RCEN: A Deep-Learning-Based Background Noise Suppression Method for DAS-VSP Records

Zhong, Tie; Cheng, Ming; Lu, Shaoping; Dong, Xintong; Li, Yue

doi:10.1109/lgrs.2021.3127637

Cited by 34 publications

(17 citation statements)

References 13 publications

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“…Although the performance of NN-based DMs has been satisfactory, there are some shortcomings. e first issue is that the decision-maker often evaluates his preferences indirectly and imprecisely, while explicit and accurate values are needed for neural network training [32,33]. e reason for using the neural network in obtaining the decision maker's preferences is to avoid any previous assumptions and maximize the flexibility of this process [34].…”

Section: Introductionmentioning

confidence: 99%

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Hong

Zhao

Kausar

et al. 2022

Computational Intelligence and Neuroscience

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This paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods.

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Section: Introductionmentioning

confidence: 99%

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Hong

Zhao

Kausar

et al. 2022

Computational Intelligence and Neuroscience

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“…However, a large amount of data during the operation of the CNN will cause hardware dependencies such as the central processing unit (CPU), trying to improve the CNN algorithm for optimization. Additionally, in the process of identifying university archives, this study is carried out under the guarantee that the fonts are not polluted, and the altered fonts are not identified and located [27][28][29][30][31][32][33]. It is hoped that the accuracy of CNN in image and font recognition will be increased, so as to be accurately recognized in the archives management system of colleges and universities.…”

Section: Discussionmentioning

confidence: 99%

University Archives Autonomous Management Control System under the Internet of Things and Deep Learning Professional Certification

Dai

Ding

2022

Computational Intelligence and Neuroscience

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The current work aims to meet the needs of the development of archives work in colleges and universities and the modernization of management to realize the standards and standardization of all aspects of archives business construction in colleges and universities, so as to improve the political and professional quality of archives cadres. First, the radio frequency identification (RFID) technology based on the Internet of things (IoT) digitizes the university archive labels. Meanwhile, the filing cabinet's intelligent security system preserves confidential files. Second, the convolutional neural network (CNN) algorithm under deep learning is introduced and college profile information is identified. Finally, the concept of professional certification is used to clarify the purpose of the university archives automation management system. Different activation functions are used to analyze the recognition accuracy loss and recognition accuracy of university archives. The identification error of You Only Look Once (YOLO) of the ReLU-convolutional neural network (R–CNN) of college archives is analyzed. The results show that the selection of rectified linear units (ReLU) activation function for CNN can effectively reduce the loss of identification accuracy of college archives and can improve the accuracy of identification of college archives. The algorithm based on the ReLU activation function has a smaller recognition error accuracy in college archives than that of the YOLO algorithm. The recognition error of the YOLO algorithm is slightly higher than that of the R–CNN. The font recognition error of archival information based on the R–CNN is relatively large. However, the conclusion is reasonable due to the recognition difficulties of handwritten archival fonts. The file positioning recognition error rate is 19.00%, the file printing font recognition error rate is 4.75%, and the image recognition error rate is 1.90%. These results have a certain reference value for the process of identifying information in the automatic management of university archives by CNN under different activation functions.

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“…The deep learning methods, fuzzy systems and ANNs are widely used for modelling and forecasting problems [19][20][21]. In a few studies, these methods have also been applied to load forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting

Tian

Firouzi

et al. 2022

Sustainability

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A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%.

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RCEN: A Deep-Learning-Based Background Noise Suppression Method for DAS-VSP Records

Cited by 34 publications

References 13 publications

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

University Archives Autonomous Management Control System under the Internet of Things and Deep Learning Professional Certification

A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting

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