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Sampling inspection by the method of variables is a well-known category of product control in which the discretion of acceptance or rejection is made hinge on some specific rule, which is framed according to the measurement of a quality characteristic under study. In this scenario, the quality feature under study is considered a continuous random variable that can be demonstrated using any continuous type statistical distribution. Birnbaum - Saunders distribution is a continuous probability distribution, which is having numerous applications in various fields. Application of Birnbaum - Saunders distribution is considered in this paper for establishing acceptance sampling inspection plans for variables based on the examination of units from a single sample. Numerical illustrations are given to demonstrate the application of proposed sampling plan. In addition, the results of numerical illustrations are explained with the help of simulated data. [ABSTRACT FROM AUTHOR]

Clearing functions that describe the expected output of a production resource as a function of its expected workload have yielded promising production planning models. However, there is as yet no fully satisfactory approach to estimating clearing functions from data. We identify several issues that arise in estimating clearing functions such as sampling issues, systematic underestimation and model misspecification. We address the model misspecification problem by introducing a generalised functional form, and the sampling issues via iterative refinement of initial parameter estimates. The iterative refinement approach yields improved performance for planning models at higher levels of utilisation, and the generalised functional form results in significantly better production plans both alone and when combined with the iterative refinement approach. The IR approach also obtains solutions of similar quality to the much more computationally demanding simulation optimisation approaches used in previous work. [ABSTRACT FROM AUTHOR]

This paper studies the stabilization problem of delayed memristive neural networks under event-triggered control. A refined switching event-trigger scheme that switches between variable sampling and continuous event-trigger can be designed by introducing an exponential decay term into the threshold function. Compared with the existing mechanisms, the proposed scheme can enlarge the interval between two successively triggered events and therefore can reduce the amount of triggering times. By constructing a time-dependent and piecewise-defined Lyapunov functional, a less-conservative criterion can be derived to ensure global stability of the closed-loop system. Based on matrix decomposition, equivalent conditions in linear matrix inequalities form of the above stability criterion can be established for the co-design of both the trigger matrix and the feedback gain. A numerical example is provided to demonstrate the effectiveness of the theoretical analysis and the advantages of the refined switching event-trigger scheme. [ABSTRACT FROM AUTHOR]

Often industrial variables can be difficult to measure due to such factors as extreme conditions or complex compositions. In such cases, soft sensors have been developed that use available system information and measurements to estimate these difficult-to-obtain variables. In practice, the measurements that are to be estimated by a soft sensor are often infrequently measured or delayed. Occasionally, these sampling times or delays are time varying. At present, most research has considered these parameters to be time invariant, and thus, there is a need to consider the time-varying case. Therefore, this paper will evaluate the impact of time-varying delays and sampling times for the design of a data-driven soft sensor. Modifications will be proposed that will increase the robustness and performance of the soft sensor. The reliability of the estimate will be shown using the Bauer–Premaratne–Durán Theorem. Furthermore, the proposed soft sensor system will be tested using simulations of a continuous stirred tank reactor (CSTR) and an reverse osmosis plant. Simulation showed that the modified soft sensor gives good estimates, whereas the traditional soft sensor gives an unstable estimate for the CSTR and reverse osmosis plant. • Samples taken from a process are not always obtained regularly. • Incorporating such values into the soft sensor framework can be difficult. • A design for the bias update term is presented that allows for such measurements to be incorporated. • This design is shown to provide good tracking in all cases. • Simulations using a CSTR and osmosis plant data show that the proposed design provides good tracking. [ABSTRACT FROM AUTHOR]

Abstract: The inspection of measurement devices according to statistical sampling plans allows conclusions to be drawn about the reliability of a whole population of devices. However, confirming high reliability levels requires large sample sizes and is thus expensive or even infeasible. For example, a reliability of 99.5% can only be guaranteed with 90% confidence by inspecting each item in a population of 280 (see ISO 2859‐2). When reliability is judged by not exceeding a certain threshold, this research provides a convenient solution allowing considerably more efficient sampling plans. Under certain distributional assumptions, in particular, we have proved that if 100q% of a population meets a tighter threshold Δ/γ, then at least 100p% of the population meets threshold Δ(with p>q, γ>1). The importance and effect of different distributional assumptions are demonstrated and relevant scenarios for the parameters (p,q,γ) presented. Verifying that a smaller portion of devices comply requires smaller sample sizes. Costs may thus decrease when more stringent specifications are verified. For example, up to 98% of utility meters in Germany are required to measure correctly at inspections, to ensure a reliability of 95% in the future. Instead of applying costly sampling plans to meters in use to demonstrate these high reliability levels, this research enables the sample size to be reduced, eg, by half. [ABSTRACT FROM AUTHOR]

An important drawback in the application of model-based predictive controllers for multiphase systems is the relatively high harmonic content. Harmonics arise due to the fixed sampling-time nature and the absence of modulation methods in the control technique. Recent research works have proposed different procedures to overcome this disadvantage at the expense of increasing the complexity of the controller and, in most cases, the computational requirements. There are, however, natural ways to face this harmonic generation that have been barely explored in the scientific literature. These alternatives include the use of variable sampling times or the application of the observer theory, whose utility has been stated without excessively increasing the computational cost of the controller. This paper presents the basis of both methodologies, analyzing their interest as natural alternatives to mitigate the generation of harmonic components in modern electrical drives when using predictive controllers. A five-phase induction machine is used as a case example to experimentally validate the study and draw conclusions.

A digital receive beamformer implementing a “one sample per pixel” variable sampling technique is described. The sampling method reduces the required sampling rates by a factor of 3, and reduces the data capture rate by a factor of 2, in comparison with the previous systems based on variable sampling. The sampling method is capable of estimating broadband pulse envelopes accurate for bandwidths up to 83.0%. This beamforming method has been implemented on a field-programmable gate array with maximum transmit and receive delay errors measured to be less than ±1.0 ns. The beamformer was tested and verified on a previously described 45-MHz 64-element phased array. The system generates images with 128 lines, 512 pixels per RF line, and 2 transmit focal zones. The system generates images with approximately 55 dB of dynamic range and was tested by imaging wire targets submersed in a water bath, wire targets embedded in a tissue phantom, and real-time in vivo imaging of a human wrist. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the procedure of audit sampling using the variable sampling methods for conducting the tests of income from insurance premiums in insurance company 'Takovo'. Since the incomes from the insurance premiums from vehicle insurance and third-party vehicle insurance have the dominant share of the insurance company's income, the application of this method will be shown in the audit examination of these incomes - incomes from VI and TPVI premiums. For investigating the applicability of these methods in testing the income of other insurance companies, we shall implement the method of variable sampling in the audit testing of the premium income from the three leading insurance companies in Serbia, 'Dunav', 'DDOR' and 'Delta Generali' Insurance.

This paper investigates the problem of robust exponential stabilization for sampled-data systems with variable sampling and packet dropouts. It is assumed that the system parameter uncertainties are norm-bounded and appear in both the state and input matrices. An input delay approach is adopted to model the sample-and-hold behavior with a time-varying delayed control input, and a switched system approach is proposed to model the data-missing phenomenon. On this basis, the sampled-data control system with variable sampling and packet dropouts is modeled as a switched system with time-varying delay. The objective is to design a sampled-data controller to guarantee the robust exponential stability of the resulting closed-loop system. Based on a new piecewise time-dependent Lyapunov functional, novel sufficient conditions are derived for the existence of robustly exponentially stabilizing sampled-data controllers. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities. Two simulation examples are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this manuscript, three repetitive types of sampling plans using the generalized process capability index of multiple characteristics are presented, which include a repetitive sampling plan, a resubmitted sampling plan and a multiple dependent state repetitive sampling plan. The plan parameters of these sampling schemes are determined through the nonlinear optimization solution. The comparison among these proposed plans is made in terms of the average sample number required. The use of the proposed plans is given with the help of an industrial example. [ABSTRACT FROM AUTHOR]

This study investigates aperiodic sampled‐data synchronisation of complex dynamical network (CDN) incorporate dynamics of actuators saturation. The sampling intervals considered here are time‐varying which allowed to be variable within the lower and upper bounds. In order to fully captures the sawtooth structure characteristic of the sampled‐data systems, a novel time‐dependent continuous Lyapunov functional is employed to obtain less conservative criteria. Furthermore, the generalised sector bound condition is utilised for the estimate about the basin of the attraction. Based on the local stability condition, the sampled‐data controller is constructed to guarantee the synchronisation of CDN in the presence of actuators saturation. The obtained sufficient conditions can be cast in two optimisation cases to maximise the admissible upper bound of sampling instants or enlarge estimate about the domain of attraction for the closed‐loop systems. Subsequently, the validity and applicability of the criteria are verified through numerical examples. [ABSTRACT FROM AUTHOR]

This article considers statistical process control (SPC) of univariate processes, and tries to make two contributions to the univariate SPC problem. First, we propose a continuously variable sampling scheme, based on a quantitative measure of the likelihood of a process distributional shift at each observation time point, provided by thep-value of the conventional cumulative sum (CUSUM) charting statistic. For convenience of the design and implementation, the variable sampling scheme is described by a parametric function in the flexible Box–Cox transformation family. Second, the resulting CUSUM chart using the variable sampling scheme is combined with an adaptive estimation procedure for determining its reference value, to effectively protect against a range of unknown shifts. Numerical studies show that it performs well in various cases. A real data example from a chemical process illustrates the application and implementation of our proposed method. This article has supplementary materials online. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of H∞ control for a stochastic sampling Markovian jump system subject to input saturation. The stochastic sampling we address is a Bernoulli distribution and two different sampling periods are considered whose occurrence probabilities are known constants. Actually the control method in this paper can be applied to a system with multiple stochastic sampling periods. By transforming the original stochastic sampling Markovian jump system into a continuous Markovian jump delayed systems, the plant can be stabilized by a state-feedback controller with input saturation. By applying an appropriate Lyapunov–Krasovskii function, some sufficient conditions for the stabilization of the system and the H∞ controller design are derived in terms of linear matrix inequalities. Finally, in order to validate the efficiency of the approach mentioned above, a simulation example is provided. [ABSTRACT FROM AUTHOR]

This work is concerned with the robust resilient control problem for uncertain networked control systems (NCSs) with variable sampling intervals, variant-induced delays and possible data dropouts, which is seldom considered in current literature. It is mainly based on the continuous time-varying-delay system approach. Followed by the nominal case, delay-dependent resilient robust stabilising conditions for the closed-loop NCS against controller gain variations are derived by employing a novel Lyapunov–Krasovskii functional which makes good use of the information of both lower and upper bounds on the varying input delay, and the upper bound on the variable sampling interval as well. A feasible solution of the obtained criterion formulated as linear matrix inequalities can be gotten. A tighter bounding technique is presented for acquiring the time derivative of the functional so as to utilise many more useful elements, meanwhile neither slack variable nor correlated augmented item is introduced to reduce overall computational burden. Two examples are given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Unannounced Interim Inspections (UIIs) in nuclear facilities of the European Union have recently attracted major attention by the International Atomic Energy Agency (IAEA) and by European Atomic Energy Community (EURATOM)in the context of the IAEA/EURATOM Partnership Approach. Therefore, a research project had been organized by the Joint Research Centre in Ispra in collaboration with the Universität der Bundeswehr München in the framework of which the assumptions have been classified which are necessary for a quantitative analysis and a few variants have been studied in detail. In that project only so-called Attribute Sampling Procedures were considered which means that only errors of the second kind (no detection of the illegal activity), but not those of the first kind (false alarms), where taken into account. It was the purpose of the work presented here to investigate the impact of errors of the first kind on UIIs which may occur if so-called Variable Sampling Procedures are used. Two kinds of planning UIIs are considered: in the sequential one both players, the inspector and the operator of the facility, decide step by step to inspect resp. to start the illegal activity – if there is one. In the hybrid-sequential one the inspector decides at the beginning of the reference time interval where to place his UIIs, whereas the operator acts again sequentially. For two UIIs during the reference time interval equilibria are determined, which generalize the results of the above mentioned research project. It turns out that in both cases, the sequential and hybrid-sequential one, the equilibrium strategies of the inspector and the equilibrium payoffs to both players are the same, but not the equilibrium strategies of the operator. We try to present a plausible explanation for this surprising result.

The problem of sampled-data control is investigated for Takagi-Sugeno (T-S) fuzzy systems with aperiodic sampling intervals based on an enhanced input-delay approach. Delay-dependent stability and stabilizability conditions for the closed-loop continuous nonuniformly sampled-data fuzzy systems are derived by constructing a novel discontinuous Lyapunov-Krasovskii (L-K) functional, which makes good use of not only the upper bound on the variable sampling interval, but also its sawtooth structure information about varying input delay often ignored in previous results. A bounding technique combined by reciprocally convex technics and linear convex combination is presented for acquiring the time derivative of the functional, wherein Jensen's inequality and Wirtinger's inequality are integratively employed. And a feasible solution of the obtained criterion formulated as parameterized linear matrix inequalities is ultimately conceived. A numerical example is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Conventional Phase II statistical process control (SPC) charts are designed using control limits; a chart gives a signal of process distributional shift when its charting statistic exceeds a properly chosen control limit. To do so, we only know whether a chart is out-of-control at a given time. It is therefore not informative enough about the likelihood of a potential distributional shift. In this paper, we suggest designing the SPC charts using p values. By this approach, at each time point of Phase II process monitoring, the p value of the observed charting statistic is computed, under the assumption that the process is in-control. If the p value is less than a pre-specified significance level, then a signal of distributional shift is delivered. This p value approach has several benefits, compared to the conventional design using control limits. First, after a signal of distributional shift is delivered, we could know how strong the signal is. Second, even when the p value at a given time point is larger than the significance level, it still provides us useful information about how stable the process performs at that time point. The second benefit is especially useful when we adopt a variable sampling scheme, by which the sampling time can be longer when we have more evidence that the process runs stably, supported by a larger p value. To demonstrate the p value approach, we consider univariate process monitoring by cumulative sum control charts in various cases. [ABSTRACT FROM AUTHOR]

This article investigates the robust synchronisation problem for uncertain nonlinear chaotic systems. The norm-bounded uncertainties enter into the chaotic systems in random ways, and such randomly occurring uncertainties (ROUs) obey certain Bernoulli distributed white noise sequences. For this synchronisation problem, the sampled-data controller that has randomly varying sampling intervals is considered. In order to fully use the sawtooth structure characteristic of the sampling input delay, a discontinuous Lyapunov functional is proposed based on the extended Wirtinger inequality. By the Lyapunov stability theory and the linear matrix inequality (LMI) framework, the existence condition for the sample-date controller that guarantees the robust mean-square synchronisation of chaotic systems is derived in terms of LMIs. Finally, in order to show the effectiveness of our result, the proposed method is applied to two numerical examples: one is Chua's chaotic systems and the other is the hyperchaotic Rössler system. [ABSTRACT FROM PUBLISHER]

Abstract: The particle filter technique has been used extensively over the past few years to track objects in challenging environments. Due to its nonlinear nature and the fact that it does not assume a Gaussian probability density function it tends to outperform other available tracking methods. A novel adaptive sample count particle filter (ASCPF) tracking method is presented in this paper for which the main motivation is to accurately track an object in crowded scenes using fewer particles and hence with reduced computational overhead. Instead of taking a fixed number of particles, a particle range technique is used where an upper and lower bound for the range is initially identified. Particles are made to switch between an active and inactive state within this identified range. The idea is to keep the number of active particles to a minimum and only to increase this as and when required. Active contours are also utilized to determine a precise area of support around the tracked object from which the color histograms used by the particle filter can be accurately calculated. This, together with the variable particle spread, allows a more accurate proposal distribution to be generated while using less computational resource. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster. [Copyright &y& Elsevier]

Abstract: This paper develops a fault-tolerant variable sampling control (VSC) scheme for a class of nonlinear networked control systems (NCSs) with time-varying state and random network delays. An uncertain continuous Takagi–Sugeno (T–S) fuzzy system with both state and input varying delays, in the presence of possible actuator faults, is obtained equivalently on the basis of the input delay methodology. A tighter bounding lemma is proposed so as to gain less conservative closed-loop stability criteria. Delay-dependent conditions in terms of linear matrix inequalities are derived for the mode-independent fault-tolerant stabilizing controller of the resulting Markovian network-based system by employing a novel stochastic Lyapunov–Krasovskii (L–K) functional. An illustrative example is simulated to show the validity of the obtained results. [Copyright &y& Elsevier]

SUMMARY This paper proposes a method for the design of a predictive controller with variable step sizes. Instead of using a fixed sampling frequency, the switching times are incorporated into an optimization problem, thus resulting in an adaptive step-size control process. The controller with the variable time steps is shown to require less tuning and reduces the number of expensive model evaluations. An alternate solution approach had to be implemented to accommodate the new formulation. Stability is proven in a context that does not require terminal cost or constraints. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes an approach which simultaneously considers the properties of cost and quality based on the Burr distribution to determine three parameters (including sample size, sampling interval between successive samples, and the control limits) when an x-bar chart monitors a manufacturing process with Weibull failure characteristic and non-normal data. Also, the cost model of Banerjee and Rahim (1988) is used as the objective function, and the probability density function of the Burr distribution is applied to derive the statistical constraints of economic statistical design of the x-bar control charts for non-normal data. The example of Banerjee and Rahim (1988) is adopted to indicate the solution procedure and sensitivity analyses. Meanwhile, the design parameters of the x-bar control charts can be obtained through the grid search method. The results show that an increase of skewness coefficient (η) results in a slight decrease for sample size (η), but is robust to the control limit width (L). Also, an increase of kurtosis coefficient (η) leads to a wider control limit width. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the problem of robust control is investigated for sampled-data systems with probabilistic sampling. The parameter uncertainties are time-varying norm-bounded and appear in both the state and input matrices. For the simplicity of technical development, only two different sampling periods are considered whose occurrence probabilities are given constants and satisfy Bernoulli distribution, which can be further extended to the case with multiple stochastic sampling periods. By applying an input delay approach, the probabilistic sampling system is transformed into a continuous time-delay system with stochastic parameters in the system matrices. By linear matrix inequality (LMI) approach, sufficient conditions are obtained, which guarantee the robust mean-square exponential stability of the system with an performance. Moreover, an controller design procedure is then proposed. An illustrative example is included to demonstrate the effectiveness of the proposed techniques. [ABSTRACT FROM AUTHOR]

The use of fixed-area plot methods was considered unsuitable to compare the responses and recovery over time of different miombo woodland species to different land-use practices (i.e. charcoal production and slash-and-burn agriculture). Young regrowth stages have many stems and the older more advanced stages have fewer stems. Different land-use practices in miombo woodland impact on the recovery potential of the woodlands. Timber harvesting does not result in significant change in stocking of the woodland compared to changes in regrowth after vegetation clearing for either charcoal production or slash-and-burn agriculture. After such clearing the woodland regrowth changes from an initial high stocking to a much lower stocking over time. This study in Zambian Copperbelt miombo woodland assessed the feasibility to use species-stem curves to determine the optimum number of stems or plants to record at a sampling point in order to compare species recovery over time in regrowth stands 2-15 years after ending the slash-and-burn and charcoal production activities. The results show that 34 and 31 stems or plants for slash-and-burn and charcoal production regrowth stands, respectively, would adequately capture the representative number of species to describe the plant community of these regrowth stands. The research has also revealed that the use of fixed-area methods would result in the measuring of too many plants in one category (younger stands) with too few in the other category (advanced stands). Therefore, the study explains why, and concludes that, variable plot size is an appropriate method for sampling species recovery in regrowth stands. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the problem of filtering for continuous-time systems under sampled measurements with probabilistic sampling. It is assumed that the occurrence probabilities of the sampling intervals are given constants and satisfy a Bernoulli distribution. Through a transformation of the discrete time instants, the filtering error system is formulated as a continuous-time system with delays and stochastic parameters. Then, filter is designed such that the filtering error system is exponentially stable in the mean square, and the -induced gain from the noise signal to the estimation error is guaranteed to be less than a prescribed level. Finally, an example is given to show the effectiveness of the theoretical results. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of robust control is investigated for sampled-data systems with probabilistic sampling. The parameter uncertainties are time-varying norm-bounded and appear in both the state and input matrices. For the simplicity of technical development, only two different sampling periods are considered whose occurrence probabilities are given constants and satisfy Bernoulli distribution, which can be further extended to the case with multiple stochastic sampling periods. By applying an input-delay approach, the probabilistic sampling system is transformed into a continuous time-delay system with stochastic parameters in the system matrices. By linear matrix inequality (LMI) approach, sufficient conditions are obtained, which guarantee the robust mean-square exponential stability of the system with an performance. Moreover, an controller design procedure is then proposed. An illustrative example is included to demonstrate the effectiveness of the proposed techniques. [Copyright &y& Elsevier]

The authors propose a new adaptive method for the estimation of frequencies for the signals whose frequencies are known in advance only roughly, which consists in updating only the sampling frequency. In this paper, we investigate an adaptive method for estimation of unknown frequencies by means of parallel processing, based on an expansion of a previously proposed method. If the range of the frequencies to be estimated can be determined, several initial synchronous signals covering that range are set up. Then, each synchronous signal is adaptively changed according to the previously proposed algorithm, and the frequencies existing within this range are estimated. Using simulation, we have established performance characteristics of the proposed method by comparing it with conventional frequency estimation methods. Finally, we propose an application of this method to the detection of DTMF signals. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(5): 43–52, 2001 [ABSTRACT FROM AUTHOR]

An important drawback in the application of model-based predictive controllers for multiphase systems is the relatively high harmonic content. Harmonics arise due to the fixed sampling-time nature and the absence of modulation methods in the control technique. Recent research works have proposed different procedures to overcome this disadvantage at the expense of increasing the complexity of the controller and, in most cases, the computational requirements. There are, however, natural ways to face this harmonic generation that have been barely explored in the scientific literature. These alternatives include the use of variable sampling times or the application of the observer theory, whose utility has been stated without excessively increasing the computational cost of the controller. This paper presents the basis of both methodologies, analyzing their interest as natural alternatives to mitigate the generation of harmonic components in modern electrical drives when using predictive controllers. A five-phase induction machine is used as a case example to experimentally validate the study and draw conclusions. [ABSTRACT FROM AUTHOR]

Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease. However, there are no widely accepted clinical methods for estimating MBF. Dynamic cardiac perfusion computed tomography (CT) holds the promise of providing a quick and easy method to measure MBF quantitatively. However, the need for repeated scans can potentially result in a high patient radiation dose, limiting the clinical acceptance of this approach. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence. These dose reduction techniques result in noisy time-attenuation curves (TACs), which can give rise to significant errors in MBF estimation. We seek to investigate whether nonuniformly varying the tube current and/or sampling intervals can yield more accurate MBF estimates for a given dose. Specifically, we try to minimize the dose and obtain the most accurate MBF estimate by addressing the following questions: when in the TAC should the CT data be collected and at what tube current(s)? We hypothesize that increasing the sampling rate and/or tube current during the time frames when the myocardial CT number is most sensitive to the flow rate, while reducing them elsewhere, can achieve better estimation accuracy for the same dose. We perform simulations of contrast agent kinetics and CT acquisitions to evaluate the relative MBF estimation performance of several clinically viable variable acquisition methods. We find that variable temporal and tube current sequences can be performed that impart an effective dose of 5.5 mSv and allow for reductions in MBF estimation root-mean-square error on the order of 20% compared to uniform acquisition sequences with comparable or higher radiation doses.