• Title/Summary/Keyword: long term neural network

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A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets

  • Hussain, Syed Nazir;Aziz, Azlan Abd;Hossen, Md. Jakir;Aziz, Nor Azlina Ab;Murthy, G. Ramana;Mustakim, Fajaruddin Bin
    • Journal of Information Processing Systems
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    • v.18 no.1
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    • pp.115-129
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    • 2022
  • Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

Artificial Neural Network Analysis for Prediction of Community Care Design Research in Spatial and Environmental Areas in Korea

  • Yumi, Jang;Jiyoung An;Jinkyung Paik
    • International Journal of Advanced Culture Technology
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    • v.11 no.2
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    • pp.249-255
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    • 2023
  • This study aims to empirically confirm the effect and impact of community care design research centered on domestic space and environment on health promotion, diagnosis treatment, disease management, rehabilitation, and mitigation through the year of publication and perspective. To this end, based on 1,227 space and environment design studies from 2,144 community care design research data conducted for about 20 years from 2002 to 2022, when care services began in earnest through the long-term care system for the elderly, SPSS 26.0 was used to create a 'Multi-layer Perceptron' artificial neural network structure model was predicted and neural network analysis was performed. Research Results First, as a result of checking studies in each field of health care by year, there is a significant difference with the number of studies related to health promotion being the highest. Second, the five perspectives are region, time, dimension, function, and content perspective. As a result of inputting these variables as independent variables and analyzing their importance in the artificial neural network, the function perspective had the most influence, followed by the region > content > dimension > time perspective.

CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data

  • Cao, Kerang;Kim, Hangyung;Hwang, Chulhyun;Jung, Hoekyung
    • Journal of Information Processing Systems
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    • v.14 no.6
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    • pp.1508-1520
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    • 2018
  • In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

A Text Sentiment Classification Method Based on LSTM-CNN

  • Wang, Guangxing;Shin, Seong-Yoon;Lee, Won Joo
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.12
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    • pp.1-7
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    • 2019
  • With the in-depth development of machine learning, the deep learning method has made great progress, especially with the Convolution Neural Network(CNN). Compared with traditional text sentiment classification methods, deep learning based CNNs have made great progress in text classification and processing of complex multi-label and multi-classification experiments. However, there are also problems with the neural network for text sentiment classification. In this paper, we propose a fusion model based on Long-Short Term Memory networks(LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification. This method will become an important way to optimize the model and improve the performance of the model.

A Study on H-CNN Based Pedestrian Detection Using LGP-FL and Hippocampal Structure (LGP-FL과 해마 구조를 이용한 H-CNN 기반 보행자 검출에 대한 연구)

  • Park, Su-Bin;Kang, Dae-Seong
    • The Journal of Korean Institute of Information Technology
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    • v.16 no.12
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    • pp.75-83
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    • 2018
  • Recently, autonomous vehicles have been actively studied. Pedestrian detection and recognition technology is important in autonomous vehicles. Pedestrian detection using CNN(Convolutional Neural Netwrok), which is mainly used recently, generally shows good performance, but there is a performance degradation depending on the environment of the image. In this paper, we propose a pedestrian detection system applying long-term memory structure of hippocampal neural network based on CNN network with LGP-FL (Local Gradient Pattern-Feature Layer) added. First, change the input image to a size of $227{\times}227$. Then, the feature is extracted through a total of 5 layers of convolution layer. In the process, LGP-FL adds the LGP feature pattern and stores the high-frequency pattern in the long-term memory. In the detection process, it is possible to detect the pedestrian more accurately by detecting using the LGP feature pattern information robust to brightness and color change. A comparison of the existing methods and the proposed method confirmed the increase of detection rate of about 1~4%.

Comparison of the effectiveness of various neural network models applied to wind turbine condition diagnosis (풍력터빈 상태진단에 적용된 다양한 신경망 모델의 유효성 비교)

  • Manh-Tuan Ngo;Changhyun Kim;Minh-Chau Dinh;Minwon Park
    • Journal of Korea Society of Industrial Information Systems
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    • v.28 no.5
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    • pp.77-87
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    • 2023
  • Wind turbines playing a critical role in renewable energy generation, accurately assessing their operational status is crucial for maximizing energy production and minimizing downtime. This study conducts a comparative analysis of different neural network models for wind turbine condition diagnosis, evaluating their effectiveness using a dataset containing sensor measurements and historical turbine data. The study utilized supervisory control and data acquisition data, collected from 2 MW doubly-fed induction generator-based wind turbine system (Model HQ2000), for the analysis. Various neural network models such as artificial neural network, long short-term memory, and recurrent neural network were built, considering factors like activation function and hidden layers. Symmetric mean absolute percentage error were used to evaluate the performance of the models. Based on the evaluation, conclusions were drawn regarding the relative effectiveness of the neural network models for wind turbine condition diagnosis. The research results guide model selection for wind turbine condition diagnosis, contributing to improved reliability and efficiency through advanced neural network-based techniques and identifying future research directions for further advancements.

Classification of Behavior of UTD Data using LSTM Technique (LSTM 기법을 적용한 UTD 데이터 행동 분류)

  • Jeung, Gyeo-wun;Ahn, Ji-min;Shin, Dong-in;Won, Geon;Park, Jong-bum
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2018.10a
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    • pp.477-479
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    • 2018
  • This study was carried out to utilize LSTM(Long Short-Term Memory) technique which is one kind of artificial neural network. Among the 27 types of motion data released by the UTD(University of Texas at Dallas), 3-axis acceleration and angular velocity data were applied to the basic LSTM and Deep Residual Bidir-LSTM techniques to classify the behavior.

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Multi-layered attentional peephole convolutional LSTM for abstractive text summarization

  • Rahman, Md. Motiur;Siddiqui, Fazlul Hasan
    • ETRI Journal
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    • v.43 no.2
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    • pp.288-298
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    • 2021
  • Abstractive text summarization is a process of making a summary of a given text by paraphrasing the facts of the text while keeping the meaning intact. The manmade summary generation process is laborious and time-consuming. We present here a summary generation model that is based on multilayered attentional peephole convolutional long short-term memory (MAPCoL; LSTM) in order to extract abstractive summaries of large text in an automated manner. We added the concept of attention in a peephole convolutional LSTM to improve the overall quality of a summary by giving weights to important parts of the source text during training. We evaluated the performance with regard to semantic coherence of our MAPCoL model over a popular dataset named CNN/Daily Mail, and found that MAPCoL outperformed other traditional LSTM-based models. We found improvements in the performance of MAPCoL in different internal settings when compared to state-of-the-art models of abstractive text summarization.

Unraveling Emotions in Speech: Deep Neural Networks for Emotion Recognition (음성을 통한 감정 해석: 감정 인식을 위한 딥 뉴럴 네트워크 예비 연구)

  • Edward Dwijayanto Cahyadi;Mi-Hwa Song
    • Annual Conference of KIPS
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    • 2023.05a
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    • pp.411-412
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    • 2023
  • Speech emotion recognition(SER) is one of the interesting topics in the machine learning field. By developing SER, we can get numerous benefits. By using a convolutional neural network and Long Short Term Memory (LSTM ) method as a part of Artificial intelligence, the SER system can be built.

Drought Forecasting Using the Multi Layer Perceptron (MLP) Artificial Neural Network Model (다층 퍼셉트론 인공신경망 모형을 이용한 가뭄예측)

  • Lee, Joo-Heon;Kim, Jong-Suk;Jang, Ho-Won;Lee, Jang-Choon
    • Journal of Korea Water Resources Association
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    • v.46 no.12
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    • pp.1249-1263
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    • 2013
  • In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.