시각화하였을 때 중요 특징점들을 더 세밀하게 표현하는 것을 확인하였다. 결과적으로 이미지 내 표정 분류 Task에서 Vanilla ResNet50, ResNet101을 사용하는 것보다 Identity-CBAM Module을 함께 사용하는 것이 더 적합함을 입증하였다.

• The Korean Journal of Food And Nutrition
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• v.37 no.4
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• pp.197-210
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• 2024

This study aims to develop a deep learning model to monitor rice serving amounts in institutional foodservice, enhancing personalized nutrition management. The goal is to identify the best convolutional neural network (CNN) for detecting rice quantities on serving trays, addressing balanced dietary intake challenges. Both a vanilla CNN and 12 pre-trained CNNs were tested, using features extracted from images of varying rice quantities on white trays. Configurations included optimizers, image generation, dropout, feature extraction, and fine-tuning, with top-1 validation accuracy as the evaluation metric. The vanilla CNN achieved 60% top-1 validation accuracy, while pre-trained CNNs significantly improved performance, reaching up to 90% accuracy. MobileNetV2, suitable for mobile devices, achieved a minimum 76% accuracy. These results suggest the model can effectively monitor rice servings, with potential for improvement through ongoing data collection and training. This development represents a significant advancement in personalized nutrition management, with high validation accuracy indicating its potential utility in dietary management. Continuous improvement based on expanding datasets promises enhanced precision and reliability, contributing to better health outcomes.

• Food Science of Animal Resources
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• v.28 no.1
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• pp.69-75
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• 2008

This study evaluated the physicochemical and sensory characteristics of vanilla ice cream treated with gamma irradiation. The general composition of the vanilla ice cream used for the study was 45.4-53.3% moisture, 5.5-5.9% fat and 3.9-4.1% protein, and these values did not change following gamma irradiation. The Hunter L, a and b values were slightly decreased following gamma irradiation. The fatty acid composition of the ice cream included caprylic acid, capric acid, lauric acid, myristic acid, palmitic acid and stearic acid, and there was no detectable change following irradiation. There was no significant difference in TBARS (2-thiobarbituric acid reactive substances) values between non-irradiated and irradiated samples at a dose of 3 kGy or less (p<0.05). Sensory evaluation indicated that gamma-irradiated vanilla ice cream did not show any difference in color relative to non-irradiated ice cream. However, gamma irradiation did affect the flavor, taste and overall acceptability of ice cream at doses above 3 kGy. These results indicate that gamma irradiation at 3 kGy is an effective treatment for sustaining the physicochemical characteristics of vanilla ice cream with minimal changes in sensory characteristics, though further studies should be carried out to reduce the deterioration of sensory qualities induced by gamma irradiation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.511-523
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• 2023

Research on the integration of unmanned aerial vehicles and deep learning for reinforced concrete damage detection is actively underway. Convolutional neural networks have a high impact on the performance of image classification, detection, and segmentation as backbones. The MobileNet, a pre-trained convolutional neural network, is efficient as a backbone for an unmanned aerial vehicle-based damage detection model because it can achieve sufficient accuracy with low computational complexity. Analyzing vanilla convolutional neural networks and MobileNet under various conditions, MobileNet was evaluated to have a verification accuracy 6.0~9.0% higher than vanilla convolutional neural networks with 15.9~22.9% lower computational complexity. MobileNetV2, MobileNetV3Large and MobileNetV3Small showed almost identical maximum verification accuracy, and the optimal conditions for MobileNet's reinforced concrete damage image feature extraction were analyzed to be the optimizer RMSprop, no dropout, and average pooling. The maximum validation accuracy of 75.49% for 7 types of damage detection based on MobilenetV2 derived in this study can be improved by image accumulation and continuous learning.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.