We present a novel temporal logic for expressing properties of behaviour in context. The logic is applied to models of continuous processes, specifically using the continuous pi-calculus as a modelling language for bi...
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We present a novel temporal logic for expressing properties of behaviour in context. The logic is applied to models of continuous processes, specifically using the continuous pi-calculus as a modelling language for biochemical systems. The logic allows the expression of the temporal behaviour of a system when placed in the context of another system. Here we study this in terms of biochemical reactions and the expression of temporal behaviour in the context of other biochemical processes. We present the syntax and semantics of the logic and study the model-checking problem over continuous time and continuous state-space process models, using the continuous pi-calculus. We present a succinct, but naive, model-checking algorithm and then show how this can be improved. We investigate the complexity of model-checking, where repeated ODE solving emerges as a particular cost;assess some limitations of the technique;and identify potential routes to overcome these. (c) 2014 Elsevier Inc. All rights reserved.
Imperfect data stream leads to tree size explosion and detrimental accuracy problems. Overfitting problem and the imbalanced class distribution reduce the performance of the original decision-tree algorithm for stream...
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Imperfect data stream leads to tree size explosion and detrimental accuracy problems. Overfitting problem and the imbalanced class distribution reduce the performance of the original decision-tree algorithm for stream mining. In this paper, we propose an incremental optimization mechanism to solve these problems. The mechanism is called Optimized Very Fast Decision Tree (OVFDT) that possesses an optimized node-splitting control mechanism. Accuracy, tree size, and the learning time are the significant factors influencing the algorithm's performance. Naturally a bigger tree size takes longer computation time. OVFDT is a pioneer model equipped with an incremental optimization mechanism that seeks for a balance between accuracy and tree size for data stream mining. It operates incrementally by a test-then-train approach. Three types of functional tree leaves improve the accuracy with which the tree model makes a prediction for a new data stream in the testing phase. The optimized node-splitting mechanism controls the tree model growth in the training phase. The experiment shows that OVFDT obtains an optimal tree structure in both numeric and nominal datasets.
In this paper we consider implementation algorithms and applications of the discretization method for diffusion equations on polygonal (2D) and polyhedral (3D) meshes recently proposed by one of the authors in [11]. T...
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In this paper we consider implementation algorithms and applications of the discretization method for diffusion equations on polygonal (2D) and polyhedral (3D) meshes recently proposed by one of the authors in [11]. The discretization method is based on the approximation of fluxes in the mixed finite element method by appropriate piecewise constant vector functions inside the mesh cells. The new piecewise constant fluxes are discontinuous inside the mesh cells but their normal components are continuous on the interfaces between neighbouring cells. Numerical results are given for test problems relevant to applications in reservoir simulation and basin modelling.
Mathematical models and numerical algorithms of substance transport over a closed graph of a cardiovascular system must, satisfy the property of conservativity. A conservative model and a numerical algorithm for calcu...
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Mathematical models and numerical algorithms of substance transport over a closed graph of a cardiovascular system must, satisfy the property of conservativity. A conservative model and a numerical algorithm for calculation of substance transport through a working heart are proposed in the paper. The efficiency of the algorithm considered here is confirmed by test calculations. A method for simulation of substance transport in tissues surrounding vessels is also proposed.
We analyze a multiserver queueing system with a finite buffer and impatient customers. The arrival customer flow is assumed to be Markovian. Service times of each server are phase-type distributed. If all servers are ...
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We analyze a multiserver queueing system with a finite buffer and impatient customers. The arrival customer flow is assumed to be Markovian. Service times of each server are phase-type distributed. If all servers are busy and a new arrival occurs, it enters the buffer with a probability depending on the total number of customers in the system and waits for service, or leaves the system with the complementary probability. A waiting customer may become impatient and abandon the system. We give an algorithm for finding the stationary distribution of system states and derive formulas for basic performance characteristics. We find Laplace-Stieltjes transforms for sojourn and waiting times. Numeric examples are given.
Large-scale scheduling problems have remained difficult to solve despite the intense research in the area and many successful algorithms. In this paper, a new decomposition algorithm is presented which finds a good fe...
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Large-scale scheduling problems have remained difficult to solve despite the intense research in the area and many successful algorithms. In this paper, a new decomposition algorithm is presented which finds a good feasible solution even for large-scale problems in a reasonable time. In each iteration, a maximum allowed number of operations is first assigned to available units, then the algorithm alternates between a production quantity maximization step and a production time minimization step in each iteration. The performance of the "rolling operation" algorithm is illustrated using an industrial example.
A new strategy for internal model control (IMC) is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM). Aimed at the chemical process with nonlinearity, the learning proce...
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A new strategy for internal model control (IMC) is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM). Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS), while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.
This paper proposes a novel self-structuring algorithm for the online adaptive fuzzy controller (SA-OAFC). The SA-OAFC capable of adding and deleting inference rules autonomously can start operating with an empty set ...
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This paper proposes a novel self-structuring algorithm for the online adaptive fuzzy controller (SA-OAFC). The SA-OAFC capable of adding and deleting inference rules autonomously can start operating with an empty set of fuzzy rules based on the desired output and actual output of the system to avoid conventional differential operation. It also takes advantage of the auxiliary fuzzy system to evaluate the approximated results with little information of the plant. The SA-OAFC is characterized by its good engineering approachability, robustness for all kinds of perturbations of the plant, and the ability to perform variable selection among a group of candidate input variables. Moreover, it manages to remarkably reduce the amount of computation and decrease the complexity of the system. This paper demonstrates the capabilities of SA-OAFC by a simulation example and then hardware-in-the-loop (HIL) experiment.
The multi-armed restless bandit problem is studied in the case where the pay-off distributions are stationary phi-mixing. This version of the problem provides a more realistic model for most real-world applications, b...
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The multi-armed restless bandit problem is studied in the case where the pay-off distributions are stationary phi-mixing. This version of the problem provides a more realistic model for most real-world applications, but cannot be optimally solved in practice, since it is known to be PSPACE-hard. The objective of this paper is to characterize a sub-class of the problem where good approximate solutions can be found using tractable approaches. Specifically, it is shown that under some conditions on the phi-mixing coefficients, a modified version of UCB can prove effective. The main challenge is that, unlike in the i.i.d. setting, the distributions of the sampled pay-offs may not have the same characteristics as those of the original bandit arms. In particular, the phi-mixing property does not necessarily carry over. This is overcome by carefully controlling the effect of a sampling policy on the pay-off distributions. Some of the proof techniques developed in this paper can be more generally used in the context of online sampling under dependence. Proposed algorithms are accompanied with corresponding regret analysis.
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