Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

Dispatch strategy of PHEVs to mitigate selected patterns of seasonally varying outputs from renewable generation

Rapid development of plug-in hybrid electric vehicles (PHEVs) brings new challenges and opportunities to the power industry. A large number of idle PHEVs can potentially be employed to form a distributed energy storage system for supporting renewable generation. To reduce the negative effects of unsteady renewable generation outputs, a stochastic optimization-based dispatch model capable of handling uncertain outputs of PHEVs and renewable generation is formulated in this paper. The mathematical expectations, second-order original moments, and variances of wind and photovoltaic (PV) generation outputs are derived analytically. Incorporated all the derived uncertainties, a novel generation shifting objective is proposed. The cross-entropy (CE) method is employed to solve this optimal dispatch model. Multiple patterns of renewable generation depending on seasons and renewable market shares are investigated. The feasibility and efficiency of the developed optimal dispatch model, as well as the CE method, are demonstrated with a 33-node distribution system.

Semantic Classes in Topic Detection and Tracking

Topic detection and tracking (TDT) is an area of information retrieval research the focus of which revolves around news events. The problems TDT deals with relate to segmenting news text into cohesive stories, detecting something new, previously unreported, tracking the development of a previously reported event, and grouping together news that discuss the same event. The performance of the traditional information retrieval techniques based on full-text similarity has remained inadequate for online production systems. It has been difficult to make the distinction between same and similar events. In this work, we explore ways of representing and comparing news documents in order to detect new events and track their development. First, however, we put forward a conceptual analysis of the notions of topic and event. The purpose is to clarify the terminology and align it with the process of news-making and the tradition of story-telling. Second, we present a framework for document similarity that is based on semantic classes, i.e., groups of words with similar meaning. We adopt people, organizations, and locations as semantic classes in addition to general terms. As each semantic class can be assigned its own similarity measure, document similarity can make use of ontologies, e.g., geographical taxonomies. The documents are compared class-wise, and the outcome is a weighted combination of class-wise similarities. Third, we incorporate temporal information into document similarity. We formalize the natural language temporal expressions occurring in the text, and use them to anchor the rest of the terms onto the time-line. Upon comparing documents for event-based similarity, we look not only at matching terms, but also how near their anchors are on the time-line. Fourth, we experiment with an adaptive variant of the semantic class similarity system. The news reflect changes in the real world, and in order to keep up, the system has to change its behavior based on the contents of the news stream. We put forward two strategies for rebuilding the topic representations and report experiment results. We run experiments with three annotated TDT corpora. The use of semantic classes increased the effectiveness of topic tracking by 10-30\% depending on the experimental setup. The gain in spotting new events remained lower, around 3-4\%. The anchoring the text to a time-line based on the temporal expressions gave a further 10\% increase the effectiveness of topic tracking. The gains in detecting new events, again, remained smaller. The adaptive systems did not improve the tracking results.

Learning polyhedral classifiers using logistic function

In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based model for the posterior probability function. We propose an alternating optimization algorithm, namely, SPLA1 (Single Polyhedral Learning Algorithm1) which maximizes the loglikelihood of the training data to learn the parameters. We also extend our method to make it independent of any user specified parameter (e.g., number of hyperplanes required to form a polyhedral set) in SPLA2. We show the effectiveness of our approach with experiments on various synthetic and real world datasets and compare our approach with a standard decision tree method (OC1) and a constrained optimization based method for learning polyhedral sets.

Optimal synthesis of adjustable planar four-bar crank-rocker type mechanisms for approximate multi-path generation

This paper deals with an optimization based method for synthesis of adjustable planar four-bar, crank-rocker mechanisms. For multiple different and desired paths to be traced by a point on the coupler, a two stage method first determines the parameters of the possible driving dyads. Then the remaining mechanism parameters are determined in the second stage where a least-squares based circle-fitting procedure is used. Compared to existing formulations, the optimization method uses less number of design variables. Two numerical examples demonstrate the effectiveness of the proposed synthesis method. (C) 2013 Elsevier Ltd. All rights reserved.

The relationship between classification of multi-domain proteins using an alignment-free approach and their functions: a case study with immunoglobulins

Establishing functional relationships between multi-domain protein sequences is a non-trivial task. Traditionally, delineating functional assignment and relationships of proteins requires domain assignments as a prerequisite. This process is sensitive to alignment quality and domain definitions. In multi-domain proteins due to multiple reasons, the quality of alignments is poor. We report the correspondence between the classification of proteins represented as full-length gene products and their functions. Our approach differs fundamentally from traditional methods in not performing the classification at the level of domains. Our method is based on an alignment free local matching scores (LMS) computation at the amino-acid sequence level followed by hierarchical clustering. As there are no gold standards for full-length protein sequence classification, we resorted to Gene Ontology and domain-architecture based similarity measures to assess our classification. The final clusters obtained using LMS show high functional and domain architectural similarities. Comparison of the current method with alignment based approaches at both domain and full-length protein showed superiority of the LMS scores. Using this method we have recreated objective relationships among different protein kinase sub-families and also classified immunoglobulin containing proteins where sub-family definitions do not exist currently. This method can be applied to any set of protein sequences and hence will be instrumental in analysis of large numbers of full-length protein sequences.

Micro-mechanical stages with enhanced range

We present concepts and an optimization-based methodology for the design of micro-mechanical stages that have not only high precision but also an enhanced range. Joint-free distributed compliant designs provide high precision and easy manufacturability at macro and micro scales. The range of motion is enhanced by using displacement-amplifying compliant mechanisms (DaCMs). The main issue addressed in this paper is how to retain the decoupling between the X and Y motions in the stage when it is equipped with DaCMs. The natural frequency of the stage is also not compromised in enhancing the range. The optimized design has 2.5 times more range than the designs reported in the literature. Furthermore, the sensitivity improved by a factor of two when the stage is optimized for an accelerometer.

A Featureless Approach to 3D Polyhedral Building Modeling from Aerial Images

This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach.

Efficient methods for stochastic optimal control

The Hamilton Jacobi Bellman (HJB) equation is central to stochastic optimal control (SOC) theory, yielding the optimal solution to general problems specified by known dynamics and a specified cost functional. Given the assumption of quadratic cost on the control input, it is well known that the HJB reduces to a particular partial differential equation (PDE). While powerful, this reduction is not commonly used as the PDE is of second order, is nonlinear, and examples exist where the problem may not have a solution in a classical sense. Furthermore, each state of the system appears as another dimension of the PDE, giving rise to the curse of dimensionality. Since the number of degrees of freedom required to solve the optimal control problem grows exponentially with dimension, the problem becomes intractable for systems with all but modest dimension.

In the last decade researchers have found that under certain, fairly non-restrictive structural assumptions, the HJB may be transformed into a linear PDE, with an interesting analogue in the discretized domain of Markov Decision Processes (MDP). The work presented in this thesis uses the linearity of this particular form of the HJB PDE to push the computational boundaries of stochastic optimal control.

This is done by crafting together previously disjoint lines of research in computation. The first of these is the use of Sum of Squares (SOS) techniques for synthesis of control policies. A candidate polynomial with variable coefficients is proposed as the solution to the stochastic optimal control problem. An SOS relaxation is then taken to the partial differential constraints, leading to a hierarchy of semidefinite relaxations with improving sub-optimality gap. The resulting approximate solutions are shown to be guaranteed over- and under-approximations for the optimal value function. It is shown that these results extend to arbitrary parabolic and elliptic PDEs, yielding a novel method for Uncertainty Quantification (UQ) of systems governed by partial differential constraints. Domain decomposition techniques are also made available, allowing for such problems to be solved via parallelization and low-order polynomials.

The optimization-based SOS technique is then contrasted with the Separated Representation (SR) approach from the applied mathematics community. The technique allows for systems of equations to be solved through a low-rank decomposition that results in algorithms that scale linearly with dimensionality. Its application in stochastic optimal control allows for previously uncomputable problems to be solved quickly, scaling to such complex systems as the Quadcopter and VTOL aircraft. This technique may be combined with the SOS approach, yielding not only a numerical technique, but also an analytical one that allows for entirely new classes of systems to be studied and for stability properties to be guaranteed.

The analysis of the linear HJB is completed by the study of its implications in application. It is shown that the HJB and a popular technique in robotics, the use of navigation functions, sit on opposite ends of a spectrum of optimization problems, upon which tradeoffs may be made in problem complexity. Analytical solutions to the HJB in these settings are available in simplified domains, yielding guidance towards optimality for approximation schemes. Finally, the use of HJB equations in temporal multi-task planning problems is investigated. It is demonstrated that such problems are reducible to a sequence of SOC problems linked via boundary conditions. The linearity of the PDE allows us to pre-compute control policy primitives and then compose them, at essentially zero cost, to satisfy a complex temporal logic specification.

Racionalidade e otimização regulatórias: um estudo a partir da teoria das falhas de regulação

A presente dissertação procura investigar o incremento da racionalidade regulatória (ou otimização regulatória) a partir da teoria das falhas de regulação. Hoje, já parece existir um consenso teórico e prático de que a regulação e seu aparato institucional as agências reguladoras constituem fenômeno irreversível. Nesse contexto, as perguntas que se colocam são as seguintes: no plano dos resultados, o Estado Regulador tem alcançado as finalidades a que se propôs? Em caso negativo, que tipo de obstáculos o tem impedido? E mais: que providências devem ser adotadas para superá-los? Responder a tais indagações depende do reconhecimento de que não apenas os mercados são imperfeitos; também a intervenção estatal na economia gera riscos e efeitos negativos. Estudar seus tipos, suas fontes e a maneira como operam é o ponto de partida para se otimizar a regulação. O trabalho propõe uma sistematização das espécies de falhas regulatórias, baseada na proposta de Cass Sunstein, mas adaptada à realidade brasileira. A exposição é precedida de explicações introdutórias sobre o conceito de regulação; as razões para se regular; e características da regulação no Estado Democrático de Direito. Tais características conformam um ideal de racionalidade regulatória, o qual é comprometido pela instauração das falhas de regulação. Reconhecer a existência dos defeitos regulatórios e conhecê-los é já um primeiro passo para se melhorar a regulação. Mas há outros encaminhamentos deveras importantes para a prevenção e correção de falhas regulatórias. Dentre eles, destaca-se um conjunto de reformas institucionais em sentido amplo e estrito, as quais envolvem o sistema de controle pelos Poderes Legislativo, Executivo e Judiciário, e mecanismos procedimentais como a análise de impacto regulatório e a elaboração de agendas regulatórias.

一种改进的自适应逃逸微粒群算法及实验分析

分析了变异操作对微粒群算法（panicle swarm optimization，简称PSO）的影响，针对收敛速度慢、容易陷入局部极小等缺点，结合生物界中物种发现生存密度过大时会自动分家迁移的习性，给出了一种自适应逃逸微粒群算法，并证明了它依概率收敛到全局最优解．算法中的逃逸行为是一种简化的确定变异操作．当微粒飞行速度过小时，通过逃逸运动使微粒能够有效地进行全局和局部搜索，减弱了随机变异操作带来的不稳定性、典型复杂函数优化的仿真结果表明，该算法不仅具有更快的收敛速度，而且能更有效地进行全局搜索．

基于时序逻辑的编译优化测试用例生成方法

提出一种基于时序逻辑公式的关键节点控制图生成方法,生成的测试用例针对性强,容易扩展;并以该方法改进了一种编译优化自动化测试工具,在很大程度上消除了其测试冗余,提高了测试效率

多对一供应链收入共享契约中的采购优化

为研究多对一供应链结构中基于契约协商的采购优化策略问题及其对改善供应链绩效的影响,针对该供应链结构中零售商具有内生保留利润的特点,建立了以制造商为主方、零售商为从方的Stackelberg主从对策模型;给出了在制造商提供契约条款的对称博弈中,零售商采购策略存在唯一最优解、制造商间的博弈存在唯一对称纳什均衡最优解的证明;讨论了收入共享契约下分散供应链决策同集中供应链决策的关系,限定了该结构中供应链协调的条件。最后,通过仿真实验分析验证了契约参数及产品的可替代性对供应链绩效的影响。

月球机器人仿真中一种消除轮地交互误差的方法

利用虚拟现实技术虚拟出月球机器人在月面上的作业环境和作业过程,是提高机器人作业的安全系数和工作效率的一条有效途径。在3D重建得到的虚拟月面环境中,如果采用通常的单纯基于运动学(或者动力学)模型的仿真方法,对机器人的作业和运动进行虚拟,那么机器人与地形交互的过程中容易产生接触偏差。而且,随着仿真时间的推进,这种接触偏差会逐渐积累并不断增大,进而严重影响仿真测试的精度和效果。为了消除月球机器人仿真中的轮地交互误差,在分析误差来源的基础上,提出了基于运动学优化的解决方法。最后利用实际的虚拟现实仿真系统,验证了所提出方法的有效性。

一种基于遗传线性搜索算法的关节手模型匹配方法

提出了一种可替代传统搜索算法的改进型模型匹配算法 这种算法将遗传算法 (GeneticAlgorithm ,GA)和经典的线性搜索算法 (LineSearch ingAlgorithm ,LSA)相结合 它能保证匹配解是全局最优的并且运算是近乎实时的