• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.49-54
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• 2007

Structural integrity assessment technique for pavement system is studied considering the uncertainties among the material properties. The artificial neural networks technique is applied for the inverse analysis to estimate the elastic modulus based on the measured deflections from the FWD test. A computer code based on the spectral element method was developed for the accurate and fast analysis of the multi-layered soil structures, and the developed program was used for generating the training and testing patterns for the neural network. Neural networks was applied to estimate the elastic modulus of pavement system using the maximum deflections with and without the uncertainties in the material properties. It was found that the estimation results by the conventiona1 neural networks were very poor when there exist the uncertainties and the estimation results could be significantly improved by adopting the proposed method for generating training patterns considering the uncertainties among material properties.

• Journal of The Korea Institute of Healthcare Architecture
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• v.15 no.2
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• pp.51-58
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• 2009

These days the trend of special education is changing from special school-based education to special class room based education, and from separated education to integrated education. In accordance with this change, special classes should be planned for multi purposes so that the class room can be used for the place of teaching and learning, guidance, job education. This research surveyed the special teachers working for 937 schools which have special classes(elementary 631, middle 217 high school 89). The result of this survey shows the different responses according to the level of the schools. For education activities, elementary and middle schools put emphasis on curriculum rather than guidance. High education, elementary school should have the places for teaching and learning, student management, play ground. Middle schools give priority to the places for individual learning, computer and practical training. High schools value the places for job education and practical training above for learning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9A
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• pp.882-890
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• 2008

To overcome the limit of the time-domain based channel estimation caused by the ADC speed, this paper present a new BPM-UWB receiver where the channel estimations and the compensations are digitally performed in the frequency domain. We theoretically show that the channel estimation can be accomplished by exploiting the periodicity of a training sequence consisting of finite number of pulses. We also present the digital receiver structure to implement the proposed system and derive its BER performances. Through computer simulations, we show the proposed receiver can dramatically improve the BER performances due to the incorporation of the estimated channel frequency response.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.2
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• pp.131-139
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• 2019

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.475-482
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• 2018

In this paper, we present a study on data augmentation methods for DNN (Deep Neural Network)-based audio tagging. In this system, an audio signal is converted into a mel-spectrogram and used as an input to the DNN for audio tagging. To cope with the problem associated with a small number of training data, we augment the training samples using time stretching, pitch shifting, dynamic range compression, and block mixing. In this paper, we derive optimal parameters and combinations for the augmentation methods through audio tagging simulations.

• Smart Media Journal
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• v.13 no.8
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• pp.16-25
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• 2024

Recently, there has been a growing emphasis on identifying both known and unknown diseases in plant disease recognition. In this task, a model trained only on images of known classes is required to classify an input image into either one of the known classes or into an unknown class. Consequently, the capability to recognize unknown diseases is critical for model deployment. To enhance this capability, we are considering three factors. Firstly, we propose a new logits-based scoring function for unknown scores. Secondly, initial experiments indicate that a compact feature space is crucial for the effectiveness of logits-based methods, leading us to employ the AM-Softmax loss instead of Cross-entropy loss during training. Thirdly, drawing inspiration from the efficacy of transfer learning, we utilize a large plant-relevant dataset, PlantCLEF2022, for pre-training a model. The experimental results suggest that our method outperforms current algorithms. Specifically, our method achieved a performance of 97.90 CSA, 91.77 AUROC, and 90.63 OSCR with the ResNet50 model and a performance of 98.28 CSA, 92.05 AUROC, and 91.12 OSCR with the ConvNext base model. We believe that our study will contribute to the community.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.83-91
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• 2021

In this paper, presents an efficient education method for training specialized edutainment SW education instructors and drone realistic media creators, not just training to acquire certificates through drone manipulation training. To this end, the NCS-based curriculum was derived. The developed curriculum includes the edutainment drone curriculum and the realistic media creator curriculum. Among them, core responsibilities were defined for the drone control curriculum and core tasks, knowledge, and attitudes were described for each. After that, a detailed curriculum for drone control was derived. In the realistic media creator curriculum, pilot education was conducted to actually produce advertisement videos to foster experts who can work directly in the industrial field. Finally, through holding an online conference in a metaverse environment, a virtual conference was operated to share and discuss media videos produced by trainees. After the end of education, the efficiency of this curriculum was proved through education satisfaction analysis for 46 education graduates. This paper presented a method to achieve internalization of SW education in non-face-to-face online education that our society must solve after post-COVID-19. In addition, an efficient educational method in a realistic media environment was suggested by showing a realistic media creator training curriculum, pilot programs, and metaverse conference management cases.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.27-35
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• 2021

Automatic post editing is a research field that aims to automatically correct errors in machine translation results. This research is mainly being focus on high resource language pairs, such as English-German. Recent APE studies are mainly adopting transfer learning based research, where pre-training language models, or translation models generated through self-supervised learning methodologies are utilized. While translation based APE model shows superior performance in recent researches, as such researches are conducted on the high resource languages, the same perspective cannot be directly applied to the low resource languages. In this work, we apply two transfer learning strategies to Korean-English APE studies and show that transfer learning with translation model can significantly improves APE performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.736-738
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• 2014

As part of real-time streaming service, which is one of the latest QoS technology has become the issue. But the current majority of streaming services are specific S/W I a separate setup program supports real-time streaming service a reality, will be stored until the end of the video, save for a video about editing and is not available. In this paper, a video about multi-threaded and distributed processing system applied to the Storm technique based on separate software or installation of programs without the H T M L 5-based Web content is produced by each device using a Web browser, real-time streaming system you want, no. This is a streaming service that provides users with real-time editing and the footage is stored as about and respond in real time between the server and the client and content sharing that need training and will be utilized in the field of multimedia streaming.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.17-24
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• 2022

The article proves that the main goal of informatization of higher education institutions in the EU countries is to improve the quality of education of future specialists by introducing digital educational resources into the education process. The main tasks of informatization of education are defined. Digital educational resources are interpreted as a set of data in digital form that is applicable for use in the learning process; it is an information source containing graphic, text, digital, speech, music, video, photo and other information aimed at implementing the goals and objectives of modern education; educational resources on the Internet, electronic textbooks, educational programs, electronic libraries, etc. The creation of digital educational resources is defined as one of the main directions of informatization of all forms and levels of Education. Types of digital educational resources by educational functions are considered. The factors that determine the effectiveness of using digital educational resources in the educational process are identified. The use of digital educational resources in the training of future specialists in the EU countries is considered in detail. European countries note that digital educational resources in professional use allow you to implement a fundamentally new approach to teaching and education, which is based on broad communication, free exchange of opinions, ideas, information of participants in a joint project, on a completely natural desire to learn new things, expand their horizons; is based on real research methods (scientific or creative laboratories), allowing you to learn the laws of nature, the basics of techniques, technology, social phenomena in their dynamics, in the process of solving vital problems, features of various types of creativity in the process of joint activities of a group of participants; promotes the acquisition by teachers of various related skills that can be very useful in their professional activities, including the skills of using computer equipment and various digital technologies.