Large Scale Hotel Resource Retrieval Algorithm Based on Characteristic Threshold Extraction

At present, the hotel resource retrieval algorithm has the problem of low retrieval efficiency, low accuracy, low security and high energy consumption, and this study proposes a large scale hotel resource retrieval algorithm based on characteristic threshold extraction. In the large-scale hotel resource data, the mass sequence is decomposed into periodic component, trend component, random error component and burst component. Different components are extracted, the singular point detection is realized by the extraction results, and the abnormal data in the hotel resource data are obtained. Based on the attribute of hotel resource data, the data similarity is processed with variable window, the total similarity of data is obtained, and the abnormal detection of redundant resource data is realized. The abnormal data detection results and redundant data detection results are substituted into the space-time filter, and the data processing is completed. The retrieval problem is identified, and the data processing results are replaced in the hotel resource retrieval based on the characteristic threshold extraction to achieve the normalization of data source and rule knowledge. The characteristic threshold and retrieval strategy are determined, and data fusion reasoning is carried out. After repeated iteration, effective solutions are obtained. The effective solution is fused to get the best retrieval result. Experimental results showed that the algorithm has higher retrieval accuracy, efficiency and security coefficient, and the average search energy consumption is 56n J/bit.

An Approach to 1/f Noise Detection Based on Adaptive T-ATFPF Algorithm

The generation of 1/f noise is closely related to the quality defects of IGBT devices. In the process of detecting IGBT single tube noise, thermal noise and shot noise show obvious white noise characteristics in the low frequency band, which are detected under the background of strong white noise 1/f noise can characterize the performance of IGBT devices. Therefore, on the basis of the Time-Frequency Peak Filtering (TFPF) algorithm, a two-dimensional time-domain adaptive T-ATFPF algorithm is proposed, and the adaptive segmentation is realized by means of the confidence interval crossing criterion based on Chebyshev’s inequality. Variable window length,use a small window length to process the signal section, which retains more detailed information of the effective signal.Use a larger window length to process the buffer section to ensure a smooth transition.Use the large window length to process the noise section, which more effectively suppresses randomness for noise, apply T-ATFPF to artificial synthesis model and actual model. Experimental results indicate that compared with the conventional algorithm, the improved method can better recover 1/f noise, and the ratio of signal to noise is greatly improved by about 1.3dB.

Differential privacy EV charging data release based on variable window

In the V2G network, the release and sharing of real-time data are of great value for data mining. However, publishing these data directly to service providers may reveal the privacy of users. Therefore, it is necessary that the data release model with a privacy protection mechanism protects user privacy in the case of data utility. In this paper, we propose a privacy protection mechanism based on differential privacy to protect the release of data in V2G networks. To improve the utility of the data, we define a variable sliding window, which can dynamically and adaptively adjust the size according to the data. Besides, to allocate the privacy budget reasonably in the variable window, we consider the sampling interval and the proportion of the window. Through experimental analysis on real data sets, and comparison with two representative w event privacy protection methods, we prove that the method in this paper is superior to the existing schemes and improves the utility of the data.

IMPROVED SPECTRAL CLUSTERING APPROACH – A NEW TOOL FOR UNSUPERVISED SEISMIC FACIES ANALYSIS OF VARIABLE WINDOW LENGTH

Traditional constant time window-based waveform classification method is a robust tool for seismic facies analysis. However, when the interval thickness is seismically variable, the fixed time window is not able to contain the complete geologic information of interest. Therefore, the constant time window-based waveform classification method is inapplicable to conduct seismic facies analysis. To expand the application scope of seismic waveform classification in the strata with varying thickness, we propose a novel scheme for unsupervised seismic facies analysis of variable window length. The input of top and bottom horizons can guarantee the comprehensive geologic information of target interval. Throughout the whole workflow, we utilize DTW (Dynamic Time Warping) distance to measure the similarities between seismic waveforms of different lengths. Firstly, we improve the traditional spectral clustering algorithm by replacing the Euclidean distance with DTW-distance. Therefore, it can be applicable in the interval of variable thickness. Secondly, to solve the problem of large computation when applying the improved spectral clustering approach, we propose the method of seismic data thinning based on the technology of superpixel. Lastly, we combine these two algorithms and perform the integrated workflow of improved spectral clustering. The experiments on synthetic data show that the proposed workflow outperforms the traditional fixed time window-based clustering algorithm in recognizing the boundaries of different lithologies and lithologic associations with varying thickness. The practical application shows great promise for reservoir characterization of interval with varying thickness. The plane map of waveform classification provides convincing reference to delineate reservoir distribution of data set.

CANOPY LIDAR POINT CLOUD DATA K-MEANS CLUSTERING WATERSHED SEGMENTATION METHOD

Airborne laser LiDAR has widely applied in the accurate extraction of single tree canopy for inventory of precision forestry. Due to the over-segmentation phenomenon occurring in the traditional watershed single-wood segmentation, this paper presents a method, called K – means clustering watershed for single tree segmentation. This method consists of four aspects: The first step is to filter the point cloud to eliminate the interference factors such as ground elevation and other factors that interfere with the LiDAR point cloud segmentation; The second step is to optimize the generation of CHM, generate a CMM based on CHM variable window detection, and obtain the treetop position to provide the pixel center position for subsequent K – means cluster segmentation; The third step is to use the K – means clustering algorithm to perform initial cluster segmentation to extract the target pixels of interest. At this time, the local maximum value detected by the variable window in the second step is used as the center pixel of the cluster; In the fourth step, an improved watershed algorithm based on the similarity of 4 neighborhoods is proposed. The improved watershed algorithm is applied to the K – means initial clustering image to segment the target area, and the over-segmentation results are subsequently processed, and the over-segmentation blocks are combined according to certain criteria. Identify the contour of single canopy from the CHM images of the experimental forest data. The experimental results show that the proposed algorithm can effectively solve the over-segmentation problem happening the traditional watershed algorithm. The accuracy of F, R and P parameters can be improved by 7.1%, 11% and 9.8%.

Glycopeptide variable window SWATH for improved Data Independent Acquisition glycoprotein analysis

N-glycosylation plays an essential role in regulating protein folding and function in eukaryotic cells. Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH) has proven useful as a data independent acquisition (DIA) MS method for analysis of glycoproteins and their glycan modifications. By separating the entire m/z range into consecutive isolation windows, DIA-MS allows comprehensive MS data acquisition and high-sensitivity detection of molecules of interest. Variable width DIA windows allow optimal analyte measurement, as peptide ions are not evenly distributed across the full m/z range. However, the m/z distribution of glycopeptides is different to that of unmodified peptides because of their large glycan structures. Here, we improved the performance of DIA glycoproteomics by using variable width windows optimized for glycopeptides. This method allocates narrow windows at m/z ranges rich in glycopeptides, improving analytical specificity and performance. We show that related glycoforms must fall in separate windows to allow accurate glycopeptide measurement. We demonstrate the utility of the method by comparing the cell wall glycoproteomes of wild-type and N-glycan biosynthesis deficient yeast and showing improved measurement of glycopeptides with different glycan structures. Our results highlight the importance of appropriately optimized DIA methods for measurement of post-translationally modified peptides.

An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors

This paper proposes an improved Empirical Mode Decomposition (EMD) method by using variable window size median filters during the Intrinsic Mode Functions (IMFs) generation. Compared to the traditional EMD, the improved EMD, namely, Median EMD (MEMD), helps to reduce mode-mixing providing an improvement in terms of separating the fundamental frequencies per IMF. The MEMD method applies the EMD to the signal and then applies a variable window size median filter to the resulting IMFs. A narrow window is used for high frequency components where a broader window is used for the lower frequency components. The filtered IMFs are then summed again and another round of EMD is applied to yield the improved MEMD IMFs. A test setup for accelerated aging of bearings in induction motors is used for the comparison of the traditional and the improved EMD methods with the goal of finding potential bearing defects in an induction motor. The potential defect at the early stage is compared with the faulty state and is used to extract the characteristics of the bearing damage that develops gradually. Comparing the EMD and MEMD, it is seen that MEMD is an improvement to EMD in terms of mode-mixing problem. The MEMD method demonstrated to have better performance compared to the traditional EMD for the extraction of the fault features from the healthy operational state of the motor.