A grey neuralnetwork model was proposed on the basis of the models. The fluctuation of data sequence is weakened by the grey theory and the neuralnetwork is capable of processing non-linear adaptable information, an...
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ISBN:
(纸本)0769529097
A grey neuralnetwork model was proposed on the basis of the models. The fluctuation of data sequence is weakened by the grey theory and the neuralnetwork is capable of processing non-linear adaptable information, and the GNN is a combination of those advantages. The results reveal, the alkalinity of sinter can be accurately predicted through this model by reference to small sample and information. It was concluded that the GNN model is effective with the advantages of high precision, less requirement of samples and comparatively simple calculation.
This paper proposes a neuralnetwork based speech separation method using spatially distributed microphones. Unlike with traditional microphone array settings, neither the number of microphones nor their spatial arran...
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ISBN:
(纸本)9781713820697
This paper proposes a neuralnetwork based speech separation method using spatially distributed microphones. Unlike with traditional microphone array settings, neither the number of microphones nor their spatial arrangement is known in advance, which hinders the use of conventional multi-channel speech separation neuralnetworks based on fixed size input. To overcome this, a novel network architecture is proposed that interleaves inter-channel processing layers and temporal processing layers. The inter-channel processing layers apply a self-attention mechanism along the channel dimension to exploit the information obtained with a varying number of microphones. The temporal processing layers are based on a bidirectional long short term memory (BLSTM) model and applied to each channel independently. The proposed network leverages information across time and space by stacking these two kinds of layers alternately. Our network estimates time-frequency (TF) masks for each speaker, which are then used to generate enhanced speech signals either with TF masking or beamforming. Speech recognition experimental results show that the proposed method significantly outperforms baseline multi-channel speech separation systems.
The visual simulation of neuronal activities was studied using the Hodgkin-Huxley equation. The complicated interaction between nerve cells from microscopic and macroscopic views were investigated employing parallel-d...
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The visual simulation of neuronal activities was studied using the Hodgkin-Huxley equation. The complicated interaction between nerve cells from microscopic and macroscopic views were investigated employing parallel-distributedprocessing and cellular automation concepts. A constructive method that simulates the spatial and temporal features of nerve cell excitement is proposed.
This paper presents a novel algorithm for distribution of user requests sent to a Web-server cluster driven by a Web switch. Our algorithm called FARD (Fuzzy Adaptive Request Distribution) is a client-and-server-aware...
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ISBN:
(纸本)0769518753
This paper presents a novel algorithm for distribution of user requests sent to a Web-server cluster driven by a Web switch. Our algorithm called FARD (Fuzzy Adaptive Request Distribution) is a client-and-server-aware, dynamic and adaptive dispatching policy. It assigns each incoming request to the server with the least expected response time, estimated for that individual request. To estimate the expected response times FARD uses the fuzzy estimation mechanism. With respect to the requirement of modifiability of the model, FARD uses a neuralnetwork provided with innate abilities for learning and adaptation. We implemented a prototype FARD-based Web switch that was used in experiments carried out to compare its performance to well known representative request distribution algorithms. The measurements show that FARD benefits can be significant, especially for heterogeneous Web clusters.
The advancement of neuralnetwork methods and technologies is finding applications in many fields and disciplines of interest to the defense, intelligence, and homeland security communities. Rapidly re-configurable se...
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ISBN:
(纸本)081946287X
The advancement of neuralnetwork methods and technologies is finding applications in many fields and disciplines of interest to the defense, intelligence, and homeland security communities. Rapidly re-configurable sensors for real or near-real time signal or image processing can be used for multi-functional purposes such as image compression, target tracking, image fusion, edge detection, thresholding, pattern recognition, and atmospheric turbulence compensation to name a few. A neuralnetwork based smart sensor is described that can accomplish these tasks individually or in combination, in real-time or near real-time. As a computationally intensive example, the case of optical imaging through volume turbulence is addressed. For imaging systems in the visible and near infrared part of the electromagnetic spectrum, the atmosphere is often the dominant factor in reducing the imaging system's resolution and image quality. The neuralnetwork approach described in this paper is shown to present a viable means for implementing turbulence compensation techniques for near-field and distributed turbulence scenarios. Representative high-speed neuralnetwork hardware is presented. Existing 2-D cellular neuralnetwork (CNN) hardware is capable of 3 trillion operations per second with peta-operations per second possible using current 3-D manufacturing processes. This hardware can be used for high-speed applications that require fast convolutions and de-convolutions. Existing 3-D artificial neuralnetwork technology is capable of peta-operations per second and can be used for fast array processing operations. Methods for optical imaging through distributed turbulence are discussed, simulation results are presented and computational and performance assessments are provided.
A wireless sensor-actuator network is formed by, nodes capable of sensing and acting upon its environment. Typical challenges in designing such networks include distributed signal processing, synchronisation and commu...
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ISBN:
(纸本)9781424443093
A wireless sensor-actuator network is formed by, nodes capable of sensing and acting upon its environment. Typical challenges in designing such networks include distributed signal processing, synchronisation and communication, as well as deployment of network nodes and scalable architectures for these networks. In this paper we look at the application of neuralnetworks to individual nodes in a wireless network, which result in a wireless sensor-actuator neuralnetwork model. We explain how the combination of sensor network and neuralnetwork, technology can describe a system which maintains desired characteristics such as scalability and adaptability We show further that such model call be successfully applied to an evacuation routing scenario.
Relaxation labeling processes are a class of parallel distributedprocessing models developed to reduce local ambiguities and achieve global consistency in labeling problems. They have become a standard technique in t...
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ISBN:
(纸本)0780314212
Relaxation labeling processes are a class of parallel distributedprocessing models developed to reduce local ambiguities and achieve global consistency in labeling problems. They have become a standard technique in the computer vision domain, and posses certain common properties with both artificial and biological neural systems. In particular, like the Hopfield network, they have a quadratic Liapunov function when a symmetry condition is satisfied. In this paper the use of relaxation processes to solve the traveling salesman problem is proposed and it is quantitatively demonstrated that the algorithm is extremely effective both in finding legitimate problem solutions and in discovering optimal tours.
distributed representations of words play a crucial role in many natural language processing tasks. However, to learn the distributed representations of words, each word in the text corpus is treated as an individual ...
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ISBN:
(纸本)9783319595696;9783319595689
distributed representations of words play a crucial role in many natural language processing tasks. However, to learn the distributed representations of words, each word in the text corpus is treated as an individual token. Therefore, the distributed representations of compound words could not be directly represented. In this paper, we introduce a recurrent neuralnetwork (RNN)-based approach for estimating distributed representations of compound words. The experimental results show that the RNN-based approach can estimate the distributed representations of compound words better than the average representation approach, which simply uses the average of individual word representations as an estimated representation of a compound word. Furthermore, the characteristic of estimated representations of compound words are closely similar to the actual representations of compound words.
A cascaded factor analysis network is proposed in this paper, which is suitable for extracting distributed semantic representations to various problems ranging from digit recognition and image classification to face r...
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ISBN:
(纸本)9783319265551;9783319265544
A cascaded factor analysis network is proposed in this paper, which is suitable for extracting distributed semantic representations to various problems ranging from digit recognition and image classification to face recognition. There are two key points in this novel model: 1. simplify and accelerate the deep convolution networks with competitive accuracy even state-of-the-art for many general image tasks;2. combine a statistical methodfactor analysis with neuralnetworks for excellent automatically learning ability and abundant semantic information. Experiments on many benchmark visual datasets demonstrate that this simple network performs efficiently and effectively while attaining competitive accuracy to the current state-of-the-art methods.
Data pathways are important in layered neuralnetworks. The problem is how to classify information pathways in the network computations. First, the architecture of the biological asymmetric network with odd-even (or e...
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ISBN:
(纸本)9780889869073
Data pathways are important in layered neuralnetworks. The problem is how to classify information pathways in the network computations. First, the architecture of the biological asymmetric network with odd-even (or even- odd) order nonlinearities is analyzed for the network computations. The stimulus with a mixture distribution is useful to evaluate their networkprocessing ability for the movement direction and its velocity, which generate a vector. Then, white noise analysis is applied to solve the problem. Thus, the characterized equation is derived in the network computations. which evaluates the processing ability of the network. Second, the movement velocity is derived, which is represented in Wiener kernels of the network computations. Thus, the information pathways for characterizing the ability of the movement detection are classified for the layered neuralnetworks computations.
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