175 resultados para Fuzzy K Nearest Neighbor
Resumo:
Carbon-doped hydrogenated silicon oxide (SiOCH) low-k films have been prepared using 13.56 MHz discharge in trimethylsilane (3MS) - oxygen gas mixtures at 3, 4, and 5 Torr sustained with RF power densities 1.3 - 2.6 W/cm2. The atomic structure of the SiOCH films appears to be a mixture the amorphous SiO2-like and the partially polycrystalline SiC-like phases. Results of the infra-red spectroscopy reflect the increment in the volume fraction of the SiC-like phase from 0.22 - 0.28 to 0.36 - 0.39 as the RF power increment. Steady-state near-UV laser-excited (364 nm wavelength, 40±2 mW) photoluminescence (PL) has been studied at room temperatures in the visible (1.8 eV - 3.1 eV) subrange of photon spectrum. Two main bands of the PL signal (at the photon energies of 2.5 - 2.6 eV and 2.8 - 2.9 eV) are observed. Intensities of the both bands are changed monotonically with RF power, whereas the bandwidth of ∼0.1 eV remains almost invariable. It is likely that the above lines are dumped by the non-radiative recombination involving E1-like centres in the amorphous-nanocrystalline SiC-like phases. Such explanation of the PL intensity dependences on the RF power density is supported by results of experimental studies of defect states spectrum in bandgap of the SiOCH films.
Resumo:
Results of experimental investigations on the relationship between nanoscale morphology of carbon doped hydrogenated silicon-oxide (SiOCH) low-k films and their electron spectrum of defect states are presented. The SiOCH films have been deposited using trimethylsilane (3MS) - oxygen mixture in a 13.56 MHz plasma enhanced chemical vapor deposition (PECVD) system at variable RF power densities (from 1.3 to 2.6 W/cm2) and gas pressures of 3, 4, and 5 Torr. The atomic structure of the SiOCH films is a mixture of amorphous-nanocrystalline SiO2-like and SiC-like phases. Results of the FTIR spectroscopy and atomic force microscopy suggest that the volume fraction of the SiC-like phase increases from ∼0.2 to 0.4 with RF power. The average size of the nanoscale surface morphology elements of the SiO2-like matrix can be controlled by the RF power density and source gas flow rates. Electron density of the defect states N(E) of the SiOCH films has been investigated with the DLTS technique in the energy range up to 0.6 eV from the bottom of the conduction band. Distinct N(E) peaks at 0.25 - 0.35 eV and 0.42 - 0.52 eV below the conduction band bottom have been observed. The first N(E) peak is identified as originated from E1-like centers in the SiC-like phase. The volume density of the defects can vary from 1011 - 1017 cm-3 depending on specific conditions of the PECVD process.
Resumo:
The mineral harmotome (Ba,Na,K)1-2(Si,Al)8O16⋅6H2O is a crystalline sodium calcium silicate which has the potential to be used in plaster boards and other industrial applications. It is a natural zeolite with catalytic potential. Raman bands at 1020 and 1102 cm−1 are assigned to the SiO stretching vibrations of three dimensional siloxane units. Raman bands at 428, 470 and 491 cm−1 are assigned to OSiO bending modes. The broad Raman bands at around 699, 728, 768 cm−1 are attributed to water librational modes. Intense Raman bands in the 3100 to 3800 cm−1 spectral range are assigned to OH stretching vibrations of water in harmotome. Infrared spectra are in harmony with the Raman spectra. A sharp infrared band at 3731 cm−1 is assigned to the OH stretching vibration of SiOH units. Raman spectroscopy with complimentary infrared spectroscopy enables the characterization of the silicate mineral harmotome.
Resumo:
This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.
Resumo:
Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us to build better training programs. Currently, there is a need to develop and evaluate Advanced Driving Assistance Systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel Intelligent Driver Training System (IDTS) that analyses crash risks for a given driving situation, providing avenues for improvement and personalisation of driver training programs. The analysis takes into account numerous variables acquired synchronously from the Driver, the Vehicle and the Environment (DVE). The system then segments out the manoeuvres within a drive. This paper further presents the usage of fuzzy set theory to develop the safety inference rules for each manoeuvre executed during the drive. This paper presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving manoeuvres and then provide a comprehensive analysis of the drive used to give feedback to novice drivers.
Resumo:
This chapter focuses on the implementation of the TS (Tagaki-Sugino) fuzzy controller for the Doubly Fed Induction Generator (DFIG) based wind generator. The conventional PI control loops for mantaining desired active power and DC capacitor voltage is compared with the TS fuzzy controllers. DFIG system is represented by a third-order model where electromagnetic transients of the stator are neglected. The effectiveness of the TS-fuzzy controller on the rotor speed oscillations and the DC capacitor voltage variations of the DFIG damping controller on converter ratings is also investigated. The results from the time domain simulations are presented to elucidate the effectiveness of the TS-fuzzy controller over the conventional PI controller in the DFIG system. The proposed TS-fuzzy con-troller can improve the fault ride through capability of DFIG compared to the conventional PI controller.
Resumo:
Modern power systems have become more complex due to the growth in load demand, the installation of Flexible AC Transmission Systems (FACTS) devices and the integration of new HVDC links into existing AC grids. On the other hand, the introduction of the deregulated and unbundled power market operational mechanism, together with present changes in generation sources including connections of large renewable energy generation with intermittent feature in nature, have further increased the complexity and uncertainty for power system operation and control. System operators and engineers have to confront a series of technical challenges from the operation of currently interconnected power systems. Among the many challenges, how to evaluate the steady state and dynamic behaviors of existing interconnected power systems effectively and accurately using more powerful computational analysis models and approaches becomes one of the key issues in power engineering. The traditional computing techniques have been widely used in various fields for power system analysis with varying degrees of success. The rapid development of computational intelligence, such as neural networks, fuzzy systems and evolutionary computation, provides tools and opportunities to solve the complex technical problems in power system planning, operation and control.
Resumo:
Biphasic vasodilatory responses to adenosine and 5'-N-ethylcarboxamidoadenosine (NECA) were observed in the coronary vasculature of K(+)-arrested perfused rat hearts. Dose-response data for both agonists were best represented by two-site models. For adenosine, two sites with negative log ED50 (pED50) values of 8.1 +/- 0.1 (mean +/- S.E.M) and 5.2 +/- 0.1 were obtained, mediating 31 +/- 2% and 69 +/- 2% of the total response. In the presence of 8-phenyltheophylline, the vasodilatory response to adenosine remained best fitted to a two-site model with pED50 values of 7.0 +/- 0.2 and 5.4 +/- 0.2. The relative contribution of each site to the total response remained unchanged. For NECA, pED50 values of 9.6 +/- 0.1 and 6.8 +/- 0.2 were obtained, representing 48 +/- 3% and 52 +/- 3% of the sites, respectively. In contrast, ATP produced a monophasic response with a pED50 value of 8.8 +/- 0.1. These results provide evidence of adenosine receptor and response heterogeneity in the in situ coronary vasculature.
Resumo:
For future planetary robot missions, multi-robot-systems can be considered as a suitable platform to perform space mission faster and more reliable. In heterogeneous robot teams, each robot can have different abilities and sensor equipment. In this paper we describe a lunar demonstration scenario where a team of mobile robots explores an unknown area and identifies a set of objects belonging to a lunar infrastructure. Our robot team consists of two exploring scout robots and a mobile manipulator. The mission goal is to locate the objects within a certain area, to identify the objects, and to transport the objects to a base station. The robots have a different sensor setup and different capabilities. In order to classify parts of the lunar infrastructure, the robots have to share the knowledge about the objects. Based on the different sensing capabilities, several information modalities have to be shared and combined by the robots. In this work we propose an approach using spatial features and a fuzzy logic based reasoning for distributed object classification.
Resumo:
Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.
Resumo:
In this paper we present an original approach for finding approximate nearest neighbours in collections of locality-sensitive hashes. The paper demonstrates that this approach makes high-performance nearest-neighbour searching feasible on Web-scale collections and commodity hardware with minimal degradation in search quality.