The single-phase brushless doubly-fed machine as a generator for wind turbines

This paper proposes a single-phase variant of the Brushless Doubly-Fed Machine, the Single-Phase BDFM, SPB. Like the BDFM it is a variable speed generator which requires a converter rated at only a fraction of the machine rating, using structure with no brushes. Unlike the BDFM, most of its power is delivered directly into a single-phase grid connection. As such it is a potential replacement for PM generators in small-ish wind turbines, potentially with a much lower cost. In this paper we give details of its operation, with reference to results from the first prototype SPB. We also suggest how it may be controlled, again with experimental results. © 2011 IEEE.

Synchronous operation control of the Brushless Doubly-Fed Machine

The paper presents a novel vector control structure for the Brushless Doubly-Fed Machine (BDFM) which is derived based on the machine synchronous operation. In fact, the synchronous operation of the BDFM provides an efficient approach for determining the required reference angle in the machine vector control structure. The utilization of such reference angle makes the vector control structure presented in this paper different from and, in fact, more effective than the existing rotor flux and stator flux orientation schemes proposed for the machine. The results of implementing the vector control scheme in simulations confirm the effectiveness of the proposed approach for the BDFM control. © 2010 IEEE.

A novel vector control approach for Single Phase Brushless Doubly Fed Machine

This paper proposed a novel control scheme for operating the Single Phase Brushless Doubly-Fed Machine (SPB) based on Stator-Flux-Oriented control algorithm. The SPB is a new type of Brushless Doubly-Fed Machine (BDFM) which shows a potential in applications which require adjustable speed such as Wind Power generation and speed adjustable Drive. The SPB can be applied to single-phase power system and the lower cost of the SPB makes the SPB suitable for low-rated power conversion applications. This paper develops the control scheme of the SPB with explicit mathematical analysis and block diagram of the controller. Experimental verification is also given. © 2011 IEEE.

Improving statistical machine translation by classifying and generalizing inflected verb forms

This paper introduces a rule-based classification of single-word and compound verbs into a statistical machine translation approach. By substituting verb forms by the lemma of their head verb, the data sparseness problem caused by highly-inflected languages can be successfully addressed. On the other hand, the information of seen verb forms can be used to generate new translations for unseen verb forms. Translation results for an English to Spanish task are reported, producing a significant performance improvement.

An ngram-based statistical machine translation decoder

In this paper we describe MARIE, an Ngram-based statistical machine translation decoder. It is implemented using a beam search strategy, with distortion (or reordering) capabilities. The underlying translation model is based on an Ngram approach, extended to introduce reordering at the phrase level. The search graph structure is designed to perform very accurate comparisons, what allows for a high level of pruning, improving the decoder efficiency. We report several techniques for efficiently prune out the search space. The combinatory explosion of the search space derived from the search graph structure is reduced by limiting the number of reorderings a given translation is allowed to perform, and also the maximum distance a word (or a phrase) is allowed to be reordered. We finally report translation accuracy results on three different translation tasks.

Phrase linguistic classification and generalization for improving statistical machine translation

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed. © 2005 Association for Computational Linguistics.

Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

Machine vision based concrete surface quality assessment

Manually inspecting concrete surface defects (e.g., cracks and air pockets) is not always reliable. Also, it is labor-intensive. In order to overcome these limitations, automated inspection using image processing techniques was proposed. However, the current work can only detect defects in an image without the ability of evaluating them. This paper presents a novel approach for automatically assessing the impact of two common surface defects (i.e., air pockets and discoloration). These two defects are first located using the developed detection methods. Their attributes, such as the number of air pockets and the area of discoloration regions, are then retrieved to calculate defects’ visual impact ratios (VIRs). The appropriate threshold values for these VIRs are selected through a manual rating survey. This way, for a given concrete surface image, its quality in terms of air pockets and discoloration can be automatically measured by judging whether their VIRs are below the threshold values or not. The method presented in this paper was implemented in C++ and a database of concrete surface images was tested to validate its performance. Read More: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CO.1943-7862.0000126?journalCode=jcemd4

Machine Vision Enhanced Post-Earthquake Inspection and Rapid Loss Estimation

Manual inspection is required to determine the condition of damaged buildings after an earthquake. The lack of available inspectors, when combined with the large volume of inspection work, makes such inspection subjective and time-consuming. Completing the required inspection takes weeks to complete, which has adverse economic and societal impacts on the affected population. This paper proposes an automated framework for rapid post-earthquake building evaluation. Under the framework, the visible damage (cracks and buckling) inflicted on concrete columns is first detected. The damage properties are then measured in relation to the column's dimensions and orientation, so that the column's load bearing capacity can be approximated as a damage index. The column damage index supplemented with other building information (e.g. structural type and columns arrangement) is then used to query fragility curves of similar buildings, constructed from the analyses of existing and on-going experimental data. The query estimates the probability of the building being in different damage states. The framework is expected to automate the collection of building damage data, to provide a quantitative assessment of the building damage state, and to estimate the vulnerability of the building to collapse in the event of an aftershock. Videos and manual assessments of structures after the 2009 earthquake in Haiti are used to test the parts of the framework.

Machine Vision Enhanced Post-earthquake Inspection

Camera motion estimation is one of the most significant steps for structure-from-motion (SFM) with a monocular camera. The normalized 8-point, the 7-point, and the 5-point algorithms are normally adopted to perform the estimation, each of which has distinct performance characteristics. Given unique needs and challenges associated to civil infrastructure SFM scenarios, selection of the proper algorithm directly impacts the structure reconstruction results. In this paper, a comparison study of the aforementioned algorithms is conducted to identify the most suitable algorithm, in terms of accuracy and reliability, for reconstructing civil infrastructure. The free variables tested are baseline, depth, and motion. A concrete girder bridge was selected as the "test-bed" to reconstruct using an off-the-shelf camera capturing imagery from all possible positions that maximally the bridge's features and geometry. The feature points in the images were extracted and matched via the SURF descriptor. Finally, camera motions are estimated based on the corresponding image points by applying the aforementioned algorithms, and the results evaluated.

Machine Vision-Based Infrastructure As-Built Documentation Using Edge Points

The existing machine vision-based 3D reconstruction software programs provide a promising low-cost and in some cases automatic solution for infrastructure as-built documentation. However in several steps of the reconstruction process, they only rely on detecting and matching corner-like features in multiple views of a scene. Therefore, in infrastructure scenes which include uniform materials and poorly textured surfaces, these programs fail with high probabilities due to lack of feature points. Moreover, except few programs that generate dense 3D models through significantly time-consuming algorithms, most of them only provide a sparse reconstruction which does not necessarily include required points such as corners or edges; hence these points have to be manually matched across different views that could make the process considerably laborious. To address these limitations, this paper presents a video-based as-built documentation method that automatically builds detailed 3D maps of a scene by aligning edge points between video frames. Compared to corner-like features, edge points are far more plentiful even in untextured scenes and often carry important semantic associations. The method has been tested for poorly textured infrastructure scenes and the results indicate that a combination of edge and corner-like features would allow dealing with a broader range of scenes.