Decomposing the user-preference in multiobjective optimization

Preference information (such as the reference point) of the decision maker (DM) is often used in multiobjective optimization; however, the location of the specified reference point has a detrimental effect on the performance of multiobjective evolutionary algorithms (MOEAs). Inspired by multiobjective evolutionary algorithm-based decomposition (MOEA/D), this paper proposes an MOEA to decompose the preference information of the reference point specified by the DM into a number of scalar optimization subproblems and deals with them simultaneously (called MOEA/D-PRE). This paper presents an approach of iterative weight to map the desired region of the DM, which makes the algorithm easily obtain the desired region. Experimental results have demonstrated that the proposed algorithm outperforms two popular preference-based approaches, g-dominance and r-dominance, on continuous multiobjective optimization problems (MOPs), especially on many-objective optimization problems. Moreover, this study develops distinct models to satisfy different needs of the DM, thus providing a new way to deal with preference-based multiobjective optimization. Additionally, in terms of the shortcoming of MOEA/D-PRE, an improved MOEA/D-PRE that dynamically adjusts the size of the preferred region is proposed and has better performance on some problems.     

矿山物联网服务承载平台与矿山购买服务

矿山综合自动化系统由于没有考虑容纳第三方的服务,逐步显露出系统封闭、标准不一、信息应用困难、对矿山技术人员要求高等问题,阻碍了其发展。明确了矿山物联网的本质为服务承载平台,矿山物联网应具有相应的公共服务能力,包括基于位置的服务、基于时间的服务、基于信息的服务、基于云的服务、基于大数据的服务、基于语义的服务等,重点论述了矿山物联网应具备的服务能力和矿山购买服务,指出这些服务能力会随着技术和需求的发展而不断充实与增加,使矿山物联网逐步向智慧矿山迈进。     

state estimation with fading measurements,randomly varying nonlinearities and probabilistic distributed delays

In this paper, the state estimation problem is investigated for a class of discrete‐time stochastic systems in simultaneous presence of three network‐induced phenomena, namely, fading measurements, randomly varying nonlinearities and probabilistic distributed delays. The channel fading is characterized by the ?th‐order Rice fading model whose coefficients are mutually independent random variables with given probability density functions. Two sequences of random variables obeying the Bernoulli distribution are utilized to govern the randomly varying nonlinearities and probabilistic distributed delays. The purpose of the problem addressed is to design an state estimator such that the dynamics of the estimation errors is stochastically stable and the prespecified disturbance rejection attenuation level is guaranteed. Through intensive stochastic analysis, sufficient conditions are established under which the addressed state estimation problem is recast as a convex optimization one that can be solved via the semi‐definite program method. Finally, a simulation example is provided to show the usefulness of the proposed state estimation scheme. Copyright © 2014 John Wiley & Sons, Ltd.     

Tree-based data retrieval algorithm for multi-item request with deadline in wireless networks

The data retrieval problem in wireless communication has attracted increasing attentions in many applications such as electronic information sharing and e-business. However, most existing studies do not consider how to retrieve the requested data items in their deadlines. In this paper, we discuss the so-called deadline-based multi-item data retrieval problem, which aims at finding an access pattern for retrieving requested data items in certain deadlines such that the access latency and data miss rate are minimized. We propose two algorithms, named EDFS and UR, to deal with the above problem. The EDFS algorithm gives priority to retrieve the requested data item with the least deadline, while the UR algorithm introduces the urgent rate that is computed by the broadcast time and deadline of requested data item for further reducing the access latency and data miss ratio. Moreover, we design a tree-based data retrieval algorithm that finds the longest downloading sequence of data items in one broadcast cycle and is applied to EDFS and UR for finding data retrieval sequence of requested data items. The efficiency of our proposed schemes has been validated by the extensive experiments.     

Study of a Class of Almost‐controllable Discrete‐time Bilinear Systems

This paper studies the controllability of a class of time‐invariant discrete‐time bilinear systems. Although the system is not controllable in the whole space, there is a very large region where control is effective. Results show that the uncontrollable region of this kind of bilinear system has a Lebesgue measure of zero. In other words, for almost any initial state and any terminal state in the state space, the former can be transferred to the latter. Further, a necessary condition for near controllability is presented. Therefore, the results in this paper unify and generalize the corresponding conclusions in the literature. ©2014 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd     

Parametric Stabilization of Quantized Interconnected Systems with Application to Coupled Inverted Pendulums

This paper studies the analysis of parametric stability and decentralized state feedback control of a kind of quantized interconnected systems. The output of each controller is quantized logarithmically before it is input to the subsystem, and the quantized density would affect the stability of the systems. First, a decentralized state feedback controller is designed for interconnected systems without quantization and the corresponding stable region is obtained. Second, for a given controller, the lower bound of the quantization density is evaluated from parameters of local controllers. Finally, the proposed method is applied to coupled inverted pendulums systems which can be viewed as quantized interconnected systems. The simulation results show that by using the proposed quantized controllers, the interconnected inverted pendulum systems are parametrically stabilized.     

An Interacting Multiple Model Approach for State Estimation with Non‐Gaussian Noise Using a Variational Bayesian Method

The conventional interacting multiple models (IMM) approach for a hybrid system under the Gaussian assumption is limited for most real applications due to the noisy measurements often being in the presence of outliers. This paper aims at accommodating the IMM approach to the non‐Gaussian cases where outliers exist. In the proposed IMM algorithm, the Student‐t distribution is used to model the non‐Gaussian measurement noise. At the interaction step, the mixed statistics of the noise parameter under a Bayesian framework are obtained via a Gamma approximation and a recently reported moments matching method. To address the state noise‐coupled intractability in Bayesian filtering, a variational Bayesian method is utilized to approximate the posterior distributions of the noise and state recursively. The proposed algorithm is tested with a maneuvering target tracking example and is shown to be robust to the outliers.