Random Threshold Research Articles

Random Policy for an Unreliable Server System With Delaying Repair and Setup Time Under Bernoulli Vacation Schedule

Abstract This paper deals with an M/G/1 queuing system with server setup and a Bernoulli vacation schedule, in which an unreliable server applies a random N-policy. The server may break down at any time, and the service channel will fail for the duration of the breakdown. When units in the queue are exhaustively served, the server will become idle until the queue size grows to meet a random threshold N(≥1). As soon as the queue size reaches N, the server will immediately begin to serve the waiting units. Upon the service completion of each unit, the server may take a vacation or may remain in the system to serve the next unit, if any. This study derives the queue size distribution during a busy period initiation epoch and a departure epoch, respectively. We also give the analytic expression for the distribution of the delay busy period, which includes breakdown periods and actual busy periods. Furthermore, numerical examples of the long-run cost functions for the random N-policy and the classical N-policy are investigated.

A Novel Technique for Multi-Class Ordinal Regression-APDC

Objectives: Ordinal regression is one which is used in Multiclass classification where there is an essential ordering among the classes. The training dataset is initially classified depending on the Random threshold values θ. Based on these values, the distance between the different class labels are predicted by one against one technique. Method: All Pairs Distance Calculation using one against one technique [APDC_1 AG 1] is Proposed to validate the work. But in the referred previous work, distance is calculated using adjacent classes, but here all pairs distance calculation is used to find the class label distance to all class label pairs. Findings: On the whole, New trained data are in the form of one dimensional representation. Here, with the knowledge of proposed work, testing data is tested with New trained data set and the results are produced. The Proposed method is seen to be ambitious when compared with previous work. Beside this, an additional set of experiments is done to study the potential quantifiability and illustratability of the proposed method when using APDC as base methodology. Improvements: Proposed work is analyzed with Kernel discriminant analysis, Logistic Regression, Classification via Regression, Multiclass Classifier and found APDC has attained better results according to all measures.

GENERAL MANPOWER-SCBZ MACHINE SYSTEM WITH MARKOVIAN PRODUCTION GENERAL SALES AND GENERAL RECRUITMENT

Two Manpower planning models with different recruitment patterns are studied. During the operation time a machine produces random number of products. After the operation time, the sale time starts, which has one among two distinct distributions depending on the magnitude of production is within or exceeding a random threshold. In this paper, the operation times have SCBZ property, production time has exponential distribution and the man power system, operation time, sales and recruitment times have general distributions. Joint transforms of the variables, their means and the covariance of operation time and recruitment time with numerical results are presented Mathematics Subject Classification: 91B70.

A Kind of Parameters Self-adjusting Extreme Learning Machine

Extreme learning machine (ELM) is a kind of feed-forward single hidden layer neural network, whose input weights and thresholds of hidden layers are generated randomly. Because the output-weights of ELM are calculated by the least-square method, the ELM presents a high speed on training and testing. However, the random input-weights and thresholds of hidden layers are not the best parameters, which can not pledge the training goals of the ELM to achieve the global minimum. In order to obtain the optimal input-weights and bias of hidden layer, this paper proposes the self-adjusting extreme learning machine, called SA-ELM. Based on the idea of the ameliorated teaching learning based optimization, the input-weights and the bias of hidden layer of extreme learning machine are adjusted with "teaching phase" and "learning phase" to minimize the objective function values. The SA-ELM is applied to the eight benchmark functions to test its validity and feasibility. Compared with ELM and fast learning network, the SA-ELM owns good regression accuracy and generalization performance. Besides, the SA-ELM is applied to build the thermal efficiency model of a 300 MW pulverized coal furnace. The experiment results reveal that the proposed algorithm owns engineering practical application value.

Study of low-threshold and high-intensity random lasing in dye doped liquid crystals

Random lasers in dye-doped nematic liquid crystal (DDNLC) cells with different structures are studied. By choosing the cell gap and the cells rubbing methods, the DDNLC random laser obtains lower energy threshold. The DDNLC random laser energy threshold can also be decreased with an Al mirror as the external feedback from 4.2 μJ/pulse to 1.5 μJ/pulse. It is worth mentioning that the random laser shifts red and intensity increases with the Al mirror. The study of the DDNLC random laser with cell structure is aimed to obtain a low power consumption laser at a lower cost.

Time to failure estimation based on degradation model with random failure threshold

As the 'point' which the system performance degrades to failure is unpredictable or random in engineering practices, it is a challenging issue to consider the failure threshold's randomicity in fault prognostic system. This paper develops an integrated methodology for time to failure estimation based on degradation model with random failure threshold. Considering both the degradation rate and the failure threshold uncertainty in failure probability analysis, the methodology for failure time forecasting is deduced, and then the fault prognostic system developing process is given in this paper. At last, the developed fault estimation method is demonstrated by an electro-hydrostatic actuator (EHA) system simulation example in aeronautical field.

Threshold optimization according to the restricted Bayes criterion in decentralized detection problems

In this paper, the restricted Bayes approach is studied in a decentralized detection problem. All decisions on which the hypothesis is true are made by local sensors through conditionally independent observations. Then these decisions are transmitted to the fusion center for thenal decision. In the conventional approach, all thresholds of local sensors and the fusion center are considered as deterministic variables and optimized according to the given criterion for given test statistics of local sensors and the fusion center. In this paper, it is shown that setting thresholds as random variables instead of deterministic ones can improve the performance according to the restricted Bayes criterion. It is proved that optimal random thresholds are dependent on each other, and the probability density function of each one consists of at most two point masses. Two methods for the implementation of this scheme are proposed. A necessary and sufficient condition for improvability of the conventional approach through replacing optimal deterministic thresholds by optimal random ones is derived. Finally, theoretical results are investigated through simulations.

Dopamine sensing and measurement using threshold and spectral measurements in random lasers.

We developed a novel dopamine sensing and measurement technique based on aggregation of gold nanoparticles in random lasers. Dopamine combined with copper ions triggers the aggregation of gold nanoparticles and thus affects the performance of random lasers. Dopamine sensing can be achieved using four parameters which are sensitive to the presence of dopamine, that is emission peak shift, emission linewidth, signal-to-noise ratio (peak emission intensity / noise) and random lasing threshold. The dopamine is most sensitively detected by a change in the emission linewidth with a limit of detection of 1 × 10(-7) M, as well as by an increase in the lasing threshold. The dopamine concentration from 1 × 10(-7) M to 1 × 10(-2) M can be determined by calibrating with the laser threshold.

Genetic analysis of the rates of conception using a longitudinal threshold model with random regression in dairy crossbreeding within a tropical environment.

This study was designed to: (i) estimate genetic parameters and breeding values for conception rates (CR) using the repeatability threshold model (RP-THM) and random regression threshold models (RR-THM); and (ii) compare covariance functions for modeling the additive genetic (AG) and permanent environmental (PE) effects in the RR-THM. The CR was defined as the outcome of an insemination. A data set of 130 592 first-lactation insemination records of 55 789 Thai dairy cows, calving between 1996 and 2011, was used in the analyses. All models included fixed effects of year × month of insemination, breed × day in milk to insemination class and age at calving. The random effects consisted of herd × year interaction, service sire, PE, AG and residual. Variance components were estimated using a Bayesian method via Gibbs sampling. Heritability estimates of CR ranged from 0.032 to 0.067, 0.037 to 0.165 and 0.045 to 0.218 for RR-THM with the second, third and fourth-order of Legendre polynomials, respectively. The heritability estimated from RP-THM was 0.056. Model comparisons based on goodness of fit, predictive abilities, predicted service results of animal, and pattern of genetic parameter estimates, indicated that the model which fit the desired outcome of insemination was the RR-THM with two regression coefficients.

Suprathreshold auditory processing deficits in noise: Effects of hearing loss and age

People with sensorineural hearing loss generally suffer from a reduced ability to understand speech in complex acoustic listening situations, particularly when background noise is present. In addition to the loss of audibility, a mixture of suprathreshold processing deficits is possibly involved, like altered basilar membrane compression and related changes, as well as a reduced ability of temporal coding. A series of 6 monaural psychoacoustic experiments at 0.5, 2, and 6 kHz was conducted with 18 subjects, divided equally into groups of young normal-hearing, older normal-hearing and older hearing-impaired listeners, aiming at disentangling the effects of age and hearing loss on psychoacoustic performance in noise. Random frequency modulation detection thresholds (RFMDTs) with a low-rate modulator in wide-band noise, and discrimination of a phase-jittered Schroeder-phase from a random-phase harmonic tone complex are suggested to characterize the individual ability of temporal processing. The outcome was compared to thresholds of pure tones and narrow-band noise, loudness growth functions, auditory filter bandwidths, and tone-in-noise detection thresholds. At 500 Hz, results suggest a contribution of temporal fine structure (TFS) to pure-tone detection thresholds. Significant correlation with auditory thresholds and filter bandwidths indicated an impact of frequency selectivity on TFS usability in wide-band noise. When controlling for the effect of threshold sensitivity, the listener's age significantly correlated with tone-in-noise detection and RFMDTs in noise at 500 Hz, showing that older listeners were particularly affected by background noise at low carrier frequencies.

Pinning model with heavy tailed disorder

We study the pinning model, which describes the behavior of a Markov chain interacting with a distinguished state. The interaction depends on an external source of randomness, called disorder. Inspired by Auffinger and Louidor (2011) and Hambly and Martin (2007), we consider the case when the disorder is heavy-tailed, while the return times of the Markov chain are stretched-exponential. We prove that the set of times at which the Markov chain visits the distinguished state, suitably rescaled, has a limit in distribution. Moreover there exists a random threshold below which this limit is trivial. Finally we complete a result of Auffinger and Louidor (2011) on the directed polymer in a random environment.

The Dynamics of Gasoline Prices: Evidence from Daily French Micro Data

Using millions of individual gasoline prices collected at a daily frequency, we examine the speed at which market refined oil prices are transmitted to consumer liquid fuel prices. We find that on average gasoline prices are modified once a week and the distribution of price changes displays a M-shape as predicted by a menu-cost model. Using a reduced form state-dependent pricing model with time-varying random thresholds, we find that the degree of pass through of wholesale prices to retail gasoline prices is on average 0.77 for diesel and 0.67 for petrol. The duration for a shock to be fully transmitted into prices is about 10 days. There is no significant asymmetry in the transmission of wholesale price to retail prices.

Genetic Prediction Models and Heritability Estimates for Functional Longevity in Dairy Cattle

Longevity is a major component of the breeding objective for dairy cattle in many countries because of its high economic value. The trait has been recommended for inclusion in the breeding objective for dairy cattle in South Africa. Linear models, random regression (RR) models, threshold models (TMs) and proportional hazard models (PH) have been used to evaluate longevity. This paper discusses these methodologies and their advantages and disadvantages. Heritability estimates obtained from these models are also reviewed. Linear methodologies can model binary and actual longevity, while RR and TM methodologies model binary survival. PH procedures model the hazard function of a cow at time t derived from survival from first calving to culling, death or censoring. It is difficult to compare methodologies for sire evaluation and ranking across countries because of the variation in the definition of longevity and the choice of model. Sire estimated breeding values (EBVs) are derived differently for the models. Sire EBVs from PH models are expressed as deviations of the culling risk from the mean of the base sires, expected percentage of daughters still alive after a given number of lactations, expected length of productive life in absolute terms or as standard deviation units. In linear, TM and RR modelling, sire EBVs for longevity have been expressed as deviations of survival from the mean estimated with Best Linear Unbiased Prediction (BLUP). Appropriate models should thus be developed to evaluate functional longevity for possible inclusion in the overall breeding objective for South African dairy cattle. Keywords: Dairy Cattle, Proportional Hazards, Random Regression, Survival Analysis

Crack phantoms: localized damage correlations and failure in network models of disordered materials

We study the initiation of failure in network models of disordered materials such as random fuse and spring models, which serve as idealized representations of fracture processes in quasi-two-dimensional, disordered material systems. We consider two different geometries, namely rupture of thin sheets and delamination of thin films, and demonstrate that irrespective of geometry and implementation of the disorder (random failure thresholds versus dilution disorder) failure initiation is associated with the emergence of typical localized correlation structures in the damage patterns. These structures (‘crack phantoms’) exhibit well-defined characteristic lengths, which relate to the failure stress by scaling relations that are typical for critical crack nuclei in disorder-free materials. We discuss our findings in view of the fundamental nature of failure processes in materials with random microstructural heterogeneity.

Random serum progesterone threshold to confirm ovulation

BackgroundSerum progesterone (P) rises after ovulation in the luteinisation process. ObjectiveTo identify an accurate progesterone threshold to confirm ovulation in the assessment of a woman’s fertility. MethodsIn a secondary analysis of an observational European multicentre study, this study included 107 women over 326 menstrual cycles and tracked daily first morning urine (FMU), changes in observed cervical mucus discharge, serum progesterone, and ultrasonography to identify the day of ovulation. A serum progesterone level was available for 102 women over a total 260 cycles with one or two P levels per cycle. ResultsIt was found that a single serum P⩾5ng/ml is highly specific with a specificity of 98.4 (95% CI 96.0–99.5), with a sensitivity of 89.6 (95% CI 85.2–92.9). ConclusionA random serum progesterone level ⩾5ng/ml confirms ovulation. This may be of use for clinicians wanting to confirm that ovulation has occurred.

Performance analysis of undoped cylindrical gate all around (GAA) MOSFET at subthreshold regime

In this work the sensitivity of process parameters like channel length (L), channel thickness (tSi), and gate work function (φM) on various performance metrics of an undoped cylindrical gate all around (GAA) metal-oxide-semiconductor field effect transistor (MOSFET) are systematically analyzed. Undoped GAA MOSFET is a radical invention as it introduces a new direction for transistor scaling. In conventional MOSFET, generally the channel doping concentration is very high to provide high on-state current, but in contrary it causes random dopant fluctuation and threshold voltage variation. So, the undoped nature of GAA MOSFET solves the above complications. Hence, we have analyzed the electrical characteristics as well as the analog/RF performances of undoped GAA MOSFET through Sentaurus device simulator.

Complex spherical 2 + 4 spin glass: A model for nonlinear optics in random media

A disordered mean field model for multimode laser in open and irregular cavities is proposed and discussed within the replica analysis. The model includes the dynamics of the mode intensity and accounts also for the possible presence of a linear coupling between the modes, due, e.g., to the leakages from an open cavity. The complete phase diagram, in terms of disorder strength, source pumping and non-linearity, consists of four different optical regimes: incoherent fluorescence, standard mode locking, random lasing and the novel spontaneous phase locking. A replica symmetry breaking phase transition is predicted at the random lasing threshold. For a high enough strength of non-linearity, a whole region with nonvanishing complexity anticipates the transition, and the light modes in the disordered medium display typical discontinuous glassy behavior, i.e., the photonic glass has a multitude of metastable states that corresponds to different mode-locking processes in random lasers. The lasing regime is still present for very open cavities, though the transition becomes continuous at the lasing threshold.

Selective Body Biasing for Post-Silicon Tuning of Sub-Threshold Designs: An Adaptive Filtering Approach

A sub-threshold design could provide a compelling approach to power critical applications. An exponential relationship exists, however, between the delay and the threshold voltage, that makes this design-time timing closure extremely difficult, if not impossible, to achieve. Several previous studies were focused on the technique of body biasing during post-silicon tuning for delay compensation. But they were mostly for super-threshold designs where spatially correlated ${L} _{\mathbf {eff}}$ variation dominates. They cannot be applied directly to sub-threshold designs in which purely random threshold voltage variations dominate. These works also assumed multiple body biasing voltage domains and multiple body biasing voltage levels, which involve significant design overhead. The problem of selective body biasing for post-silicon tuning of sub-threshold designs is examined in this paper. The possibility of using only one body bias voltage domain with a single body bias voltage is explored. The problem was formulated first as a linearly constrained statistical optimization model. The adaptive filtering concept from the signal processing community was then adopted so that an efficient, yet novel, solution could be developed. Using several 65 nm industrial designs, experimental results suggest that, compared with a seemingly more intuitive approach, the proposed approach can improve the pass rate by 57% on average with similar standby power and the same number of body biasing gates. This approach can reduce the standby power, on average, by 84%, with a 20% pass rate loss, more than the approach to bias all the gates.

Phenotype accessibility and noise in random threshold gene regulatory networks.

Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes.

An estimator for the tail index of an integrated conditional Pareto–Weibull-type model

We introduce a nonparametric regression estimator for a tail heaviness parameter in an integrated conditional Pareto–Weibull-type model. The estimator is based on local log excesses over a high random threshold. Asymptotic properties are derived under proper regularity conditions.