Descriptor observer approaches for multivariable systems with measurement noises and application in fault detection and diagnosis
In this paper, new descriptor observer design approaches are presented for multivariable systems with measurement noises. If the system matrix is stable, the proposed modified proportional and derivative observer technique allows us not only to decouple the measurement noise in any forms completely, but also obtain accurate estimations of both system states and measurement noises. If the system is detectable and the noise is bounded, it has been shown that two design parameters can provide more degrees of freedom. In this context, the derivative gain parameter is chosen to reduce the amplification of the noise, and the proportional gain is then selected to guarantee the stability of the error dynamics. For systems with both measurement output noises and input disturbances, a modified proportional, derivative and integral observer is constructed to asymptotically estimate states, input disturbances, and output noises at the same time. The applications of the proposed observers for systems with actuator and sensor faults are also discussed. Numerical examples are given to illustrate the design procedures, and the simulation results show the satisfactory tracking performance.

title: An ILC-Based Adaptive Control for General Stochastic Systems With Strictly Decreasing Entropy
abstract: In this paper, a new method for adaptive control of general nonlinear and non-Gaussian unknown stochastic systems has been proposed. The method applies the minimum entropy control scheme to decrease the closed-loop randomness of the output under an iterative learning control (ILC) basis. Both modeling and control of the plant are performed using dynamic neural networks. For this purpose, the whole control horizon is divided into a certain number of time domain subintervals called batches and a pseudo-D-type ILC law is employed to train the plant model and controller parameters so that the entropy of the closed-loop tracking error is made to decrease batch by batch. The method has the advantage of decreasing the output uncertainty versus the advances of batches along the time horizon. The analysis on the proposed ILC convergence is made and a set of demonstrable experiment results is also provided to show the effectiveness of the obtained control algorithm, where encouraging results have been obtained.

title: Estimation delay compensation in high-gain observer-based parameter identification
abstract: In this paper, the properties of the disturbance estimation in a high gain observer are analyzed, and a time delay calculation method is proposed to improve the performance of parameter identification. This paper shows that the estimation delay depends on the observer gain, but has nothing to do with the parameter variation. Then, a novel algorithm is proposed to calculate the delay according to the phase response of disturbance estimation transfer function. Hence, in terms of system identification, this delay is compensated by lagging the state estimate by the same amount of delay. This technique, on one side, improves the performance of parameter identification accuracy, and, on the other side, gives a new insight into the high-gain observer design.

title: Minimum Entropy Control Algorithm for General Dynamic Stochastic Systems
abstract: In order to measure the uncertainty of the stochastic systems subjected to arbitrary noise disturbance instead of Gaussian white noise, the minimum entropy control of tracking errors for dynamic stochastic systems is presented in this paper. Different from conventional hypothesis, it is assumed that the system output and noise obey multi-to-one mapping, which is more general in the practical application. A controller design was described based on minimizing system output error entropy and a recursive optimization algorithm was set up for dynamic, non-Gaussian and nonlinear system. This approach only used the formula of the probability density function of the tracking error to calculate the controller and it did not need to know the style of the system model and the probability density function of noise, which often is difficult to measure in fact. An illustrative example is utilized to demonstrate the efficiency of the minimum entropy control algorithm and the approving simulation results have been gained

title: An Algorithm for Identification of Reduced-Order Dynamic Models of Gas Turbines
abstract: Model based approaches show a lot of advantages for fault detection and condition monitoring. Particularly, it is true in employing reduced order models for real-time parameter identification and output prediction of gas turbines. Many algorithms have been developed, but most of them focus on one-step-ahead prediction models and involve complex computation. These algorithms are not acceptable for long-term prediction and real-time condition monitoring. In this paper, an improved gradient method (dynamic gradient descent) is proposed. The idea is to take account of the dependency of prediction errors and calculate the gradient information recursively. Not only low computation expense is achieved, but the non-Gaussian errors can also be overcome when this approach is applied to estimate parameters of a reduced order gas turbine model and to improve long-term prediction

title: MIMO Probability Density Function Control Using Simple LOG-MLP Neural Networks
abstract: This paper presents a new model for the control of multivariable output probability density function (PDF). Firstly, a Multi-Layer Perceptron (MLP) neural network is adopted to approximate the static output PDF of the MIMO systems. Nonlinear principal component analysis (NLPCA) is then used to reduce the order of the obtained static neural network model and the dynamics of the system is considered based on the lower-order model. After this, an integrated solution is provided to set up the system with lower-order dynamics for the purpose of stochastic distribution control. The controller design is then presented in detail. Finally, a simulation example is given to demonstrate the effectiveness of the method and encouraging results have been obtained.

title: Packing Three Copies of a Tree into a Complete Graph
abstract: A three T of order n is said to be 3-placeable if there are three edge-disjoint copies of T in Kn. In this paper we characterize all the trees T such that T is 3-placeable.

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: Processor Performance Modeling using Symbolic Simulation
abstract: We propose a method of analytically characterizing processor performance as a function of circuit latencies. In our approach, we modify traditional simulation to use variables instead of fixed latencies for the internal functional units. The simulation engine then algebraically computes execution times, and the result is a mathematical equation which characterizes the performance space across numerous processor configurations. We discuss the computational complexity issues of this approach and show that instruction chunking and simple equation redundancy checking can make this approach feasible-we can model a large multi-dimensional design space with thousands to millions of design parameter combinations for about 10times the simulation time of a single conventional simulation run. We demonstrate our approach by exploring two different machines: a traditional MlPS-style in-order pipeline and the Intel Graphics

title: Vertex-disjoint hexagons with chords in a bipartite graph
abstract: In this paper, we explore vertex-disjoint subgraphs in a bipartite graph. Let G = ( V 1 , V 2 ; E ) be a bipartite graph with | V 1 | = | V 2 | = s k, where s ⩾ 2 and k ⩾ 1 are integers. We conjecture that if the minimum degree of G is at least ( s − 1) k + 1, then G contains k vertex-disjoint subgraphs isomorphic to K s,s . We verify this conjecture for k ⩽ 3. When s = 3, we show that G contains k vertex-disjoint hexagons such that each of them has two chords, i.e., G k( K 3,3 − e).

title: Mitosis: A Speculative Multithreaded Processor Based on Precomputation Slices
abstract: This paper presents the Mitosis framework, which is a combined hardware-software approach to speculative multithreading, even in the presence of frequent dependences among threads. Speculative multithreading increases single-threaded application performance by exploiting thread-level parallelism speculatively, that is, executing code in parallel, even when the compiler or runtime system cannot guarantee that the parallelism exists. The proposed approach is based on predicting/computing thread input values via software through a piece of code that is added at the beginning of each thread (the precomputation slice). A precomputation slice is expected to compute the correct thread input values most of the time but not necessarily always. This allows aggressive optimization techniques to be applied to the slice to make it very short. This paper focuses on the microarchitecture that supports this execution model. The primary novelty of the microarchitecture is the hardware support for the execution and validation of precomputation slices. Additionally, this paper presents new architectures for the register file and the cache memory in order to support multiple versions of each variable and allow for efficient rollback in case of misspeculation. We show that the proposed microarchitecture, together with the compiler support, achieves an average speedup of 2.2 for applications that conventional nonspeculative approaches are not able to parallelize at all.