Threshold Estimation Method Research Articles

Novel empirical curvelet denoising strategy for suppressing mixed noise of microseismic data

We present a novel denoising strategy based on empirical curvelet transform (ECT) for noisy microseismic data. Our approach can simultaneously suppress high-frequency, low-frequency, and shared-bandwidth noises and preserve detailed information on the noisy microseismic data. Initially, we design a new threshold estimation method by adding a scale factor for ECT threshold denoising. Subsequently, we construct an adaptive parameter model employing the similarity standard deviation for the non-local means (NLM) algorithm. Then, we divide the coefficients obtained from the ECT decomposition into two sets based on the energy spectrum, subjecting each set to improved adaptive thresholding and improved NLM denoising algorithms. Eventually, we reconstruct the denoised signals using the empirical curvelet inverse transform. Our results demonstrate that under a signal-to-noise ratio (SNR) of −10 dB, the proposed strategy achieves a correlation coefficient of 0.9524, a root mean square error of 0.198, an SNR of 1.36 dB, and reduces the first arrival picking error to 0.00382 s. Furthermore, application on the real microseismic data further confirms that the proposed method can clarify the corresponding first arrival.

Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier

Automatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining their utility in observing brain development from infancy to late adulthood. In our research, we introduce a novel approach for segmenting and classifying neonatal brain images. Our methodology capitalizes on minimum spanning tree (MST) segmentation employing the Manhattan distance, complemented by a shrunken centroid classifier empowered by the Brier score. This fusion enhances the accuracy of tissue classification, effectively addressing the complexities inherent in age-specific segmentation. Moreover, we propose a novel threshold estimation method utilizing the Brier score, further refining the classification process. The proposed approach yields a competitive Dice similarity index of 0.88 and a Jaccard index of 0.95. This approach marks a significant step toward neonatal brain tissue segmentation, showcasing the efficacy of our proposed methodology in comparison to the latest cutting-edge methods.

Threshold Effect of Financial Inclusion on Poverty Reduction in Africa: Does institutional quality play a role?

ABSTRACT In Africa, poverty levels are still very high, especially with the advent of the COVID-19 pandemic, despite efforts by governments and development partners to address it. Financial inclusion has been identified as an important concept in fighting poverty due to its ability to increase the income level of households. This study sought to estimate the threshold level at which financial inclusion, aided by strong institutions, will lead to poverty reduction in Africa, using Hansen’s threshold estimation method, covering a period of 2004–2020. The Hansen threshold estimation method uses an endogenous approach to identify threshold values, which eliminates deterministic process by using a systematic approach, as compared to the linear methods of sample-splitting of threshold-level specifications by other methods. This therefore makes the Hansen threshold estimation method robust in threshold estimations compared to other methods. The study found double threshold values of 0.0534 and 0.341, indicating that a financial inclusion value below 0.0534 would increase household consumption expenditure, leading to poverty reduction. However, a threshold value between 0.0534 and 0.3141 has a negative impact on poverty reduction. Furthermore, beyond a threshold value of 0.3141, financial inclusion will improve household consumption and as such reduce poverty in Africa. The study also established a threshold value of institutional quality index of −1.6325, indicating that below that institutional quality index, financial inclusion will have a negative impact on household consumption as well as poverty reduction in Africa. The results further indicated that dependency ratio, gross national income, interest rate, inflation, education, and government expenditure contribute significantly to poverty reduction in Africa. It is recommended that development partners, central banks and governments in the region should consciously develop and implement financial literacy policies, with the collaboration of all financial institutions that are aimed at promoting financial inclusion due to its ability to end poverty.

Residual evaluation of fault detection in discrete‐time systems

AbstractThis paper studies residual evaluation of fault detection in discrete‐time systems considering unknown‐but‐bounded disturbance. By applying Lyapunov stability principle and zonotopic technique, respectively, two dynamic threshold estimation methods are proposed to detect fault of discrete‐time systems. The proposed methods are extended to systems with time‐delay to improve the applicability in practical systems. The effectiveness and comparison of the proposed methods are illustrated by numerical examples.

Validation of the adaptive scan method in the quest for time-efficient methods of testing auditory processes

A major barrier to the clinical application of psychophysical testing of central auditory processes is the time required to obtain precise estimates of different listening abilities. In this study, we validate a novel adaptive scan (AS) method of threshold estimation that is designed to adapt on a range of values around threshold rather than on a single threshold value. This method has the advantage of providing the listener with greater familiarity with the stimulus characteristics near threshold while maintaining precise measurement and increasing time-efficiency. Additionally, we explore the time-efficiency of AS through comparison with two more conventional adaptive algorithms and the method of constant stimuli in two common psychophysical tasks: the detection of a gap in noise and the detection of a tone in noise. Seventy undergraduates without hearing complaints were tested using all four methods. The AS method provided similar threshold estimates with similar precision to those from the other adaptive methods and, thus, it is a valid adaptive method of psychophysical testing. We also provide an analysis of the AS method based on precision metrics to propose a shortened version of the algorithm that maximizes the time/precision tradeoff and can achieve similar thresholds to the adaptive methods tested in the validation. This work lays the foundation for using AS across a wide variety of psychophysical assessments and experimental situations where different levels of precision and/or time-efficiency may be required.

Comparison of anchor-based methods for estimating thresholds of meaningful within-patient change using simulated PROMIS PF 20a data under various joint distribution characteristic conditions.

To compare the performance of anchor-based methods for estimating thresholds of meaningful within-patient change (i.e., individual change) of clinical outcome assessments in conditions reflecting data characteristics of small- to medium-sized clinical trials. Datasets were generated from the joint distributions of the PROMIS PF 20a T-score changes and a seven-point global change anchor measure. The 108 simulation conditions (1000 replications per condition) included combinations of three marginal distributions of T-score changes, three improvement percentages in the anchor measure, four levels of responsiveness correlations, and three sample sizes. Threshold estimation methods included mean change, median change, ROC curve, predictive modeling, half SD, and SEM. Relative bias, precision, accuracy, and measurement significance of the estimates were evaluated based on comparison with true thresholds and IRT-based individual reliable changes of PROMIS scores. Quantile regression models were applied to select and interpret effects of simulation conditions on estimation bias. When PROMIS T-score changes were distributed normally, the predictive modeling method performed best with 50% or more responders identified by the anchor; the mean and median methods were preferred with 30% responders. For skewed distributions, the median method and ROC method gained more advantages. Among the evaluated study conditions, the improvement percentage condition had the most obvious effects on estimation bias. To establish accurate and precise thresholds, clinical researchers are recommended to prioritize study designs with at least 50% anchor-defined responders and strongly responsive target endpoints with highly reliable scoring calibration and to select optimal anchor-based methods given the data characteristics.

Impact of financial inclusion on banking stability in ASEAN countries –threshold financial inclusion

ABSTRACT This paper aims to investigate the effect of financial inclusion on bank stability in ASEAN countries. FEM, threshold estimation, and GMM method, respectively. The findings show that financial inclusion decreases the stability of banks, but the influence varies between banks and market structures. Furthermore, we examine the impact of the financial inclusion threshold on bank stability. The results show countries with financial inclusion above the threshold will increase stability, whereas countries with financial inclusion below the threshold will decrease the bank’s strength. Research on the non-linear impact of financial inclusion on bank stability through threshold model.

A new intelligent ECG recognition approach based on CNN and improved ALO-SVM

Cardiovascular disease is one of the most common diseases, which seriously threatens people’s life and health. Therefore, cardiovascular disease prevention becomes one of the most attractive research topics in health care system design. Intelligent recognition of electrocardiogram (ECG) signals represents an effective method for rapid diagnosis and the evaluation of cardiovascular diseases in medicine. Realization and efficiency of the classification of ECG signals in real time play major roles in the detection of cardiovascular diseases. This paper is concerned with the proposition of an intelligent ECG signal recognition method based on a convolutional neural network (CNN) and support vector machines (SVM) with an improved antlion algorithm (ALO). First, the ECG signal is denoised and pre-processed by lifting the wavelet. Subsequently, CNN is used to extract the signal characteristics of the denoising signal, and the extracted signal characteristics are used as the input characteristics of the SVM. Finally, an improved ALO algorithm is used to optimize the relevant input functions of the SVM to achieve a better signal classification. In our algorithm, the performance is enhanced by improving the threshold estimation method of the lifting wavelet, to improve the filtering effect. The proposed CNN architecture is tested with multi-lead ECG signals from the MIT-BIH ECG signal data set. The results display that the method has obtained an average accuracy, sensitivity, and specificity values of \(99.97\%\), \(99.97\%\), and \(99.99\%\), respectively. Compared with the existing results, the proposed approach has a better recognition performance.

Detection thresholds for electrostimulation combined with robotic leg support in sub-acute stroke patients.

Stroke is one of the leading causes of disability in adults in the European Union. It often leads to motor impairments, such as a hemiparetic lower extremity. Research indicates that early task-specific and intensive training promotes neuroplasticity and leads to recovery and/or compensation. One way to provide intensive training early after a stroke is via robot-supported training. A rehabilitation robot was designed by Life Science Robotics (Aalborg, Denmark) that can provide continuous repetitive movements of the hip, knee, and/or ankle in e.g., a lying position. In order to emphasize active contribution by the patient, actively triggered electrical stimulation (via muscle activation) can be combined with robotic assistance. The current study aims to compare different threshold estimation methods for detection of movement intention from muscle activity for actively triggered electrical stimulation during robot-supported leg movement in stroke patients. Three sub-acute stroke patients were included for a single measurement session. They performed knee extension and/or ankle dorsal flexion with four different threshold estimation methods to assess the intention detection threshold to initiate electrostimulation. The thresholds were based on the resting level of muscle activity (of m. rectus femoris or m. tibialis anterior) plus two or three times the standard deviation of the average resting value, or the resting level plus 5% or 10% of the peak muscle activity during a maximal voluntary contraction. The results showed that the method based on the resting muscle activity plus two times the standard deviation was the most stable across the three included stroke patients. This method had a detection success rate of 86.7% and was experienced as moderately comfortable. In conclusion, performing knee extension and/or ankle dorsal flexion with electromyography triggered electrostimulation is feasible in sub-acute stroke patients. Muscle activity-triggered electrostimulation combined with robotic support based on a threshold of resting levels plus two times the standard deviation seems to detect movement initiation most consistently in this small sample of sub-acute stroke patients.

Panel data models with two threshold variables

Abstract We develop threshold estimation methods for panel data models with two threshold variables and individual fixed specific effects covering short time periods. In the static panel data model, we propose least squares estimation of the threshold and regression slopes using fixed effects transformations; while in the dynamic panel data model, we propose maximum likelihood estimation of the threshold and slope parameters using first difference transformations. In both models, we propose to estimate the threshold parameters sequentially. We apply the methods to a 15-year sample of 565 U.S. firms to test whether financial constraints affect investment decisions.

Threshold detection and slot clustering algorithms in DPIM-VLC systems

In this paper it is presented two adaptive threshold estimation methods for Digital Pulse Interval Modulation (DPIM) in Visible Light Communication (VLC) systems. First method, which is called I-DPIMwAL, is proposed for mobile users without Ambient Light (AL) while second scheme that is referred to as I-DPIM ensures to accomplish the mobile data transmission under AL effect. Addition to this, the paper introduces a receiver based on K-means algorithm that classifies the logical levels of DPIM slots without using any threshold value. In order to compare with improved methods, two traditional systems are considered in the simulations. The FP-DPIM, which can be called Full Perfect-DPIM, has the perfect knowledge of threshold value that is determined by using both ambient light level and distance between receiver and transmitter whereas HP-DPIM, which can be defined as Half Perfect-DPIM, uses only one static threshold value for all distances between receiver and transmitter. According to experimental results, it can be considered that AL becomes either square wave or exponential signals. In practical systems, FP-DPIM cannot know the threshold value that is caused by ambient light effect however, it was given to obtain the exact performance analysis in simulation results.

The triggering threshold estimation of Majiagou landslide based on data mining

The deformation of the reservoir landslide is mainly governed by the combined action of rainfall and the fluctuation of the reservoir water level. The determination of the threshold of triggering factors is of great significant in the stability analysis and evaluation of the potential landslide. Existing empirical threshold model is mainly based on statistical analysis to fit the explicit function between triggering factors and displacement, which is widely used in rainfall-triggered landslides. However, for reservoir landslides, the relationship among rainfall intensity, reservoir water level, fluctuation rate and landslide displacement is highly nonlinear, which hindered the application of existing empirical threshold model. To tackle the scientific challenge, a novel data mining-based threshold estimation method is proposed in this study. The Majiagou landslide, located in the Three Gorges Reservoir (TGR) region, is selected as the study stie. Firstly, the Distributed Fiber Optical Sensing (DFOS) technology has been adopted to record the rainfall, reservoir water level fluctuations, and deformation information for two years in real time; Then, the evolution pattern of Majiagou landslide was analyzed in depth; Finally, the cluster analysis and decision tree algorithm are used to determine the threshold value of the rainfall and the fluctuation of the reservoir water level. Among which, 80% of the data set is used for training model, and the remaining 20% is used for validation. The study here provides a new and effective method to estimate the triggering threshold and contribute to the prediction and early warning of reservoir landslides.

A New Three-Part Phase Unwrapping Algorithm for 3-D Measurement of Dynamic Objects

For three-dimensional (3-D) measurement of dynamic objects, a new three-part phase unwrapping algorithm is proposed. First, a novel fringe modulation degree threshold estimation method is proposed. Then, the unwrapped phase can be obtained by three segments. The first-segment is calculated by 2-D spacial phase unwrapping algorithm while the fringe modulation degree is within the threshold. The second-segment is calculated between two frames when their fringe modulation degree difference is within the threshold. The third-segment is calculated by a estimation method of 2π jump order. This method only need one wrapped phase to calculate the unwrapped phase. Simulations and experiments have proved the effectiveness of the proposed method.

Automated Adaptive Wideband Acoustic Reflex Threshold Estimation in Normal-hearing Adults.

Acoustic stapedius reflex threshold (ART) tests are included in a standard clinical acoustic immittance test battery as an objective cross-check with behavioral results and to help identify site of lesion. In traditional clinical test batteries, middle-ear admittance of a 226 Hz probe is estimated using ear-canal measurements in the presence of a reflex-activating stimulus. In the wideband (WB) acoustic immittance ART test used in this study, the pure-tone probe is replaced by a WB probe stimulus and changes in absorbed power are estimated using ear-canal measurements in the presence of the activator. The ART is defined as the lowest level at which a criterion change in admittance (clinical) or absorbed power (WB) is observed in the presence of the activator. In the present study, ARTs were obtained in adults with normal hearing using the clinical, manual method and with a new WB automated adaptive threshold detection method. It was hypothesized that the WB test would result in lower ARTs than the clinical test because reflex-related changes in power absorbance could be observed across multiple frequency bands in the WB test compared with a single frequency in the traditional test. Data were collected in a prospective research design. ARTs were obtained in ipsilateral and contralateral conditions using 500, 1000, 2000 Hz, and broadband noise (BBN) activators on a clinical system and on an experimental WB system. The bandwidth of the BBN activator was 125 to 4000 Hz on the clinical system and 200 to 8000 Hz on the wideband system. ARTs were estimated at both tympanometric peak pressure (TPP) and ambient pressure on the WB system. Data were collected in both ears of 39 adults (21 males) of mean age 47.7 years (range 23-72 years). Differences in ARTs among the three threshold estimation methods (clinical, WB at TPP, WB at ambient) were examined using the general linear model repeated measures test in SPSS. Post-hoc pairwise comparisons were completed with Bonferroni correction for multiple comparisons. Statistical significance was defined as p < 0.05 for all analyses. ARTs obtained on the WB system at TPP and ambient pressure were significantly lower than obtained on the clinical system. ARTs obtained on the WB system at TPP were significantly higher than at ambient pressure in the 500 and 2000 Hz ipsilateral conditions. WB automated adaptive ARTs in normal-hearing adults were lower than for clinical methods when measured at TPP and ambient pressure. Lower presentation levels required to estimate ART in the WB test may be more tolerable to patients. Patients with ARTs that are not present at the maximum level of a traditional reflex test may have present ARTs with a WB ART test, which may reduce the need to refer for additional testing for possible retrocochlear involvement. Automation of the test may allow clinicians more time to attend to the other requisite tasks of a hearing evaluation and make the system useful for telehealth applications.

Aesthesiometer-Based Testing for Laryngopharyngeal Hyposensitivity.

To develop a method for threshold estimation of the laryngeal adductor reflex (LAR) response using Cheung-Bearelly monofilaments. Cross-sectional. Twenty-two healthy adults (12 men, 10 women) were tested for LAR response outcome using 30 mm 5-0 and 4-0 nylon monofilaments. Tactile stimuli were delivered to the aryepiglottic (AE) fold and medial pyriform sinus (MPS). Dichotomous classifier features and performance (area under the curve (AUC)), the LAR response outcome agreement and disagreement matrix, and test-retest reliability were examined. From those data, a test protocol that would minimize patient burden to estimate the LAR triggering threshold was formulated. Classifier performance of 5-0 monofilament stimulation of the AE fold (sensitivity=0.63, specificity=0.63, PPV=0.74, NPV=0.25) and MPS (sensitivity=0.45, specificity=0.77, PPV=0.74, NPV=0.23,) was slightly below that of 4-0 monofilament stimulation of the AE fold (sensitivity=0.82, specificity=0.50, PPV=0.82, NPV=0.50) and MPS (sensitivity =0.84, specificity=0.64, PPV=0.90, NPV=0.56), based on AUC. LAR response outcome agreement for 5-0 and 4-0 stimulations was high (93%) for 5-0 positive response, but low (29%) for 5-0 negative response. Aesthesiometer test-retest reliability for LAR response outcome was excellent (Cronbach's alpha=0.97). Threshold estimation of the LAR response may be operationalized by adopting a decision tree protocol. For negative LAR response to initial 5-0 monofilament stimulation and positive response to subsequent 4-0 monofilament stimulation, the higher threshold is confirmed. Positive LAR response to 5-0 or 4-0 monofilament stimulation is expected in over 90% of asymptomatic adults. Negative LAR response to 4-0 monofilament stimulation identifies patients at risk for laryngopharyngeal hyposensitivity. 3b Laryngoscope, 132:163-168, 2022.

Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations

Partial discharge (PD) signals are an important index to evaluate the operation state of intelligent substations. The correct distinction of PD pulse and interference pulse has become a challenging task. Because of the noise and the low signal-to-noise ratio, the stable signals become non-stationary. The selection of a wavelet basis, the selection rule of threshold λ and the design of the threshold function are the key factors affecting the final denoising effect. Therefore, an enhanced ant colony optimisition wavelet (ACOW) algorithm was applied to find the global optimal threshold through the continuous derivative threshold function and the ant colony optimisation (ACO) algorithm. At the same time the efficiency of adaptive search calculation, was also significantly improved. The method of the ACOW algorithm was compared with the soft wavelet method, gradient-based wavelet method and the genetic optimisation wavelet (GOW) method. Using these four methods to denoise four typical signals, different mean square errors (MSE), magnitude errors (ME) and time costs were obtained. Interestingly, the results show that the ACOW method can achieve the minimum MSE and has less time cost. It generates significantly smaller waveform distortion than the other three threshold estimation methods. In addition, the high efficiency and good quality of the output signals are beneficial to the diagnosis of local discharge signals in intelligent substations.

Threshold Detection Scheme Based on Parametric Distribution Fitting for Optical Fiber Channels

Background: When data is transmitted by encoding it in the amplitude of the transmitted signal, such as in Amplitude Shift Keying, the receiver makes a decision on the transmitted data by observing the amplitude of the received signal. Objective: Depending on the level of the received signal, the receiver has to decode the received data, with minimum probability of error. For binary transmission, this is achieved by choosing a decision threshold. Methods: A threshold detection mechanism is evaluated in this paper, which uses parametric probability distribution fitting to the received signal. Results: Based on our earlier work on visible light communication, the idea is extended to a fiber optic channel and it is found that in a fiber optic channel, the threshold value obtained by fitting a Rayleigh distribution to the received data results in error probability approaching zero. Conclusion: It is found that the proposed threshold estimation method works well for both equal as well as unequal apriori probabilities of the binary symbols. The proposed method of threshold detection is adaptive when the apriori probabilities for 1’s and 0’s change.

Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold

AbstractThis article proposes an innovative unsupervised learning method for early damage detection and long‐term structural health monitoring of civil structures under environmental variability. This method consists of three main parts including a novelty detector based on kernel null Foley–Sammon transform (KNFST), a practical approach to choosing an optimal Gaussian kernel parameter, and a probabilistic method for the threshold estimation. The crux of KNFST is to map all original samples to a kernel feature space and project the kernelized features into a single point in a null space. The proposed threshold estimation method exploits the extreme value theory, the generalized Pareto distribution, and the peak‐over‐threshold. The major contribution of this article is to propose an innovative novelty detection method by a one‐class kernel null space algorithm and a probabilistic threshold estimation approach. Dealing with the problem of environmental variations and estimating a reliable alarming threshold are the main advantages of the proposed method. The effectiveness and reliability of the proposed method are validated by the Wooden Bridge in a laboratory environment and the full‐scale Z24 Bridge. Results demonstrate that the proposed unsupervised learning method highly succeeds in detecting damage even under strong environmental variations.

Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network

Continuous monitoring of wind turbine health conditions using anomaly detection methods can improve the reliability and reduce maintenance costs during operation of wind turbine. Anomaly detection aims at identifying the root causes leading to unexpected changes of product performance. Most existing methods make less use of temporal order of the data and are poor at extracting features from these data. To address these problems, a method based on long short-term memory (LSTM) and auto-encoder (AE) neural network is introduced to assess sequential condition monitoring data of the wind turbine. First, a performance assessment model is constructed using LSTM neural units and AE networks to calculate the performance indices for evaluation of the degree of anomalies in wind turbine performance. Then, an adaptive threshold estimation method based on support vector regression model is developed to identify the abnormal data instances. The mutual information theory is subsequently explored to analyze the relationships between various monitoring parameters and performance abnormal instances to identify critical condition monitoring parameters. The effectiveness of the proposed method has been verified by a case study using real-world wind turbine condition monitoring (CM) data.

Bridge health monitoring in environmental variability by new clustering and threshold estimation methods

Environmental variability is a major challenging issue in bridge health monitoring because bridges are more prone to such variability than other civil structures. To deal with this challenge, this article proposes a new machine-learning method for early damage detection under environmental variability by means of the k-medoids clustering, a new damage indicator, and an innovative approach for selecting a proper cluster number. Estimation of a reliable alarming threshold is another important challenge for early damage detection via most of the machine-learning methods. On this basis, a novel probabilistic approach using the theory of extreme value and a goodness-of-fit measure is proposed to estimate an alarming threshold. The major contributions of this article include proposing a new damage indicator suitable for decision making by clustering-based algorithms, an innovative cluster selection algorithm for dealing with the problem of environmental variability and increasing damage detectability, and a novel probabilistic method for threshold estimation. Modal-based features of the well-known Z24 Bridge are considered to verify the accuracy and effectiveness of the proposed approaches along with several comparative studies. Results show that the methods presented here are highly able to detect early damage even under strong environmental variations and estimate a reliable threshold.