Trailing edge noise theory for rotating blades in uniform flow

This paper presents a new formulation for trailing edge noise radiation from rotating blades based on an analytical solution of the convective wave equation. It accounts for distributed loading and the effect of mean flow and spanwise wavenumber. A commonly used theory due to Schlinker and Amiet (1981) predicts trailing edge noise radiation from rotating blades. However, different versions of the theory exist; it is not known which version is the correct one and what the range of validity of the theory is. This paper addresses both questions by deriving Schlinker and Amiet's theory in a simple way and by comparing it to the new formulation, using model blade elements representative of a wind turbine, a cooling fan and an aircraft propeller. The correct form of Schlinker and Amiet's theory (1981) is identified. It is valid at high enough frequency, i.e. for a Helmholtz number relative to chord greater than one and a rotational frequency much smaller than the angular frequency of the noise sources.

Exploring an effective model of new product development in medical devices: A knowledge cluster approach

This study proposes a new product development (NPD) model that aims to improve the effectiveness of innovative NPD in the medical devices. By adopting open innovation theory and applying an in-depth investigation methodology, this paper proposes a knowledge cluster that improves the integration of interdisciplinary human resources and enhances the acquirement of innovative technologies. A knowledge cluster approach helps gather, organise, synthesise, and accumulate knowledge in order to become the impetus for innovation. Although enterprises are no longer the principals of research and development, they should still be capable of integrating professional physicians, external groups, and individuals through the knowledge cluster platform. However, in order to support an effective NPD model, enterprises should provide adequate incentives and trust to external individuals or groups willing to contribute their expertise and knowledge to this knowledge cluster platform. Copyright © 2013 Inderscience Enterprises Ltd.

New robotics: design principles for intelligent systems.

New robotics is an approach to robotics that, in contrast to traditional robotics, employs ideas and principles from biology. While in the traditional approach there are generally accepted methods (e. g., from control theory), designing agents in the new robotics approach is still largely considered an art. In recent years, we have been developing a set of heuristics, or design principles, that on the one hand capture theoretical insights about intelligent (adaptive) behavior, and on the other provide guidance in actually designing and building systems. In this article we provide an overview of all the principles but focus on the principles of ecological balance, which concerns the relation between environment, morphology, materials, and control, and sensory-motor coordination, which concerns self-generated sensory stimulation as the agent interacts with the environment and which is a key to the development of high-level intelligence. As we argue, artificial evolution together with morphogenesis is not only "nice to have" but is in fact a necessary tool for designing embodied agents.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

Effects of magnetic field on the electronic structure of wurtzite quantum dots: Calculations using effective-mass envelope function theory

The Hamiltonian of the wurtzite quantum dots in the presence of an external homogeneous magnetic field is given. The electronic structure and optical properties are studied in the framework of effective-mass envelope function theory. The energy levels have new characteristics, such as parabolic property, antisymmtric splitting, and so on, different from the Zeeman splitting. With the crystal field splitting energy Delta(c)=25 meV, the dark excitons appear when the radius is smaller than 25.85 A in the absence of external magnetic field. This result is more consistent with the experimental results reported by Efros [Phys. Rev. B 54, 4843 (1996)]. It is found that dark excitons become bright under appropriate magnetic field depending on the radius of dots. The circular polarization factors of the optical transitions of randomly oriented dots are zero in the absence of external magnetic field and increase with the increase of magnetic field, in agreement with the experimental results. The circular polarization factors of single dots change from nearly 0 to about 1 as the orientation of the magnetic field changes from the x axis of the crystal structure to the z axis, which can be used to determine the orientation of the z axis of the crystal structure of individual dots. The antisymmetric Hamiltonian is very important to the effects of magnetic field on the circular polarization of the optical transition of quantum dots.

Manifold covering theory in biomimetic pattern recognition

We describe a new model which is based on the concept of cognizing theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite manifold covering in biomimetic pattern recognition, and study its property. Furthermore, we propose this manifold covering algorithm based on Biomimetic Pattern Recognition. At last, the experimental results for face recognition demonstrates that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

Blind image restoration based on the theory of high-dimensional space geometry

In practical situations, the causes of image blurring are often undiscovered or difficult to get known. However, traditional methods usually assume the knowledge of the blur has been known prior to the restoring process, which are not practicable for blind image restoration. A new method proposed in this paper aims exactly at blind image restoration. The restoration process is transformed into a problem of point distribution analysis in high-dimensional space. Experiments have proved that the restoration could be achieved using this method without re-knowledge of the image blur. In addition, the algorithm guarantees to be convergent and has simple computation.

Information geometry theory of high-dimension space and application for speaker independent continuous digit speech recognition

In the light of descriptive geometry and notions in set theory, this paper re-defines the basic elements in space such as curve and surface and so on, presents some fundamental notions with respect to the point cover based on the High-dimension space (HDS) point covering theory, finally takes points from mapping part of speech signals to HDS, so as to analyze distribution information of these speech points in HDS, and various geometric covering objects for speech points and their relationship. Besides, this paper also proposes a new algorithm for speaker independent continuous digit speech recognition based on the HDS point dynamic searching theory without end-points detection and segmentation. First from the different digit syllables in real continuous digit speech, we establish the covering area in feature space for continuous speech. During recognition, we make use of the point covering dynamic searching theory in HDS to do recognition, and then get the satisfying recognized results. At last, compared to HMM (Hidden Markov models)-based method, from the development trend of the comparing results, as sample amount increasing, the difference of recognition rate between two methods will decrease slowly, while sample amount approaching to be very large, two recognition rates all close to 100% little by little. As seen from the results, the recognition rate of HDS point covering method is higher than that of in HMM (Hidden Markov models) based method, because, the point covering describes the morphological distribution for speech in HDS, whereas HMM-based method is only a probability distribution, whose accuracy is certainly inferior to point covering.

Mandarin speech emotion recognition based on high dimensional geometry theory

In this paper, a novel approach for mandarin speech emotion recognition, that is mandarin speech emotion recognition based on high dimensional geometry theory, is proposed. The human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. According to the characteristics of these emotional speech signals, the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. The new method called high dimensional geometry theory is applied for recognition. Compared with traditional GSVM model, the new method has some advantages. It is noted that this method has significant values for researches and applications henceforth.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

New type of Fano resonant tunneling via Anderson impurities in superlattice

The spectrum of differential tunneling conductance in Si-doped GaAs/AlAs superlattice is measured at low electric fields. The conductance spectra feature a zero-bias peak and a low-bias dip at low temperatures. By taking into account the quantum interference between tunneling paths via superlattice miniband and via Coulomb blockade levels of impurities, we theoretically show that such a peak-dip structure is attributed to a Fano resonance where the peak always appears at the zero bias and the line shape is essentially described by a new function \xi\/\xi\+1 with the asymmetry parameter q approximate to 0. As the temperature increases, the peak-dip structure fades out due to thermal fluctuations. Good agreement between experiment and theory enables us to distinguish the zero-bias resonance from the usual Kondo resonance.

New algorithms of the TSM and TOM methods for calibrating microwave test fixtures

Based on the conventional through-short-match (TSM) method, an improved TSM method has been proposed in this Letter. This method gives an analytical solution and has almost all the advantages of conventional TSM methods. For example, it has no phase uncertainty and no bandwidth limitation. The experimental results show that the accuracy can be significantly improved with this method. The proposed theory can be applied to the through-open-match (TOM) method. (C) 2002 Wiley Periodicals. Inc.

A New Technique for Side Pumping of Double-Clad Fiber Lasers

We report a new technique, called SAP, for side pumping of double-clad, rare-earth-doped fiber lasers using fiber-coupled pump sources. The highest coupling efficiency can even exceed 92% in theory with this structure.

Study on the Dissociation Behavior of Gas Hydrate in Porous Sediment Based on Fractal Theory

The dissociation process of gas hydrate was regarded as a gas-solid reaction without solid production layer when the temperature was above the zero centigrade. Based on the shrinking core model and the fractal theory, a fractional dimension dynamical model for gas hydrate dissociation in porous sediment was established. The new approach of evaluating the fractal dimension of the porous media was also presented. The fractional dimension dynamical model for gas hydrate dissociation was examined with the previous experimental data of methane hydrate and carbon dioxide hydrate dissociations, respectively. The calculated results indicate that the fractal dimensions of porous media acquired with this method agree well with the previous study. With the absolute average deviation (AAD) below 10%, the present model provided satisfactory predictions for the dissociation process of methane hydrate and carbon dioxide hydrate.