Researching the Link Between the Geometric and R鑞yi Discord for Special Canonical Initial States Based on Neural Network Method

Li, Xiaoyü; Zhu, Qihe; Meng, Qingyu; You, Caishu; Zhu, Mingzheng; Hu, Yong; Huang, Yiming; Wu, Hao; Zheng, Dongqi

doi:10.32604/cmc.2019.06060

Cited by 10 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The convolutional layer is composed of multiple convolution kernels, and the shape and number of convolution kernels directly determine the performance of the network [13]. The parameter sharing of each convolution kernel can reduce the number of network model parameters, making the trained model stronger in generalization [14]. The convolution process is described shown in Eq.…”

Section: Convolutional Layermentioning

confidence: 99%

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Zhang¹,

Lin²,

Chen³

et al. 2022

Computer Systems Science and Engineering

Self Cite

View full text Add to dashboard Cite

Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to flight safety. On the basis of analyzing the mechanism of aero-engine surge, an aero-engine surge fault diagnosis method based on deep learning technology is proposed. In this paper, key sensor data are obtained by analyzing different engine sensor data. An aero-engine surge dataset acquisition algorithm (ASDA) is proposed to sample the fault and normal points to generate the training set, validation set and test set. Based on neural network models such as one-dimensional convolutional neural network (1D-CNN), convolutional neural network (RNN), and long-short memory neural network (LSTM), different neural network optimization algorithms are selected to achieve fault diagnosis and classification. The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis. The aero-engine surge fault diagnosis network (ASFDN) proposed in this paper achieves better results. Through training, the network achieves more than 99% classification accuracy for the test set.

show abstract

Section: Convolutional Layermentioning

confidence: 99%

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Zhang¹,

Lin²,

Chen³

et al. 2022

Computer Systems Science and Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In unclear data, they often search for patterns and connections and are specifically tailored for complex problems where there are no classical mathematical and conventional procedures or formal underlying theories. ANN differs from statistical and algorithmic techniques such as regression sampling in that ANN learns from examples to give generalized solutions [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. ANN consists of multiple layers, and, in every layer, there exist nonlinear processing and fundamental computation units called neurons that perform tasks such as feature extraction.…”

Section: Artificial Intelligence and Its Application In Shear Strementioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

Ahmed

Naseem

et al. 2021

Sensors

View full text Add to dashboard Cite

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

show abstract

“…The biggest advantage of data-based detection is that it does not need accurate mathematical or physical models, but it can detect faults by datamining. Fault detection based on machine learning [9][10][11][12] and deep learning [13][14][15] has gradually become the mainstream of current detection methods. In particular, engine fault detection based on deep learning can automatically learn predictive characteristics directly through the constructed model without relying on previous assumptions and raw data.…”

Section: Introductionmentioning

confidence: 99%

Surge Fault Detection of Aeroengines Based on Fusion Neural Network

Zheng¹,

Tang²,

Wu³

et al. 2021

Intelligent Automation &Amp; Soft Computing

Self Cite

View full text Add to dashboard Cite

Aeroengine surge fault is one of the main causes of flight accidents. When a surge occurs, it is hard to detect it in time and take anti-surge measures correctly. Recently, people have been applying detection methods based on mathematical models and expert knowledge. Due to difficult modeling and limited failure-mode coverage of these methods, early surge detection cannot be achieved.To address these problems, firstly, this paper introduced the data of six main sensors related to the aeroengine surge fault, such as, total pressure at compressor (high pressure rotor) outlet (Pt3), high pressure compressor rotor speed (N2), power level angle (PLA), etc. Secondly, aiming at preprocessing of data sets, this paper proposed a data standardization preprocessing algorithm based on batch sliding window (DSPABSW) to build a training set, validation set and test set. Thirdly, aeroengine surge fault detection fusion neural network (ASFDFNN) was provided and optimized to improve the detection accuracy of aeroengine surge faults. Finally, the experimental results showed that the model achieved 95.7%, 93.6% and 94.7% in precision rate, recall rate and F1_Score respectively and consequently it can detect the aeroengine surge fault 260 ms in advance.

show abstract

Researching the Link Between the Geometric and R鑞yi Discord for Special Canonical Initial States Based on Neural Network Method

Cited by 10 publications

References 7 publications

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Surge Fault Detection of Aeroengines Based on Fusion Neural Network

Contact Info

Product

Resources

About