Algorithm for identification in the v-gap metric

This paper suggests a method for identification in the v-gap metric. For a finite number of frequency response samples, a problem for identification in the v-gap metric is formulated and an approximate solution is described. It uses an iterative technique for obtaining an L2-gap approximation. Each stage of the iteration involves solving an LMI optimisation. Given a known stabilising controller and the L2-gap approximation, it is shown how to derive a v-gap approximation.

Redesigning the design process through interactive simulation: A case study of life-cycle engineering in jet engine conceptual design

Many aerospace companies are currently making the transition to providing fully-integrated product-service offerings in which their products are designed from the outset with life-cycle considerations in mind. Based on a case study at Rolls-Royce, Civil Aerospace, this paper demonstrates how an interactive approach to process simulation can be used to support the redesign of existing design processes in order to incorporate life-cycle engineering (LCE) considerations. The case study provides insights into the problems of redesigning the conceptual stages of a complex, concurrent engineering design process and the practical value of process simulation as a tool to support the specification of process changes in the context of engineering design. The paper also illustrates how development of a simulation model can provide significant benefit to companies through the understanding of process behaviour that is gained through validating the behaviour of the model using different design and iteration scenarios. Keywords: jet engine design; life-cycle engineering; LCE; process change; design process simulation; applied signposting model; ASM. Copyright © 2011 Inderscience Enterprises Ltd.

Closing the gap: Improved bounds on optimal POMDP solutions

POMDP algorithms have made significant progress in recent years by allowing practitioners to find good solutions to increasingly large problems. Most approaches (including point-based and policy iteration techniques) operate by refining a lower bound of the optimal value function. Several approaches (e.g., HSVI2, SARSOP, grid-based approaches and online forward search) also refine an upper bound. However, approximating the optimal value function by an upper bound is computationally expensive and therefore tightness is often sacrificed to improve efficiency (e.g., sawtooth approximation). In this paper, we describe a new approach to efficiently compute tighter bounds by i) conducting a prioritized breadth first search over the reachable beliefs, ii) propagating upper bound improvements with an augmented POMDP and iii) using exact linear programming (instead of the sawtooth approximation) for upper bound interpolation. As a result, we can represent the bounds more compactly and significantly reduce the gap between upper and lower bounds on several benchmark problems. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

Performance evaluation of multiplexed model predictive control for a large airliner in nominal and contingency scenarios

Model predictive control allows systematic handling of physical and operational constraints through the use of constrained optimisation. It has also been shown to successfully exploit plant redundancy to maintain a level of control in scenarios when faults are present. Unfortunately, the computational complexity of each individual iteration of the algorithm to solve the optimisation problem scales cubically with the number of plant inputs, so the computational demands are high for large MIMO plants. Multiplexed MPC only calculates changes in a subset of the plant inputs at each sampling instant, thus reducing the complexity of the optimisation. This paper demonstrates the application of multiplexed model predictive control to a large transport airliner in a nominal and a contingency scenario. The performance is compared to that obtained with a conventional synchronous model predictive controller, designed using an equivalent cost function. © 2012 AACC American Automatic Control Council).

Distributed flow algorithms for scalable similarity visualization

We describe simple yet scalable and distributed algorithms for solving the maximum flow problem and its minimum cost flow variant, motivated by problems of interest in objects similarity visualization. We formulate the fundamental problem as a convex-concave saddle point problem. We then show that this problem can be efficiently solved by a first order method or by exploiting faster quasi-Newton steps. Our proposed approach costs at most O(|ε|) per iteration for a graph with |ε| edges. Further, the number of required iterations can be shown to be independent of number of edges for the first order approximation method. We present experimental results in two applications: mosaic generation and color similarity based image layouting. © 2010 IEEE.

Cubically convergent iterations for invariant subspace computation

We propose a Newton-like iteration that evolves on the set of fixed dimensional subspaces of ℝ n and converges locally cubically to the invariant subspaces of a symmetric matrix. This iteration is compared in terms of numerical cost and global behavior with three other methods that display the same property of cubic convergence. Moreover, we consider heuristics that greatly improve the global behavior of the iterations.

Predictive control for spacecraft rendezvous in an elliptical orbit using an FPGA

A field programmable gate array (FPGA)-based predictive controller for a spacecraft rendezvous manoeuvre is presented. A linear time varying prediction model is used to accommodate elliptical orbits, and a variable prediction horizon is used to facilitate finite time completion of manoeuvres. The resulting constrained optimisation problems are solved using a primal dual interior point algorithm. The majority of the computational demand is in solving a set of linear equations at each iteration of this algorithm. To accelerate this operation, a custom circuit is implemented, using a combination of Mathworks HDL Coder and Xilinx System Generator for DSP, and used as a peripheral to a MicroBlaze soft core processor. The system is demonstrated in closed loop by linking the FPGA with a simulation of the plant dynamics running in Simulink on a PC, using Ethernet. © 2013 EUCA.

Field programmable gate array based predictive control system for spacecraft rendezvous in elliptical orbits

Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd. Summary A field programmable gate array (FPGA) based model predictive controller for two phases of spacecraft rendezvous is presented. Linear time-varying prediction models are used to accommodate elliptical orbits, and a variable prediction horizon is used to facilitate finite time completion of the longer range manoeuvres, whilst a fixed and receding prediction horizon is used for fine-grained tracking at close range. The resulting constrained optimisation problems are solved using a primal-dual interior point algorithm. The majority of the computational demand is in solving a system of simultaneous linear equations at each iteration of this algorithm. To accelerate these operations, a custom circuit is implemented, using a combination of Mathworks HDL Coder and Xilinx System Generator for DSP, and used as a peripheral to a MicroBlaze soft-core processor on the FPGA, on which the remainder of the system is implemented. Certain logic that can be hard-coded for fixed sized problems is implemented to be configurable online, in order to accommodate the varying problem sizes associated with the variable prediction horizon. The system is demonstrated in closed-loop by linking the FPGA with a simulation of the spacecraft dynamics running in Simulink on a PC, using Ethernet. Timing comparisons indicate that the custom implementation is substantially faster than pure embedded software-based interior point methods running on the same MicroBlaze and could be competitive with a pure custom hardware implementation.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.

Photon iterative numerical technique for steady-state simulation of gain-clamped semiconductor optical amplifiers

The photon iterative numerical technique, which chooses the outputs of the amplified spontaneous emission spectrum and lasing mode as iteration variables to solve the rate equations, is proposed and applied to analyse the steady behaviour of conventional semiconductor optical amplifiers (SOAs) and gain-clamped semiconductor optical amplifiers (GCSOAs). Numerical results show that the photon iterative method is a much faster and more efficient algorithm than the conventional approach, which chooses the carrier density distribution of the SOAs as the iterative variable. It is also found that the photon iterative method has almost the same computing efficiency for conventional SOAs and GCSOAs.

一种风险驱动的迭代开发需求优先级排序方法

需求优先级排序是指系统参与者为需求指定实现的优先次序,是迭代开发过程中开发者制定项目迭代计划的基础.现存的需求优先级排序方法对系统参与者之间的协商和调整优先级的支持能力不足,导致根据需求优先级所制定的迭代计划难以作出符合需求变更和环境改变的调整.提出一种风险驱动的需求优先级自适应排序方法.该方法将自适应计划方法学与风险驱动相结合,将风险作为排序决策的依据,以自适应的过程为迭代开发排序需求优先级.该方法能够改善需求优先级排序过程中系统参与者之间的协商和调整需求优先级的能力,增强在迭代开发中对需求的控制,降低因需求导致项目失败的可能性.

缺陷驱动的软件过程量化管理方法研究

随着软件应用范围的不断扩大和复杂程度的不断提高，软件开发过程越来越难以控制，软件质量也越来越难以保障。质量管理的思想和理念，已经从单纯的以面向软件产品的检验为主要手段的质量控制，发展到更加成熟、更加主动地对软件产品生产过程进行管理的质量保障。 作为高成熟度软件过程的特征，量化过程管理逐渐被软件组织接受并实施。通过实施量化管理，能够刻画项目或过程目标的满足程度，找到造成过程或产品重大偏差的根本原因。然而，在量化过程管理实施期间，软件组织面对不同的软件开发过程、众多的过程性能度量指标、复杂的统计分析方法，既要考虑量化管理方法的合理性和复杂程度，又要权衡量化管理的实施成本，这使得实施有效的量化过程管理充满挑战。本文以缺陷数据为中心，提出了一种缺陷驱动的量化过程管理框架，以及基于该框架的两个量化管理方法，支持软件组织收集量化过程管理所需数据，建立过程性能基线和过程性能模型，量化管理软件项目。该框架适合迭代、瀑布等不同的开发方法，支持项目全生命周期的量化管理。 本文主要贡献包括： 提出了一种缺陷驱动的量化过程管理框架(Defect-driven Quantitative Process Management framework, DefQPM)。量化管理中，保障软件质量是核心。质量和缺陷密切相关，软件开发过程中各类工程活动（如：需求、设计、编码、测试等）都伴随着缺陷的注入、排除和遗留。DefQPM框架以缺陷数据作为量化管理的出发点，自底向上的通过数据层、模型层、使用层来指导软件组织分析过程性能，识别度量指标间的相关性，建立符合自身情况的过程性能基线和过程性能模型，有效的实施量化过程管理。DefQPM框架给出了实施量化管理的过程和机制。基于DefQPM框架，可以建立针对特定应用场景的量化管理方法，以及针对特定软件组织的量化管理解决方案。 提出了一种基于DefQPM的迭代项目量化管理方法(process performance Baseline based Defect-driven iteration management, BiDefect)。迭代开发方法由于其灵活性和管理需求变更的能力，得到了广泛应用。然而，如何对迭代项目实施量化管理依然充满挑战。迭代项目中，各种活动多次并行执行，难以找到合适的控制点，也缺乏针对迭代项目的度量指标及分析方法。基于DefQPM框架，本文研究了迭代开发项目典型的量化管理需要（例如：通过控制每次迭代工作产品的质量来保障最终交付软件产品的质量），提出了一种针对迭代项目的量化管理方法，解决了量化管理迭代项目的几个主要挑战。该方法关注缺陷的注入、排除、遗留情况，指导项目策划期间建立整体估算和度量，在项目执行期间评价软件过程执行情况及软件产品的质量，及时识别异常并采取纠正措施，进而为项目后续工作中成本、进度、质量等方面提供估算、控制方面的指导。 提出了一种基于DefQPM的测试过程量化管理方法(Quantitatively Managing Testing process, TestQM)。测试是重要的质量控制活动，对于高成熟度软件组织来说也是需要进行量化管理的活动。缺陷检测和缺陷修复是测试过程的两类主要活动，需要不同技能的人员执行。目前流行的软件估算方法多是将缺陷检测和缺陷修复的工作量和进度统一纳入测试活动中进行估算和管理，不够准确。基于DefQPM框架，本文提出了一种专门针对测试过程的量化管理方法。该方法关注缺陷按注入阶段分布情况，缺陷与修复工作量的相关性，以及缺陷与修复进度的相关性，指导在早期项目建立测试过程的估算，在测试过程中根据缺陷按注入阶段分布情况调整缺陷修复工作量和进度，使得测试过程受控。同时，介绍了TestQM针对Web应用开发项目的经验模型。 最后，介绍了上述量化管理方法在国内软件组织中的应用，包括BiDefect方法在迭代开发项目中的应用，以及TestQM方法在Web应用开发项目中的应用。软件组织实施量化过程管理前后的过程性能变化表明，应用本文方法能够对项目进行有效的估算、度量、重新估算和控制，进而提高产品质量，改善客户满意度。

PREDICTION OF VORTEX-INDUCED VIBRATION OF FLEXIBLE RISER USING AN IMPROVED WAKE-OSCILLATOR MODEL

Based on improving the wake-oscillator model, an analytical model for vortex-induced vibration (VIV) of flexible riser under non-uniform current is presented, in which the variation of added mass at lock-in and the nonlinear relationship between amplitude of response and reduced velocity are considered. By means of empirical formula combining iteration computation, the improved analytical model can be conveniently programmed into computer code with simpler and faster computation process than CFD so as to be suitable to application of practical engineering. This model is validated by comparing with experimental result and numerical simulation. Our results show that the improved model can predict VIV response and lock-in region more accurately. At last, illustrative examples are given in which the amplitude of response of flexible riser experiencing VIV under action of non-uniform current is calculated and effects of riser tension and flow distribution along span of riser are explored. It is demonstrated that with the variation of tension and flow distribution, lock-in region of mode behaves in different way, and thus the final response is a synthesis of response of locked modes.