Empirical Threshold Research Articles

Diagnostic Performance of Blood Pressure Measurement Modalities in Living Kidney Donor Candidates.

Precise BP measurement to exclude hypertension is critical in evaluating potential living kidney donors. Ambulatory BP monitoring is considered the gold standard method for diagnosing hypertension, but it is cumbersome to perform. We sought to determine whether lower BP cutoffs using office and automated BP would reduce the rate of missed hypertension in potential living donors. We measured BP in 578 prospective donors using three modalities: (1) single office BP, (2) office automated BP (average of five consecutive automated readings separated by 1 minute), and (3) ambulatory BP. Daytime ambulatory BP was considered the gold standard for diagnosing hypertension. We assessed both the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7) and the American College of Cardiology/American Heart Association (ACC/AHA) definitions of hypertension in the cohort. Empirical thresholds of office BP and automated BP for the detection of ambulatory BP-diagnosed hypertension were derived using Youden index, which maximizes the sum of sensitivity and specificity and gives equal weight to false positive and false negative values. Hypertension was diagnosed in 90 (16%) prospective donors by JNC-7 criteria and 198 (34%) prospective donors by ACC/AHA criteria. Masked hypertension was found in 3% of the total cohort by JNC-7 using the combination of office or automated BP, and it was seen in 24% by ACC/AHA guidelines. Using Youden index, cutoffs were derived for both office and automated BP using JNC-7 (<123/82 and <120/78 mm Hg) and ACC/AHA (<119/79 and <116/76 mm Hg) definitions. Using these lower cutoffs, the sensitivity for detecting hypertension improved from 79% to 87% for JNC-7 and from 32% to 87% by ACC/AHA definition, with negative predictive values of 95% and 87%, respectively. Missed (masked) hypertension was reduced to 2% and 4% of the entire cohort by JNC-7and ACC/AHA, respectively. The prevalence of hypertension was higher in living donor candidates using ACC/AHA compared JNC-7 definitions. Lower BP cutoffs in the clinic improved sensitivity and led to a low overall prevalence of missed hypertension in prospective living kidney donors.

Evaluation of Space Traffic Effects in SBUV Polar Mesospheric Cloud Data.

Water-rich rocket exhaust plumes, in particular those emitted by the National Aeronautics and Space Administration Space Shuttle, have been suggested to make a significant contribution to long-term trends in polar mesospheric cloud (PMC) ice water content. We investigate this claim using the combined Solar Backscatter Ultraviolet (SBUV) PMC data record from eight separate instruments, which includes 60 Shuttle launches during PMC seasons between 1985 and 2011. No statistically significant postlaunch signal in PMC total ice is observed based on superposed epoch analysis of the SBUV record. Only a few launches show individual peaks in total ice anomaly above the seasonal background that exceed an empirical threshold, and the maximum cumulative signature from these infrequent cases is typically less than 5% of the season total in ice mass. Other non-Shuttle launches show circumstantial evidence of possible PMC effects, although supporting evidence for plume transport is not available. We conclude that space traffic effects have been a negligible component of long-term PMC behavior.

A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation

The video-based separation of foreground (FG) and background (BG) has been widely studied due to its vital role in many applications, including intelligent transportation and video surveillance. Most of the existing algorithms are based on traditional computer vision techniques that perform pixel-level processing assuming that FG and BG possess distinct visual characteristics. Recently, state-of-the-art solutions exploit deep learning models targeted originally for image classification. Major drawbacks of such a strategy are the lacking delineation of FG regions due to missing temporal information as they segment the FG based on a single frame object detection strategy. To grapple with this issue, we excogitate a 3D convolutional neural network (3D CNN) with long short-term memory (LSTM) pipelines that harness seminal ideas, viz., fully convolutional networking, 3D transpose convolution, and residual feature flows. Thence, an FG-BG segmenter is implemented in an encoder-decoder fashion and trained on representative FG-BG segments. The model devises a strategy called double encoding and slow decoding, which fuses the learned spatio-temporal cues with appropriate feature maps both in the down-sampling and up-sampling paths for achieving well generalized FG object representation. Finally, from the Sigmoid confidence map generated by the 3D CNN-LSTM model, the FG is identified automatically by using Nobuyuki Otsu's method and an empirical global threshold. The analysis of experimental results via standard quantitative metrics on 16 benchmark datasets including both indoor and outdoor scenes validates that the proposed 3D CNN-LSTM achieves competitive performance in terms of figure of merit evaluated against prior and state-of-the-art methods. Besides, a failure analysis is conducted on 20 video sequences from the DAVIS 2016 dataset.

Integrated decision making for predictive maintenance of belt conveyor systems

Belt conveyor systems are widely utilized for continuous transport of bulk materials. Maintenance activities are essential to ensure the reliability of belt conveyor systems. Conventional diagnosis decision is achieved based on empirical constant thresholds. The Challenge of this study is to propose a framework of integrated maintenance decision making for belt conveyor idlers. Information from operational conditions, reliability estimation of idlers and condition monitoring data are integrated for accurate decision making. Innovatively, in the proposed framework threshold of the monitoring parameter can vary according to real time operational conditions and reliability estimation results. A simulation study is presented to demonstrate the effectiveness of framework. Simulation results show that the framework can result in more accurate maintenance decision making compared to conventional approaches.

Estimating the rainfall threshold of a deep-seated landslide by integrating models for predicting the groundwater level and stability analysis of the slope

Rainfall thresholds of landslides often are determined by empirical meteorological thresholds, but the reliability of this approach sometimes is limited by the lack of information about the hydrological processes that trigger landslides. Groundwater plays a critical role in triggering deep-seated landslides. In this study, we propose a methodology to estimate the rainfall threshold for a deep-seated landslide based on an integrated model that combines a model for predicting the level of the groundwater with a finite-element, strength-reduction model. First, in order to obtain more accurate results when predicting the level of the groundwater, a method is proposed to estimate the groundwater level fluctuation caused by rainfall (GLFR). Then, two different machine learning methods, i.e., the genetic algorithm back-propagation neural network (GA-BPNN) method and the genetic algorithm support vector machine (GA-SVM) method, are proposed for predicting the GLFR of the Duxiantou landslide located in Zhejiang Province, China. The results of the predictions showed that the performance of the GA-SVM model was better than that of the GA-BPNN model. Then, a series of numerical simulations was conducted to investigate the factor of safety (Fs) of the slope at different groundwater levels. At last, the probabilities of the occurrences of Duxiantou landslides for different return periods of rainfall intensity were evaluated to determine the rainfall threshold.

Rainfall threshold and soil moisture indexes for the initiation of landslide in Banjarmangu sub-district, central Java, Indonesia

Rainfall is an identified trigger of landslides, and researchers have long attempted to ensure the total precipitation that can trigger slope failures under different local climate condition, a problem of scientific interest. In this study, we propose new empirical rainfall thresholds combining with antecedent soil moisture indexes for analysing the initiation of landslides in Banjarmangu districts, located in Central Java. Daily rainfall and landslide information were obtained from Banjarnegara Geophysical Station and Regional Agency for Disaster Management (BPBD); evapotranspiration data were obtained from the United States Geological Survey (USGS). We plotted the mean rainfall intensity (mm per hour) and rainfall duration values in logarithmic function. For the results, we found that the increase of rainfall duration led the decrease of rainfall intensity defined by intensity-duration (ID) threshold I = 43.2D−0.87. In addition, we determine a formula for a cumulative rainfall threshold (CT), P3 = 120.75–0.475P15, recognized by rainfall amounts (in mm) during the last 3 days and previous 15 days, was constructed from landslides historical data from 2011 to 2017. Both thresholds have to be used with the combination of soil moisture index for failure above 0 mm at positive pore pressure for developing a landslide warning.

The wait-and-judge scenario approach applied to antenna array design

The scenario optimisation approach is a methodology for finding solutions to uncertain convex problems by resorting to a sample of data, which are called “scenarios”. In a min–max set-up, the solution delivered by the scenario approach comes with tight probabilistic guarantees on its risk defined as the probability that an empirical cost threshold will be exceeded when the scenario-based solution is adopted. While the standard theory of scenario optimisation has related the risk of the data-based solution to the number of optimisation variables, a more recent approach, called the “wait-and-judge” scenario approach, enables the user to assess the risk of the solution in a data-dependent way, based on the number of decisive scenarios (“support scenarios”). The aim of this paper is to illustrate the potentials of the wait-and-judge approach for min–max sample-based design and we shall consider to this purpose an antenna array design problem.

Processing of impedance cardiogram differential for non-invasive cardiac function detection

The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.

A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan

BackgroundDetecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems.MethodsWe applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week.ResultsThe MCM and SRM yielded complete estimates for each of Japan’s 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R2 = 0.82, p < 0.001 vs. R2 = 0.34, p < 0.001 for epidemic onset; R2 = 0.18, p < 0.001 vs. R2 = 0.05, p < 0.001 for epidemic end; R2 = 0.28, p < 0.001 vs. R2 < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM.ConclusionsThe Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.

High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold.

High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) and wavelet threshold is proposed. Firstly, EMD decomposition is performed on the output of the main accelerometer to obtain the intrinsic mode function (IMF). Then, the continuous mean square error rule is used to find energy cut-off point, and then the corresponding high frequency IMF component is denoised by wavelet threshold. Finally, the processed high-frequency IMF component is superposed with the low-frequency IMF component, and the reconstructed signal is denoised signal. Experimental results show that this method integrates the advantages of EMD and wavelet threshold and can retain useful signals to the maximum extent. The impact peak and vibration characteristics are 0.003% and 0.135% of the original signal, respectively, and it reduces the noise of the original signal by 96%.

An image layer difference index method to extract light area from NPP/VIIRS nighttime light monthly data

ABSTRACTNighttime light data have been proven to perform well for economic measurements and built-up area extractions. However, the new generation of monthly NPP/VIIRS data series products, which collected from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite contains background values, which relatively restrict the potential application of nighttime images. Therefore, the image layer difference index (LDI) was constructed to obtain high-intensity pixels that have radiance mainly caused by urban light glow. In addition, an empirical threshold was used to extract low-intensity pixels. Thirty-eight counties in Chongqing Province in China were used as an example for this experimental research. To verify the accuracy of the proposed method, a linear regression model, which was based on the quantitative statistics of the pixels, exhibited the coefficient of determination R2 values of 0.980 and 0.947. Considering the interrelation between the light intensity and economic level, statistical data of the county-level economy was also used for further confirmation; the R2 values of the linear regression between the computed indexes were 0.924 and 0.909.

Total and partial electron impact ionization cross sections of fusion-relevant diatomic molecules.

We report calculations of total (and absolute) electron-impact ionization cross sections (EICSs) for the fusion-relevant diatomic molecular species BeH, BeN, BeO, WH, WBe, WN, WO, O2, and N2 by means of the Deutsch-Märk and the binary-encounter-Bethe methods in the energy range from threshold to 10 keV. In addition, we discuss an empirical scheme to estimate partial cross sections from the total ones based on reaction energetics and empirical threshold laws and explore its accuracy by assessing available experimental data on total and partial EICSs. Finally, we also report parameters obtained by fitting the calculated cross sections to an expression commonly used in fusion edge plasma modeling.

APPLYING EMPIRICAL THRESHOLDING ALGORITHM FOR A KEYSTROKE DYNAMICS BASED AUTHENTICATION SYSTEM

Through the application of a password-based authentication technique, users are granted permission to access a secure system when the username and password matches with that logged in database of the system. Furthermore, anyone who provides the correct username and password of a valid user will be able to log in to the secure network. In current circumstances, impostors can hack the system to obtain a user’s password, while it has also been easy to find out a person’s private password. Thus, the existing structure is exceptionally flawed. One way to strengthen the password-based authentication technique, is by keystroke dynamics. In the proposed keystroke dynamics based authentication system, despite the password match, the similarity between the typing pattern of the typed password and password samples in the training database are verified. The timing features of the user’s keystroke dynamics are collected to calculate the threshold values. In this paper, a novel algorithm is proposed to authenticate the legal users based on the empirical threshold values. The first step involves the extraction of timing features from the typed password samples. The password training database for each user is constructed using the extracted features. Moreover, the empirical threshold limits are calculated from the timing features in the database. The second step involves user authentication by applying these threshold values. The experimental analyses are carried out in MATLAB simulation, and the results indicate a significant reduction in false rejection rate and false acceptance rate. The proposed methodology yields very low equal error rate of 0.5% and the authentication accuracy of 99.5%, which are considered suitable and efficient for real-time implementation. The proposed method can be a useful resource for identifying illegal invasion and is valuable in securing the system as a correlative or substitute form of client validation.

Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

Aiming at the situation that complementary ensemble empirical mode decomposition (CEEMD) noise suppression method may produce redundant noise and wavelet transform easily loses high-frequency detail information, considering wavelet packet transform can be used to perform better time-frequency localization analysis on signals containing a large amount of medium and high frequency information, according to the noise and useful signal components of both the characteristic of self-correlation function is different, the CEEMD and wavelet packet threshold jointed method is proposed. The method uses the energy concentration ratio to find noise and useful signal component demarcation point to denoise the microseismic signals. Firstly, we utilize adaptively decompose the signal from high frequency to low frequency by the CEEMD; Secondly, using the self-correlation method to select the intrinsic mode function (IMF) that needs noise suppression, the wavelet suppression method is used to suppress the noise of several high-frequency components whose self-correlation coefficient is below the critical value K; Finally, the IMF component after the wavelet packet threshold noise suppression is reconstructed with the noise-free IMF component. In order to verify the effectiveness of the proposed method on the noise suppression of microseismic signal, we added a Gaussian white noise to the Ricker wavelet signal similar to the microseismic signal. The experimental results show that the signal-to-noise ratio (SNR) of the signal is raised more than 10dB. The energy percentage is higher than 92%. In practical engineering, our proposal achieves an effective noise suppression effect on the microseismic signal.

A Fusion Methodology to Bridge GPS Outages for INS/GPS Integrated Navigation System

The performance of an inertial navigation system (INS) and global positioning system (GPS) integrated navigation system is reduced during GPS outages. To bridge GPS outages, a fusion methodology to provide pseudo GPS position information is proposed. The methodology consists of two parts, empirical mode decomposition threshold filtering (EMDTF) and a long short-term memory (LSTM) neural network. The EMDTF eliminates the noise in inertial sensors and provides more accurate data for subsequent calculations. The LSTM uses the current specific forces and angular rates to predict the pseudo GPS position during GPS outages. To evaluate the effectiveness of the proposed methodology, numerical simulations and real field tests are employed. Compared with the traditional artificial neural networks, the results illustrate the proposed methodology can significantly improve the navigation accuracy during GPS outages and the model is simpler.

Study on Threshold Selection Methods in Calculation of Ocean Environmental Design Parameters

In marine engineering design, the threshold selection is a basic and very important part for the analysis of measured data and subsequent acquisition of sample data for probability analysis. In this paper, threshold selection methods are deeply studied from the two perspectives of time domain and frequency domain, and the obtained calculation results with different thresholds are provided. First, the theory of mean residual life plot is used to quantify the threshold value of the measured tide levels of Hangzhou Bay. For the 100-year return periods design values calculated in the Gumbel distribution, the results under the threshold u = 3.9 m shows an increasing of 1.70% over those under the threshold u = 3.75 m; and the results under the threshold u = 3.75 m shows an increase of 0.97% over those under the threshold u = 3.5 m. The quantitative selection method of a threshold is simple and practical, and eliminates the tedious process of multiple trials based on empirical threshold determination. Besides, for the first time, the coherence spectrum threshold is used to explore the significant correlations of different tide level sequences in the frequency domain. Based on the threshold selection method used in the WOSA method-based coherence spectrum significance test, the coherence spectrum thresholds of different tide level in Hangzhou Bay are provided to effectively avoid the false identification of false peaks. It is suggested by the calculation results that, at the overlapping ratio of 50%, the segment number of three and the segment length of 16, and the spectral analysis shows that the extreme tide level of Hangzhou Bay has a vibration period of 21.3 years.

Advanced methodology for multiple sclerosis lesion exploring: Towards a computer aided diagnosis system

Abstract The exploration and diagnosis of severe neuropathologies is one of the essential tasks that could be reinforced and aided by advanced techniques in medical imagery. Cortex lesions and tumors are examples of pathologies where Magnetic Resonance Imagery modalities are the essential tools for exploration and tracking. Our main added value in this research field was focused on Multiple Sclerosis lesions exploration aiming to offer a clinical computer aided tool capable of helping clinicians during their daily explorations. An automatic method for brain segmentation and Multiple Sclerosis lesion exploration was in fact attentively conceived and tested. Firstly, the brain segmentation was performed using an atlas based Gaussian mixture model, followed by the calculation of a probabilistic lesion map on which an empirical threshold, characterized by a set of constraints, was applied together with a lesion refinement algorithm in order to create a binary lesion mask. The approach was applied to four clinical databases. Compared to various existing methods, the approach showed its efficiency in differentiating normal brain tissues with averages of 0.78 ± 0.04 , 0.85 ± 0.04 and 0.89 ± 0.04 for Dice, sensitivity and specificity, respectively. Besides, it proved the possibility of accurately identifying lesions even in noisy images, and low lesion load, giving averages of 0.75 ± 0.04 , 0.90 ± 0.08 and 0.75 ± 0 . 12 for the previous metrics, respectively. Furthermore, it is able to distinguish between the lesion center and the contiguous dirty white matter, which was carefully validated by clinicians. As a perspective, our approach could be extended to explore other neuropathologies such as Alzheimer’s or Parkinson’s diseases.

The HOMA-IR Performance to Identify New Diabetes Cases by Degree of Urbanization and Altitude in Peru: The CRONICAS Cohort Study.

Aims Prognostic thresholds to identify new type 2 diabetes mellitus (T2DM) cases using the HOMA-IR have not been defined. We studied the HOMA-IR performance to identify incident T2DM cases and to assess if the thresholds varied according to urbanization and altitude in Peru. Methods Longitudinal analysis. The outcome was incident T2DM cases: self-report diagnosis and fasting glucose. The exposure was the HOMA-IR. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was estimated with 95% confidence intervals (95% CIs). Results are presented overall and stratified by study site (Lima, Tumbes, urban Puno, and rural Puno), rurality (urban, semiurban, and rural), and altitude (low and high). Results A total of 3120 participants (mean age: 55.6 years, 51.2% females) contributed data to this analysis. The median baseline HOMA-IR was 1.7 (IQR 1.0–2.9), with median values ranging from 1.1 in rural Puno to 2.0 in Lima and Tumbes (p < 0.001). Overall for incident T2DM, the AUC was 0.69 (95% CI: 0.64–0.74) with an empirical threshold of 2.8 yielding a positive likelihood ratio of 2.30 and a negative one of 0.61; the positive and negative predictive values were 14.6% and 95.7%, respectively. The empirical thresholds varied within the variables of interest, for example, from 0.9 in urban Puno to 2.9 in Lima. Conclusions Using the HOMA-IR to identify incident T2DM cases seems to yield moderate accuracy. The HOMA-IR could help improve identifying people at high risk of T2DM.

Wavelet methods for studying the onset of strong plasma turbulence

Recent simulations have demonstrated that coherent current sheets dominate the kinetic-scale energy dissipation in strong turbulence of magnetized plasma. Wavelet basis functions are a natural tool for analyzing turbulent flows containing localized coherent structures of different spatial scales. Here, wavelets are used to study the onset and subsequent transition to fully developed turbulence from a laminar state. Originally applied to neutral fluid turbulence, an iterative wavelet technique decomposes the field into coherent and incoherent contributions. In contrast to Fourier power spectra, finite time Lyapunov exponents, and simple measures of intermittency such as non-Gaussian statistics of field increments, the wavelet technique is found to provide a quantitative measure for the onset of turbulence and to track the transition to fully developed turbulence. The wavelet method makes no assumptions about the structure of the coherent current sheets or the underlying plasma model. Temporal evolution of the coherent and incoherent wavelet fluctuations is found to be highly correlated (a Pearson correlation coefficient of &amp;gt;0.9) with the magnetic field energy and plasma thermal energy, respectively. The onset of turbulence is identified with the rapid growth of a background of incoherent fluctuations spreading across a range of scales and a corresponding drop in the coherent components. This is suggestive of the interpretation of the coherent and incoherent wavelet fluctuations as measures of coherent structures (e.g., current sheets) and dissipation, respectively. The ratio of the incoherent to coherent fluctuations Ric is found to be fairly uniform in the turbulent state across different plasma models and provides an empirical threshold of ∼0.1 for turbulence onset. The utility of this technique is illustrated through examples. First, it is applied to the Kelvin–Helmholtz instability from different simulation models including fully kinetic, hybrid (kinetic ion/fluid electron), and Hall MHD simulations. Second, the wavelet diagnostic is applied to the development of turbulence downstream of the bowshock in a global magnetosphere simulation. Finally, the wavelet technique is also shown to be useful as a de-noising method for particle simulations.

A Novel Method to Extract Narrow Water Using a Top-Hat White Transform Enhancement Technique

A novel water extraction method is proposed to improve the ability of delineation of the narrow water for the modified normalized difference water index (MNDWI) derived from remotely sensed images. This method introduces a morphological white top-hat transforming operation on the MNDWI to construct a morphological narrow water index (MNWI). The MNWI can effectively enhance the local contrast of linear objects, such that the narrow water can be easily separated from mountain shadows, bare patches and other land covers in the MNDWI image. Furthermore, a dual-threshold segmentation method was also used to extract the potential water bodies by combining an empirical threshold segmentation on the MNDWI and an automatic threshold segmentation on the MNWI. The effectiveness of the proposed method was validated with three experimental datasets, clipped from two different Landsat images, and the results demonstrate that the narrow water bodies can be effectively extracted with the overall accuracy being over 90%. Visually, most of the narrow streams or rivers keep continuous shapes in space and their water boundaries are also precisely delineated.