Due to the economic importance and ecological vulnerability of coastal areas, the optimization of spatial patterns is important for the sustainable development of coastal zones. In this study, a new landscape pattern ...
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Due to the economic importance and ecological vulnerability of coastal areas, the optimization of spatial patterns is important for the sustainable development of coastal zones. In this study, a new landscape pattern index gradient analysis method coupled with wavelet algorithm (GA -WA) was explored to balance the development and protection activities in the coastal zone. First, based on the classification results of remote sensing images, the gradient detection of the coastal zone was carried out using the cumulative moving window method, and the calculation of the landscape pattern index was carried out using Fragstats software to obtain the gradient change curve of the landscape pattern index. Secondly, the wavelet algorithm was applied to micro -analyze the above gradient change curves, and the results of multiscale analysis of the landscape pattern index in the study area were obtained. Finally, it is demonstrated how this method can help in practical decision making. It was found that: (1) The landscape pattern index gradient analysis method coupled with wavelet algorithms offers a new approach to balance the development and protection of coastal zones. This approach presents an innovative method for optimizing the ecological pattern of coastal zone landscapes from the perspective of macro -micro combination, which is not only applicable to coastal zones but also can be extended to other strip corridor landscapes. (2) The gradient analysis of the coastal zone landscape pattern index provides results at multiple spatial scales, which is superior to the analysis at a single spatial scale. The results of the gradient analysis method are finer and more stable, which solves the uncertainty problem of traditional landscape pattern index analysis. There is a significant difference between the landscape indices of the overall landscape and the gradient landscape, in which the AREA_MN index of the ecological land has the largest difference, reaching 110.18%. (3) Wavele
This paper describes the experimental investigation of steel pallet rack beam-to-column connec-tions. Total behavior of moment-rotation (M-& phi;) curve and the effect of particular characteristics on the behavior...
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This paper describes the experimental investigation of steel pallet rack beam-to-column connec-tions. Total behavior of moment-rotation (M-& phi;) curve and the effect of particular characteristics on the behavior of connection were studied and the associated load strain relationship and corre-sponding failure modes are presented. In this respect, an estimation of SPRBCCs moment and rotation are highly recommended in early stages of design and construction. In this study, a new approach based on Support Vector Machines (SVMs) coupled with discrete wavelet transform (DWT) is designed and adapted to estimate SPRBCCs moment and rotation according to four input parameters (column thickness, depth of connector and load, beam depth,). Results of SVM-wavelet model was compared with genetic programming (GP) and artificial neural networks (ANNs) models. Following the results, SVM-wavelet algorithm is helpful in order to enhance the accuracy compared to GP and ANN. It was conclusively observed that application of SVM-wavelet is especially promising as an alternative approach to estimate the SPRBCCs moment and rotation.
In order to effectively analyze the fault signals of non-linear and non-stationary rolling bearings and obtain high-precision fault diagnosis results of rolling bearings, the derivative and enhanced discrete analytic ...
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In order to effectively analyze the fault signals of non-linear and non-stationary rolling bearings and obtain high-precision fault diagnosis results of rolling bearings, the derivative and enhanced discrete analytic wavelet algorithm for rolling bearing fault diagnosis is studied. The morphological spectrum of fault signal is obtained by multi-scale morphological opening operation. The morphological spectrum entropy is calculated from the morphological spectrum curve to describe the morphological characteristics of different signals of rolling bearing. The morphological features of the obtained signals are decomposed by approximate analytic complex wavelet transform, rearrangement of wavelet packet subspace and cross-combination of wavelet packet subspace. The derivative and enhanced discrete analytic wavelet algorithm is used to diagnose rolling bearing faults. The experimental results show that the algorithm can accurately diagnose the fault of rolling bearings, and can completely diagnose 10 specific faults of rolling bearings. The error of diagnosing the diameter of pits is less than 0.1%. It can be applied to the actual fault diagnosis of rolling bearings.
In the present study hydraulic scaled model was conducted to evaluate an intake structure and checking its safety hydraulic performance. An investigation on the structural and mechanical equipment performance was perf...
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In the present study hydraulic scaled model was conducted to evaluate an intake structure and checking its safety hydraulic performance. An investigation on the structural and mechanical equipment performance was performed by testing a scaled model to determine discharge capacity and head losses. In addition, the novel method established on Support Vector Machines (SVM) coupled through discrete wavelet transform was designed and adapted to estimate head loss at inlet and outlet section of the horizontal intake structure. Estimation and prediction results of SVM-wavelet model was compared with genetic programming (GP) and artificial neural networks (ANNs) models. The model test results of SVM wavelet approach reveal more accuracy in prediction and also attain improved generalization capabilities than GP and ANN. Furthermore, results specified that advanced SVM-wavelet model can be applied confidently for auxiliary research to formulate predictive model for head loss at inlet and outlet section. Consequently, it was found that using of SVM-wavelet is principally encouraging as an alternate strategy to predict the head loss as a representative of inner pressure head at intake structure. (C) 2017 Elsevier B.V. All rights reserved.
Recently a novel pain recognition indicator derived from electroencephalogram(EEG) signals, pain threshold index(PTI) has been developed. The aim of this study was to determine whether PTI can be used for prediction o...
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Recently a novel pain recognition indicator derived from electroencephalogram(EEG) signals, pain threshold index(PTI) has been developed. The aim of this study was to determine whether PTI can be used for prediction of postoperative acute pain while surgical pleth index(SPI) applied as control. Eighty patients undergoing laparoscopic urological surgery under general anesthesia were enrolled. Data of SPI, PTI and a sedative index-wavelet index(WLI) were recorded within last 10 min at the end of surgery. The postoperative pain scores (NRS, numerical rating scale) were obtained. The Bland-Altman analysis was used for evaluation of consistency between PTI and SPI, whereas receiver-operating characteristic (ROC) curves was used for the mean values of PTI, SPI, and WLI to distinguish between mild (NRS 0-3) and moderate-severe (NRS 4-10) pain, and calculate their "best-fit" cut-off values. Data from 76 patients were included for final analysis. There was a good agreement between SPI and PTI values at the end of surgery. The ROC analysis showed a cut-off PTI value of 53 to discriminate between mild and moderate-to-severe pain, while SPI is 44 for this discrimination. Further analysis indicated that PTI had a best predictive accuracy reflected by highest area under curve (AUC)(0.772, 95% CI: 0.661-0.860)with sensitivity(62.50%) and specificity(90.91%) and a best positive predictive value(83.3%,95% CI: 68.4-98.2%). PTI obtained at the end of surgery, which have better predictive accuracy for postoperative pain than SPI, could differentiate the patients with moderate-to-severe pain from those with mild pain after they awaken from anesthesia. Clinical trial registrationChinese Clinical Trials Registry: ChiCTR1900024789.
A method of Rotating Machinery fault feature extraction based on wavelet transform and Hilbert demodulation is been studied. On the basis of rotating machinery fault mechanism and spectral characteristics, wavelet tra...
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ISBN:
(纸本)9783037858813
A method of Rotating Machinery fault feature extraction based on wavelet transform and Hilbert demodulation is been studied. On the basis of rotating machinery fault mechanism and spectral characteristics, wavelet transform is used to be decompose the vibration acceleration signals of bearing faults into different frequency bands, Which is then used to achieve accurate fault information by Hilbert demodulation. The result shows the method can effectively improve the frequency resolution and realize accurate extraction of fault feature, and it has certain practical value for industrial production of rotating machinery faults diagnosis when applied to the production industry.
In the present report, we describe the case of a 63-year-old man who received an inappropriate implantable cardioverter defibrillator (ICD) shock due to an epileptic seizure. He experienced an acute myocardial infarct...
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In the present report, we describe the case of a 63-year-old man who received an inappropriate implantable cardioverter defibrillator (ICD) shock due to an epileptic seizure. He experienced an acute myocardial infarction 12 months previously, and his left ventricular (LV) ejection fraction was markedly reduced (21,1%) due to the presence of advanced LV remodeling and an LV aneurysm, An implantable cardioverter-defibrillator (ICD, Medtronic Protecta XT VR) was implanted for the primary prevention of sudden cardiac death. After the implantation, ICD shock data were transmitted via a remote monitoring system. Although many episodes of tachycardia due to atrial fibrillation (AF) were detected, inappropriate discharge was avoided by the use of the wavelet(TM) morphology discrimination algorithm (Medtronic Inc., MN, USA). However, an ICD shock was inappropriately delivered for AF tachycardia accompanied by frequent noise detected in the intracardiac electrocardiogram. A detailed analysis showed that the observed noise was derived from the myopotential induced by an epileptic seizure, which overlapped with the QRS wave. This resulted in inappropriate ICD shock delivery that could not be avoided with the use of wavelet algorithm. To eliminate the involvement of the rnyopotential derived from an epileptic seizure, the nominal direction of the intracarcliac electrocardiogram was changed. This adjustment prevented inappropriate ICD shock delivery during subsequent epileptic seizures. Here, we describe for the first time a case of inappropriate ICD shock delivery induced by an epileptic seizure, suggesting a possible limitation of the wavelet discrimination algorithm. (C) 2014 Japanese Heart Rhythm Society. Published by Elsevier B.V. All rights reserved.
This paper mainly selects four diseases and pests of corn for image recognition and classification,selects wavelet neural network algorithm for image processing,and then uses yolov3 neural network for image loss itera...
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This paper mainly selects four diseases and pests of corn for image recognition and classification,selects wavelet neural network algorithm for image processing,and then uses yolov3 neural network for image loss iteration to achieve better results,Through this research,we can better understand the application and depth of convolutional neural network in the field of image *** this paper,the relevant data sets are used for neural network operation,and the data sets are processed *** the data set is obtained,the correlation degree is deleted and the wavelet algorithm is used for denoising to obtain the denoised picture and algorithm,which is equivalent to modifying and fitting the *** the appropriate convolutional neural network on the existing basis,modify the convolutional neural network into several different neural networks,and then apply the convolutional neural network to the data set to obtain the classification effect after passing through the neural network and achieve the classification *** this time,the accuracy of the effect after continuous optimization can reach%,and achieve the corresponding effect,It is consistent with the expected effect.
An unconstrained visual display terminal (VDT) visual fatigue monitoring method based on BCG is studied. A VDT visual fatigue test is designed and implemented in this study, also acceleration signals are obtained by u...
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An unconstrained visual display terminal (VDT) visual fatigue monitoring method based on BCG is studied. A VDT visual fatigue test is designed and implemented in this study, also acceleration signals are obtained by using an acceleration sensor, which is installed on the seat back. Then, BCG is obtained by adaptive threshold wavelet denoising, FFT, frequency domain filtering in the range of heart rate, and IFFT. Low frequency (LF) is extracted after the RR interval and JJ interval are calculated by the wavelet algorithm. Curves of two kinds of LF with the usage of VDT were obtained and were identical. Thus, the authors can draw conclusions that BCG can replace ECG to achieve unconstrained monitoring of VDT visual fatigue and LF gradually increases with VDT visual fatigue becoming more and more serious. Finally, we compare LF curves of BCG and ECG during use VDT to draw a conclusion that BCG can replace ECG to monitor VDT visual fatigue in an unconstrained way. Also, the confidence interval is used to verify the reliability of SD subjective evaluation, and the conclusion is reliable.
In this paper, the grounding line selection technology is applied to the field. Under a substation model, the SL-01 grounding line selection device is used for small current system, and multiple tests are carried out ...
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ISBN:
(纸本)9781538685495
In this paper, the grounding line selection technology is applied to the field. Under a substation model, the SL-01 grounding line selection device is used for small current system, and multiple tests are carried out in the field, and the corresponding waveform analysis is carried out. In this paper, the principle of small current system single-phase ground fault line selection based on transient traveling wave is studied, and the influence of neutral grounding method on traveling wave line selection method is analyzed. The corresponding device hardware and software algorithm is designed to solve the high frequency problem. Field test results and waveform analysis show that under the existing technical conditions, it is feasible to use high-speed sampling technology and wavelet algorithm for traveling wave selection. SL-01 traveling wave line selection device can meet the needs of the site.
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