varunr14/newsummary huggingface dataset:
Scalar and vector drives have been the cornerstones of control of industrial motors for decades . In both the elimination of mechanical speed sensor consists in a trend of modern drives . This work proposes the development of an adaptive neuro fuzzy inference system angular rotor speed estimator applied to vector and scalar drives . A multi frequency training of ANFIS is proposed initially for a V f scheme and after that a vector drive with magnetizing flux oriented control is proposed . In the literature ANFIS has been commonly proposed as a speed controller in substitution of the classical PI controller of the speed control loop . This paper investigates the ANFIS as an open loop speed estimator instead . The subtractive clustering technique was used as strategy for generating the membership functions for all the incoming signal inputs of ANFIS . This provided a better analysis of the training data set improving the comprehension of the estimator . Additionally the subtractive cluster technique allowed the training with experimental data corrupted by noise improving the estimator robustness . Simulations to evaluate the performance of the estimator considering the V f and vector drive system were realized using the Matlab Simulink software . Finally experimental results are presented to validate the ANFIS open loop estimator . angular speed of the magnetizing flux oriented reference frame angular slip speed direct sum overall function implemented by the adaptive network arbitrary function instantaneous value of magnetizing current instantaneous value of direct component stator current instantaneous value of quadrature component stator current magnetizing inductance stator inductance predicted network output for the pattern k differential operator positive constant positive constant stator resistance set of parameters desired network output for the pattern k rotor leakage time constant rotor time constant instantaneous value of direct component stator voltage instantaneous value of quadrature component stator voltage vector of input variables moment of inertia number of training pattern number of pole pairs 


Summary: It was modeled a ANFIS speed estimator applied to both vector and scalar induction motor drives. Subtractive clustering was used to generate the membership functions. Subtractive clustering allowed training the ANFIS with experimental data with noise. In the scalar drive the ANFIS estimator used the RMS values of voltages and currents as incoming signals. Magnetizing FOC was used in the vector drive instead of Rotor FOC.
Most of the real world problems have dynamic characteristics where one or more elements of the underlying model for a given problem including the objective constraints or even environmental parameters may change over time . Hyper heuristics are problem independent meta heuristic techniques that are automating the process of selecting and generating multiple low level heuristics to solve static combinatorial optimization problems . In this paper we present a novel hybrid strategy for applicability of hyper heuristic techniques on dynamic environments by integrating them with the memory search algorithm . The memory search algorithm is an important evolutionary technique that have applied on various dynamic optimization problems . We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark . The former problem is extended from the generalized assignment problem by changing resource consumptions capacity constraints and costs of jobs over time and the latter one is a well known synthetic problem that generates and updates a multidimensional landscape consisting of several peaks . Experimental evaluation performed on various instances of the given two problems validates that our hyper heuristic integrated framework significantly outperforms the memory search algorithm . 


Summary: We propose a novel hybrid strategy for applicability of hyper heuristic techniques on dynamic environments. Performance of our method is validated with the dynamic generalized assignment problem and the moving peaks benchmark. Our approach outperforms the related work for various problem instances with respect to quality of solutions.
Artificial chromosomes with genetic algorithm is one of the latest versions of the estimation of distribution algorithms . This algorithm has already been applied successfully to solve different kinds of scheduling problems . However due to the fact that its probabilistic model does not consider variable interactions ACGA may not perform well in some scheduling problems particularly if sequence dependent setup times are considered . This is due to the fact that the previous job will influence the processing time of the next job . Simply capturing ordinal information from the parental distribution is not sufficient for a probabilistic model . As a result this paper proposes a bi variate probabilistic model to add into the ACGA . This new algorithm is called the ACGA2 and is used to solve single machine scheduling problems with sequence dependent setup times in a common due date environment . A theoretical analysis is given in this paper . Some heuristics and local search algorithm variable neighborhood search are also employed in the ACGA2 . The results indicate that the average error ratio of this ACGA2 is half the error ratio of the ACGA . In addition when ACGA2 is applied in combination with other heuristic methods and VNS the hybrid algorithm achieves optimal solution quality in comparison with other algorithms in the literature . Thus the proposed algorithms are effective for solving the scheduling problems . 


Summary: The major motivation of ACGA2 is to take the bi variate probabilistic models into the consideration. We further provide a theoretical analysis of the two models EDAs. This studied conducted extensive experiments on the single machine scheduling problems with sequence dependent setup times in a common due date environment. The experimental result shows the proposed ACGA2 outperforms ACGA significantly because the average error ratio of ACGA2 is half of ACGA.
Four wave mixing crosstalk is the dominant nonlinear effect in long haul repeaterless wavelength division multiplexing lightwave fiber optical communication systems . To reduce FWM crosstalk in optical communication systems unequally spaced channel allocation method is used . One of the unequal bandwidth channel allocation techniques is designed by using the concept of Golomb ruler . It allows the gradual computation of an optimally allocated channel set such that degradations caused by inter channel interference and FWM is minimal . This paper applies two soft computing based approaches i.e . Genetic Algorithm and Biogeography Based Optimization to generate near optimal Golomb ruler sequences in reasonable time . The generated sequences have been compared with the two other classical approaches namely Extended Quadratic Congruence and Search Algorithm . It has been observed that BBO GA outperforms the other two approaches . 


Summary: The application of Genetic Algorithm and Biogeography Based Optimization to generate near optimal Golomb ruler sequences is being proposed. Both the approaches produce near optimal Golomb ruler sequences very efficiently in reasonable execution time. The performances have been compared with the two other classical methods namely Extended Quadratic Congruence and Search Algorithm. The preliminary results indicate that BBO and GA appear to be most efficient approach to such NP complete problems. BBO approach outperforms the GA slightly for larger mark values.
In this paper some multi item inventory models for deteriorating items are developed in a random planning horizon under inflation and time value money with space and budget constraints . The proposed models allow stock dependent consumption rate and partially backlogged shortages . Here the time horizon is a random variable with exponential distribution . The inventory parameters other than planning horizon are deterministic in one model and in the other the deterioration and net value of the money are fuzzy available budget and space are fuzzy and random fuzzy respectively . Fuzzy and random fuzzy constraints have been defuzzified using possibility and possibility probability chance constraint techniques . The fuzzy objective function also has been defuzzified using possibility chance constraint against a goal . Both deterministic optimization problems are formulated for maximization of profit and solved using genetic algorithm and fuzzy simulation based genetic algorithm . The models are illustrated with some numerical data . Results for different achievement levels are obtained and sensitivity analysis on expected profit function is also presented . Scope and purpose The traditional inventory model considers the ideal case in which depletion of inventory is caused by a constant demand rate . However for more sale inventory should be maintained at a higher level . Of course this would result in higher holding or procurement cost etc . Also in many real situations during a shortage period the longer the waiting time is the smaller the backlogging rate would be . For instance for fashionable commodities and high tech products with short product life cycle the willingness for a customer to wait for backlogging diminishes with the length of the waiting time . Most of the classical inventory models did not take into account the effects of inflation and time value of money . But at present the economic situation of most of the countries has been much deteriorated due to large scale inflation and consequent sharp decline in the purchasing power of money . So it has not been possible to ignore the effects of inflation and time value of money any further . The purpose of this article is to maximize the expected profit of two inventory control systems in the random planning horizon . 


Summary: Multi item inventory models allowing partial backlogging deterioration under random planning horizon. Inventory models with random planning horizon in imprecise environment with the effect of inflation and discounting. A random fuzzy constraint has been successively introduced for the first time in the inventory models. A real life inventory system with limitations on available budget and storing space constraints in both fuzzy and random fuzzy. Maximum profit has been compared by genetic algorithm and fuzzy simulation based genetic algorithm.
This study applies the Multiple Criteria Decision Making to evaluate the service quality of some Turkish hospitals . In general the service quality has abstract properties which mean that using the previously known measurement approach is insufficient . It is for this reason that the fuzzy set theory is adopted as a research template . In Istanbul Turkey there are four B class hospitals classed as private hospitals that are covered by the Social Security Institution and for which we propose to represent the service performance measurement using triangular fuzzy numbers . In this study importance weights of performance criteria are found with AHP . Then the Multiple Criteria Decision Making methods TOPSIS and Yager s min max approach are applied to find and rank the crisp performance values . In a second step an aggregation of performance criteria with OWA and Compensatory AND operators are looked at instead of the TOPSIS method and min max approach . Thereby numerical applications are supplied by the four methods and the obtained results are compared . 


Summary: We have applied Multiple Criteria Decision Making MCDM to evaluate the service quality of Turkish hospitals. We adopted fuzzy set theory as a research template. Importance weights of the performance criteria have found with AHP. TOPSIS which is MCDM method has applied to find and rank the crisp performance values. OWA operators Compensatory AND operator and Yager s Min Max method have investigated instead of TOPSIS method.
In this paper we suggest a block image encryption algorithm which can give us an efficient scheme to hide and encrypt image data . Only the diffusion function instead of classical permutation plus diffusion operations is adopted . The plain image is firstly divided into two equal parts randomly by vertical horizontal or diagonal directions . Then encryption of one part depends on the other part in which the keystream is generated by the plain image i.e . one of the two parts . An error concept is added in the initial conditions in every round . It means that the keystreams are different in the process of encryption steps . The error may be positive or negative decided by a rule of sign function . Experiment results show that the proposed method can provide a high security of cryptosystem and can reduce the computation redundancy compared with that of the traditional architectures such as Arnold map based method and totally shuffling based method . 


Summary: Self adaptive error is added in the initial conditions in every round of which shows the novelty of this manuscript. A rule is designed to let errors be positive or negative. A new control parameter is generated dependent on errors it is also self adaptive. Only diffusion function is applied but it can reach a high security and less time cost. The keystream depends on the plain image which can resist efficiently the known plaintext and chosen plaintext attacks. UACI unified average changing intensity and NPCR number of pixels change rate results confirm that the proposed method can reach the ideal effect.
We develop an orthogonal forward selection approach to construct radial basis function network classifiers for two class problems . Our approach integrates several concepts in probabilistic modelling including cross validation mutual information and Bayesian hyperparameter fitting . At each stage of the OFS procedure one model term is selected by maximising the leave one out mutual information between the classifier s predicted class labels and the true class labels . We derive the formula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for model term selection . Furthermore a Bayesian procedure of hyperparameter fitting is also integrated into the each stage of the OFS to infer the l 2 norm based local regularisation parameter from the data . Since each forward stage is effectively fitting of a one variable model this task is very fast . The classifier construction procedure is automatically terminated without the need of using additional stopping criterion to yield very sparse RBF classifiers with excellent classification generalisation performance which is particular useful for the noisy data sets with highly overlapping class distribution . A number of benchmark examples are employed to demonstrate the effectiveness of our proposed approach . 


Summary: An orthogonal forward selection algorithm is proposed for constructing radial basis function classifiers based on maximises the leave one out mutual information between the classifier s predicted class labels and the true class labels. Integrated within each OFS step a Bayesian procedure of hyperparameter fitting is introduced to infer the l 2 norm local regularisation parameter from the data. The results obtained demonstrate that the proposed algorithm automatically constructs very parsimonious RBF classifiers with excellent classification generalisation performance.
In medical system there may be many critical diseases where experts do not have sufficient knowledge to handle those problems . For these cases experts may provide their opinion only about certain aspects of the disease and remain silent for those unknown features . Feeling the need of prioritizing different experts based on their given information this article uses a novel concept for assigning confident weights to different experts which are mainly based on their provided information . Experts provide their opinions about various symptoms using intuitionistic fuzzy soft matrix . In this article we propose an algorithmic approach based on intuitionistic fuzzy soft set which explores a particular disease reflecting the agreement of all experts . This approach is guided by the group decision making model and uses cardinals of IFSS as novel concept . We have used choice matrix as an important parameter which is based on choice parameters of individual expert . This article has also validated the proposed approach using distance measurements and consents of the majority of experts . The effectiveness of the proposed approach is demonstrated using a suitable case study . 


Summary: Group decision making for medical diagnosis. Intuitionistic fuzzy soft set and fuzzy soft matrix. Hamming distance and Euclidean approach. Cardinal of intuitionistic fuzzy soft set to compute the weight. Viral fever related diagnosis.
JCSE SPIHT an algorithm of joint compression and selective encryption based on set partitioning in hierarchical trees is proposed to achieve image encryption and compression simultaneously . It can protect SPIHT compressed images by only fast scrambling a tiny portion of crucial data during the coding process while keeping all the virtues of SPIHT intact . Intensive experiments are conducted to validate and evaluate the proposed algorithm the results show that the efficiency and the compression performance of JCSE SPIHT are very close to original SPIHT . In security analysis JCSE SPIHT is proved to be immune to various attacks not only from traditional cryptanalysis but also by utilizing sophisticated image processing techniques . 


Summary: We propose JCSE SPIHT an algorithm of joint compression and selective encryption based on SPIHT. We design a fast random insertion to accelerate the encryption from taking O n 2 time to O n . JCSE SPIHT can generate plaintext dependent keystream by cryptographically secure PRNG. We exam the security of JCSE SPIHT by traditional cryptanalysis and image processing techniques.
All dynamic crop models for growth and development have several parameters whose values are usually determined by using measurements coming from the real system . The parameter estimation problem is raised as an optimization problem and optimization algorithms are used to solve it . However because the model generally is nonlinear the optimization problem likely is multimodal and therefore classical local search methods fail in locating the global minimum and as a consequence the model parameters could be inaccurate estimated . This paper presents a comparison of several evolutionary and bio inspired algorithms considered as global optimization methods such as Differential Evolution Covariance Matrix Adaptation Evolution Strategy Particle Swarm Optimization and Artificial Bee Colony on parameter estimation of crop growth SUCROS model . Subsequently the SUCROS model for potential growth was applied to a husk tomato crop using data coming from an experiment carried out in Chapingo Mexico . The objective was to determine which algorithm generates parameter values that give the best prediction of the model . An analysis of variance was carried out to statistically evaluate the efficiency and effectiveness of the studied algorithms . Algorithm s efficiency was evaluated by counting the number of times the objective function was required to approximate an optimum . On the other hand the effectiveness was evaluated by counting the number of times that the algorithm converged to an optimum . Simulation results showed that standard DE rand 1 bin got the best result . 


Summary: An approach based on evolutionary and bio inspired algorithms is proposed for solving the parameter estimation problem in crop growth dynamic models. Differential Evolution algorithm showed the best performance in solving the parameter estimation problem of a dynamic crop growth model. A statistical analysis and ANOVA were applied to quantitatively evaluate the efficiency and effectiveness of Differential Evolution CMA ES PSO ABC and LSE algorithms.
This paper presents a novel parameter automation strategy for particle swarm optimization algorithm for solving non convex emission constrained economic dispatch problems . Many evolutionary techniques such as particle swarm optimization differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods . But often these methods converge to a sub optimal solution prematurely . This paper presents a new improved particle swarm optimization technique called self organizing hierarchical particle swarm optimization technique with time varying acceleration coefficients for non convex emission constrained economic dispatch problems to avoid premature convergence . Generator ramp rate limits and prohibited operating zones are taken into account in problem formulation . Non convex emission constrained economic dispatch problem is obtained by considering both the economy and emission objectives . The performance of the proposed method is demonstrated on two sample test systems . The results of the proposed method are compared with other methods . It is found that the results obtained by the proposed method are superior in terms of fuel cost emission output and losses . 


Summary: Nonconvex emission constrained economic dispatch NECED is a complex optimization problem. Many optimization techniques and algorithm have been applied to solve it. A new improved particle swarm optimization technique is proposed for the same. The effectiveness of the proposed method is tested on two practical systems. The results are compared with classical as well as other heuristic technique.
This paper presents a real coded chemical reaction based algorithm to solve the short term hydrothermal scheduling problem . Hydrothermal system is highly complex and related with every problem variables in a nonlinear way . The objective of the hydro thermal scheduling is to determine the optimal hourly schedule of power generation for different hydrothermal power system for certain intervals of time such that cost of power generation is minimum . Chemical reaction optimization mimics the interactions of molecules in term of chemical reaction to reach a low energy stable state . A real coded version of chemical reaction optimization known as real coded chemical reaction optimization is considered here . To check the effectiveness of the RCCRO 3 different test systems are considered and mathematical remodeling of the algorithm is done to make it suitable for solving short term hydrothermal scheduling problem . Simulation results confirm that the proposed approach outperforms several other existing optimization techniques in terms quality of solution obtained and computational efficiency . Results also establish the robustness of the proposed methodology to solve STHS problems . 


Summary: The paper solved different short term hydro thermal scheduling STHS problems. The paper considered both small and large size test systems. Real coded chemical reaction optimization RCCRO is applied to solve the problem. Performance of RCCRO has been compared with different PSO DE modified DE etc. Performance of RCCRO has been found to be more encouraging to solve STHS problems.