Adaptive Tracking Control for the Output PDFs Based on Dynamic Neural Networks
In this paper, a novel adaptive tracking control strategy is established for general non-Gaussian stochastic systems based on two-step neural network models. The objective is to control the conditional PDF of the system output to follow a given target function by using dynamic neural network models. B-spline neural networks are used to model the dynamic output probability density functions (PDFs), then the concerned problem is transferred into the tracking of given weights corresponding to the desired PDF. The dynamic neural networks with undetermined parameters are employed to identify the nonlinear relationships between the control input and the weights. To achieve control objective, an adaptive state feedback controller is given to estimate the unknown parameters and control the nonlinear dynamics.

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: 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: 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: 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: Implementing a scalable ASC processor
abstract: Previous papers (Walker et al. (2001); Wu et al. (2002)) have described our implementation of a small prototype processor and control unit for associative computing, called the ASC processor. That initial prototype was implemented on an Altera education board using an Altera FLEX 10K FPGA, and was limited to an unrealistic 4 processing elements (PE). This paper describes a more complete implementation - a scalable ASC processor that can scale up to 52 PE on an Altera APEX 20KE board, or further on larger FPGA. This paper also proposes extensions to support multiple control units and control parallelism.

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: Vertex-disjoint quadrilaterals in graphs
abstract: Let k be a positive integer and G a graph of order n. In ( (1999) 229), we proved that if n=4k and @d(G)>=2k then G contains k-1 vertex-disjoint quadrilaterals. In this paper, we investigate vertex-disjoint quadrilaterals in G with n@?0(mod4), and we have found the best minimum degree condition @d(G)>=@?12n@? such that G contains @?14n@? vertex-disjoint quadrilaterals. As a result of our method, we have improved our previous result as follows: If n=4k and @d(G)>=2k-1 then G contains k-1 vertex-disjoint quadrilaterals.

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: Merge: a programming model for heterogeneous multi-core systems
abstract: In this paper we propose the Merge framework, a general purpose programming model for heterogeneous multi-core systems. The Merge framework replaces current ad hoc approaches to parallel programming on heterogeneous platforms with a rigorous, library-based methodology that can automatically distribute computation across heterogeneous cores to achieve increased energy and performance efficiency. The Merge framework provides (1) a predicate dispatch-based library system for managing and invoking function variants for multiple architectures; (2) a high-level, library-oriented parallel language based on map-reduce; and (3) a compiler and runtime which implement the map-reduce language pattern by dynamically selecting the best available function implementations for a given input and machine configuration. Using a generic sequencer architecture interface for heterogeneous accelerators, the Merge framework can integrate function variants for specialized accelerators, offering the potential for to-the-metal performance for a wide range of heterogeneous architectures, all transparent to the user. The Merge framework has been prototyped on a heterogeneous platform consisting of an Intel Core 2 Duo CPU and an 8-core 32-thread Intel Graphics and Media Accelerator X3000, and a homogeneous 32-way Unisys SMP system with Intel Xeon processors. We implemented a set of benchmarks using the Merge framework and enhanced the library with X3000 specific implementations, achieving speedups of 3.6x -- 8.5x using the X3000 and 5.2x -- 22x using the 32-way system relative to the straight C reference implementation on a single IA32 core.