• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.324-330
• /
• 2021

Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investigated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.3
• /
• pp.481-489
• /
• 2020

Safety and security during the handling of hazardous materials is a great concern for anyone in the field. One driving point in the security field is the ability to detect the source of the danger and take action against it as quickly as possible. Via the usage of a fully convolutional network, it is possible to create the label map of an input image, indicating what object is occupying the specific area of the image. This research employs the usage of U-net, which was constructed in biomedical field segmentation to segment cells, instead of the original FCN. One of the challenges that this research faces is the availability of ground truth with precise labeling for the dataset. Testing the network after training resulted in some images where the network pronounces even better detail than the expected label map. With better detailed label map, the network might be able to produce better segmentation is something to be studied in further research.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.706-708
• /
• 2023

In this study, we embark on a journey to uncover the essence of emotions by exploring the depths of transfer learning on three pre-trained transformer models. Our quest to classify five emotions culminates in discovering the KLUE (Korean Language Understanding Evaluation)-BERT (Bidirectional Encoder Representations from Transformers) model, which is the most exceptional among its peers. Our analysis of F1 scores attests to its superior learning and generalization abilities on the experimental data. To delve deeper into the mystery behind its success, we employ the powerful SHAP (Shapley Additive Explanations) method to unravel the intricacies of the KLUE-BERT model. The findings of our investigation are presented with a mesmerizing text plot visualization, which serves as a window into the model's soul. This approach enables us to grasp the impact of individual tokens on emotion classification and provides irrefutable, visually appealing evidence to support the predictions of the KLUE-BERT model.

• The Journal of Korean Association of Computer Education
• /
• v.19 no.6
• /
• pp.55-68
• /
• 2016

Informatics curriculum revised 2015 proposed the use of block type and text type of programming language by organizing problem solving and the programming unit in a spiral. The purpose of this study is to find out whether the algorithms helps programming learning and whether there is a positive transition effect in block type programming learning to text type programming trailing learning. For 15 elementary school students was conducted block type and text type programming learning. As a result of the research, it is confirmed that writing the algorithm in a limited way can interfere with the learner's expression of thinking, but the block type programming learning has a positive transition to the text type programming learning. This study is meaningful that it suggested a plan for the programming education which is sequential from elementary school.

• Journal of East Asia Management
• /
• v.2 no.1
• /
• pp.93-103
• /
• 2021

Struggle refers to the process of overcoming various difficulties for a goal. The spirit of struggle is a positive attitude and reaction reflected in the process of struggle. Cultivating the spirit of struggle of college students is the call of the new era. In essence, the cultivation of the spirit of struggle is a process of learning, which is in line with Bandura's Observation Learning Theory(Bandura, 1977):Attention, Maintenance, Reproduction and Motivation. The cultivation of College Students' spirit of struggle in the new era is also a learning process of enriched experience. It is necessary to cultivate the spirit of struggle into the soul of college students and make it become a habit of students. Moreover, it is crucial to carry out adaptive transformation of Bandura's observation learning theory. By studying the mechanism of the spirit of struggle of college students, taking innovation and entrepreneurship education as a means, and aiming at cultivating the connotation of President Xi's thought on socialism with Chinese characteristics for a new era, this paper constructs the AIST model for cultivating the spirit of struggle of college students in the new era. This model includes online learning acceptance platform(Acceptance), classroom experience stimulation platform(Inspiration), iterative training solidified platform (Solidification), and competition practice transfer platform(Transfer). The purpose of this model is to provide a practical way for universities to fulfill the fundamental task of moral education and cultivate qualified socialist builders and successors. The number of students using the online learning acceptance platform ranked the first among that of the similar courses in China; The classroom experience stimulation platform and the iterative training solidified platform support each other, with an effective rate of 97%; The competition practice transfer platform has realized the continuous growth of the number of awards won in competitions for three years. The direction of future efforts is to establish the external mechanism of the spirit of struggle, to ensure the effectiveness of classroom experience and iterative training, to cultivate teachers with coaching skills, and to accurately measure the transformation point of external and endogenous motivation.

• Nuclear Engineering and Technology
• /
• v.55 no.11
• /
• pp.4102-4111
• /
• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• Annual Conference on Human and Language Technology
• /
• 2020.10a
• /
• pp.223-226
• /
• 2020

엔터티 링킹을 위해서는 엔터티 링킹을 수행 할 후보 엔터티의 정보를 얻어내는 것이 필요하다. 하지만, 엔터티 정보를 획득하기 어려운 경우, 엔터티 링킹을 수행 할 수 없다. 이 논문에서는 이를 해결하기 위해 데이터셋으로부터 엔터티의 가상 엔터티 설명문을 작성하고, 이를 통해 엔터티 링킹을 수행함으로써 엔터티 정보가 없는 환경에서도 2.58%p밖에 성능 하락이 일어나지 않음을 보인다.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.65-70
• /
• 2021

Waste accumulation is becoming a significant challenge in most urban areas and if it continues unchecked, is poised to have severe repercussions on our environment and health. The massive industrialisation in our cities has been followed by a commensurate waste creation that has become a bottleneck for even waste management systems. While recycling is a viable solution for waste management, it can be daunting to classify waste material for recycling accurately. In this study, transfer learning models were proposed to automatically classify wastes based on six materials (cardboard, glass, metal, paper, plastic, and trash). The tested pre-trained models were ResNet50, VGG16, InceptionV3, and Xception. Data augmentation was done using a Generative Adversarial Network (GAN) with various image generation percentages. It was found that models based on Xception and VGG16 were more robust. In contrast, models based on ResNet50 and InceptionV3 were sensitive to the added machine-generated images as the accuracy degrades significantly compared to training with no artificial data.

• Journal of Science Education
• /
• v.38 no.3
• /
• pp.657-669
• /
• 2014

The purpose of this research is to investigate the effect of students' storytelling on the science learning motivation and science related attitude in elementary science class. We had developed storytelling materials for 11 class hours on the 'Heat transfer and our life' unit based on the analysis of the 4th grade-science curriculum. The research sample was 22 4th graders who were belonged to one of the classes at an elementary school in Gwangju. The test of students' science learning motivation and science related attitude were administered before and after the storytelling treatment, and the difference was checked by the paired t-test using SPSS program. Students' perceptions on the storytelling were also investigated. The conclusions of this research are as follows. First, the students' storytelling tend to have somewhat positive influence on learning motivation. The each mean of post-test in the domain of attention, confidence and satisfaction was significantly higher than that of pre-test. Second, students' storytelling have a positive influence on scientific attitude. Although the means of post-test were higher than those of pre-test in both science subject attitude and scientific attitude domain, a statistically significant difference was found only in the scientific attitude domain. For future researches, the development of more story-materials or strategies for effective storytelling is needed.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.36 no.3
• /
• pp.113-120
• /
• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.