Dynamic modelling and control for multivariable output distribution systems
Following the recently developed strategies for the modelling and control for multi-input multi-output (MIMO) distribution control systems, this paper presents a dynamic model for the control of combined probability density function. A multilayer perceptron (MLP) neural network is used to approximate the probability density function (PDF) of the systems and a high-order dynamic nonlinear state-space model is obtained. Then nonlinear principal component analysis (NLPCA) is adopted to reduce the obtained model order and a lower-order time-varying system is achieved. The controller design for the lower-order time-varying system is given. The effectiveness of the result has been demonstrated by two examples in the end.

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: 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: 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: 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: A framework for modeling and optimization of prescient instruction prefetch
abstract: This paper describes a framework for modeling macroscopic program behavior and applies it to optimizing prescient instruction prefetch -- novel technique that uses helper threads to improve single-threaded application performance by performing judicious and timely instruction prefetch. A helper thread is initiated when the main thread encounters a spawn point, and prefetches instructions starting at a distant target point. The target identifies a code region tending to incur I-cache misses that the main thread is likely to execute soon, even though intervening control flow may be unpredictable. The optimization of spawn-target pair selections is formulated by modeling program behavior as a Markov chain based on profile statistics. Execution paths are considered stochastic outcomes, and aspects of program behavior are summarized via path expression mappings. Mappings for computing reaching, and posteriori probability; path length mean, and variance; and expected path footprint are presented. These are used with Tarjan's fast path algorithm to efficiently estimate the benefit of spawn-target pair selections. Using this framework we propose a spawn-target pair selection algorithm for prescient instruction prefetch. This algorithm has been implemented, and evaluated for the Itanium Processor Family architecture. A limit study finds 4.8%to 17% speedups on an in-order simultaneous multithreading processor with eight contexts, over nextline and streaming I-prefetch for a set of benchmarks with high I-cache miss rates. The framework in this paper is potentially applicable to other thread speculation techniques.

title: Inferno: a functional simulation infrastructure for modeling microarchitectural data speculations
abstract: This paper presents key insights and design rationales behind Inferno, a functional simulation construction framework developed at Intel to support execution-driven cycle-accurate performance modeling and simulation of advanced microarchitectural data speculation techniques for future processor designs and explorations. Inferno divides the task of functional simulation into three essential components, namely: (1) context manager of in-flight speculatively executed instructions, (2) stateless emulator of instruction semantics, and (3) a high speed functional simulator capable of booting OS and running large-scale system workloads. These building block components work together in concert via a set of well-architected functional model convergence APIs. With a novel abstraction called speculative domain, the context manager serves effectively as a relational database about the in-flight instructions and on-going speculations. Through a set of functional model usage APIs, the context manager enables performance models to express arbitrary microarchitectural data speculation scenarios. The contribution of this paper is to demonstrate the importance of providing a functional model with modular construction, proper abstraction and expressive APIs for speculative state management. Inferno is such a functional model construction framework and has significantly improved productivity in modeling a variety of sophisticated data speculation microarchitecture techniques.

title: A Scalable Pipelined Associative SIMD Array with Reconfigurable PE Interconnection Network for Embedded Applications
abstract: This paper describes the FPGA implementation of a specialized SIMD processor array for embedded applications. An alternative to traditional SoC or MPSoC architectures, this array combines the massive parallelism inherent in SIMD architectures with the search capabilities of associative computing, producing a SIMD Processor Array System on a Chip (PASoC) well suited for applications such as data mining and bioinformatics. This paper first describes the architecture of the system, and then the pipelining of the processing elements and the addition of a reconfigurable interconnection network. Finally, the paper concludes with a brief description of a string-matching algorithm that can be used for the applications cited above.

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: Existence of two disjoint long cycles in graphs
abstract: Let n,h be integers with n>=6 and h>=7. We prove that if G is a graph of order n with @s"2(G)>=h, then G contains two disjoint cycles C"1 and C"2 such that |V(C"1)|+|V(C"2)|>=min{h,n}.