Modeling update for the Thirty Meter Telescope laser guide star dual-conjugate adaptive optics system

This paper describes the modeling efforts undertaken in the past couple of years to derive wavefront error (WFE) performance estimates for the Narrow Field Infrared Adaptive Optics System (NFIRAOS), which is the facility laser guide star (LGS) dual-conjugate adaptive optics (AO) system for the Thirty Meter Telescope (TMT). The estimates describe the expected performance of NFIRAOS as a function of seeing on Mauna Kea, zenith angle, and galactic latitude (GL). They have been developed through a combination of integrated AO simulations, side analyses, allocations, lab and lidar experiments

Algorithms for output feedback, multiple-model, and decentralized control problems

The optimal stochastic output feedback, multiple-model, and decentralized control problems with dynamic compensation are formulated and discussed. Algorithms for each problem are presented, and their relationship to a basic output feedback algorithm is discussed. An aircraft control design problem is posed as a combined decentralized, multiple-model, output feedback problem. A control design is obtained using the combined algorithm. An analysis of the design is presented

An Evolutionary Algorithm to Optimize Log/Restore Operations within Optimistic Simulation Platforms

In this work we address state recoverability in advanced optimistic simulation systems by proposing an evolutionary algorithm to optimize at run-time the parameters associated with state log/restore activities. Optimization takes place by adaptively selecting for each simulation object both (i) the best suited log mode (incremental vs non-incremental) and (ii) the corresponding optimal value of the log interval. Our performance optimization approach allows to indirectly cope with hidden effects (e.g., locality) as well as cross-object effects due to the variation of log/restore parameters for different simulation objects (e.g., rollback thrashing). Both of them are not captured by literature solutions based on analytical models of the overhead associated with log/restore tasks. More in detail, our evolutionary algorithm dynamically adjusts the log/restore parameters of distinct simulation objects as a whole, towards a well suited configuration. In such a way, we prevent negative effects on performance due to the biasing of the optimization towards individual simulation objects, which may cause reduced gains (or even decrease) in performance just due to the aforementioned hidden and/or cross-object phenomena. We also present an application-transparent implementation of the evolutionary algorithm within the ROme OpTimistic Simulator (ROOT-Sim), namely an open source, general purpose simulation environment designed according to the optimistic synchronization paradigm

Cakar ayam shaping machine

Cakar ayam (Figure 7.1) is one of the Malay traditional cookies that are made from sliced sweet potatoes deep-fried in the coconut candy. In current practice of moulding the cookies, the fried sweet potatoes are molded using traditional manual tools, which are inefficient and less productive for the mass production purposes. “Kuih cakar ayam” associated with the meaning of the idiom means less messy handwriting has a somewhat negative connotation .This cookies may just seem less attractive in shape but still likeable . In fact, this cookie is considered a popular snack even outside the holiday season. The choice of the name of this cookie is more to shape actually resembles former chicken scratches made by the paw the ground while foraging. The value of wisdom, beauty and creativity of the Malays is clearly evident through the Malay cookie. Although it is attacked by the invention of modern cakes that look far more interesting, these cakes will be able to survive a long time until now

Exploiting visual salience for the generation of referring expressions

In this paper we present a novel approach to generating referring expressions (GRE) that is tailored to a model of the visual context the user is attending to. The approach integrates a new computational model of visual salience in simulated 3-D environments with Dale and Reiter’s (1995) Incremental Algorithm. The advantage of our GRE framework are: (1) the context set used by the GRE algorithm is dynamically computed by the visual saliency algorithm as a user navigates through a simulation; (2) the integration of visual salience into the generation process means that in some instances underspecified but sufficiently detailed descriptions of the target object are generated that are shorter than those generated by GRE algorithms which focus purely on adjectival and type attributes; (3) the integration of visual saliency into the generation process means that our GRE algorithm will in some instances succeed in generating a description of the target object in situations where GRE algorithms which focus purely on adjectival and type attributes fail

Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation

Multivariable proportional-integral-plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data-based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete-time transfer function models obtained in this manner are represented in a non-minimum state space form suitable for PIP control system design. Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resorting to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non-minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm

Integration of a mean-torque diesel engine model into a hardware-in-the-loop shipboard network simulation using lambda tuning

This study describes the creation of a hardware-in-the-loop (HIL) environment for use in evaluating network architecture, control concepts and equipment for use within marine electrical systems. The environment allows a scaled hardware network to be connected to a simulation of a multi-megawatt marine diesel prime mover, coupled via a synchronous generator. This allows All-Electric marine scenarios to be investigated without large-scale hardware trials. The method of closing the loop between simulation and hardware is described, with particular reference to the control of the laboratory synchronous machine, which represents the simulated generator(s). The fidelity of the HIL simulation is progressively improved in this study. First, a faster and more powerful field drive is implemented to improve voltage tracking. Second, the phase tracking is improved by using two nested proportional–integral–derivative–acceleration controllers for torque control, tuned using lambda tuning. The HIL environment is tested using a scenario involving a large constant-power load step. This provides a very severe test of the HIL environment, and also reveals the potentially adverse effects of constant-power loads within marine power systems