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neural processing-type fiber-optic strain sensor

Neural processing-type fiber-optic strain sensor

引用

IEEE Instrumentation and Measurement Technology conference

作者： W.J. Bock E. Porada M.B. Zaremba Laboratoire d'optoélectronique Département d'informatique Université du Quàbec à Hull Hull QUE Canada

The authors present a neural-processing-type strain sensor insensitive to thermal variation and describe calibration of the device through modulation of the internal parameters of the processing system. The sensor exploits the variation of the far-field polarization pattern in a single-mode birefringent fiber under the influence of longitudinal strain. A temperature-compensating fiber element is built in, making the sensor assembly immune to thermal variation. Sampling of the sensor output and parallel distributed processing of the samples are integrated with the sensor. The processor contains both a training function and a generalization function. The training function modulates a small linear network built into the system. In the working phase, the generalization function is used to recover the measurement information. Provided the sensor is thermally compensated, the network gives the reading of the measurand with an error not exceeding 0.1%.< >

关键词： Optical fiber sensors Capacitive sensors Optical fiber devices Optical fiber polarization Thermal sensors Sensor phenomena and characterization Sensor systems Calibration Birefringence Temperature sensors

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Sone Applications of neural network Classifier Techniques to Sonar Beanforaing

Sone Applications of Neural Network Classifier Techniques to...

引用

IJCNN International Joint conference on neural networks

作者： GONG Xian Yi YAN Qi SONG Ming Kai Hangzhou Applied Acoustics Research Institute

Adaptive pattern classifiers using neural networks are being developed for several *** include back propagation classifiers and other feed-forward classifiers. Sonar beamforming is a versatile approach in which data of measured field by sensor array are weighted and summed,weighting factors being prodicted field, steering vector or steering matrix,according to parameter *** of sonar beamformer will depend on parallel distributed processing techniques. Therefore,neural networks,especially neural net pattern classifier techniques,can be successfully applied to underwater acoustics signal processing,e.g.,spatial filtering. Our study puts emphases on conventional beamforming with 1-D search(bearing) in a homogeneous medium and modal beamforming with 2-D search(depth and range) in a waveguide which are implemented by neural net pattern classifier techniques employing back propagation or other learning—training *** simulation experiment results are also given.

关键词： Sone Applications of neural network Classifier Techniques to Sonar Beanforaing

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Relations between fault tolerance and internal representations for multi-layer perceptrons

Relations between fault tolerance and internal representatio...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： M.D. Emmerson R.I. Damper Department of Electronics and Computer Science University of Southampton Southampton UK

Fault tolerance is often mentioned as an important, intrinsic property of neural networks but it has not often been the subject of directed study. Such fault tolerance as does exist must depend strongly upon the exact nature of the internal representations captured during training, and the way these are distributed across the network. A representative pattern-recognition task is used to assess the fault tolerance of feedforward neural nets as a function of hidden-layer size. Damage resistance is found to increase with the number of hidden neurons, although this finding is sensitive to the exact performance metric employed. The technique of augmentation, which can increase the fault tolerance of a net of given size, is described. To understand the relations between the obtained measures of fault tolerance and the nets' internal representations, a number of analyses are used. These techniques have more usually been used to simplify network structure by identifying and removing redundancies, but they are equally applicable to the study of fault tolerance.< >

关键词： Fault tolerance Multilayer perceptrons neural networks Redundancy Neurons Very large scale integration Computer science Feedforward neural networks Electrical resistance measurement Fault diagnosis

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SATISFACTORY-SOLUTION PRINCIPLE BY neural COMPUTING

SATISFACTORY-SOLUTION PRINCIPLE BY NEURAL COMPUTING

引用

IJCNN International Joint conference on neural networks

作者： Jin Fan Fan Junbo Hu Fei Southwest Jiaotong University

In addition to well- known massively parallel distributed processing features of neural networks,the main reason why the brain sysytem of human being is more clever than traditional computers of Von Neumann type is that it only seeks for a satisfacory solution and not an exact ***,a satisfactory-solution principle for neural computing is suggested in this paper,definitions and theorems of which are also given.

关键词： SATISFACTORY-SOLUTION PRINCIPLE BY neural COMPUTING

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Adaptive distributed orthogonalization processing for principal components analysis

Adaptive distributed orthogonalization processing for princi...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： H. Chen R.-W. Liu Department of Electrical Engineering University of Notre Dame Notre Dame IN USA

Adaptive extraction of principal components of a vector stochastic process is a topic currently receiving much attention. The authors propose a learning algorithm implemented on a neural-like network. This algorithm is shown to be superior to previous ones. The convergence of this algorithm can be proved, but only an outline of the proof is presented.< >

关键词： Principal component analysis Stochastic processes Signal processing algorithms Convergence Data mining Autocorrelation Statistics Intelligent networks Adaptive signal processing Data analysis

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neural network lipreading system for improved speech recognition

Neural network lipreading system for improved speech recogni...

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International Joint conference on neural networks (IJCNN)

作者： D.G. Stork G. Wolff E. Levine Ricoh California Research Center Menlo Park CA USA Department of Electrical Engineering University of Stanford Stanford CA USA

A modified time-delay neural network (TDNN) has been designed to perform both automatic lipreading (speech reading) in conjunction with acoustic speech recognition in order to improve recognition both in silent environments as well as in the presence of acoustic noise. The system is far more robust to acoustic noise and verbal distractors than is a system not incorporating visual information. Specifically, in the presence of high-amplitude pink noise, the low recognition rate in the acoustic only system (43%) is raised to 75% by the incorporation of visual information. The system responds to (artificial) conflicting cross-modal patterns in a way closely analogous to the McGurk effect in humans. The power of neural techniques is demonstrated in several difficult domains: pattern recognition; sensory integration; and distributed approaches toward 'rule-based' (linguistic-phonological) processing.< >

关键词： neural networks Speech recognition Automatic speech recognition Acoustic noise Humans Working environment noise Noise robustness 1f noise Pattern recognition Speech enhancement

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Designing Energy Surface for Truck Back-Upper Problem

Designing Energy Surface for Truck Back-Upper Problem

引用

IJCNN International Joint conference on neural networks

作者： Cheng Yuan Liou Jiann Ming Wu Dept.of Computer Science and Information Engineer National Taiwan University

In this work,the idea of energy surfaces which are designed by analogy to the flow 5eld in fluid dynamics is employed to build controller for truck-and-trailer back-upper problem with presence of dynamic obstacles in arbitrary *** first demonstrate that the truck back upper problem in free space,which is an unstable nonlinear control problem,can be naturally solved by the controller based on the flow field of the two dimensional doublet in fluid ***,parallel and distributed algorithms on two dimensional lattices are further developed to compute the emergent flow field,abbreviated as EFF,for general obstacle avoidance navigation with kinematic *** new controller generally guides the navigation along the tangent direction of the streamlines in the developed emergent flow *** the kinematic constraints of the controller,a greedy search for the minimum of the orientation moments surrounding the motor will guide the motor to the desired *** efficiency and direct use of raw images are main advantages of our approach in many real time applications.

关键词： autonomous navigation neural network cellular automata parallel and distributed processing obstacle avoidance Al planning lattice architecture

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Integration of visual and somatosensory information for preshaping hand in grasping movements 92

Integration of visual and somatosensory information for pres...

引用

Proceedings of the 6th International conference on neural Information processing Systems

作者： Yoji Uno Naohiro Fukumura Ryoji Suzuki Mitsuo Kawato ATR Human Information Processing Research Laboratories Kyoto Japan Parallel Distributed Processing Research Dept. Sony Corporation Tokyo Japan and Faculty of Engineering University of Tokyo Tokyo Japan Faculty of Engineering University of Tokyo Tokyo Japan

ISBN: (纸本)9781558602748

The primate brain must solve two important problems in grasping movements. The first problem concerns the recognition of grasped objects: specifically, how does the brain integrate visual and motor information on a grasped object? The second problem concerns hand shape planning: specifically, how does the brain design the hand configuration suited to the shape of the object and the manipulation task? A neural network model that solves these problems has been developed. The operations of the network are divided into a learning phase and an optimization phase. In the learning phase, internal representations, which depend on the grasped objects and the task, are acquired by integrating visual and somatosensory information. In the optimization phase, the most suitable hand shape for grasping an object is determined by using a relaxation computation of the network.

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Moving object recognition and guidance of robots using neural networks

Moving object recognition and guidance of robots using neura...

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IEEE National conference on Aerospace and Electronics (NAECON)

作者： A. Neogy S.N. Balakrishnan C.H. Dagli University of Missouri Rolla Rolla MO USA

The design of a robust guidance system for a robot is discussed. The two major tasks for this guidance system are the online recognition of a moving object invariant to rotation and translation, and tracking the moving object using a neural-network-driven vision system. This system included computer software ported to the IBM PC and interfaced with an IBM 7535 robot. The operation of this guidance system involved recognition of a moving object and the ability to track it till the robot and effector was in close proximity of the object. It was found that the robot was able to track a moving object as long as it did not leave the region visible to the camera. Recognition was successfully demonstrated for objects of arbitrary shapes. Even similarly shaped objects of different sizes were correctly recognized.< >

关键词： Object recognition neural networks Robot kinematics Machine vision Pattern recognition Backpropagation Robot vision systems Image processing Robustness distributed processing

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Neuro chips with on-chip backprop and/or Hebbian learning

Neuro chips with on-chip backprop and/or Hebbian learning

引用

IEEE International conference on Solid-State Circuits (ISSCC)

作者： T. Shima T. Kimura Y. Kamatani T. Itakura Y. Fujita T. Iida Toshiba Research and Development Center Toshiba Corporation Japan Toshiba Semiconductor System Engineering Center Toshiba Corporation Japan

In a neural network, neurons and synapses are two unit functions. If the learning procedures are assigned appropriately, they can be placed in tiling form. This arrangement is potentially expandable if constructed of two separate LSI chips, a synapse chip and a neuron chip. The authors describe such an implementation. A single synapse configuration is shown along with a synapse group with 64 synapses and a learning control circuit. A single neuron functional block diagram is presented, and synapse characteristics are shown.< >

关键词： Hebbian theory Neurons distributed processing Computer networks Computational modeling Circuit simulation Computer simulation

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