Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Multi-point and multi-objective optimization of a centrifugal compressor impeller based on genetic algorithm

The design of high efficiency, high pressure ratio, and wide flow range centrifugal impellers is a challenging task. The paper describes the application of a multiobjective, multipoint optimization methodology to the redesign of a transonic compressor impeller for this purpose. The aerodynamic optimization method integrates an improved nondominated sorting genetic algorithm II (NSGA-II), blade geometry parameterization based on NURBS, a 3D RANS solver, a self-organization map (SOM) based data mining technique, and a time series based surge detection method. The optimization results indicate a considerable improvement to the total pressure ratio and isentropic efficiency of the compressor over the whole design speed line and by 5.3% and 1.9% at design point, respectively. Meanwhile, surge margin and choke mass flow increase by 6.8% and 1.4%, respectively. The mechanism behind the performance improvement is further extracted by combining the geometry changes with detailed flow analysis

Sensitivity analysis for multidisciplinary design optmization

When designing a complex industrial product, the designer often has to optimise simultaneously multiple conflicting criteria. Such a problem does not usually have a unique solution, but a set of non-dominated solutions known as Pareto solutions. In this context, the progress made in the development of more powerful but more computationally demanding numerical methods has led to the emergence of multi-disciplinary optimisation (MDO). However, running computationally expensive multi-objective optimisation procedures to obtain a comprehensive description of the set of Pareto solutions might not always be possible. The aim of this research is to develop a methodology to assist the designer in the multi-objective optimisation process. As a result, an approach to enhance the understanding of the optimisation problem and to gain some insight into the set of Pareto solutions is proposed. This approach includes two main components. First, global sensitivity analysis is used prior to the optimisation procedure to identify non- significant inputs, aiming to reduce the dimensionality of the problem. Second, once a candidate Pareto solution is obtained, the local sensitivity is computed to understand the trade-offs between objectives. Exact linear and quadratic approximations of the Pareto surface have been derived in the general case and are shown to be more accurate than the ones found in literature. In addition, sufficient conditions to identify non-differentiable Pareto points have been proposed. Ultimately, this approach enables the designer to gain more knowledge about the multi-objective optimisation problem with the main concern of minimising the computational cost. A number of test cases have been considered to evaluate the approach. These include algebraic examples, for direct analytical validation, and more representative test cases to evaluate its usefulness. In particular, an airfoil design problem has been developed and implemented to assess the approach on a typical engineering problem. The results demonstrate the potential of the methodology to achieve a reduction of computational time by concentrating the optimisation effort on the most significant variables. The results also show that the Pareto approximations provide the designer with essential information about trade-offs at reduced computational cost

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

ターボ機械の最適化と設計知識マイニング

Tohoku University大林 茂課

Efficient Frontier for Robust Higher-order Moment Portfolio Selection

This article proposes a non-parametric portfolio selection criterion for the static asset allocation problem in a robust higher-moment framework. Adopting the Shortage Function approach, we generalize the multi-objective optimization technique in a four-dimensional space using L-moments, and focus on various illustrations of a four-dimensional set of the first four L-moment primal efficient portfolios. our empirical findings, using a large European stock database, mainly rediscover the earlier works by Jean (1973) and Ingersoll (1975), regarding the shape of the extended higher-order moment efficient frontier, and confirm the seminal prediction by Levy and Markowitz (1979) about the accuracy of the mean-variance criterion.Efficient frontier, portfolio selection, robust higher L-moments, shortage function, goal attainment application.

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

Experimental time-domain controlled source electromagnetic induction for highly conductive targets detection and discrimination

The response of geological materials at the scale of meters and the response of buried targets of different shapes and sizes using controlled-source electromagnetic induction (CSEM) is investigated. This dissertation focuses on three topics; i) frac- tal properties on electric conductivity data from near-surface geology and processing techniques for enhancing man-made target responses, ii) non-linear inversion of spa- tiotemporal data using continuation method, and iii) classification of CSEM transient and spatiotemporal data. In the first topic, apparent conductivity profiles and maps were studied to de- termine self-affine properties of the geological noise and the effects of man-made con- ductive metal targets. 2-D Fourier transform and omnidirectional variograms showed that variations in apparent conductivity exhibit self-affinity, corresponding to frac- tional Brownian motion. Self-affinity no longer holds when targets are buried in the near-surface, making feasible the use of spectral methods to determine their pres- ence. The difference between the geology and target responses can be exploited using wavelet decomposition. A series of experiments showed that wavelet filtering is able to separate target responses from the geological background. In the second topic, a continuation-based inversion method approach is adopted, based on path-tracking in model space, to solve the non-linear least squares prob- lem for unexploded ordnance (UXO) data. The model corresponds to a stretched- exponential decay of eddy currents induced in a magnetic spheroid. The fast inversion of actual field multi-receiver CSEM responses of inert, buried ordnance is also shown. Software based on the continuation method could be installed within a multi-receiver CSEM sensor and used for near-real-time UXO decision. In the third topic, unsupervised self-organizing maps (SOM) were adapted for data clustering and classification. The use of self-organizing maps (SOM) for central- loop CSEM transients shows potential capability to perform classification, discrimi- nating background and non-dangerous items (clutter) data from, for instance, unex- ploded ordnance. Implementation of a merge SOM algorithm showed that clustering and classification of spatiotemporal CSEM data is possible. The ability to extract tar- get signals from a background-contaminated pattern is desired to avoid dealing with forward models containing subsurface response or to implement processing algorithm to remove, to some degree, the effects of background response and the target-host interactions

Automatic object classification for surveillance videos.

PhDThe recent popularity of surveillance video systems, specially located in urban scenarios, demands the development of visual techniques for monitoring purposes. A primary step towards intelligent surveillance video systems consists on automatic object classification, which still remains an open research problem and the keystone for the development of more specific applications. Typically, object representation is based on the inherent visual features. However, psychological studies have demonstrated that human beings can routinely categorise objects according to their behaviour. The existing gap in the understanding between the features automatically extracted by a computer, such as appearance-based features, and the concepts unconsciously perceived by human beings but unattainable for machines, or the behaviour features, is most commonly known as semantic gap. Consequently, this thesis proposes to narrow the semantic gap and bring together machine and human understanding towards object classification. Thus, a Surveillance Media Management is proposed to automatically detect and classify objects by analysing the physical properties inherent in their appearance (machine understanding) and the behaviour patterns which require a higher level of understanding (human understanding). Finally, a probabilistic multimodal fusion algorithm bridges the gap performing an automatic classification considering both machine and human understanding. The performance of the proposed Surveillance Media Management framework has been thoroughly evaluated on outdoor surveillance datasets. The experiments conducted demonstrated that the combination of machine and human understanding substantially enhanced the object classification performance. Finally, the inclusion of human reasoning and understanding provides the essential information to bridge the semantic gap towards smart surveillance video systems

Meta-optimizations for Cluster Analysis

This dissertation thesis deals with advances in the automation of cluster analysis.This dissertation thesis deals with advances in the automation of cluster analysis