• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.91-98
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• 2023

Recently, according to development of artificial intelligence, a wide range of industry being automatic and optimized. Also we can find out some research of using supervised learning for deteceting defect of railway in domestic rail industry. However, there are structures other than rails on the track, and the fastener is a device that binds the rail to other structures, and periodic inspections are required to prevent safety accidents. In this paper, we present a method of reducing cost for labeling using semi-supervised and transfer model trained on rail fastener data. We use Resnet50 as the backbone network pretrained on ImageNet. At first we randomly take training data from unlabeled data and then labeled that data to train model. After predict unlabeled data by trained model, we adopted a method of adding the data with the highest probability for each class to the training data by a predetermined size. Futhermore, we also conducted some experiments to investigate the influence of the number of initially labeled data. As a result of the experiment, model reaches 92% accuracy which has a performance difference of around 5% compared to supervised learning. This is expected to improve the performance of the classifier by using relatively few labels without additional labeling processes through the proposed method.

• The Journal of Society for e-Business Studies
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• v.12 no.4
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• pp.185-209
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• 2007

The development of information technologies has contributed on-line training as one of important education methods. On-line training in firms, which is similar to e-learning or virtual education, provides trainees with more education opportunities in diverse ways. It has developed a range of innovative services with a one-stop solution of education within the electronic sector. Also under the on-line training environment, trainees can undertake customized training packages at anytime and any places. Moreover, information technology allows both the trainers and other trainees to be decoupled in any of the elements of tune, place, and space. Two research questions are investigated : what are the determinants affecting the on-line training effectiveness and how those variables affect the two aspects of training effectiveness: learning performance and transfer performance. Based on the previous literature conducted on the traditional training environment, the determinants of training effectiveness are derived. Eight hypotheses are developed based on literature reviews and tested by questionnaires survey data. The collected data have been analyzed by LISREL. It is found that the relationship between individual, organizational and on-line site design variables and training effectiveness (learning and transfer) are significant. The contribution and limitations of this research are also discussed with future studies.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• 대한공업교육학회지
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• v.30 no.1
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• pp.133-148
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• 2005

This research attempts to establish a strategy through which to secure the introduction of the KEM 2.0 system. Another aim of this research is to eventually establish a service system which can be used to automatically generate and provide the metadata information contained in the Cylearn system. in order to facilitate Korean vocational high school students access to teaching-learning materials. The main research tasks associated with this research were: 1) To establish the components of the file server system while taking into consideration the environment in which each educational organization operates; 2) To utilize the KEM 2.0 in order to optimize the configuration of the Cylearn system and the related software. To do so, the KEM 2.0. should be applied in close coordination with Edunet: The results of this research can be summarized as follows: First, a strategy to introduce the KEM 2.0 was established. To achieve this, the researcher analyzed the characteristics of the sequencing and presentation methods and suggested teaching-learning materials based on the KEM 2.0.s sequencing system. Second, the file server was constructed using the KEM 2.0. The established file server took into account the environmental conditions in which the Edunet system operates so as to facilitate the creation of a network system with Edunet. Third, the Cylearn service system was linked to Edunet. The researcher developed a module using the KEM 2.0. that could be used to transmit the metadata related to teaching-learning materials to Edunet. To achieve this, an input device and a databank which could be used to transfer the generated metadata to Edunet were developed. The input device and databank developed using the KEM 2.0. were used to transfer the metadata to Edunet by linking the Cylearn system to the Edunet system.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.1-9
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• 2021

Traditionally, most malicious codes have been analyzed using feature information extracted by domain experts. However, this feature-based analysis method depends on the analyst's capabilities and has limitations in detecting variant malicious codes that have modified existing malicious codes. In this study, we propose a ResNet-Variational AutoEncder-based variant malware classification method that can classify a family of variant malware without domain expert intervention. The Variational AutoEncoder network has the characteristics of creating new data within a normal distribution and understanding the characteristics of the data well in the learning process of training data provided as input values. In this study, important features of malicious code could be extracted by extracting latent variables in the learning process of Variational AutoEncoder. In addition, transfer learning was performed to better learn the characteristics of the training data and increase the efficiency of learning. The learning parameters of the ResNet-152 model pre-trained with the ImageNet Dataset were transferred to the learning parameters of the Encoder Network. The ResNet-Variational AutoEncoder that performed transfer learning showed higher performance than the existing Variational AutoEncoder and provided learning efficiency. Meanwhile, an ensemble model, Stacking Classifier, was used as a method for classifying variant malicious codes. As a result of learning the Stacking Classifier based on the characteristic data of the variant malware extracted by the Encoder Network of the ResNet-VAE model, an accuracy of 98.66% and an F1-Score of 98.68 were obtained.

• Journal of the Korean Society for Heat Treatment
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• v.35 no.6
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• pp.327-336
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• 2022

In this study, a model for automatically recognizing several steel parts through a camera before charging materials was developed under the assumption that the temperature distribution in the pre-air atmosphere was known. For model development, datasets were collected in random environments and factories. In this study, the YOLO-v5 model, which is a YOLO model with strengths in real-time detection in the field of object detection, was used, and the disadvantages of taking a lot of time to collect images and learning models was solved through the transfer learning methods. The performance evaluation results of the derived model showed excellent performance of 0.927 based on mAP 0.5. The derived model will be applied to the model development study, which uses the model to accurately recognize the material and then match it with the temperature distribution in the atmosphere to determine whether the material layout is suitable before charging materials.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.158-168
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• 2024

There is hardly any person in modern times who has not taken soft drinks instead of drinking water. The rate of people taking soft drinks being surprisingly high, researchers around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases and so on. Therefore, in this work an image-based tool is developed to monitor the nutritional information of soft drinks by using deep convolutional neural network with transfer learning. At first, visual saliency, mean shift segmentation, thresholding and noise reduction technique, collectively known as 'pre-processing' are adopted to extract the location of drinks region. After removing backgrounds and segment out only the desired area from image, we impose Discrete Wavelength Transform (DWT) based resolution enhancement technique is applied to improve the quality of image. After that, transfer learning model is employed for the classification of drinks. Finally, nutrition value of each drink is estimated using Bag-of-Feature (BoF) based classification and Euclidean distance-based ratio calculation technique. To achieve this, a dataset is built with ten most consumed soft drinks in Bangladesh. These images were collected from imageNet dataset as well as internet and proposed method confirms that it has the ability to detect and recognize different types of drinks with an accuracy of 98.51%.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• Educational Technology International
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• v.19 no.1
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• pp.35-60
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• 2018

The purpose of this study was to analyze the types of lesson and its effectiveness with digital textbook. To address those goals, we had observed five classes of the primary school, which designated as a research pilot school for digital textbook. Based on the result of observation, 3 types of lesson with digital textbook were categorized: Teacher-directed lecture (type 1), Blended learning (type 2), and Flipped learning (type 3). Depending on the type of lesson was analyzed the positive and negative effectiveness by means of matrix analysis method. As a result, in Teacher-directed lecture (type 1), there was found out the participation of the lesson in atmosphere of stable and comfortable as positive experience, also digital textbook operating immature and boring as negative experience. In Blended learning (type 2), there was found out the fun by sharing the product and peer feedback, and flow by learning transfer as positive experience, also digital textbook operating immature and understanding the difference between assignments as negative experience. In Flipped learning (type 3), there was shown the positive attitude and ownership in the lesson as positive experience, also distracting and boring in the lesson when learner was excluded in participation as negative experience. Based on the results, we suggested some strategies for improving positive experience and protecting negative experience in the lesson with using digital textbook.

• Journal of The Korean Association of Information Education
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• v.10 no.1
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• pp.93-104
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• 2006

The purpose of this study is to examine the effects of GLAS(Guided-Learner Adaptable Scaffolding) strategies in web-based collaborative learning environments. Through the extensive literature reviews, web-based collaborative learning environments considering GLAS types were developed. 93 sixth graders were selected from a elementary school in Seoul, and they learned in the web-based system for 4 weeks. The results revealed that the impact of scaffolding on transfer of learning, cognitive overload by reflective scaffolding, learning motivation affected intrinsic scaffolding.