Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.     

变速永磁同步发电机系统稳压控制（连载之五）直接电压控制在发电机系统中的应用

在所研究的具有强弱磁能力变速永磁同步发电机的270V直流电能系统中,应用PWM整流器,提出直接电压控制技术,设计了直流母线电压PI控制器,并结合有源阻尼技术,在Matlab/Simulink仿真及测试平台中加以实现。最后,对本系列连载所研究的各类控制算法的性能进行对比研究。     

Future train traffic control: control by re-planning

Improving train traffic control can be a cost-efficient way to improve train traffic punctuality and increase utilization of existing and future railway infrastructure. However, performance in train traffic control tasks currently involves working on a technical level in order to regulate the traffic flow. Working in a preventive manner is poorly supported and train traffic controllers are usually restricted to just solving problems as they occur. This often results in unnecessarily long delays and decreased timeliness of train traffic. The main objective of this paper is to describe a proposed control strategy and a case study, which evaluates the control strategy and the prototype tool derived from the research. By shifting the control paradigm to a high-level control strategy, many of today’s problems may be avoided, with benefits of the reduction in delays, improved timeliness and better utilization of the infrastructure. Twenty-one train traffic controllers participated in a case study, with a simulated prototype environment. The majority of the participating train traffic controllers were positive to the new concepts and ideas. Many of the important aspects of the proposed control strategy can be investigated with the simulation, but due to the complexity of train traffic some issues must be evaluated in an operative environment.     

Recombination luminescence from defects in boron-ion implantation-doped diamond using low fluences

 Two identical, high purity, natural type IIa diamonds, which displayed the ubiquitous blue cathodoluminescence (CL) band at ≈ 2.9 eV, as well as an indication of the corresponding green band at ≈ 2.4 eV, have been equivalently doped by using extremely low dose B⁺- and C⁺-ion CIRA-implantations respectively. Comparative CL measurements showed changes in the intensities of the 2.9 and 2.4 eV bands and the generation of bands at ≈ 4 eV, as well as at ≈ 3.5 and ≈ 4.6 eV (the latter two in the B⁺-CIRA diamond). The results are commensurate with the model (proposed previously) in which the 2.9 and 4 eV bands are generated respectively by electron-hole recombinations at negatively charged acceptor- and positively charged donor-like, intrinsic defects. The present results indicate that Coulomb interactions between the latter defects and (at least partially) compensated, negatively charged, boron acceptors, generate the 3.5 and 4.6 eV bands, which may be considered as higher energy (≈ 0.6 eV) replicas of the 2.9 and 4 eV bands. In both cases, two electrons and a hole interact just before the hole combines with an electron. Such a configuration of charges seems related to, and could possibly be described as, a type of ”ionised exciton molecule”, where the ”bonding” of two negative ”nuclei” is facilitated by the presence of the hole. The CL measurements further indicate that the 2.4 eV band forms when a high enough density of, in this case, neutral acceptors are present. These neutral acceptors compete with the valence band to supply holes for recombination at the negatively charged, acceptor-type, intrinsic defects which are, in the absence of the boron, responsible for the generation of the blue, 2.9 eV band. Received: 5 December 1997 / Accepted: 13 December 1997     

Load forecasting using a multivariate meta-learning system

Although over a thousand scientific papers address the topic of load forecasting every year, only a few are dedicated to finding a general framework for load forecasting that improves the performance, without depending on the unique characteristics of a certain task such as geographical location. Meta-learning, a powerful approach for algorithm selection has so far been demonstrated only on univariate time-series forecasting. Multivariate time-series forecasting is known to have better performance in load forecasting. In this paper we propose a meta-learning system for multivariate time-series forecasting as a general framework for load forecasting model selection. We show that a meta-learning system built on 65 load forecasting tasks returns lower forecasting error than 10 well-known forecasting algorithms on 4 load forecasting tasks for a recurrent real-life simulation. We introduce new metafeatures of fickleness, traversity, granularity and highest ACF. The meta-learning framework is parallelized, component-based and easily extendable.     

Development of a Modelica Compiler Using JastAdd

This paper describes experiences from implementing key parts of a compiler for Modelica, an object-oriented language supporting declarative modeling and simulation of complex physical systems. Our implementation uses the attribute-grammar based tool JastAdd. In particular, we discuss the implementation of Modelica name analysis which is highly context-dependent, type analysis which is based on structural subtyping, and flattening which is a fundamental part of the Modelica compilation process.of so called modifications, Modelica.     

Enforcing structural regularities in software using IntensiVE

The design and implementation of a software system is often governed by a variety of coding conventions, design patterns, architectural guidelines, design rules, and other so-called structural regularities. To prevent a deterioration of the system’s source code, it is important that these regularities are verified and enforced upon evolution of the system. The Intensional Views Environment (IntensiVE), presented in this article, is a tool suite for specifying relevant structural regularities in an (object-oriented) software system and verifying them against the current and later versions of the system. At the heart of the IntensiVE tool suite are (logic) program queries and the model of intensional views and relations, through which regularities are expressed. Upon verification of these regularities in the source code of the system, IntensiVE reports the code entities (i.e. classes, methods, variables, statements, etc.) that violate these constraints. We present IntensiVE and illustrate its application to the verification of an Abstract Factory design pattern in the implementation of a software system.     

The effect of random grain distributions on fatigue crack initiation in a notched coarse grained superalloy specimen

Coarse grained superalloys are of large interest in high temperature applications, and can be found in e.g. gas turbine components, where great care must be given with respect to high temperature fatigue. Due to the large grain size, the material behaviour at e.g. sharp notches cannot be considered homogeneous. As a consequence, the fatigue behaviour is likely to expose a large variation. In order to numerically investigate this variation, a Monte Carlo analysis has been carried out by 100 FE-simulations of notched specimens, where placements and orientations of the grains were randomised. Furthermore, each grain was modelled as a unique single-crystal, displaying both anisotropic elastic and plastic behaviour and tension/compression asymmetry. The effect of randomness was investigated by the obtained dispersion in fatigue crack initiation life. It was concluded that the fatigue life behaviour of coarse grained nickel-base superalloys may show a considerable variation, which cannot be captured by one single deterministic analysis based on data for a homogenised material. Furthermore, the dispersion is of such a magnitude that it needs to be taken into account in industrial applications where highly stressed coarse grained materials are used.     

Separation of L‐aspartic acid and L‐glutamic acid mixtures for use in the production of bio‐based chemicals

BACKGROUND: Amino acids are promising feedstocks for the chemical industry due to their chemical functionality. They can be obtained by the hydrolysis of potentially inexpensive protein streams such as the byproduct of biofuel production. However, individual amino acids are required before they can be used for the further production of chemicals. Here, the separation of L‐aspartic acid (Asp) and L‐glutamic acid (Glu) mixture, which can be isolated from protein hydrolysis solutions at low pH or from electrodialysis of complex amino acid mixtures, was studied. RESULTS: Glu was converted into L‐pyroglutamic acid (pGlu) which can be separated from the mixture of Asp and Glu due to its higher solubility in water. The conversion was carried out under aqueous or melt conditions. Under aqueous conditions, the conversion was studied as a factor of time, temperature and the amount of Glu. The conversion was specific with high yield and not effected by Asp. After pGlu was separated from Asp and residual Glu by solubility difference, it can be transferred back to Glu through hydrolysis. CONCLUSION: The conversion of Glu to pGlu is specific and can be applied to separation Asp and Glu for their use in the production of bio‐based chemicals. Copyright © 2012 Society of Chemical Industry     

Degradation of erythromycin in honey and selection of suitable marker residues for food safety analysis

Extra-label use of veterinary medications in apiculture is a practice which is known to result in the presence of drug residues in honey. Erythromycin has been used by some beekeepers in an attempt to control foulbrood diseases in honey bees. It is recognised that erythromycin degrades in acidic aqueous solutions to yield predominantly anhydroerythromycin. Honey is an acidic medium in which erythromycin should also degrade. Three degradation products with a molecular weight of 715 Da (anhydroerythromycin, erythromycin enol ether, and an unidentified but suspected related isomer) have been confirmed to be formed in honey. Erythromycin was found to degrade rapidly in honey at typical hive temperature with a half-life of less than one day. Based on these results, it is recommended that food safety laboratories include degradation products in analytical testing protocols when examining honey for erythromycin residues.