Random Threshold Research Articles

Remining Useful Life Prediction of Equipment Driven by Multi-Sensor Data

This paper presents a method for predicting the remining useful life (RUL) of equipment by using multi-sensor monitoring data of equipment. Firstly, the performance degradation of equipment is evaluated based on the maximum information coefficient. Then the performance degradation model is built by using Wiener process with non-linear drift. The maximum likelihood is used to estimate the parameters of the model, and the probability density distribution of RUL considering random failure threshold is deduced. Finally, the parameters of the model are updated by improved particle filter, and the RUL of the remaining equipment is predicted.

On a relationship between randomly and non-randomly thresholded empirical average excesses for heavy tails

Motivated by theoretical similarities between the classical Hill estimator of the tail index of a heavy-tailed distribution and one of its pseudo-estimator versions featuring a non-random threshold, we show a novel asymptotic representation of a class of empirical average excesses above a high random threshold, expressed in terms of order statistics, using their counterparts based on a suitable non-random threshold, which are sums of independent and identically distributed random variables. As a consequence, the analysis of the joint convergence of such empirical average excesses essentially boils down to a combination of Lyapunov’s central limit theorem and the Cramér-Wold device. We illustrate how this allows to improve upon, as well as produce conceptually simpler proofs of, very recent results about the joint convergence of marginal Hill estimators for a random vector with heavy-tailed marginal distributions. These results are then applied to the proof of a convergence result for a tail index estimator when the heavy-tailed variable of interest is randomly right-truncated. New results on the joint convergence of conditional tail moment estimators of a random vector with heavy-tailed marginal distributions are also obtained.

Development of the SR Based Auto-tuning Image Enhancement System and Evaluation of the Enhanced Image Quality using PQM

This paper proposes the development and evaluation of an auto-tuning stochastic resonance (SR) for image enhancement on images under various illumination conditions. Perceptual Quality Metric (PQM) is used for evaluating and quantifying the image quality. The current process is developed being based on our previous works related to the image enhancement by using the manual tuning stochastic resonance. The process was performed by adding the random noise and threshold in an image. The process works properly in the dark and very low contrast images as well as bright images. This image enhancement system works on dark and bright images as well. The system was tested with the face detection algorithm on the dark and illumination variant images. In this paper, we present the idea of auto-tuning of the SR iteration with random noise and threshold value 0 by using the process related to the histogram calculation, mean and median. In this paper, we performed various experiments on object and human detection as well under different conditions and confirmed the effectiveness of our auto-tuning SR based image enhancement algorithm. Finally, we conducted experiments using Perceptual Quality Metric which is an image quality metric to apply the proposed algorithm in various fields.

Random laser action in dye-doped xerogel with inhomogeneous TiO2 nanoparticles distribution

Rhodamine 6G-doped TEOS-derived xerogels containing different concentrations of TiO2 nanoparticles with average diameter of about 350 nm were designed to study random laser action. Due to the decantation of TiO2 nanoparticles during the sol–gel process, the obtained xerogels samples exhibits an inhomogeneous TiO2 distribution, with a higher concentration at one of the sample surfaces. By pumping the samples at each of the surfaces at a time with a ps-laser at 532 nm, differences in the emission behavior were observed. The higher concentration of TiO2 in one side of the sample, which occurs during its preparation, was shown to play an important role in the random laser energy threshold and emission spectra, depending on what side of the sample the excitation takes place. The difference in the laser threshold shows that the way scatterers are volumetrically distributed can increase or decrease the pump energy required for laser action.

Proposing autotuning image enhancement method using stochastic resonance

AbstractThis paper is associated with an autotuning stochastic resonance (SR) approach for image enhancement on illumination variant images. This new process is being developed based on our previous works related to image enhancement by using SR through the manual tuning process. The process was performed by adding the random noise and threshold 0 in an image. The process works properly in the dark and very low contrast images as well as bright images based and mixed illumination variant images. This system works on the images with the mixture of darkness and brightness. We also present the idea of autotuning of the iteration with random noise and threshold value 0 by using the process related to the histogram calculation or mean and median. We performed various experiments on various types of images including human faces under different conditions and confirmed the effectiveness of our image enhancement technique.

Modelling and Assessment of Survival Probability of Shock Model with Two Kinds of Shocks

The study aims at modelling and assessment of survival probability of a component experiencing two kinds of shocks namely, damage shock and fatal shock. Shocks are occurring randomly in time as events of a Poisson Process. The two cases of fixed/random threshold of components are studied. Survival probabilities of proposed models are derived. Maximum likelihood estimators (MLEs) of survival probabilities are obtained using the data from life testing experiments. Fisher information and asymptotic distribution of MLEs of parameters are obtained when a constant threshold is considered. Computation and comparison of estimators of two cases (constant threshold and random threshold) are made through simulation studies. The study recommends the consideration of threshold as a random variable.

光调控的偶氮染料掺杂聚合物分散液晶微管随机激光器

This study demonstrated the optically tunable random lasing from azo-dye-doped polymer dispersed liquid crystal (PDLC) in capillary tubes, in order to expand new applications for this device. The azo-dye-doped PDLC was prepared in glass capillaries with different core diameters, and the random lasing thresholds of these samples were measured respectively. The spectra of random lasers were measured with increasing the intensity of ultraviolet radiation. The experimental results show that the random laser threshold measurements of different core diameter capillary samples (100 μm, 300 μm, 500 μm) are 11.8 μJ/pulse, 8.6 μJ/pulse and 13.2 μJ/pulse. The experimental results also show that as the intensity of ultraviolet radiation increases (from 0 mW/cm2 to 150 mW/cm2), the random laser intensity decreases gradually, and the FWHM of the spectrum becomes wider. And the random lasing showed similar properties under the UV beam with different polarized directions during the optical tuning process. Our work verifies the possibility to fabricate miniature laser devices in cylindrical confinement structures, and extends the application scope of PDLC.

RETRACTED ARTICLE: The construction and simulation of internet financial product diffusion model based on complex network and consumer decision-making mechanism

Nowadays, Internet financial products, like Internet banking service and e-payment, have permeated into every corner of civil life in China. Via the Internet and media, consumers can obtain big data and information of financial products. Consumer big data analytics capabilities directly affect consumers’ cognition and thus affect their adoption of Internet financial products. Upon this background, this paper aims to study the diffusion of Internet financial products in a big data environment based on consumer analytics capabilities. First, in light of the consumer decision-making mechanism, we build a random threshold model for the diffusion of Internet financial products. Next, we explore the impact of consumer big data analysis capabilities on the diffusion of Internet financial products under different proportion of initial adopter and network densities in a specific social network topology. The simulation result shows that the consumer big data analytics capabilities have a significant impact on the speed and depth of the diffusion of Internet financial products. However, when proportion of initial adopter and network density reaches a certain value, the impact of consumer big data analysis ability on diffusion speed and depth has decreased. This study has a guiding significance for new strategies of Internet financial products promotion.

Non-decreasing threshold variances in mixed generalized ordered response models: A negative correlations approach to variance reduction

Mixed Generalized Ordered Response (MGOR) models, that allow random heterogeneity in thresholds, are widely used to model ordered outcomes such as accident injury severity. This study highlights a potential limitation of these models, as applied in most empirical research, that the variances of the random thresholds are implicitly assumed to be in a non-decreasing order. This restriction is unnecessary and can lead to difficulty in estimation of random parameters in higher order thresholds. In this study, we investigate the use of negative correlations between random parameters as a variance reduction technique to relax the property of non-decreasing variances of thresholds in MGOR models. To this end, a simulation-based approach was used (where multiple datasets were simulated assuming a known negative correlation structure between the true parameters), and two models were estimated on each dataset – one allowing correlations between random parameters and the other not allowing such correlations. Allowing negative correlations helped relax the non-decreasing variance property of MGOR models. However, maximum simulated likelihood estimation of parameters on data with correlations occasionally encountered model convergence and parameter identification issues. Comparison of the models that did converge suggests that ignoring correlations leads to an estimation of fewer random parameters in the higher order thresholds and results in bias and/or loss of precision for a few parameter estimates. However, ignoring correlations leads to an adjustment of other parameter estimates such that overall likelihood values, predicted percentage shares, and the marginal effects are similar to those from the models with correlations.

<b>Evaluation of imputed genomic data in discrete traits using Random forest and Bayesian threshold methodsb

The objectives of this study were (1) to quantify imputation accuracy and to assess the factors affecting it; and (2) to evaluate the accuracy of threshold BayesA (TBA), Bayesian threshold LASSO (BTL) and random forest (RF) algorithms to analyze discrete traits. Genomic data were simulated to reflect variations in heritability (h 2 = 0.30 and 0.10), number of QTL (QTL = 81 and 810), number of SNP (10 K and 50 K) and linkage disequilibrium (LD=low and high) for 27 chromosomes. For real condition simulating, we randomly masked markers with 90% missing rate for each scenario; afterwards, hidden markers were imputed using FImpute software. In imputed genotypes, a wide range of accuracy was observed for RF (0.164-0.512) compared to TBA (0.283-0.469) and BTL (0.272-0.504). Comparing to original genotypes, using imputed genotypes decreased the average accuracy of genomic prediction about 0.0273 (range of 0.024 to 0.036). Comparing to Bayesian threshold, using RF was improved rapidly accuracy of genomic prediction with increase in the marker density. Despite the higher accuracy of BTL and TBA at different levels of LD and heritability, the increase in accuracy was greater for RF. Furthermore, the best method for prediction of genomic accuracy depends on genomic architecture of population.

Modelling decisions of control transitions and target speed regulations in full-range Adaptive Cruise Control based on Risk Allostasis Theory

Adaptive Cruise Control (ACC) and automated vehicles can contribute to reduce traffic congestion and accidents. Recently, an on-road study has shown that drivers may prefer to deactivate full-range ACC when closing in on a slower leader and to overrule it by pressing the gas pedal a few seconds after the activation of the system. Notwithstanding the influence of these control transitions on driver behaviour, a theoretical framework explaining driver decisions to transfer control and to regulate the target speed in full-range ACC is currently missing.This research develops a modelling framework describing the underlying decision-making process of drivers with full-range ACC at an operational level, grounded on Risk Allostasis Theory (RAT). Based on this theory, a driver will choose to resume manual control or to regulate the ACC target speed if its perceived level of risk feeling and task difficulty falls outside the range considered acceptable to maintain the system active. The feeling of risk and task difficulty evaluation is formulated as a generalized ordered probit model with random thresholds, which vary between drivers and within drivers over time. The ACC system state choices are formulated as logit models and the ACC target speed regulations as regression models, in which correlations between system state choices and target speed regulations are captured explicitly. This continuous-discrete choice model framework is able to address interdependencies across drivers’ decisions in terms of causality, unobserved driver characteristics, and state dependency, and to capture inconsistencies in drivers’ decision making that might be caused by human factors.The model was estimated using a dataset collected in an on-road experiment with full-range ACC. The results reveal that driver decisions to resume manual control and to regulate the target speed in full-range ACC can be interpreted based on the RAT. The model can be used to forecast driver response to a driving assistance system that adapts its settings to prevent control transitions while guaranteeing safety and comfort. The model can also be implemented into a microscopic traffic flow simulation to evaluate the impact of ACC on traffic flow efficiency and safety accounting for control transitions and target speed regulations.

Dynamics of an adaptive randomly reinforced urn

Adaptive randomly reinforced urn (ARRU) is a two-color urn model where the updating process is defined by a sequence of non-negative random vectors $\{(D_{1,n}, D_{2,n});n\geq1\}$ and randomly evolving thresholds which utilize accruing statistical information for the updates. Let $m_1=E[D_{1,n}]$ and $m_2=E[D_{2,n}]$. Motivated by applications, in this paper we undertake a detailed study of the dynamics of the ARRU model. First, for the case $m_1 \neq m_2$, we establish $L_1$ bounds on the increments of the urn proportion at fixed and increasing times under very weak assumptions on the random threshold sequence. As a consequence, we deduce weak consistency of the evolving urn proportions. Second, under slightly stronger conditions, we establish the strong consistency of the urn proportions for all finite values of $m_1$ and $m_2$. Specifically, we show that when $m_1=m_2$ the proportion converges to a non-degenerate random variable. Third, we establish the asymptotic distribution, after appropriate centering and scaling, of the proportion of sampled balls in the case $m_1=m_2$. In the process, we settle the issue of asymptotic distribution of the number of sampled balls for a randomly reinforced urn (RRU). To address the technical issues, we establish results on the harmonic moments of the total number of balls in the urn at different times under very weak conditions, which is of independent interest.

Random lasing emission from WO3 particles dispersed in Rhodamine 6G solution

We experimentally investigate the random lasing emission from tungsten oxide (WO3) particles dispersed in Rhodamine 6G solution. Due to the growing interest in tungsten oxide materials and also their importance and applications in gas sensors, electrochromic and optochromic materials, photoelectrodes and other optoelectrical devices, we choose this material as the scattering elements in a random laser system. Up to our knowledge, the use of WO3 materials as the scattering elements in a random laser system has not yet been reported. WO3 particles provide optical feedback by multiple light scattering. Optical gain is provided by Rhodamine 6G solution through stimulated emission process. Our results show that by adding WO3 particles to Rhodamine 6G solution, single peak random lasing emission is achieved. Furthermore, we investigate the effects of changing the concentration of WO3 particles on the random lasing characteristics. Our results confirm that the increase of WO3 particles leads to the decrease of random lasing threshold, blue-shift of the emission peak, enhancement of the laser emission peak and decrease of its line-width. Due to electrochromic and gasochromic properties of WO3 nanostructures this work can open an avenue for design and fabrication of new tunable and controllable random lasers.

Effect of localized loading on failure threshold of fiber bundles

We investigate the global failure threshold of an interconnected set of elements, when a finite fraction of the elements initially share an externally applied load. The study is done under the framework of random fiber bundle model, where the fibers are linear elastic objects attached between two plates. The failure threshold of the system varies non-monotonically with the fraction of the system on which the load is applied initially, provided the load sharing mechanism following a local failure is sufficiently wide. In this case, there exists a finite value for the initial loading fraction, for which the damage on the system will be maximum, or in other words the global failure threshold will be minimum for a finite value of the initial loading fraction. This particular value of initial loading fraction, however, goes to zero when the load sharing is sufficiently local. Such crossover behavior, seen for both one and two dimensional versions of the model, can give very useful information about stability of interconnected systems with random failure thresholds.

Optimal weights decoding of M-ary suprathreshold stochastic resonance in stochastic pooling network

Suprathreshold stochastic resonance (SSR) is a noise enhancing signal processing phenomenon, occurring in a parallel array of nonlinear elements. In this paper, we investigate the optimal decoding scheme of SSR in stochastic pooling network with a quantization evolution of the output signal-to-quantization-noise ratio (SQNR) by studying the optimal weights and the optimal thresholds. Firstly, we introduce an effective method of weights decoding which makes the better SSR effects than the Wiener linear decoding and is defined as the optimal weights decoding. Moreover, in order to find the optimal thresholds, we select three common forms of thresholds in [0,1] interval the uniform thresholds, the random thresholds and the group thresholds. The result indicates that the group thresholds make a better SSR effect than uniform thresholds, but worse than the random thresholds. Therefore, the random thresholds are the optimal thresholds setting in the M-ary stochastic pooling network. Finally, we discuss the influences of number of elements N and threshold level M on SSR, and find that changing the number of the comparators N in stochastic pooling network is more easier to enhance the performance of SSR than changing the values of M. These works as a complement to the optimal quantification theory will be helpful for the study of optimal thresholds in stochastic pooling network.

Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions.

Protein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.

Reliability‐based topology optimization against geometric imperfections with random threshold model

SummaryThis paper develops a new reliability‐based topology optimization framework considering spatially varying geometric uncertainties. Geometric imperfections arising from manufacturing errors are modeled with a random threshold model. The projection threshold is represented by a memoryless transformation of a Gaussian random field, which is then discretized by means of the expansion optimal linear estimation. The structural response and their sensitivities are evaluated with the polynomial chaos expansion, and the accuracy of the proposed method is verified by Monte Carlo simulations. The performance measure approach is adopted to tackle the reliability constraints in the reliability‐based topology optimization problem. The optimized designs obtained with the present method are compared with the deterministic solutions and the reliability‐based design considering random variables. Numerical examples demonstrate the efficiency of the proposed method.

Robust Multithreaded Object Tracker through Occlusions for Spatial Augmented Reality

A spatial augmented reality (SAR) system enables a virtual image to be projected onto the surface of a real-world object and the user to intuitively control the image using a tangible interface. However, occlusions frequently occur, such as a sudden change in the lighting environment or the generation of obstacles. We propose a robust object tracker based on a multithreaded system, which can track an object robustly through occlusions. Our multithreaded tracker is divided into two threads: the detection thread detects distinctive features in a frame-to-frame manner, and the tracking thread tracks features periodically using an optical-flow-based tracking method. Consequently, although the speed of the detection thread is considerably slow, we achieve real-time performance owing to the multithreaded configuration. Moreover, the proposed outlier filtering automatically updates a random sample consensus distance threshold for eliminating outliers according to environmental changes. Experimental results show that our approach tracks an object robustly in real-time in an SAR environment where there are frequent occlusions occurring from augmented projection images.

On a random lead time and threshold shock model using phase‐type geometric processes

This study investigates two random threshold shock models for a repairable deteriorating system with nonnegligible maintenance times, with and without a spare via a phase‐type geometric process. The system fails whenever the intershock arrival time is less than a random threshold. The provision of stochastic lead time is incorporated in Model II so that an ordering policy N−1 and a replacement policy N based on the number of failures of the system are also considered. An explicit expression of the average cost rate is derived for both models and the optimal replacement policy N* is obtained by minimizing the long‐run average cost rate analytically. The numerical illustrations and sensitivity analysis provided therein conform to the observations made in the study.

Availability analysis for general repairable systems with repair time threshold

ABSTRACTAvailability analysis is an important issue in many practical fields. This paper investigates the availability for general repairable systems with repair time threshold. Based on practical applications, a repair time threshold is introduced. If the period of a repair is less than a predefined time threshold, then the system may be considered as working during this period, i.e., the effect of the repair could be neglected. Otherwise, if the period of a repair is longer than the given threshold, then the system is considered as working from the beginning of the system failure until the repair time exceeding the threshold, i.e., the time point of the system down could be delayed. We consider both constant and random repair time threshold. This paper valuates the user-perceived availability, when the user does not experience any service interruption because the duration of repair is too short. The results can be applied in reliability engineering, queueing theory and many other fields. A numerical example for ventilator system is presented to demonstrate the application of the developed approach.