Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (6)
  • Open Access


    Soil NOx Emission Prediction via Recurrent Neural Networks

    Zhaoan Wang1, Shaoping Xiao1,*, Cheryl Reuben2, Qiyu Wang2, Jun Wang2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 285-297, 2023, DOI:10.32604/cmc.2023.044366

    Abstract This paper presents designing sequence-to-sequence recurrent neural network (RNN) architectures for a novel study to predict soil NOx emissions, driven by the imperative of understanding and mitigating environmental impact. The study utilizes data collected by the Environmental Protection Agency (EPA) to develop two distinct RNN predictive models: one built upon the long-short term memory (LSTM) and the other utilizing the gated recurrent unit (GRU). These models are fed with a combination of historical and anticipated air temperature, air moisture, and NOx emissions as inputs to forecast future NOx emissions. Both LSTM and GRU models can capture the intricate pulse patterns… More >

  • Open Access


    MSCNN-LSTM Model for Predicting Return Loss of the UHF Antenna in HF-UHF RFID Tag Antenna

    Zhao Yang1, Yuan Zhang1, Lei Zhu2,*, Lei Huang1, Fangyu Hu3, Yanping Du1, Xiaowei Li1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2889-2904, 2023, DOI:10.32604/cmc.2023.037297

    Abstract High-frequency (HF) and ultrahigh-frequency (UHF) dual-band radio frequency identification (RFID) tags with both near-field and far-field communication can meet different application scenarios. However, it is time-consuming to calculate the return loss of a UHF antenna in a dual-band tag antenna using electromagnetic (EM) simulators. To overcome this, the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory (MSCNN-LSTM) for predicting the return loss of UHF antennas instead of EM simulators. In the proposed MSCNN-LSTM, the MSCNN has three branches, which include three convolution layers with different kernel sizes and numbers. Therefore, MSCNN… More >

  • Open Access


    An Improved Time Feedforward Connections Recurrent Neural Networks

    Jin Wang1,2, Yongsong Zou1, Se-Jung Lim3,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2743-2755, 2023, DOI:10.32604/iasc.2023.033869

    Abstract Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel branch was introduced for the… More >

  • Open Access


    A Novel Ultra Short-Term Load Forecasting Method for Regional Electric Vehicle Charging Load Using Charging Pile Usage Degree

    Jinrui Tang*, Ganheng Ge, Jianchao Liu, Honghui Yang

    Energy Engineering, Vol.120, No.5, pp. 1107-1132, 2023, DOI:10.32604/ee.2023.025666

    Abstract Electric vehicle (EV) charging load is greatly affected by many traffic factors, such as road congestion. Accurate ultra short-term load forecasting (STLF) results for regional EV charging load are important to the scheduling plan of regional charging load, which can be derived to realize the optimal vehicle to grid benefit. In this paper, a regional-level EV ultra STLF method is proposed and discussed. The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles, and then constructed by our collected EV charging transaction data in the field. Secondly, these usage degrees… More >

  • Open Access


    Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification

    Rakesh Kumar Pattanaik1, Mihir N. Mohanty1,*, Srikanta Ku. Mohapatra2, Binod Ku. Pattanayak3

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 195-208, 2023, DOI:10.32604/csse.2023.029591

    Abstract System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems. As most practical systems don’t have prior information about the system behaviour thus, mathematical modelling is required. The authors have proposed a stacked Bidirectional Long-Short Term Memory (Bi-LSTM) model to handle the problem of nonlinear dynamic system identification in this paper. The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions. The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways: one from the past… More >

  • Open Access


    Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI

    Ayesha Sarwar1, Kashif Javed1, Muhammad Jawad Khan1, Saddaf Rubab1, Oh-Young Song2,*, Usman Tariq3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3825-3840, 2021, DOI:10.32604/cmc.2021.016893

    Abstract Brain-Computer Interface (BCI) is a system that provides a link between the brain of humans and the hardware directly. The recorded brain data is converted directly to the machine that can be used to control external devices. There are four major components of the BCI system: acquiring signals, preprocessing of acquired signals, features extraction, and classification. In traditional machine learning algorithms, the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data. The major reason for this is, features are selected manually, and we are not able to get those features that give… More >

Displaying 1-10 on page 1 of 6. Per Page