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In this paper, we show how quantum Boolean circuits can be used to implement the oracle and the inversion-about-average function in Grover's search algorithm. Before illustrating how this can be done, we present the circuit design principle using the satisfiability (SAT) problem as an example. Then, based on this principle, we show the quantum circuits for two different kinds of applications. The first one is searching a phone book. Although this is a typical example of Grover's algorithm, we show that it is impractical as a real-world application. As the second application, we give the quantum circuits for a more practical application—breaking a symmetric cryptosystem. Although these two applications have quite different types of search criteria, they are both one-way functions and the proposed circuits can actually be generalized to any such problems. In this perspective, we conclude this paper by proposing a template of quantum circuits that is capable of searching the solution of a certain class of one-way functions. [ABSTRACT FROM AUTHOR]

Purpose |!|#8211; The purpose of this paper is to identify a new and simple two-end algorithm for fault location identification and characterization, in electrical distribution systems. Design/methodology/approach |!|#8211; The developed diagnostic algorithm is based on a simple model of the network using a lumped parameters representation. Findings |!|#8211; Test results have proved the approach to be efficient, allowing a precise fault identification and location while not requiring synchronized measures from the two ends. Research limitations/implications |!|#8211; There is a need for measurement systems at all MV/LV substations. Practical implications |!|#8211; Applicability with limited investments is not possible where metering systems are not so diffused, although smart grids and DG units require such infrastructures. Moreover, utilities are quite interested in such issues, since the new required quality standards put severe constraints on faults management and clearance. Originality/value |!|#8211; The paper presents a new and easier diagnostic algorithm for faults diagnosis in distribution systems. [ABSTRACT FROM AUTHOR]

Abstract: In crisp run control rules, usually it is stated that a process moves very sharply from in-control condition to out-of-control act. This causes an increase in both false-alarm rate and control chart sensitivity. Moreover, the classical run control rules are not implemented on an intelligent sampling strategy that changes control charts’ parameters to reduce error probability when the process appears to have a shift in parameter values. This paper presents a new hybrid method based on a combination of fuzzified sensitivity criteria and fuzzy adaptive sampling rules, which make the control charts more sensitive and proactive while keeping false alarms rate acceptably low. The procedure is based on a simple strategy that includes varying control chart parameters (sample size and sample interval) based on current fuzzified state of the process and makes inference about the state of process based on fuzzified run rules. Furthermore, in this paper, the performance of the proposed method is examined and compared with both conventional run rules and adaptive sampling schemes. [Copyright &y& Elsevier]

Purpose - The main intention of this paper is to state the benefits of using online videogames as a research environment, where AI algorithms are improved by means of learning from real-human-behaviour examples. Design/methodology/approach - The manner of taking advantage from the flux of real-human-behaviour examples inside an online videogame is stated. Then Mad University, a prototype online videogame specifically conceived and developed for this purpose, is explained. Findings - Human-like AI in artificial algorithms can be boosted by means of a specific kind of online videogame called MMORPGs, used as a research environment. Research limitations/implications - Mad University is a prototype videogame which has been developed to experiment with AI algorithms that aim to learn strategies in a generalized fashion. The next research step will be to improve Mad University and to put it to work with hundreds of players and then research and test the effectiveness of the AI algorithms. Originality/value - This paper proposes a new way of testing and experimenting with AI algorithms in order to obtain more human-like results, and claims to have attempted to develop a generalized learning method. [ABSTRACT FROM AUTHOR]

The article discusses the priorities of researchers who post, share and recommend papers on academic social networks in terms of profit, user data and open access publishing. Topics include the value of academic social networks as a way of getting publications online and making them publicly available, the impact of this process on speeding up the pace of discovery and facilitating a revolution in peer review, and the influence of networks' discovery algorithm on academic life.

Abstract: This paper describes selected developments in transonic flow simulation technology that have utilized nonlinear potential methods for external aerodynamic applications. In particular, the research efforts in this field at Ames Research Center are highlighted. Included are a review of the various potential equation forms, the pertinent characteristics associated with key potential equation numerical algorithms, and a variety of numerical results for various aerodynamic applications to highlight key discussion points. [Copyright &y& Elsevier]

Abstract: This paper investigates probabilistic logics endowed with independence relations. We review propositional probabilistic languages without and with independence. We then consider graph-theoretic representations for propositional probabilistic logic with independence; complexity is analyzed, algorithms are derived, and examples are discussed. Finally, we examine a restricted first-order probabilistic logic that generalizes relational Bayesian networks. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce weights into Pawlak rough set model to balance the class distribution of a data set and develop a weighted rough set based method to deal with the class imbalance problem. In order to develop the weighted rough set based method, we design first a weighted attribute reduction algorithm by introducing and extending Guiasu weighted entropy to measure the significance of an attribute, then a weighted rule extraction algorithm by introducing a weighted heuristic strategy into LEM2 algorithm, and finally a weighted decision algorithm by introducing several weighted factors to evaluate extracted rules. Furthermore, in order to estimate the performance of the developed method, we compare the weighted rough set based method with several popular methods used for class imbalance learning by conducting experiments with twenty UCI data sets. Comparative studies indicate that in terms of AUC and minority class accuracy, the weighted rough set based method is better than the re-sampling and filtering based methods, and is comparable to the decision tree and SVM based methods. It is therefore concluded that the weighted rough set based method is effective for class imbalance learning. [Copyright &y& Elsevier]

Abstract: Applying dither to highly nonlinear systems may suppress chaotic phenomena, but dynamic performance, such as convergence rate and disturbance attenuation, is usually not guaranteed. This paper presents a dithered H ∞ robust fuzzy control scheme to stabilize chaotic systems that ensures disturbance attenuation bounds. In the proposed scheme, Takagi–Sugeno (T–S) fuzzy linear models are used to describe the relaxed models of the dithered chaotic system, and fuzzy controllers are designed based on an extension to the concept of parallel distributed compensation (PDC). Sufficient condition for the existence of the H ∞ robust fuzzy controllers is presented in terms of a novel linear matrix inequalities (LMI) form which takes full consideration of modeling error and disturbances, but cannot be solved by the standard procedures. In order to solve the LMI problem and to identify the chaotic systems as T–S fuzzy modes, we propose a compound optimization strategy called the island-based random-walk algorithm (IRA). The algorithm is composed of a set of communicating random-walk optimization procedures concatenated with the down-hill simplex method. The design procedure and validity of the proposed scheme is demonstrated via numerical simulation of the dithered fuzzy control of a chaotic system. [Copyright &y& Elsevier]

Abstract: In this paper, the notions of L-fuzzy convex sets and L-fuzzy numbers are introduced where L is a completely distributive lattice. The notions of [0,1]-fuzzy convex sets and [0,1]-fuzzy numbers are generalized. Furthermore their properties and characterizations are presented in terms of cut sets of L-fuzzy sets. [Copyright &y& Elsevier]

Abstract: This paper studies evolutionary programming and adopts reinforcement learning theory to learn individual mutation operators. A novel algorithm named RLEP (Evolutionary Programming based on Reinforcement Learning) is proposed. In this algorithm, each individual learns its optimal mutation operator based on the immediate and delayed performance of mutation operators. Mutation operator selection is mapped into a reinforcement learning problem. Reinforcement learning methods are used to learn optimal policies by maximizing the accumulated rewards. According to the calculated Q function value of each candidate mutation operator, an optimal mutation operator can be selected to maximize the learned Q function value. Four different mutation operators have been employed as the basic candidate operators in RLEP and one is selected for each individual in different generations. Our simulation shows the performance of RLEP is the same as or better than the best of the four basic mutation operators. [Copyright &y& Elsevier]

Abstract: Transparent models search for a balance between interpretability and accuracy. This paper is about the estimation of transparent models of chaotic systems from data, which are accurate and simple enough for their expression to be understandable by a human expert. The models we propose are discrete, built upon common blocks in control engineering (gain, delay, sum, etc.) and optimized both in their complexity and accuracy. The accuracy of a discrete model can be measured by means of the average error between its prediction for the next sampling period and the true output at that time, or ‘one-step error’. A perfect model has zero one-step error, but a small error is not always associated with an approximate model, especially in chaotic systems. In chaos, an arbitrarily low difference between two initial states will produce uncorrelated trajectories, thus a model with a low one-step error may be very different from the desired one. Even though a recursive evaluation (multi-step prediction) improves the fitting, in this work we will show that a learning algorithm may not converge to an appropriate model, unless we include some terms that depend on estimates of certain properties of the model (so called ‘invariants’ of the chaotic series). We will show this graphically, by means of the reconstructed attractors of the original system and the model. Therefore, we also propose to follow a multi-objective approach to model chaotic processes and to apply a simulated annealing-based optimization to obtain transparent models. [Copyright &y& Elsevier]

The small signal behavior of a frequency controlled resonant converter is modeled as an equivalent pulsewidth modulation (PWM) converter. By this model, a recently developed simulation algorithm for PWM convener Is modified to simulate both the open loop and closed loop small signal behaviour. Experimental results show that this approach is accurate and efficient. Resonant converters have many advantages over conventional PWM converters. There are many research works on the analysis and design of resonant converters, covering both the steady states and theft small-signal responses.