The kinematic design of spatial,hybrid closed chains including planar parallelograms

It is presented an integral approach for the kinematic design of spatial, hybrid closed chains which include planar parallelograms into their kinematic structure. It is based on a systematic application of recursive formulae intended for describing the evolution of screws through time. Due to the particular nature of the proposed approach, it can be closely related with Lie algebras and allows to overcome the lacking of group structure offered by a parallelogram when it is going to be considered as a component of a hybrid closed chain. Several application examples are presented in order to show the potential of the proposed approach.     

Consensus and disturbance attenuation in multi‐agent chains with nonlinear control and time delays

In this paper, we investigate consensus and disturbance attenuation in a chain of mobile agents, which include non‐autonomous agents, semi‐autonomous agents and autonomous agents. In particular, the nonlinear dynamics of non‐autonomous agents is given and cannot be designed, while the dynamics of semi‐autonomous and autonomous agents can be partially and fully designed, respectively. To improve the robustness of multi‐agent chains against disturbances, we propose a nonlinear control framework for semi‐autonomous and autonomous agents such that they mimic the behavior of non‐autonomous agents for compatibility while also exploiting long‐range connections with distant agents. This framework ensures the existence of a unique consensus equilibrium, which is independent of the network size, connectivity topologies, control gains and information delays. Robustness of multi‐agent chains against disturbances is investigated by evaluating the frequency response at the nonlinear level. For infinitely long multi‐agent chains with recurrent patterns, we also derive a condition that ensures the disturbance attenuation but only requires the analysis of the linearized model. A case study is conducted for a connected vehicle system where numerical simulations are used to validate the analytical results. Copyright © 2016 John Wiley & Sons, Ltd.     

传感器网络高效密钥建立方案

随着时间的推移,传感器网络中正常工作的节点越来越少。为了确保网络正常工作,必须适时地添加新节点。针对传感器网络,提出了一个高效的密钥建立方案。该方案构造了n个相互独立的子密钥池,两相邻子密钥池之间存在联系密钥。第j次部署的传感器节点只从第j个子密钥池中选取密钥。理论分析及模拟结果表明与使用固定密钥池的方案相比,该方案的抗毁性显著提高。     

Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains

In the real-world manufacturing/distribution planning decision (MDPD) integration problems in supply chains, the environmental coefficients and parameters are normally imprecise due to incomplete and/or unavailable information. This work presents a fuzzy linear programming approach based on the possibility theory. It applies this approach to solve multi-product and multi-time period MDPD problems with imprecise goals and forecast demand by considering the time value of money of related operating cost categories. The proposed approach attempts to minimize the total manufacturing and distribution costs by considering the levels of inventory, subcontracting and backordering, the available machine capacity and labor levels at each source, forecast demand and available warehouse space at each destination. This study utilizes an industrial case study to demonstrate the feasibility of applying the proposed approach to practical MDPD problems. The primary contribution of this paper is a fuzzy mathematical programming methodology for solving the MDPD integration problems in uncertain environments.     

Approximate steady-state analysis of large Markov models based on the structure of their decision diagram encoding

We propose a new approximate numerical algorithm for the steady-state solution of general structured ergodic Markov models. The approximation uses a state-space encoding based on multiway decision diagrams and a transition rate encoding based on a new class of edge-valued decision diagrams. The new method retains the favorable properties of a previously proposed Kronecker-based approximation, while eliminating the need for a Kronecker-consistent model decomposition. Removing this restriction allows for a greater utilization of event locality, which facilitates the generation of both the state-space and the transition rate matrix, thus extends the applicability of this algorithm to larger and more complex models.     

Effective dimensions and relative frequencies

Consider the problem of calculating the fractal dimension of a set X consisting of all infinite sequences S over a finite alphabet Σ that satisfy some given condition P on the asymptotic frequencies with which various symbols from Σ appear in S. Solutions to this problem are known in cases where

(i)

the fractal dimension is classical Hausdorff or packing dimension (by work of Volkmann and Olsen), or

(ii)

the fractal dimension is effective (even finite-state) and the condition Pcompletely specifies an empirical distribution π over Σ, i.e., a limiting frequency of occurrence for every symbol in Σ.

In this paper, we show how to calculate the finite-state dimension (equivalently, the finite-state compressibility) of such a set X when the condition P only imposes partial constraints on the limiting frequencies of symbols. Our results automatically extend to less restrictive effective fractal dimensions (e.g., polynomial-time, computable, and constructive dimensions), and they have the classical results (i) as immediate corollaries. Our methods are nevertheless elementary and, in most cases, simpler than those by which the classical results were obtained.     

Efficient Tate pairing computation using double-base chains

Pairing-based cryptosystems have developed very fast in the last few years. The efficiencies of these cryptosystems depend on the computation of the bilinear pairings, In this paper, a new efficient algorithm based on double-base chains for computing the Tate pairing is proposed for odd characteristic p 〉 3. The inherent sparseness of double-base number system reduces the computational cost for computing the Tate pairing evidently. The new algorithm is 9% faster than the previous fastest method for the embedding degree k = 6.     

On the Convergence of Temporal-Difference Learning with Linear Function Approximation

The asymptotic properties of temporal-difference learning algorithms with linear function approximation are analyzed in this paper. The analysis is carried out in the context of the approximation of a discounted cost-to-go function associated with an uncontrolled Markov chain with an uncountable finite-dimensional state-space. Under mild conditions, the almost sure convergence of temporal-difference learning algorithms with linear function approximation is established and an upper bound for their asymptotic approximation error is determined. The obtained results are a generalization and extension of the existing results related to the asymptotic behavior of temporal-difference learning. Moreover, they cover cases to which the existing results cannot be applied, while the adopted assumptions seem to be the weakest possible under which the almost sure convergence of temporal-difference learning algorithms is still possible to be demonstrated.