Delay-dependent fault detection and diagnosis using B-spline neural networks and nonlinear filters for time-delay stochastic systems
A new fault detection and diagnosis (FDD) scheme is studied in this paper for the continuous-time stochastic dynamic systems with time delays, where the available information for the FDD is the input and the measured output probability density functions (PDFs) of the system. The square-root B-spline neural networks is used to formulate the output PDFs with the dynamic weightings. As a result, the concerned FDD problem can be transformed into a robust FDD problem subjected to a continuous time uncertain nonlinear system with time delays. Delay-dependent criteria to detect and diagnose the system fault are provided by using linear matrix inequality (LMI) techniques. It is shown that this new criterion can provide higher sensitivity performance than the existing result. Simulations are given to demonstrate the efficiency of the proposed approach.

title: An Intelligent Mill Load Switching Control of the Pulverizing System for an Alumina Sintering Process
abstract: This paper proposes an intelligent switching control strategy for the pulverizing system for an alumina sintering process. The control strategy consists of a coordinating controller, a proportional-integral (PI) controller, a rule-based reasoning controller and a switching mechanism. The practical application to a pulverizing system in an alumina sintering process has shown that when the hot air temperature is low and varies frequently, this control strategy ensures that the mill load and the temperature of the mill outlet are both within their required ranges, and also controls the mill load at the level corresponding to the maximum mill output. Moreover, it has been observed that the proposed control strategy can avoid the fault operating condition and effectively reduce the unit energy consumption.

title: Advances in stochastic distribution control
abstract: Stochastic distribution control systems aims at the controller design so as to realize a shape control of the distributions of certain random variables in the process. Once the probability density functions (PDFs) of these variables are used to describe their distributions, the control task is to obtain control signals so that the output PFDs of the system are made to follow their target PDFs. In this paper, a survey of the recent developments on the research of stochastic distribution control systems will be made.

title: Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine
abstract: In this study, a novel identification technique, that is high-gain observer-based identification approach, is proposed for systems with bounded process and measurement noises. For system parameters with abnormal changes, an adaptive change detection and parameter identification algorithm is next presented. The presented technique and algorithm are finally applied to the parameter identification of the gas turbine engine by using the recorded input data from the engine test-bed. The identified parameters and the response curves are desirable. The simulations have proved the effectiveness of the proposed procedure compared with the previous identification approach.

title: Minimum entropy control of nonlinear ARMA systems over a communication network
abstract: In this paper, the entropy concept has been utilized to characterize the uncertainty of the tracking error for nonlinear ARMA stochastic systems over a communication network, where time delays in the communication channels are of random nature. A recursive optimization solution has been developed. In addition, an alternative algorithm is also proposed based on the probability density function of the tracking error, which is estimated by a neural network. Finally, a simulation example is given to illustrate the efficiency and feasibility of the proposed approach.

title: Robust minimum entropy tracking control with guaranteed stability for nonlinear stochastic systems under modeling errors
abstract: In this paper, robust minimum entropy tracking control problem is considered for nonlinear stochastic systems. The controlled systems are described by nonlinear non-Gaussian difference equations with the un-modeled uncertainty and modeling error, as well as time delays. Entropy is adopted to characterize the uncertainty of the tracking error. The nonlinear multi-step-ahead predictive cost function is used and the relationship between the probability density functions of the input and the tracking error via the uncertain mapping is established. With these formulations, the cost function can be bounded as a nonlinear functional of the control input and the known bounds of the errors. Explicit design algorithms are presented for the robust suboptimal controller and further for the stabilization controllers. The Renyi's entropy has also been used to simplify the cost function. Simulations are given to demonstrate the effectiveness of the proposed control algorithm.

title: Maximal Total Length Of k Disjoint Cycles In Bipartite Graphs
abstract: Let k, s and n be three integers with s≥k≥2, n≥2k+1. Let G=(V1,V2;E) be a bipartite graph with |V1|=|V2|=n. If the minimum degree of G is at least s+1, then G contains k vertex-disjoint cycles covering at least min(2n,4s) vertices of G.

title: A Fast and Robust Ellipse Detection Algorithm Based on Pseudo-random Sample Consensus
abstract: Ellipse is one of the most common features that appears in images. Over years in research, real-timing and robustness have been two very challenging problems aspects of ellipse detection. Aiming to tackle them both, we propose an ellipse detection algorithm based on pseudo-random sample consensus (PRANSAC). In PRANSAC we improve a contour-based ellipse detection algorithm (CBED), which was presented in our previous work. In addition, the parallel thinning algorithm is employed to eliminate useless feature points, which increases the time efficiency of our detection algorithm. In order to further speed up, a 3-point ellipse fitting method is introduced. In terms of robustness, a "robust candidate sequence" is proposed to improve the robustness performance of our detection algorithm. Compared with the state-of-the-art ellipse detection algorithms, experimental results based on real application images show that significant improvements in time efficiency and performance robustness of the proposed algorithm have been achieved.

title: Analysis of DC characteristics of GaAs double heterojunction bipolar transistors (DHBTs) with a pseudomorphic GaAsSb base
abstract: In this paper, DC characteristics in GaAs DHBTs with pseudomorphic GaAsSb base layer are investigated using an analytical approach. Devices using different GaAs 1− x Sb x base thickness with different Sb compositions were simulated to gain an in-depth understanding on the effect of base layer design on the device DC performance. Our calculated results show that, in the GaAsSb base with high Sb composition, the strain plays a non-negligible role in determining the overall device performance. The reduction of B–E turn-on voltage using a large Sb composition of GaAs 1− x Sb x could be partially compensated by strain in the base. Furthermore, for the devices using high Sb composition, a thin base layer has to be used to ensure elastic strain. In order to maintain reasonable base conductivity that is critical for device microwave performance, high base doping is required. However, our simulation reveals a drastic reduction of DC gain with the increase in base doping, while a high emitter doping may be helpful for the compensation of the degradation of β. A proper design of the device structure by taking the aforementioned issues into account is important for improving device DC and microwave performance.

title: Register renaming and scheduling for dynamic execution of predicated code
abstract: To achieve higher processor performance requires greater synergy between advanced hardware features and innovative compiler techniques. Recent advancement in compilation techniques for predicated execution has provided significant opportunity in exploiting instruction level parallelism. However, little research has been done on how to efficiently execute predicated code in a dynamic microarchitecture. In this paper, we evaluate hardware optimizations for executing predicated code on a dynamically scheduled microarchitecture. We provide two novel ideas to improve the efficiency of executing predicated code. On a generic Intel Itanium processor pipeline model, we demonstrate that, with some microarchitecture enhancements, a dynamic execution processor can achieve about 16% performance improvement over an equivalent static execution processor.

title: On Quadrilaterals and Cycle Covers in a Bipartite Graph