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Memristor is introduced as the fourth basic circuit element. In this paper, we show that piecewise linear function can be used to model memristor. Piecewise linear function is used as a window function to model the memristor device. The piecewise linear window function is linear at each subregion of the domain. Because of this good character, explicit relation between the thickness of the doped region and the charge can be delivered, together with the explicit memristance expression. Models using a general nonlinear window function may not have these explicit results. The piecewise linear window function is also more flexible than the existing window functions. Piecewise linear function can also be used to model the more general memristive systems.

In buildings with many elevators, elevator group control algorithm is used to efficiently dispatch the elevators to serve each hall call generated from different floors. Recent researches for energy saving of elevator system have attracted widespread interest, most of which achieve energy saving by minimizing the estimated energy consumption. This paper propose a rule-based algorithm for elevator group control, which focuses on design of dispatching rules for energy saving based on the inner mechanism of the elevator system. As the weights and other parameters for the rules matters a lot to the performance of the algorithm, coarse-to-fine searching strategy is used for optimization. It can overcome the combinatorial explosion of feasible set and the limitation of simulation platform. The proposed algorithm is tested on a very practical simulation platform. The result is reliable, and shows the efficiency of the algorithm on energy saving.the broad application of energy technology in this century, energy issue brings many problems, such as environmental pollution and energy shortage [1], and this causes the public's extensive attention to energy saving. As a great part of power consumption in city, the proliferation use of elevator system causes the public's concern, making reducing the energy cost demanding. This paper concentrates on energy saving by optimization of group elevator scheduling in mid-rise and high-rise buildings, as non-optimized elevator group control leads to poor service performance and much more power consumption. Elevator group control system (EGCS) manages systematically three or more elevators in a group to efficiently transport passengers. Efforts have been made to reduce energy cost of elevators, some focusing on the special assignment of idle cages [2], some on optimization of zoning [3], and others on minimization of estimated energy cost [4], [5] and so on. Although these efforts can achieve energy saving, more or less, the cause is not quite clear.This paper uses rule-based dispatching algorithm (RB) for EGCS, and Fig. 1 shows the structure. There are several

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This paper describes continuous piecewise linear (CPWL) programming where the objective and constraints are in the form of hinging hyperplane (HH). And HH has received wide attention due to its simplicity and good performance in system identification. When solving a CPWL programming problem, some excellent features inspire us to come up with more efficient algorithms: the two distinguished states of a hinge function reminds us of application of genetic algorithm, while the piecewise linearity and concavity of the problem of minimization of HH naturally lead to the usage of well developed methods for concave programming, such as the cutting plane method. In order to find the global minima, we propose an improved genetic algorithm (GA) incorporating the cutting plane method. The main improvement lies in three aspects. First, it utilizes binary strings that derive local minima as chromosomes, with the proposed local minima locating method. Second, a stopping criterion has been established to ensure the global optimality of GA, with the structure information provided by γ extension of local minima. And third, genetic operations have also been revised to enhance the performance of the algorithm, which is assessed by the computational experiments.

Memristor is introduced as the fourth basic circuit element. Memristor exhibits great potential for numerous applications, such as emulating synapse, while the mathematical model of the memristor is still an open subject. In the linear-drift model, the boundary condition of the device is not considered. This paper proposes an extended linear-drift model of the memristor. The extended linear-drift model keeps the linear characteristic and simplicity of the linear-drift model and considers the boundary condition of the device. A piecewise linear approximation model of the extended linear-drift model is given. Both models are suitable for describing the memristor.

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