Optimal Window Length Research Articles

Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control.

The brain-computer interface (BCI) plays an important role in assisting patients with amyotrophic lateral sclerosis (ALS) to enable them to participate in communication and entertainment. In this study, a novel channel projection-based canonical correlation analysis (CP-CCA) target identification method for steady-state visual evoked potential- (SSVEP-) based BCI system was proposed. The single-channel electroencephalography (EEG) signals of multiple trials were recorded when the subject is under the same stimulus frequency. The CCAs between single-channel EEG signals of multiple trials and sine-cosine reference signals were obtained. Then, the optimal reference signal of each channel was utilized to estimate the test EEG signal. To validate the proposed method, we acquired the training dataset with two testing conditions including the optimal time window length and the number of the trial of training data. The offline experiments conducted a comparison of the proposed method with the traditional canonical correlation analysis (CCA) and power spectrum density analysis (PSDA) method using a 5-class SSVEP dataset that was recorded from 10 subjects. Based on the training dataset, the online 3D-helicopter control experiment was carried out. The offline experimental results showed that the proposed method outperformed the CCA and the PSDA methods in terms of classification accuracy and information transfer rate (ITR). Furthermore, the online experiments of 3-DOF helicopter control achieved an average accuracy of 87.94 ± 5.93% with an ITR of 21.07 ± 4.42 bit/min.

Prediction of acid radical ion binding residues by K-nearest neighbors classifier

BackgroundProteins perform their functions by interacting with acid radical ions. Recently, it was a challenging work to precisely predict the binding residues of acid radical ion ligands in the research field of molecular drug design.ResultsIn this study, we proposed an improved method to predict the acid radical ion binding residues by using K-nearest Neighbors classifier. Meanwhile, we constructed datasets of four acid radical ion ligand (NO2−, CO32−, SO42−, PO43−) binding residues from BioLip database. Then, based on the optimal window length for each acid radical ion ligand, we refined composition information and position conservative information and extracted them as feature parameters for K-nearest Neighbors classifier. In the results of 5-fold cross-validation, the Matthew’s correlation coefficient was higher than 0.45, the values of accuracy, sensitivity and specificity were all higher than 69.2%, and the false positive rate was lower than 30.8%. Further, we also performed an independent test to test the practicability of the proposed method. In the obtained results, the sensitivity was higher than 40.9%, the values of accuracy and specificity were higher than 84.2%, the Matthew’s correlation coefficient was higher than 0.116, and the false positive rate was lower than 15.4%. Finally, we identified binding residues of the six metal ion ligands. In the predicted results, the values of accuracy, sensitivity and specificity were all higher than 77.6%, the Matthew’s correlation coefficient was higher than 0.6, and the false positive rate was lower than 19.6%.ConclusionsTaken together, the good results of our prediction method added new insights in the prediction of the binding residues of acid radical ion ligands.

Novel data-driven short-frequency mutual information entropy threshold filtering and its application to bearing fault diagnosis

A rotating mechanical vibration signal is time-varying and non-stationary because of the effect of defects on the rotating components and working conditions. The commonly used short-time Fourier transform (STFT) divides the time-domain signal into several segments by a sliding window. However, the STFT cannot optimize the time and frequency resolution. Considerable prior knowledge and much expertise are needed to further analyze these complex vibration signals. Therefore, a novel processing method comprising a fault vibration signal and named short-frequency mutual information entropy threshold filtering is proposed to filter and reconstruct the non-stationary vibration signal for diagnosing the impulse fault features. In this method, the improved STFT with an adaptive optimal window length is used to transform the vibration signal into the time-frequency domain. Then, a Meyer wavelet filter bank is used to divide the frequency spectrum into several sub-bands in each moment. The minimum mutual information entropy between the sub-bands and the original frequency is used to remove the partial sub-bands and filter the useful features from each frequency spectrum. Then, the frequency spectra are rearranged and an inverse STFT is used to recover the original signal. The envelope spectrum method is used to extract the fault features from the recovered signal. This method is completely determined by the characteristics of the vibration signal data. Finally, the fault vibration signal from our testing rig and wind turbine gearbox are used to verify the performance of the proposed method. The results show that this method can efficiently detect impact fault features in the vibration signal.

Optimal Window Length and Performance Analysis of FEDS Based Adaptive Beam Forming Approach over Rayleigh Fading Channel

Optimal Window Length and Performance Analysis of FEDS Based Adaptive Beam Forming Approach over Rayleigh Fading Channel

The Determination Method of Optimal Window Length in DEA Window Analysis

본 연구에서는 시계열데이터에 대한 효율성 분석방법으로 이용되는 DEA(Data Envelopment Analysis) Window 분석에서 최적 윈도우 길이를 결정할 수 있는 방법을 제시하였다. 최적 윈도우의 길이를 결정하기 위해서 효율성 값들에 대한 변별력 평가기준으로 Shannon의 엔트로피 기반중요도 지수를 이용하였으며, 실증분석을 통해 제안된 방법의 유용성을 입증하였다. 본 연구에서 제안된 최적 윈도우 길이 결정방법의 실증분석을 위하여 3개 예제가 적용되었다. 적용결과에 따르면 제안된 방법이 기존 방법보다 적용 가능성이 높은 것으로 분석되었다. Kruskal-Wallis 순위합 검정에 따르면 윈도우 길이에 따른 DMU들 간의 효율성 순위에는 차이가 없는 것으로 분석되었다. 따라서 최적 윈도우 길이 결정 알고리즘을 DEA Window분석에 이용할수 있다. 기존 연구에서는 명확한 변별력 평가기준 없이 비교대상 DMU의 수가 가장 많은 경우가 효율성값들에 대한 변별력이 높을 것이라는 가정을 하고 있다. 본 연구에서는 이런 문제를 해결하기 위해 엔트로피 기반 변별력 평가기준을 적용한 최적 윈도우의 길이를 결정할 수 있는 방법을 제안하였다. 본 연구에서 제안한 최적 윈도우 길이 결정방법을 적용한 결과에 따르면 전체 비교횟수가 가장 많은 경우보다 1회 비교횟수가 가장 많은 경우의 윈도우 길이가 최적 윈도우 길이라는 것이 입증되었다.This paper presents a method for determining the optimal window length in DEA window analysis which is used as an efficiency analysis method of time series data. To determine the optimal window length, an algorithm was presented to determine the optimal window length using Shannon’s Entropy-based importance weights as a criterion for discriminating against efficiency values, and empirical analysis proved the usefulness of the proposed method. In this paper, three examples are applied for the empirical analysis of the optimal window length determination method proposed in this study. According to the application results, the proposed method is more applicable than the existing methods. As a result of Kruskal-Wallis rank sum test, it is analyzed that there is no difference in efficiency ranking between DMU(Decision Making Units) according to window length, and the optimal window length selection algorithm can be used for DEA window analysis. In the previous study, it is assumed that the largest number of DMU in the comparison will have a high discriminative power on the efficiency values without a clear discriminant evaluation criterion. In this paper, we propose a method to determine optimal window length using Entropy based discriminant evaluation criterion to solve this problem. According to the result of applying the optimal window length determination method proposed in this study, it is proved that the window length in the case of the largest number of comparison times at one time is the optimal window length.

Determination of optimal window in spectrum analysis process with moving average method gravity data measurement

Bouguer anomaly is separated by moving average method with MATLAB 13. In moving average method there is spectrum analysis process to get optimal window length. The window length value used is 5. The depth of the regional anomaly boundary area is 1871.75 m, while the depth of the residual anomaly boundary area is 359.33 m. After anomaly separation, 2.5D modeling was made with Oasis Montaj 6.4.2. The gravity anomaly data used to construct the model is the residual gravity anomaly. The result of 2.5D modeling is seen 4 formations of rock, among others Batilembu Formation with density 2.4 gr / cc, Batumadi Formation with density 2.37 gr / cc, Tangustabun Formation 2.42 gr / cc, and Molu Complex with density 2.7 gr / cc.

An Improved Method to Determine Coda‐Q, Earthquake Magnitude, and Site Amplification: Theory and Application to Southern California

AbstractSeismic coda waves can be used to constrain attenuation, estimate earthquake magnitudes, and determine site amplification factors. We have developed a new multistation and multievent method to determine these three important seismic parameters simultaneously. We analyze 642 representative local (≤100 km) and shallow (≤20 km) earthquakes with catalog magnitudes between 1.8 and 5.4 in southern California at multiple frequency bands centered at 1.5, 3, 6, and 12 Hz. We find that the length of the moving average time window can affect the measurement of coda attenuation QC, but our tests indicate that the optimal window length is about 15 times the dominant data period. We use linear regression to fit each coda section and use only those portions that agree with the model decay rate with a correlation coefficient larger than 0.9. For a frequency‐dependent coda‐QC model (QC = Q0fn) at 1‐Hz reference frequency, our results yield estimates for Q0 and n of 107–288 and 0.42–1.14, respectively. Our coda magnitude estimates are linearly correlated with catalog magnitudes, and our observed lateral variations in coda‐QC and our site amplification factors are in general agreement with previous results, although there are notable differences at some locations. This approach provides a unified, accurate, and stable method to measure coda‐QC, earthquake magnitude, and site amplification using coda waves of locally recorded earthquakes.

A strategy of electrical energy management for internal combustion engine vehicle based on driving cycle recognition and electrical load perception

Electrical energy consumption is an important component of energy consumption for internal combustion engine vehicle, which directly affects the fuel economy. A bus-based electrical energy management system is built, and an electrical energy management strategy based on driving cycle recognition and electrical load perception is presented to achieve the refined management of vehicle energy. Six typical driving cycles are selected to establish an improved learning vector quantization neural network model for driving cycle recognition. The corresponding model training algorithm is designed by utilizing a similar driving cycle classification and the gradient optimization so that the better recognition accuracy and the less computation intensity can be obtained. An online recognition mechanism based on sliding time window is devised, and the optimal time window length is determined. To minimize fuel consumption, a dynamic optimal regulation strategy for the output power of the alternator and battery, which considers driving cycle recognition and electrical load perception, is proposed. Experimental results show that the strategy can effectually improve the vehicle fuel economy according to the driving cycle and the electrical load change and decrease the fuel consumption per 100 miles of vehicle.

Optimizing Window Length for Turbulent Heat Flux Calculations from Airborne Eddy Covariance Measurements under Near Neutral to Unstable Atmospheric Stability Conditions

Airborne eddy covariance (EC) is one of the most effective ways to directly measure turbulent flux at a regional scale. This study aims to find the optimum spatial window length for turbulent heat fluxes calculation from airborne eddy covariance measurements under near neutral to unstable atmospheric stability conditions, to reduce the negative influences from mesoscale turbulence, and to estimate local meaningful turbulent heat fluxes accurately. The airborne flux measurements collected in 2008 in the Netherlands were used in this study. Firstly, the raw data was preprocessed, including de-spike, segmentation, and stationarity test. The atmospheric stability conditions were classified as near neutral, moderately unstable, or very unstable; the stable condition was excluded. Secondly, Ogive analysis for turbulent heat fluxes from all available segmentations of the airborne measurements was used to determine the possible window length range. After that, the optimum window length for turbulent heat flux calculations was defined based on the analysis of all possible window lengths and their uncertainties. The results show that the choice of the optimum window length strongly depends on the atmospheric stability conditions. Under near neutral conditions, local turbulence is mixed insufficiently and vulnerable to heterogeneous turbulence. A relatively short window length is needed to exclude the influence of mesoscale turbulence, and we found the optimum window length ranges from 2000 m to 2500 m. Under moderately unstable conditions, the typical scale of local turbulence is relative large, and the influence of mesoscale turbulence is relatively small. We found the optimum window length ranges from 3900 m to 5000 m. Under very unstable conditions, large convective eddies dominate the transmission of energy so that the window length needs to cover the large eddies with large energy transmission. We found the optimum window length ranges from 4500 m to 5000 m. This study gives a comprehensive methodology to determine the optimizing window length in order to compromise a balance between the accuracy and the surface representativeness of turbulent heat fluxes from airborne EC measurements.

The Voice of the Heart: Vowel-Like Sound in Pulmonary Artery Hypertension.

Increased blood pressure in the pulmonary artery is referred to as pulmonary hypertension and often is linked to loud pulmonic valve closures. For the purpose of this paper, it was hypothesized that pulmonary circulation vibrations will create sounds similar to sounds created by vocal cords during speech and that subjects with pulmonary artery hypertension (PAH) could have unique sound signatures across four auscultatory sites. Using a digital stethoscope, heart sounds were recorded at the cardiac apex, 2nd left intercostal space (2LICS), 2nd right intercostal space (2RICS), and 4th left intercostal space (4LICS) undergoing simultaneous cardiac catheterization. From the collected heart sounds, relative power of the frequency band, energy of the sinusoid formants, and entropy were extracted. PAH subjects were differentiated by applying the linear discriminant analysis with leave-one-out cross-validation. The entropy of the first sinusoid formant decreased significantly in subjects with a mean pulmonary artery pressure (mPAp) ≥ 25 mmHg versus subjects with a mPAp < 25 mmHg with a sensitivity of 84% and specificity of 88.57%, within a 10-s optimized window length for heart sounds recorded at the 2LICS. First sinusoid formant entropy reduction of heart sounds in PAH subjects suggests the existence of a vowel-like pattern. Pattern analysis revealed a unique sound signature, which could be used in non-invasive screening tools.

Intermittent Fault Detection with T2 Control Chart

Multivariate statistical analysis using the Hotellings T2 statistic (T2 control chart) is one of the most effective data-driven methods for fault detection. However, existing T2 control charts in the literature mainly focus on permanent faults, and intermittent faults are relatively rarely considered. In fact, due to the small magnitude and short duration characteristics, traditional multivariate statistical methods are not sensitive to intermittent faults, resulting in much missed detection. Thus in this paper, the T2 control chart with consecutive observations is designed for permanent fault detection and intermittent fault detection respectively. In order to analyze the detection ability of proposed methods, definitions of being guaranteed detectable are introduced both for permanent faults and intermittent faults. Then, sufficient conditions for the defined fault detectability are derived for both kinds of faults respectively. Moreover, optimal window length for intermittent fault detection is discussed. Finally a simulation study is carried out to show the effectiveness of the proposed methods.

The optimal window size for analysing longitudinal networks

The time interval between two snapshots is referred to as the window size. A given longitudinal network can be analysed from various actor-level perspectives, such as exploring how actors change their degree centrality values or participation statistics over time. Determining the optimal window size for the analysis of a given longitudinal network from different actor-level perspectives is a well-researched network science problem. Many researchers have attempted to develop a solution to this problem by considering different approaches; however, to date, no comprehensive and well-acknowledged solution that can be applied to various longitudinal networks has been found. We propose a novel approach to this problem that involves determining the correct window size when a given longitudinal network is analysed from different actor-level perspectives. The approach is based on the concept of actor-level dynamicity, which captures variability in the structural behaviours of actors in a given longitudinal network. The approach is applied to four real-world, variable-sized longitudinal networks to determine their optimal window sizes. The optimal window length for each network, determined using the approach proposed in this paper, is further evaluated via time series and data mining methods to validate its optimality. Implications of this approach are discussed in this article.

Accounting for CO2 variability over East Asia with a regional joint inversion system and its preliminary evaluation

A regional surface carbon dioxide (CO2) flux inversion system, the Tan-Tracker-Region, was developed by incorporating an assimilation scheme into the Community Multiscale Air Quality (CMAQ) regional chemical transport model to resolve fine-scale CO2 variability over East Asia. The proper orthogonal decomposition-based ensemble four-dimensional variational data assimilation approach (POD-4DVar) is the core algorithm for the joint assimilation framework, and simultaneous assimilations of CO2 concentrations and surface CO2 fluxes are applied to help reduce the uncertainty in initial CO2 concentrations. A persistence dynamical model was developed to describe the evolution of the surface CO2 fluxes and help avoid the “signal-to-noise” problem; thus, CO2 fluxes could be estimated as a whole at the model grid scale, with better use of observation information. The performance of the regional inversion system was evaluated through a group of single-observation-based observing system simulation experiments (OSSEs). The results of the experiments suggest that a reliable performance of Tan-Tracker-Region is dependent on certain assimilation parameter choices, for example, an optimized window length of approximately 3 h, an ensemble size of approximately 100, and a covariance localization radius of approximately 320 km. This is probably due to the strong diurnal variation and spatial heterogeneity in the fine-scale CMAQ simulation, which could affect the performance of the regional inversion system. In addition, because all observations can be artificially obtained in OSSEs, the performance of Tan-Tracker-Region was further evaluated through different densities of the artificial observation network in different CO2 flux situations. The results indicate that more observation sites would be useful to systematically improve the estimation of CO2 concentration and flux in large areas over the model domain. The work presented here forms a foundation for future research in which a thorough estimation of CO2 flux variability over East Asia could be performed with the regional inversion system.

Methodology to determine window length for unknown target detection in electronic warfare system

To detect threat signals in electronic warfare support systems, a detector that uses a plurality of windows with various sizes should be designed such that the length of all the signal sources can be considered. Since a large number of these windows cause excessive computational complexity, the number of windows of the detector is reduced by using a small number of representative windows. In this case, since a window is dedicated to the unknown signal of a certain interval, deterioration of the detection performance is inevitable owing to the inconsistency between the lengths of the received signal and the window size. Hence, the deterioration of the detection performance should be minimised by analysing the relation between the lengths of a window and a signal. However, the conventional analysis methods of detection performance are not suitable because they are based on the premise that the lengths of the signal and window are consistent with each other. The authors propose a novel analysis method using processing gain to overcome this limitation, which can be applied irrespective of the inconsistency between the lengths of a window and a signal. Based on this analysis, they present a method to obtain an optimal window length that minimises degradation of the detection performance and subsequently verify the result using simulation.

Scanner Data Price Indexes: Addressing Some Unresolved Issues

Scanner data are increasingly being used in the calculation of price indexes such as the CPI. The preeminent approach is the RYGEKS method (Ivancic, Diewert and Fox 2011). This uses multilateral methods to construct price parities across a rolling year then links these to construct a nonrevisable index. While this approach performs well there remain some unresolved issues, in particular; the optimal window length and the linking method. In this note, these questions are addressed. A novel linking method is proposed along with the use of weighted GEKS as opposed to a fixed window. These approaches are illustrated empirically on a large scanner dataset and perform well.

The Procedure for Subspace Identification Optimal Parameters Selection in Application to the Turbine Blade Modal Analysis

This paper considers a problem of parameters selection in stochastic subspace identification algorithm (SSI). There are two user-defined parameters in SSI, such as the “window length” and the model order. To introduce the model order selection, the stabilization diagram (SD) in combination with the modal similarity index and stabilization criteria is used. Furthermore, SD is adopted to indicate spurious modes resulting from noise. Some singular spectral analysis features are used for the optimal window length selection. The algorithm has been tested with the data obtained from the turbine blade modal testing. The obtained modal parameters in combination with blade FE-model are used to simulate the dynamic response due to multiharmonic loading. Good correlation between simulation and corresponding experimental results confirms the correctness of the proposed algorithm.

Retrieval of Land Surface Model State Variables through Assimilating Screen Level Humidity and Temperature Measurements

This study demonstrates successful variational retrieval of land surface states by assimilating screen level atmospheric measurements of specific humidity and air temperature. To this end, the land surface scheme is first validated against the Oklahoma Atmospheric Surface Layer Instrumentation System measurements with necessary refinements to the forward model implemented. The retrieval scheme involves a 1D land surface-atmosphere model, the corresponding adjoint codes, and a cost function that measures residuals between observed and modeled screen level atmospheric temperature and specific humidity. The retrieval scheme is robust when subjected to observational errors with magnitudes comparable to instrument accuracy and for initial guess errors larger than typical model forecast errors. Using varying assimilation window lengths centered on different periods of a day, the sampling strategy is assessed. The daytime observations are more informative compared to nocturnal observations. An assimilation window as narrow as four hours, if centered on local noon, contains comparable information to an expanded window covering the whole day. There exists an optimal assimilation window length resulting from the contest between degrading forecast accuracy and increasing information content. For an assimilation window less than two days, the “optimal” assimilation window length is inversely proportional to the data ingesting frequency.

An improved car-following model considering relative velocity fluctuation

To explore and evaluate the impacts of relative velocity fluctuation on the dynamic characteristics and fuel consumptions of traffic flow, we present an improved car-following model considering relative velocity fluctuation based on the full velocity difference model, then we carry out several numerical simulations to determine the optimal time window length and to explore how relative velocity fluctuation affects cars’ velocity and its fluctuation as well as fuel consumptions. It can be found that the improved car-following model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, and that taking relative velocity fluctuation into account in designing the advanced adaptive cruise control strategy can improve the traffic flow stability and reduce fuel consumptions.

The Unique Heart Sound Signature of Children with Pulmonary Artery Hypertension

We hypothesized that vibrations created by the pulmonary circulation would create sound like the vocal cords during speech and that subjects with pulmonary artery hypertension (PAH) might have a unique sound signature. We recorded heart sounds at the cardiac apex and the second left intercostal space (2LICS), using a digital stethoscope, from 27 subjects (12 males) with a median age of 7 years (range: 3 months-19 years) undergoing simultaneous cardiac catheterization. Thirteen subjects had mean pulmonary artery pressure (mPAp) < 25 mmHg (range: 8-24 mmHg). Fourteen subjects had mPAp ≥ 25 mmHg (range: 25-97 mmHg). We extracted the relative power of the frequency band, the entropy, and the energy of the sinusoid formants from the heart sounds. We applied linear discriminant analysis with leave-one-out cross validation to differentiate children with and without PAH. The significance of the results was determined with a t test and a rank-sum test. The entropy of the first sinusoid formant contained within an optimized window length of 2 seconds of the heart sounds recorded at the 2LICS was significantly lower in subjects with mPAp ≥ 25 mmHg relative to subjects with mPAp < 25 mmHg, with a sensitivity of 93% and specificity of 92%. The reduced entropy of the first sinusoid formant of the heart sounds in children with PAH suggests the existence of an organized pattern. The analysis of this pattern revealed a unique sound signature, which could be applied to a noninvasive method to diagnose PAH.

Estimation of S-wave static corrections using CMP cross-correlation of surface waves

We utilize the CMP Cross-Correlation of Surface Waves (CCSW) to obtain a high-resolution S-wave velocity model for the calculation of PS receiver static corrections. We cross-correlate each trace of a shot record with a reference trace that is selected from within the shot gather based on high signal to noise ratio. New midpoints that relate to the correlated traces are then calculated. Thereafter, we calculate the phase velocity for each CMP gather, and we convert the resulting dispersion curve to an S-wave model. Moreover, we show that, to achieve a precise estimate of a dispersion curve in a CMP gather, we should only consider those traces whose offsets lay in a spatial window length. We empirically find that an optimum window length (aperture) should be about one to one and a half times of the maximum wavelength of the fundamental mode of surface wave. When the aperture is optimum, we observe a distinguishable trend of the phase velocity of each mode within the dispersion curve that avoids mode misidentification. In addition, we show the importance of wavelength-dependent aperture for estimating the phase velocity when there is a wide range of wavelength in a data set. We obtain static corrections based on an S-wave velocity model obtained from CCSW and successfully apply them to a field data.