• Journal of Digital Convergence
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• v.12 no.11
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• pp.349-355
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• 2014

In this paper, we propose the artificial vision system using human visual information processing and wavelet. Artificial vision system may be used for the visually impaired person and the machine recognition system. In this paper, we have constructed the information compression process to ganglion cells from the human retina. And we have reconstructed the primary visual information using recovery process to primary visual cortex from ganglion. Primary visual information is constructed by wavelet transformation using a high frequency and low frequency response. In the experiment, we used the faces database of AT&T. And the proposed method was able to improve the accuracy of face recognition considerably. And it was verified through experiments.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.173-178
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• 2022

Recently, a recommendation system using deep learning in social network services has been actively studied. However, in the case of a recommendation system using deep learning, the cold start problem and the increased learning time due to the complex computation exist as the disadvantage. In this paper, the user-tailored exercise routine recommendation algorithm is proposed using the user's metadata. Metadata (the user's height, weight, sex, etc.) set as the input of the model is applied to the designed model in the proposed algorithms. The exercise recommendation system model proposed in this paper is designed based on the neural collaborative filtering (NCF) algorithm using multi-layer perceptron and matrix factorization algorithm. The learning proceeds with proposed model by receiving user metadata and exercise information. The model where learning is completed provides recommendation score to the user when a specific exercise is set as the input of the model. As a result of the experiment, the proposed exercise recommendation system model showed 10% improvement in recommended performance and 50% reduction in learning time compared to the existing NCF model.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.37-45
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• 2022

As of 2020, about 500 hours of videos are uploaded to YouTube, a representative online video platform, per minute. As the number of users acquiring information through various uploaded videos is increasing, online video platforms are making efforts to provide better recommendation services. The currently used recommendation service recommends videos to users based on the user's viewing history, which is not a good way to recommend videos that deal with specific purposes and interests, such as educational videos. The recent recommendation system utilizes not only the user's viewing history but also the content features of the item. In this paper, we extract the content features of educational video for educational video recommendation based on YouTube, design a recommendation system using it, and implement it as a web application. By examining the satisfaction of users, recommendataion performance and convenience performance are shown as 85.36% and 87.80%.

• Annual Conference of KIPS
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• 2018.10a
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• pp.786-789
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• 2018

우리는 가상 현실 환경에서 사람이 직접 옷을 입지 않은 상황에서 미리 피팅 상황을 확인할 수 있는 시스템을 구현하였다. 이를 위하여 우리는 시스템을 이용한 카메라 촬영과 결과를 출력하는 기능, 그리고 영상처리에서 사람 얼굴 인지, 및 그에 대한 내용을 기반으로 옷을 오버레이하는 기능을 구현하였다. 추가 기능으로는 사람 손동작 인지를 통한 커맨드 제어, 딥러닝을 이용하여 사람의 얼굴에 어울리는 옷을 추천해주는 추천 서비스를 구현하였다.

• Annual Conference of KIPS
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• 2018.05a
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• pp.402-404
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• 2018

본 논문은 기존에 주어진 문장 다음에 올 수 있는 문장에 대해 딥러닝을 활용하여 예측하는 시스템이며, 데이터 전처리, 문장 목적 파악, 문맥 파악의 세가지 파트로 구성되어 있다. 전처리 과정에서는 문장에 쓰인 단어에 대한 품사 정보를 Input Feature 로 추가한다. 이어서 문장 목적 파악을 위해서는 상황별로 문장을 표현하는 방법이나 단어들의 순서가 다르기 때문에 단어의 순서보다는 문장의 특징점을 학습한다. 마지막으로 문맥 파악을 위해서 이전 단계에서 학습된 문장별 목적 데이터를 기반으로 데이터의 시간적 흐름에 대한 학습을 진행함으로써 이후에 나올 수 있는 문장을 예측한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.58-64
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• 2021

As the number of registered vehicles increases, traffic congestion will worsen worse, which may act as an inhibitory factor for urban social and economic development. Through accurate traffic flow prediction, various AI techniques have been used to prevent traffic congestion. This paper uses the data from a VDS (Vehicle Detection System) as input variables. This study predicted traffic flow in five levels (free flow, somewhat delayed, delayed, somewhat congested, and congested), rather than predicting traffic flow in two levels (free flow and congested). The Catboost model, which is a machine-learning algorithm, was used in this study. This model predicts traffic flow in five levels and compares and analyzes the accuracy of the prediction with other algorithms. In addition, the preprocessed model that went through RandomizedSerachCv and One-Hot Encoding was compared with the naive one. As a result, the Catboost model without any hyper-parameter showed the highest accuracy of 93%. Overall, the Catboost model analyzes and predicts a large number of categorical traffic data better than any other machine learning and deep learning models, and the initial set parameters are optimized for Catboost.

• Convergence Security Journal
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• v.21 no.4
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• pp.109-115
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• 2021

Currently, with the development of 5G and IoT technology, it is being used in connection with the things used in real life through a network. However, attempts to use networked computers for malicious purposes are increasing, and attacks using malicious codes that infringe the confidentiality and integrity of user information are becoming more intelligent. As a countermeasure to this, research is being conducted on a method of detecting malicious packets using a security control system and AI technology, supervised learning. The cyber security control system is being operated inefficiently in terms of manpower and cost. In addition, in the era of the COVID-19 pandemic, remote work has increased, making it difficult to respond immediately. In addition, malicious code detection using the existing AI technology, supervised learning, does not detect variant malicious code, and has an inaccurate malicious code detection rate depending on the quantity and quality of data. Therefore, in this study, by converging malicious packet detection technologies through various machine learning and deep learning models, the accuracy of malicious packet detection is increased, the false positive rate and the false positive rate are reduced, and a new type of malicious packet can be efficiently detected when intrusion. We propose a malicious packet detection technology.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.509-514
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• 2020

The conventional TTS system consists of several modules, including text preprocessing, parsing analysis, grapheme-to-phoneme conversion, boundary analysis, prosody control, acoustic feature generation by acoustic model, and synthesized speech generation. But TTS system with deep learning is composed of Text2Mel process that generates spectrogram from text, and vocoder that synthesizes speech signals from spectrogram. In this paper, for the optimal Korean TTS system construction we apply Tacotron2 to Tex2Mel process, and as a vocoder we introduce the methods such as WaveNet, WaveRNN, and WaveGlow, and implement them to verify and compare their performance. Experimental results show that WaveNet has the highest MOS and the trained model is hundreds of megabytes in size, but the synthesis time is about 50 times the real time. WaveRNN shows MOS performance similar to that of WaveNet and the model size is several tens of megabytes, but this method also cannot be processed in real time. WaveGlow can handle real-time processing, but the model is several GB in size and MOS is the worst of the three vocoders. From the results of this study, the reference criteria for selecting the appropriate method according to the hardware environment in the field of applying the TTS system are presented in this paper.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.161-168
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• 2019

Recently, video equipments such as CCTV, which is spreading rapidly, is being used as a means to monitor and cope with abnormal situations in almost governments, companies, and households. However, in most cases, since recognizing the abnormal situation is carried out by the monitoring person, the immediate response is difficult and is used only for post-analysis. In this paper, we present the results of the development of video surveillance system that automatically recognizing the abnormal situations and sending such events to the smartphone immediately using the latest deep learning technology. The proposed system extracts skeletons from the human objects in real time using Openpose library and then recognizes the human behaviors automatically using deep learning technology. To this end, we reconstruct Openpose library, which developed in the Caffe framework, on Darknet framework to improve real-time processing. We also verified the performance improvement through experiments. The system to be introduced in this paper has accurate and fast behavioral recognition performance and scalability, so it is expected that it can be used for video surveillance systems for various applications.