A power waveform design based on OVSF-PPM for stress wave based wireless power transfer

Zhang, Guangmin; Yang, Ping; He, Sihong; Zheng, Yu; Song, Gangbing

doi:10.1016/j.ymssp.2020.107111

Cited by 5 publications

(4 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(5) Repeat steps (3) to (4) until the remaining number of data is less than N W , and obtain the filtered data X ′ filter . (6) Calculate the MSE of the original data X train and the filtered data X ′ filter as the loss function, and use Equation (10) to calculate the gradient value of this on the standard deviation threshold T. (7) Use the gradient value from step (6) to update the standard deviation threshold according to Equations ( 8) and ( 9). (8) Repeat steps (2) to (7), and perform multiple iterations to update until the loss function converges to a certain value and the optimal standard deviation threshold T W is obtained.…”

Section: Algorithmmentioning

confidence: 99%

“…Consequently, it is challenging to meet the real-time logging engineering requirements with this transmission rate. While stress wave-based communication using piezoceramic transducers offers higher data transmission rates [ 7 , 8 , 9 ], this technology is still in the laboratory research stage and has not yet been commercialized [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

Song,

Zhao,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

show abstract

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

Song,

Zhao,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, the operating environment of power equipment is very complicated and the quality of equipment is uneven. Therefore, in recent years, there have been many power supply and distribution accidents caused by power equipment faults, which have caused huge losses to various power supply and distribution units [11][12][13] . When the power equipment fails, it will produce a fault section with strong pertinence.…”

Section: Introductionmentioning

confidence: 99%

Design of automatic identification algorithm for double-feature fault signal waveform of power equipment

Tang,

Li,

Lei

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

The conventional automatic identification algorithm of double-feature fault signal waveform of power equipment mainly uses ART (Adaptive Resonnance Theory) network for classification and discrimination, which is easily influenced by the identification mapping relationship, resulting in low correct identification rate of fault signal waveform. Therefore, it is necessary to design a brand-new automatic identification algorithm of double-feature fault signal waveform of power equipment. That is to say, the waveform characteristics of dual-feature fault signal of power equipment are extracted, and the optimization algorithm for automatic identification of dual-feature fault signal waveform of power equipment is generated, so that the automatic identification of fault signal waveform is realized. The experimental results show that the designed double-feature fault signal waveform automatic identification algorithm for power equipment has a high correct fault identification rate, which proves that the designed double-feature fault signal waveform automatic identification algorithm for power equipment has good identification effect, reliability and certain application value, and has made certain contributions to improving the operation safety of power equipment.

show abstract

“…There are a few studies on leakage signal simulation at home and abroad, which can be divided into two groups, building a real pipe network system for leakage signal acquisition and using signal simulation software to simulate the leakage signal of complex pipe network. In terms of actual pipeline construction, Guangmin Zhang [1]. proposed a pipeline leakage positioning method that can adjust the resolution in order to improve the resolution of the pipeline leakage positioning.…”

Section: Introductionmentioning

confidence: 99%

A Pipeline Leakage Signal Simulation and Generation System

2021

View full text Add to dashboard Cite

To test the effectiveness of the detection and positioning technology of the pipe leakage, the propagation law of pipeline leakage signal is studied in this paper, and a pipeline leakage signal simulation and generation system is proposed. It can simulate the leakage pressure wave signals at different positions of the pipeline. Changing pipe’s parameters though the computer, the simulation and output of the leakage signal under various working conditions can be realized. It can test the reliability and accuracy of the detection and location technology of the pipe leakage, and verify the applicability of the pipe leakage detection and location technology to different pipe structures. The results show that the output signal of system can replace the real signal, and located the pre-set leakage point by cross-correlation method. The purpose of studying the effectiveness and accuracy of the existing leak location algorithm base on largescale complex pipe network system in laboratory conditions was realized.

show abstract

A power waveform design based on OVSF-PPM for stress wave based wireless power transfer

Cited by 5 publications

References 81 publications

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

Design of automatic identification algorithm for double-feature fault signal waveform of power equipment

A Pipeline Leakage Signal Simulation and Generation System

Contact Info

Product

Resources

About