Transactions of the institute of measurement and control pdf

These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable. A new model order abatement method based on the clustering of poles and zeros of a large-scale continuous time system is proposed.

The clustering of poles and zeros are used for finding the cluster centres. The abated model is identified from the cluster centres, which reflect the effectiveness of the dominant poles of the clusters.

The cluster centre is determined by taking [Formula: see text] root of the sum of the inverse of [Formula: see text] power of poles zeros in a particular cluster.

It is famous that the magnitude of the pole cluster centre plays an important role in the clustering technique for the simplification of large-scale systems.

The magnitude of the cluster centres computed by the modified pole clustering method or some other methods based on the pole clustering techniques is large as compared to the proposed technique.

The less magnitude of pole cluster centre reflects the better approximations and proper matching of the abated model with the original system.

Therefore, the proposed method offers better approximations matching between actual and abated systems during the transient period compared to some other clustering methods, which supports the replacement of large-scale systems by proposed abated systems.

The proposed technique is a generalized version of the standard pole clustering technique. The proposed method guarantees the retention of dominant poles, stability and other fundamental control properties of the actual plant in the abated model. The proposed algorithm is illustrated by the five standard systems taken from the literature. The accuracy and effectiveness of the proposed method are verified by comparing the time responses and various performance error indices.

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Optimal tuning of a PID controller using a wound healing algorithm based on the clonal selection principle. First probability distribution of non- random censoring and investigated the application of conforming proportion is determined using the Bayesian Bayesian decision theory.

Fallahnezhad et al. Aslam According to Berger , a non-informative prior et al. A non-informative prior is obtained by the principle of determined time.

Fallahnezhad and Aslam proposed a indifference, which equal probabilities are assigned to all new approach on selecting the different actions for a lot based probability events. They applied Bayesian modeling to matter which metric is used to solve this problem. Then the required sample size is deter- errors. Beta distribution function can be discretized to obtain In this research, first using the sample data, the probability required probabilities for decision tree Mazzuchi and Soyer, distribution of nonconforming proportion is determined In other words, the probability mass functions for pt determined, and then this variable is discretized.

The decision can be obtained as follows, tree approach is employed where e , a are decision nodes and z , p are chance nodes. It is one approach to design a solution pt thus probability of observing j defectives in sample of i algorithm. A decision tree is a flowchart-like structure in items follows binomial distribution. Decision trees Therefore based on the law of total probability, following have three kinds of nodes. In the decision node, a decision is obtained, must be selected.

It proceeds by first considering a decision might be m X m made at a terminal node. For plastics is inspected by experimenters. Thus we have: in this section. If we define N as the lot size, n as the! It is seen that in all cases, the expected cost of the pro- :Prfprobability of rejecting the lotg posed model is less than the expected cost of SSP. The result of the proposed model in all cases is reason- Thus the following is obtained, able.

For example, the expected cost of the proposed model increases by increasing the cost of the model! It is observed when the inspection cost during sam-! Optimal solution for different values of parameters. It is seen that when the nonconforming proportion 5. Thus in contrast with traditional sampling plans where z0! We have extended the z0! The optimal decision can be determined using the back- : ward induction approach.

The z1! First the sample sizes is determined optimal value of i then the optimal decision about the lot is obtained for each outcome In general, it is concluded that the optimal policy in the zj.

As items in the lot, then all nonconforming items are removed explained, the optimal decision is obtained using decision tree and replaced by good items thus we can be sure that best decision has been selected based d4 : Tumble and then screen all items in the lot on cost parameters. Tumbling is an action A comparison study for different value of a and b are carried of decreasing proportion of the defectives used by some on in this section, and the results are given in Table 3.

Assume industries Raiffa and Schlaifer, The amount of vitamin C in the juice is inspected by the proportion of defectives that are eliminated during tum- experimenters.

According to the presented approach, first a bling process. Depending on different values of a and b, various acceptance sampling models may be obtained.

In the rest of this paper, we used the above model to solve As can be seen in Table 3 , the optimal cost in the pro- the case study again. Now we start from terminal nodes of posed methodology is less than the optimal cost in single sam- decision trees. Assume that the cost for action of eliminating pling plan as was expected.

Table 4. When the nonconforming proportion is more than p 2 , ! When the nonconforming proportion is more than 0. Also, the required sample size d1 , d2 , d2 , d2 6. Then, the decision-tree approach is d3 , d2 , d2 7.

The inspection errors have also e2 than 0. Since inspection errors affect d2 , d2 , d2 on the nonconforming proportion, thus the correct values of Downloaded from tim.

The terms type I and type II errors are standard terms function in the presence of inspection errors. Cost objective that evaluated in sampling methods. A type I error is the function includes the cost of 1 Accepting the lot, 2 Rejecting incorrect rejection of an acceptable item.

A type II error is the the lot, 3 Inspecting all items in the lot. Inspection errors are incorrect acceptation of unacceptable item. Cost-objective function includes the cost of rejecting, accepting the lot and 1 cost of inspecting all items in the lot. Attribute acceptance Industrial Engineering Department, University of Yazd, Yazd, Iran 2 Department of Industrial Engineering, University of Yazd sampling and variable sampling models are two important categories in designing sampling plan.

The former is used Corresponding author: when the products are justified classified as good or bad. It has Email: Fallahnezhad yazd. The terms type I and type II errors proportion of the lot and expected cost of different actions.

A type I error is the incorrect rejection of a models has been analysed by many researchers for years. A type II error is the incorrect acceptation Some of such approaches are investigated by Ferrell , of nonconforming item that are defined as follows, Ferrell and Chhoker , Moskowitz and Tang and Schneider and Tang The cost of different decisions is evaluated based Where I1 , I2 denote the probability of type I and type II on the cost of terminal node using the backward induction.

Many author discussed about different Different decisions are summarized as follows: methods of sampling. Wortham and Wilson proposed an optimal sequential sampling plan using backward recursive 1. Decision on received lot inference. They presented a procedure based on dynamic pro- 2. Decision on sample size gramming for designing optimal acceptance sampling plans for item-by-item inspection. Using a Bayesian procedure, a Sample size should be determined via solving decision tree prior distribution is specified, and a suitable cost model is where the cost of terminal nodes is evaluated based on two employed, depicting the cost of sampling, accepting or reject- state variables.

The first one is nonconforming proportion ing the lot. Probability distribution function of noncon- tree approach for acceptance sampling model. They proposed forming proportion is obtained using Bayesian inference and a new acceptance sampling design to accept or reject a lot follows Beta distribution. Since the distribution is continuous, based on Bayesian modelling to update the probability distri- a discretization method is applied to have discrete values for bution function of the nonconforming proportion, then they state variable with known probabilities.

Then we can deter- determined the required sample size using backwards induc- mine the required probabilities for second state variable which tion in the decision tree approach. Finally, they determined is the number of defectives in the new sample by binomial an optimal n, c design. Tong et al. Chen et al. Cost of new sample size Fallahnezhad and Yousefi Babadi used stochastic 2. Cost of decision about the lot dynamic programming concept and Bayesian inference to decide among decisions of accepting or rejecting a lot.

Most of the sampling plans in the literature use the infor- Avinadav and Perlman proposed an economic inspec- mation in sample for decision-making about the lot, but the tion plan in which only a fraction of the lots, rather than each proposed sampling plan uses the data in sample for determin- lot, is inspected. They proved that the expected total cost is a ing how many more samples should be gathered, and it also strictly quasi-convex function of the inspection interval.

They deter- mined the expected total cost of quality control per lot under the assumption that rejected lots are scrapped. Jianwei et al. Notations presented a general model of sampling acceptance plan Consider a lot of size N with an unknown nonconforming for the exponential distribution with exponentially distributed proportion p is received.

First probability distribution of non- random censoring and investigated the application of conforming proportion is determined using the Bayesian Bayesian decision theory. Fallahnezhad et al.

Aslam According to Berger , a non-informative prior et al. A non-informative prior is obtained by the principle of determined time. Fallahnezhad and Aslam proposed a indifference, which equal probabilities are assigned to all new approach on selecting the different actions for a lot based probability events. They applied Bayesian modeling to matter which metric is used to solve this problem.

Then the required sample size is deter- errors. Beta distribution function can be discretized to obtain In this research, first using the sample data, the probability required probabilities for decision tree Mazzuchi and Soyer, distribution of nonconforming proportion is determined In other words, the probability mass functions for pt determined, and then this variable is discretized. The decision can be obtained as follows, tree approach is employed where e , a are decision nodes and z , p are chance nodes.

It is one approach to design a solution pt thus probability of observing j defectives in sample of i algorithm.