Zero Assignment for Robust  $H_{2}/H_{\infty}$ Fault Detection Filter Design
In practical engineering, it is inevitable that a system is perturbed by noise signals. Unfortunately, H infin /H infin filtering may fail to detect some faults when the noise distribution matrix are the same as the fault distribution matrix. In this paper, it is shown that the dynamic feedback gain of a dynamic filter introduces additional zeros to the filter, and both the filter poles and the additional zeros can be assigned arbitrarily. In order to attenuate band-limited noises, the zero assignment technique is used, and an optimal dynamic fault detection filtering approach is proposed by locating the zeros to the noise frequencies and optimizing the poles. Compared to other dynamic filter design approaches, the zero assignment technique gives a better tradeoff between more design freedom and computation costs. As shown in the simulation, a better noise attenuation and fault detection performance have been obtained. The zero assignment in multivariable fault detection filter design would be the main contribution of this paper.

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: 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: 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: 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: A dependency chain clustered micro architecture
abstract: In this paper we explore a new clustering approach for reducing the complexity of wide issue in-order processors based on EPIC architectures. Complexity effectiveness is achieved by heavily clustering the pipeline from decode to commit stage without the need for any direct bypass between clusters. This is made possible by assuming support for executing compiler-constructed traces. One trace is executed at a time by executing its coarse-grained dependency chains (DCs) in different in-order clusters. Since the DCs of a trace are mutually data independent of each other they can be executed in different clusters without any direct communication between them. To execute DCs in narrower clusters without compromising ILP, a compiler algorithm that splits large DCs by duplicating instructions is proposed. Through cycle accurate simulations we show that a DC processor with one 3-wide, one 2-wide and one 1-wide in-order pipeline, could achieve performance equivalent to a 6-wide in-order superscalar processor. Since a clustered DC microarchitecture is complexity efficient, it is amenable to higher clock frequencies and will also be easier to design and validate than a 6-wide monolithic design.

title: Directed Bipartite Graphs Containing Every Possible Pair of Directed Cycles

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: Control Flow Optimization Via Dynamic Reconvergence Prediction
abstract: This paper presents a novel microarchitecture technique for accurately predicting control flow reconvergence dynamically. A reconvergence point is the earliest dynamic instruction in the program where we can expect program paths to reconverge regardless of the outcome or target of the current branch. Thus, even if the immediate control flow after a branch is uncertain, execution following the reconvergence point is certain. This paper proposes a novel hardware re-convergence predictor which is both implementable and accurate, with a 4KB predictor achieving more than 95% accuracy for SPEC INT, and larger implementations achieving greater than 99% accuracy. The information provided from reconvergence prediction can increase the effectiveness of a range of previously proposed performance optimizations, including speculative multithreading, control independence, and squash reuse. This paper also demonstrates a new technique that takes advantage of the dynamic reconvergence prediction information in order to predict a wrong path excursion ahead of branch resolution. On average, 34% of wrong path fetches are eliminated.

title: Color-based road detection in urban traffic scenes
abstract: Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.