• Title/Summary/Keyword: 게이트 순환 유닛

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Snoring sound detection method using attention-based convolutional bidirectional gated recurrent unit (주의집중 기반의 합성곱 양방향 게이트 순환 유닛을 이용한 코골이 소리 검출 방식)

  • Kim, Min-Soo;Lee, Gi Yong;Kim, Hyoung-Gook
    • The Journal of the Acoustical Society of Korea
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    • v.40 no.2
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    • pp.155-160
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    • 2021
  • This paper proposes an automatic method for detecting snore sound, one of the important symptoms of sleep apnea patients. In the proposed method, sound signals generated during sleep are input to detect a sound generation section, and a spectrogram transformed from the detected sound section is applied to a classifier based on a Convolutional Bidirectional Gated Recurrent Unit (CBGRU) with attention mechanism. The applied attention mechanism improved the snoring sound detection performance by extending the CBGRU model to learn discriminative feature representation for the snoring detection. The experimental results show that the proposed snoring detection method improves the accuracy by approximately 3.1 % ~ 5.5 % than existing method.

Passive sonar signal classification using attention based gated recurrent unit (어텐션 기반 게이트 순환 유닛을 이용한 수동소나 신호분류)

  • Kibae Lee;Guhn Hyeok Ko;Chong Hyun Lee
    • The Journal of the Acoustical Society of Korea
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    • v.42 no.4
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    • pp.345-356
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    • 2023
  • Target signal of passive sonar shows narrow band harmonic characteristic with a variation in intensity within a few seconds and long term frequency variation due to the Lloyd's mirror effect. We propose a signal classification algorithm based on Gated Recurrent Unit (GRU) that learns local and global time series features. The algorithm proposed implements a multi layer network using GRU and extracts local and global time series features via dilated connections. We learns attention mechanism to weight time series features and classify passive sonar signals. In experiments using public underwater acoustic data, the proposed network showed superior classification accuracy of 96.50 %. This result is 4.17 % higher classification accuracy compared to existing skip connected GRU network.

Multi-channel EEG classification method according to music tempo stimuli using 3D convolutional bidirectional gated recurrent neural network (3차원 합성곱 양방향 게이트 순환 신경망을 이용한 음악 템포 자극에 따른 다채널 뇌파 분류 방식)

  • Kim, Min-Soo;Lee, Gi Yong;Kim, Hyoung-Gook
    • The Journal of the Acoustical Society of Korea
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    • v.40 no.3
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    • pp.228-233
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    • 2021
  • In this paper, we propose a method to extract and classify features of multi-channel ElectroEncephalo Graphy (EEG) that change according to various musical tempo stimuli. In the proposed method, a 3D convolutional bidirectional gated recurrent neural network extracts spatio-temporal and long time-dependent features from the 3D EEG input representation transformed through the preprocessing. The experimental results show that the proposed tempo stimuli classification method is superior to the existing method and the possibility of constructing a music-based brain-computer interface.

A Study on Recognition of Citation Metadata using Bidirectional GRU-CRF Model based on Pre-trained Language Model (사전학습 된 언어 모델 기반의 양방향 게이트 순환 유닛 모델과 조건부 랜덤 필드 모델을 이용한 참고문헌 메타데이터 인식 연구)

  • Ji, Seon-yeong;Choi, Sung-pil
    • Journal of the Korean Society for information Management
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    • v.38 no.1
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    • pp.221-242
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    • 2021
  • This study applied reference metadata recognition using bidirectional GRU-CRF model based on pre-trained language model. The experimental group consists of 161,315 references extracted by 53,562 academic documents in PDF format collected from 40 journals published in 2018 based on rules. In order to construct an experiment set. This study was conducted to automatically extract the references from academic literature in PDF format. Through this study, the language model with the highest performance was identified, and additional experiments were conducted on the model to compare the recognition performance according to the size of the training set. Finally, the performance of each metadata was confirmed.

Novel two-stage hybrid paradigm combining data pre-processing approaches to predict biochemical oxygen demand concentration (생물화학적 산소요구량 농도예측을 위하여 데이터 전처리 접근법을 결합한 새로운 이단계 하이브리드 패러다임)

  • Kim, Sungwon;Seo, Youngmin;Zakhrouf, Mousaab;Malik, Anurag
    • Journal of Korea Water Resources Association
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    • v.54 no.spc1
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    • pp.1037-1051
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    • 2021
  • Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

A Study on Deep Learning Model for Discrimination of Illegal Financial Advertisements on the Internet

  • Kil-Sang Yoo; Jin-Hee Jang;Seong-Ju Kim;Kwang-Yong Gim
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.8
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    • pp.21-30
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    • 2023
  • The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

Forecasting of erythrocyte sedimentation rate using gated recurrent unit (GRU) neural network (Gated recurrent unit (GRU) 신경망을 이용한 적혈구 침강속도 예측)

  • Lee, Jaejin;Hong, Hyeonji;Song, Jae Min;Yeom, Eunseop
    • Journal of the Korean Society of Visualization
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    • v.19 no.1
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    • pp.57-61
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    • 2021
  • In order to determine erythrocyte sedimentation rate (ESR) indicating acute phase inflammation, a Westergren method has been widely used because it is cheap and easy to be implemented. However, the Westergren method requires quite a long time for 1 hour. In this study, a gated recurrent unit (GRU) neural network was used to reduce measurement time of ESR evaluation. The sedimentation sequences of the erythrocytes were acquired by the camera and data processed through image processing were used as an input data into the neural network models. The performance of a proposed models was evaluated based on mean absolute error. The results show that GRU model provides best accurate prediction than others within 30 minutes.

Analysis and Recognition of Depressive Emotion through NLP and Machine Learning (자연어처리와 기계학습을 통한 우울 감정 분석과 인식)

  • Kim, Kyuri;Moon, Jihyun;Oh, Uran
    • The Journal of the Convergence on Culture Technology
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    • v.6 no.2
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    • pp.449-454
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    • 2020
  • This paper proposes a machine learning-based emotion analysis system that detects a user's depression through their SNS posts. We first made a list of keywords related to depression in Korean, then used these to create a training data by crawling Twitter data - 1,297 positive and 1,032 negative tweets in total. Lastly, to identify the best machine learning model for text-based depression detection purposes, we compared RNN, LSTM, and GRU in terms of performance. Our experiment results verified that the GRU model had the accuracy of 92.2%, which is 2~4% higher than other models. We expect that the finding of this paper can be used to prevent depression by analyzing the users' SNS posts.

A study on training DenseNet-Recurrent Neural Network for sound event detection (음향 이벤트 검출을 위한 DenseNet-Recurrent Neural Network 학습 방법에 관한 연구)

  • Hyeonjin Cha;Sangwook Park
    • The Journal of the Acoustical Society of Korea
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    • v.42 no.5
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    • pp.395-401
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    • 2023
  • Sound Event Detection (SED) aims to identify not only sound category but also time interval for target sounds in an audio waveform. It is a critical technique in field of acoustic surveillance system and monitoring system. Recently, various models have introduced through Detection and Classification of Acoustic Scenes and Events (DCASE) Task 4. This paper explored how to design optimal parameters of DenseNet based model, which has led to outstanding performance in other recognition system. In experiment, DenseRNN as an SED model consists of DensNet-BC and bi-directional Gated Recurrent Units (GRU). This model is trained with Mean teacher model. With an event-based f-score, evaluation is performed depending on parameters, related to model architecture as well as model training, under the assessment protocol of DCASE task4. Experimental result shows that the performance goes up and has been saturated to near the best. Also, DenseRNN would be trained more effectively without dropout technique.

Development of Demand Forecasting Model for Public Bicycles in Seoul Using GRU (GRU 기법을 활용한 서울시 공공자전거 수요예측 모델 개발)

  • Lee, Seung-Woon;Kwahk, Kee-Young
    • Journal of Intelligence and Information Systems
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    • v.28 no.4
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    • pp.1-25
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    • 2022
  • After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.