P.M. Session Chair: Prof Daniel Ho Students Contribution 2:00 pm – 3:00 pm 1) Shifang Kuang (SCUT) Stability and Numerical Methods of Nonlinear Stochastic Systems with Time-Delays 3:00 pm – 4:00 pm 2) Lulu Li (CityU) Practical Consensus in Multi-agent Networks with Communication Constrains 4:00 pm – 4:20 pm Coffee Break 4:20 pm – 5:20 pm 3) Xueyan Zhao (SCUT) Stability of Stochastic Functional ...

Determination of facilities, such as factories or warehouses, location and availability conditions is one of the important and strategic decisions for an organization to make. Transportation costs that form a major part of goods price are dependent to this decision making. There are verity of methods have been presented to achieve the optimal locations of these facilities which are generally de...

Motivated by problems coming from planning and operational management in power generation companies, this work extends the traditional two-stage linear stochastic program by adding probabilistic constraints in the second stage. In this work we describe, under special assumptions, how the two-stage stochastic programs with mixed probabilities can be treated computationally. We obtain a convex co...

For a document collection in which structural elements are identiied with markup, it is often necessary to construct a grammar retrospectively that constrains element nesting and ordering. This has been addressed by others as an application of grammatical inference. We describe an approach based on stochastic grammatical inference which scales more naturally to large data sets and produces mode...

The optimal reactive power dispatch (ORPD) is a very important problem aspect of power system planning and is a highly nonlinear, non-convex optimization problem because consist of both continuous and discrete control variables. Since the power system has inherent uncertainty, hereby, this paper presents both of the deterministic and stochastic models for ORPD problem in multi objective and sin...

This paper deals with the optimal control problem for systems with state linear equality constraints. For deterministic linear systems, first we find various existence conditions for constraining state feedback control and determine all constraining feedback gains, from which the optimal feedback gain is derived by using the result of singular optimal control. For systems with stochastic proces...

Sparsified Randomization Monte Carlo (SRMC) algorithms for solving systems of linear algebraic equations introduced in our previous paper [34] are discussed here in a broader context. In particular, I present new randomized solvers for large systems of linear equations, randomized singular value (SVD) decomposition for large matrices and their use for solving inverse problems, and stochastic si...

the purpose of the study was to identify the constrains affecting urban agriculture in tehran, iran. the statistical population of this study consisted of city dwellers within the 22 municipal districts of tehran out of which 320 individuals were selected as the sample of the study. cochran’s formula was used to determine the sampling size based on stratified sampling method. a panel of experts...

We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation class of stochastic nonlinear systems. It uses spectrally-normalized deep neural network to construct contraction metric, sampled via simplified convex optimization in the setting. Spectral normalization constrains state-derivatives metric be Lipschitz continuous, th...

In the course of a (man-machine) dialogue, the system’s belief concerning the user’s intention is continuously being built up. Moreover, restricting the discourse to a narrow application domain further constrains the variety of possible user reactions. In this paper, we will show how these knowledge sources may be utilized in a stochastic framework to improve speech understanding. On field-test...