Neural networks models for wear patterns recognition of single-point dresser

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.

Um software de reconhecimento de voz para português brasileiro

Descreve a implementação de um software de reconhecimento de voz para o Português Brasileiro. Dentre os objetivos do trabalho tem-se a construção de um sistema de voz contínua para grandes vocabulários, apto a ser usado em aplicações em tempo-real. São apresentados os principais conceitos e características de tais sistemas, além de todos os passos necessários para construção. Como parte desse trabalho foram produzidos e disponibilizados vários recursos: modelos acústicos e de linguagem, novos corpora de voz e texto. O corpus de texto vem sendo construído através da extração e formatação automática de textos de jornais na Internet. Além disso, foram produzidos dois corpora de voz, um baseado em audiobooks e outro produzido especificamente para simular testes em tempo-real. O trabalho também propõe a utilização de técnicas de adaptação de locutor para resolução de problemas de descasamento acústico entre corpora de voz. Por último, é apresentada uma interface de programação de aplicativos que busca facilitar a utilização do decodificador Julius. Testes de desempenho são apresentados, comparando os sistemas desenvolvidos e um software comercial.

Deep learning architecture for gesture recognition

Pós-graduação em Engenharia Mecânica - FEG

The effects of the C-terminal amidation of mastoparans on their biological actions and interactions with membrane-mimetic systems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Dynamic Features for Iris Recognition

The human eye is sensitive to visible light. Increasing illumination on the eye causes the pupil of the eye to contract, while decreasing illumination causes the pupil to dilate. Visible light causes specular reflections inside the iris ring. On the other hand, the human retina is less sensitive to near infra-red (NIR) radiation in the wavelength range from 800 nm to 1400 nm, but iris detail can still be imaged with NIR illumination. In order to measure the dynamic movement of the human pupil and iris while keeping the light-induced reflexes from affecting the quality of the digitalized image, this paper describes a device based on the consensual reflex. This biological phenomenon contracts and dilates the two pupils synchronously when illuminating one of the eyes by visible light. In this paper, we propose to capture images of the pupil of one eye using NIR illumination while illuminating the other eye using a visible-light pulse. This new approach extracts iris features called "dynamic features (DFs)." This innovative methodology proposes the extraction of information about the way the human eye reacts to light, and to use such information for biometric recognition purposes. The results demonstrate that these features are discriminating features, and, even using the Euclidean distance measure, an average accuracy of recognition of 99.1% was obtained. The proposed methodology has the potential to be "fraud-proof," because these DFs can only be extracted from living irises.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

Synthetic strategies of new spin-labelled supramolecular systems

Molecular recognition and self-assembly represent fundamental issues for the construction of supramolecular systems, structures in which the components are held together through non-covalent interactions. The study of host-guest complexes and mechanical interlocked molecules, important examples in this field, is necessary in order to characterize self-assembly processes, achieve more control over the molecular organization and develop sophisticated structures by using properly designed building blocks. The introduction of paramagnetic species, or spin labelling, represents an attractive opportunity that allows their detection and characterization by the Electron Spin Resonance spectroscopy, a valuable technique that provides additional information to those obtained by traditional methods. In this Thesis, recent progresses in the design and the synthesis of new paramagnetic host-guest complexes and rotaxanes characterized by the presence of nitroxide radicals and their investigation by ESR spectroscopy are reported. In Chapter 1 a brief overview of the principal concepts of supramolecular chemistry, the spin labelling approach and the development of ESR methods applied to paramagnetic systems are described. Chapter 2 and 3 are focused on the introduction of radicals in macrocycles as Cucurbiturils and Pillar[n]arenes, due to the interesting binding properties and the potential employment in rotaxanes, in order to investigate their structures and recognition properties. Chapter 4 deals with one of the most studied mechanical interlocked molecules, the bistable [2]rotaxane reported by Stoddart and Heath based on the ciclobis (paraquat-p-phenylene) CBPQT4+, that represents a well known example of molecular switch driven by external stimuli. The spin labelling of analogous architectures allows the monitoring by ESR spectroscopy of the switch mechanism involving the ring compound by tuning the spin exchange interaction. Finally, Chapter 5 contains the experimental procedures used for the synthesis of some of the compounds described in Chapter 2-4.

Approaching water stress in the Alps: Transdisciplinary co-production of systems, target and transformation knowledge

There is increasing recognition that transdisciplinary approaches are needed to create suitable knowledge for sustainable water management. However, there is no common understanding of what transdisciplinary research may be and there is very limited debate on potentials and challenges regarding its implementation. Against this background, this paper presents a conceptual framework for transdisciplinary co-production of knowledge in water management projects oriented towards more sustainable use of water. Moreover, first experiences with its implementation are discussed. In so doing, the focus lies on potentials and challenges related to the co-production of systems, target and transformation knowledge by researchers and local stakeholders.

Offline grammar-based recognition of handwritten sentences

This paper proposes a sequential coupling of a Hidden Markov Model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using Stochastic Context-Free Grammars (SCFG) extracted from a text corpus. Based on extensive experiments, we conclude that syntax analysis helps to improve recognition rates significantly.

The syndromology of anorectal malformations revisited: from patterns of associated malformations to the recognition of syndromes

ackground: Although the frequency of associated malformation is high, the incidence of inheritable syndromes is widely underestimated in children with anorectal malformation (ARM). Data sources: OMIM database, patient records and charts of the Department of Pediatric Surgery, Johannes Gutenberg-University, Mainz, Germany. Methods: We analyzed all associations, sequences and syndromes listed in the OMIM database that can be accompanied by ARM. A large cohort of children born with ARM was then retrospectively investigated as to the type of ARM, presence of additional malformations and possible categorization as a syndrome, sequence or association. For this process a syndrome finder was developed and employed. This simplistic tool allows for a rapid first check of possible syndromes before a more complex analysis is started using the OMIM database and consulting specialists. Results: Among 317 children with ARM, associated malformations were present in 77.7% of 127 children with high ARM, in 68.7% of 32 with intermediate ARM, and in 25.3% of 158 with a low type ARM. Three or more organ systems were involved in 29.1% children with high type ARM and 25% with intermediate ARM and 8.2% with a low type ARM. An association of the vertebral anal tracheo-esophageal renal (VATER) and vertebral anal cardiac tracheo-esophageal renal limb (VACTERL) type was found in a total of 35 patients. Before analysis, 11 syndromes and 35 associations which were not clear previously in this patient cohort were described. In other 17 patients, 14 syndromes and 3 associations were identified. Conclusions: The high number of only retrospectively identified syndromes suggests that a routine search is necessary in every patient with ARM and additional malformations.