Hybrid Correction Method Research Articles

Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements.

.SignificanceFunctional near-infrared spectroscopy (fNIRS) is a promising optical neuroimaging technique, measuring the hemodynamic signals from the cortex. However, improving signal quality and reducing artifacts arising from oscillation and baseline shift (BS) are still challenging up to now for fNIRS applications.AimConsidering the advantages and weaknesses of the different algorithms to reduce the artifact effect in fNIRS signals, we propose a hybrid artifact detection and correction approach.ApproachFirst, distinct artifact detection was realized through an fNIRS detection strategy. Then the artifacts were divided into three categories: BS, slight oscillation, and severe oscillation. A comprehensive correction was applied through three main steps: severe artifact correction by cubic spline interpolation, BS removal by spline interpolation, and slight oscillation reduction by dual-threshold wavelet-based method.ResultsUsing fNIRS data acquired during whole night sleep monitoring, we compared the performance of our approach with existing algorithms in signal-to-noise ratio (SNR) and Pearson’s correlation coefficient (). We found that the proposed method showed improvements in performance in SNR and with strong stability.ConclusionsThese results suggest that the new hybrid artifact detection and correction method enhances the viability of fNIRS as a functional neuroimaging modality.

A hybrid correcting method considering heterozygous variations by a comprehensive probabilistic model

BackgroundThe emergence of the third generation sequencing technology, featuring longer read lengths, has demonstrated great advancement compared to the next generation sequencing technology and greatly promoted the biological research. However, the third generation sequencing data has a high level of the sequencing error rates, which inevitably affects the downstream analysis. Although the issue of sequencing error has been improving these years, large amounts of data were produced at high sequencing errors, and huge waste will be caused if they are discarded. Thus, the error correction for the third generation sequencing data is especially important. The existing error correction methods have poor performances at heterozygous sites, which are ubiquitous in diploid and polyploidy organisms. Therefore, it is a lack of error correction algorithms for the heterozygous loci, especially at low coverages.ResultsIn this article, we propose a error correction method, named QIHC. QIHC is a hybrid correction method, which needs both the next generation and third generation sequencing data. QIHC greatly enhances the sensitivity of identifying the heterozygous sites from sequencing errors, which leads to a high accuracy on error correction. To achieve this, QIHC established a set of probabilistic models based on Bayesian classifier, to estimate the heterozygosity of a site and makes a judgment by calculating the posterior probabilities. The proposed method is consisted of three modules, which respectively generates a pseudo reference sequence, obtains the read alignments, estimates the heterozygosity the sites and corrects the read harboring them. The last module is the core module of QIHC, which is designed to fit for the calculations of multiple cases at a heterozygous site. The other two modules enable the reads mapping to the pseudo reference sequence which somehow overcomes the inefficiency of multiple mappings that adopt by the existing error correction methods.ConclusionsTo verify the performance of our method, we selected Canu and Jabba to compare with QIHC in several aspects. As a hybrid correction method, we first conducted a groups of experiments under different coverages of the next-generation sequencing data. QIHC is far ahead of Jabba on accuracy. Meanwhile, we varied the coverages of the third generation sequencing data and compared performances again among Canu, Jabba and QIHC. QIHC outperforms the other two methods on accuracy of both correcting the sequencing errors and identifying the heterozygous sites, especially at low coverage. We carried out a comparison analysis between Canu and QIHC on the different error rates of the third generation sequencing data. QIHC still performs better. Therefore, QIHC is superior to the existing error correction methods when heterozygous sites exist.

Geometry-Load Based Hybrid Correction Method for the Pre-Deformation Design of a Steam Turbine Blade

To solve the problem of the slow convergence of the geometry-based correction (GC) method in the design of a steam turbine blade, this paper proposes a geometry-load-based hybrid correction (GLHC) method. In this method, the deformation of the blade caused by the centrifugal load is still corrected by the GC method, while the deformation caused by the aerodynamic load is corrected by the load-based correction (LC) method instead of the GC method. The LC method updates the cold shape of the blade by reversely applying the aerodynamic load to the ideal shape according to the balance between the internal force generated by the deformation of the blade and the aerodynamic load acting on surface of the hot blade shape, thereby reducing the number of iterations by reducing the shape deviation in each step of the iteration. The GLHC method, which combines the GC and LC methods, is used to improve the design process. The efficiency of the GLHC and GC methods are compared with the maximum number of position deviations of the corresponding mesh nodes between the hot blade and ideal blade shapes, which acts as the criterion. The results show that the GLHC method reduces the number of iterations.

Multiatlas Fusion with a Hybrid CT Number Correction Technique for Subject-Specific Pseudo-CT Estimation in the Context of MRI-Only Radiation Therapy

ObjectiveTo propose a hybrid multiatlas fusion and correction approach to estimate a pseudo–computed tomography (pCT) image from T2-weighted brain magnetic resonance (MR) images in the context of MRI-only radiotherapy. Materials and MethodsA set of eleven pairs of T2-weighted MR and CT brain images was included. Using leave-one-out cross-validation, atlas MR images were registered to the target MRI with multimetric, multiresolution deformable registration. The subsequent deformations were applied to the atlas CT images, producing uncorrected pCT images. Afterward, a three-dimensional hybrid CT number correction technique was used. This technique uses information about MR intensity, spatial location, and tissue label from segmented MR images with the fuzzy c-means algorithm and combines them in a weighted fashion to correct Hounsfield unit values of the uncorrected pCT images. The corrected pCT images were then fused into a final pCT image. ResultsThe proposed hybrid approach proved to be performant in correcting Hounsfield unit values in terms of qualitative and quantitative measures. Average correlation was 0.92 and 0.91 for the proposed approach by taking the mean and the median, respectively, compared with 0.86 for the uncorrected unfused version. Average values of dice similarity coefficient for bone were 0.68 and 0.72 for the fused corrected pCT images by taking the mean and the median, respectively, compared with 0.65 for the uncorrected unfused version indicating a significant bone estimation improvement. ConclusionA hybrid fusion and correction method is presented to estimate a pCT image from T2-weighted brain MR images.

A systematic evaluation of contemporary impurity correction methods in ITS-90 aluminium fixed point cells

The fixed points of the International Temperature Scale of 1990 (ITS-90) are the basis of the calibration of standard platinum resistance thermometers (SPRTs). Impurities in the fixed point material at the level of parts per million can give rise to an elevation or depression of the fixed point temperature of order of millikelvins, which often represents the most significant contribution to the uncertainty of SPRT calibrations. A number of methods for correcting for the effect of impurities have been advocated, but it is becoming increasingly evident that no single method can be used in isolation. In this investigation, a suite of five aluminium fixed point cells (defined ITS-90 freezing temperature 660.323 °C) have been constructed, each cell using metal sourced from a different supplier. The five cells have very different levels and types of impurities. For each cell, chemical assays based on the glow discharge mass spectroscopy (GDMS) technique have been obtained from three separate laboratories. In addition a series of high quality, long duration freezing curves have been obtained for each cell, using three different high quality SPRTs, all measured under nominally identical conditions. The set of GDMS analyses and freezing curves were then used to compare the different proposed impurity correction methods. It was found that the most consistent corrections were obtained with a hybrid correction method based on the sum of individual estimates (SIE) and overall maximum estimate (OME), namely the SIE/Modified-OME method. Also highly consistent was the correction technique based on fitting a Scheil solidification model to the measured freezing curves, provided certain well defined constraints are applied. Importantly, the most consistent methods are those which do not depend significantly on the chemical assay.

A new hybrid correction method for short-term load forecasting based on ARIMA, SVR and CSA

Accurate load-forecasting problem is a significant and vital issue, especially in the new competitive electricity market. The models that are employed for forecasting purposes would determine how reliable the last forecasted results are. Therefore, this paper proposes a new hybrid correction method based on autoregressive integrated moving average (ARIMA) model, support vector regression (SVR) and cuckoo search algorithm (CSA) to achieve a more reliable forecasting model. The proposed method gets use of the autocorrelation function (ACF) and the partial ACF to search the stationary or non-stationary behaviour of the investigated time series. In the case of non-stationary data, it will be differenced one or more times to become stationary. After that, Akaike information criterion is utilised to find the appropriate ARIMA model such that the linear component of the data would be captured. Therefore, the ARIMA residuals would contain the non-linear components that should be modelled by use of the SVR model. The role of CSA as a successful optimisation algorithm is to find the optimal SVR parameters for more accurate forecasting. Meanwhile, a novel self-adaptive modification method based on CSA is proposed to empower the total search ability of the algorithm effectively. The proposed method is applied to the empirical peak load data of Fars Electrical Power Company in Iran.

Development of Filtering Methods for PET Signals Contaminated by RF Pulses for Combined PET-MRI

This paper presents the development of filtering methods for positron emission tomography (PET) signals contaminated by radio frequency (RF) pulses for combined PET and clinical 3-T magnetic resonance imaging (MRI). The filtering methods include software, hardware, and hybrid correction methods. In the software correction method, PET signals are assessed, and valid signals are identified based on the characteristics of a typical PET signal using Field-Programmable Gate Array (FPGA)-based programming. The hardware correction method makes use of differential-to-single-ended and low-pass filter circuits for PET analog signals. The hybrid correction method involves the sequential application of both the hardware and software methods. Both valid and contaminated PET signals are measured with an oscilloscope. An evaluation is then made of the performance (energy resolution, photopeak channel, total counts, and coincidence timing resolution) of the PET detector modules with and without various MR sequences (gradient echo, spin echo T1 sequence). For all correction methods, the energy resolution, photopeak position, and coincidence timing resolution with MR sequences are similar (<; 3%) to those without MR sequences. However, the total count of each module depends greatly on the method applied. The hybrid correction method displays an ability to preserve (<; 1%) the total counts of the modules during various MR sequences. The results show that this filtering method, which can reject noise signals and reduce count loss while preserving the valid analog signals of MR sequences, is reliable and useful for the development of simultaneous PET-MRI.

Next Day Load Curve Forecasting Using Hybrid Correction Method

This work presents an approach for short-term load forecast problem, based on hybrid correction method. Conventional artificial neural network based short-term load forecasting techniques have limitations especially when weather changes are seasonal. Hence, we propose a load correction method by using a fuzzy logic approach in which a fuzzy logic, based on similar days, corrects the neural network output to obtain the next day forecasted load. An Euclidean norm with weighted factors is used for the selection of similar days. The load correction method for the generation of new similar days is also proposed. The neural network has an advantage of dealing with the nonlinear parts of the forecasted load curves, whereas, the fuzzy rules are constructed based on the expert knowledge. Therefore, by combining these two methods, the test results show that the proposed forecasting method could provide a considerable improvement of the forecasting accuracy especially as it shows how to reduce neural network forecast error over the test period by 23% through the application of a fuzzy logic correction. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan.