• Journal of Platform Technology
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• v.11 no.6
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• pp.3-12
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• 2023

In today's system operation, it is difficult to detect failures and take immediate action in the case of a shortage of manpower compared to the number of equipment or failures in vulnerable time zones, which can lead to delays in failure recovery. In addition, various algorithms exist to detect abnormal symptom data, and it is important to select an appropriate algorithm for each problem. In this paper, an ensemble-based isolation forest model was used to efficiently detect multivariate point anomalies that deviated from the mean distribution in the data set generated to predict system failure and minimize service interruption. And since significant changes in memory space usage are observed together with changes in CPU usage, the problem is solved by using LSTM-Auto Encoder for a collective anomaly in which another feature exhibits an abnormal pattern according to a change in one by comparing two or more features. did In addition, evaluation indicators are set for the performance evaluation of the model presented in this study, and then AI model evaluation is performed.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.578-586
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• 2021

We propose a semi-supervised learning method based on Variational AutoEncoder (VAE) and Unsupervised Data Augmentation (UDA) to improve the performance of an end-to-end speech recognizer. In the proposed method, first, the VAE-based augmentation model and the baseline end-to-end speech recognizer are trained using the original speech data. Then, the baseline end-to-end speech recognizer is trained again using data augmented from the learned augmentation model. Finally, the learned augmentation model and end-to-end speech recognizer are re-learned using the UDA-based semi-supervised learning method. As a result of the computer simulation, the augmentation model is shown to improve the Word Error Rate (WER) of the baseline end-to-end speech recognizer, and further improve its performance by combining it with the UDA-based learning method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.174-176
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• 2022

In this paper, we present a mobile application for 'personalized customized learning' for children with articulation disorders using an artificial intelligence (AI) algorithm. A dataset (Data Set) to analyze, judge, and predict the learner's articulation situation and degree. In particular, we designed a prototype model by looking at how AI can be improved and advanced compared to existing applications from the UX/UI (GUI) aspect. So far, the focus has been on visual experience, but now it is an important time to process data and provide a UX/UI (GUI) experience to users. The UX/UI (GUI) of the proposed mobile application was to be provided according to the learner's articulation level and situation by using CRNN (Convolution Recurrent Neural Network) of DeepLearning and Auto Encoder GPT-3 (Generative Pretrained Transformer). The use of artificial intelligence algorithms will provide a learning environment with a high degree of perfection to children with articulation disorders, thereby enhancing the learning effect. I hope that you do not have any fear or discomfort in conversation by improving the perfection of articulation with 'personalized and customized learning'.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.89-103
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• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.243-252
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• 2023

Satellite imageries are at a greatest importance for land cover examining. Numerous studies have been conducted with satellite images and uses semantic segmentation techniques to extract information which has higher altitude viewpoint. The device which is taking these images must employee wireless communication links to send them to receiving ground stations. Wireless communications from a satellite are inevitably affected due to transmission errors. Evidently images which are being transmitted are distorted because of the information loss. Current semantic segmentation techniques are not made for segmenting distorted images. Traditional image enhancement methods have their own limitations when they are used for satellite images enhancement. This paper proposes an auto-encoder based image pre-enhancing method for satellite images. As a distorted satellite images dataset, images received from a real radio transmitter were used. Training process of the proposed auto-encoder was done by letting it learn to produce a proper approximation of the source image which was sent by the image transmitter. Unlike traditional image enhancing methods, the proposed method was able to provide more applicable image to a segmentation model. Results showed that by using the proposed pre-enhancing technique, segmentation results have been greatly improved. Enhancements made to the aerial images are contributed the correct assessment of land resources.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.888-893
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• 2021

The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1-2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-based denoising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. Convolutional AutoEncoder (CAE) was employed for denoising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images; the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.624-629
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• 2022

에세이의 창의성을 자동으로 분류하는 기존의 주요 연구는 말뭉치에서 빈번하게 등장하지 않는 단어에 초점을 맞추어 기계학습을 수행한다. 그러나 이러한 연구는 에세이의 주제와 상관없이 단순히 참신한 단어가 많아 창의적으로 분류되는 문제점이 발생한다. 본 논문에서는 어텐션(Attention)과 역문서 빈도(Inverse Document Frequency; IDF)를 이용하여 에세이 내용 전달에 있어 중요하면서 참신한 단어에 높은 가중치를 두는 문맥 벡터를 구하고, 자기부호화기(AutoEncoder) 모델을 사용하여 문맥 벡터들로부터 창의적인 에세이와 창의적이지 않은 에세이의 특징 벡터를 추출한다. 그리고 시험 단계에서 새로운 에세이의 특징 벡터와 비교하여 그 에세이가 창의적인지 아닌지 분류하는 딥러닝 모델을 제안한다. 실험 결과에 따르면 제안 방안은 기존 방안에 비해 높은 정확도를 보인다. 구체적으로 제안 방안의 평균 정확도는 92%였고 기존의 주요 방안보다 9%의 정확도 향상을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.708-710
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• 2021

본 논문에서는 X-ray 진단에서 산란선으로 인한 영상의 왜곡을 보정하는 방법으로서 수정된 구조의 AE(Auto-Encoder) 모델에 기반한 방법론을 제안한다. 기존 AE 모델의 계층에 따라 특징지도의 크기가 축소되고 팽창되는 과정에서 영상 복원에 필요한 정보가 소실될 가능성을 보완하기 위하여 동일 레벨 계층 간에 스킵 연결을 추가하였다. 또한 X-ray 영상에서 피사체 세부 부위의 두께와 밀도에 따라 산란선의 영향이 서로 다른 형태로 나타난다는 특성을 학습 과정에 효과적으로 반영하기 위하여 어텐션 모듈을 추가한 네트워크 구조를 도입하였다. 총 80 쌍의 흉부 X-ray 영상 데이터에 대하여 기존의 AE 모델을 사용한 방법 및 U-Net 과 FFA-Net 모델을 사용한 영상 복원 기법의 실험 결과를 상호 비교함으로써 제안된 방법의 타당성을 평가하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1134-1141
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• 1999

In this paper, we implement an optical code division multiple access(OCDMA) system and analyze the performance of the implemented system. In the implemented system, a transmitter encodes input data into optical pulses by using laser diode, and spreads the encoded pulses in an encoder which consists of 4 stage delay lines. The decoder which is the same structure as that of encoder delays and combines the spreaded pulses, and thus recovers the original data. At first, we discuss the auto- and cross-correlations of OCDMA signals under both environments of single user and multi-users, and then verify the simulation results with experimental results. We also evaluate the effect of a number of stages of delay line and code length on the system performance through computer simulations. As experimental results we can see that if the decoder have the same configuration as that of encoder, the peak auto-correlation characteristics can be achieved, and thus we can recover the original data from received data. As simulation results we can see that although bit error rate decreases as code length decreases or the number of stage of delay line increases, it is difficult to implement the system because the pulse width becomes narrow. From the results, we can apply CDMA technologies to optical communication networks.