The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The sugge...
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Auditory perception is one of the most important functions for robotics applications. Microphone arrays are widely used for auditory perception in which the spatial structure of microphones is usually known. In practi...
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The basic function of VSCs is to convert the DC voltage stored in a capacitor into AC voltages or AC voltages into DC voltage. One of the advantages of the VSC is that this kind of electronic converters allow to contr...
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The main goal in design of grid connected photo voltaic systems is to maximize the energy generation based on local energy requirements, weather conditions, economic and social impact. These factors ought to be taken ...
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In this paper Group Method of Data Handling (GMDH) technique is implemented and applied to the system identification problem of a Magneto-Rheological (MR) damper. GMDH networks are used to approximate the forward and ...
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electrical muscle stimulation demonstrates potential for restoring functional movement and for preventing muscle atrophy after spinal cord injury (SCI). To optimize delivery of electrical stimulation protocols, an acc...
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High-frequency power Doppler imaging of angiogenesis can be challenging given the presence of small blood vessels and slow flow velocities. In the presence of substantial Doppler artifacts such as false-positive color...
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Wireless Sensor Network (WSN) is a network of sensors that has a very large scale of nodes with a tight resources limitation. WSN is application specific, with a wide range of applications in military, survey, industr...
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ISBN:
(纸本)9781424455317;9781424455324
Wireless Sensor Network (WSN) is a network of sensors that has a very large scale of nodes with a tight resources limitation. WSN is application specific, with a wide range of applications in military, survey, industrial, up to home usage. The major problems for WSN are resource limitations and dynamic network, which require unique algorithms for unique applications. Our study focused on the implementation and the characterization of the Gravity algorithm, with the Flooding algorithm as a comparison. The Gravity algorithm uses the gravity weight which is stored on each node to form a virtual contour. This contour will guide the message to reach the sink. This work is the first implementation of the Gravity model in OMNeT++. Simulations conducted in the full mesh and the layered topology show that the Gravity algorithm uses less energy and a higher diversity factor compared to the Flooding algorithm. A similar result is also acquired when the probability of failures are considered. The result shows that the Gravity algorithm outperforms the Flooding algorithm while still maintaining the simplicity of the algorithm. This is important to a WSN which is resources limited. Only a small amount of memory is needed for the gravity weight calculation and storage.
We perform numerical calculations for the diffraction efficiency of single-sided microprism designs with optical beams spread over several microprisms. For small angles (less than 1.5 degrees) the far-field diffractio...
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This paper introduces a granular, interval-based evolving modeling (IBeM) approach to develop system models from a stream of data. IBeM is an evolving rule-based modeling scheme that gradually adapts its structure (in...
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This paper introduces a granular, interval-based evolving modeling (IBeM) approach to develop system models from a stream of data. IBeM is an evolving rule-based modeling scheme that gradually adapts its structure (information granules and rule base) and rules antecedent and consequent parameters from data (inductive learning). Its main purpose is continuous learning, self-organization, and adaptation to unknown environments. The IBeM approach develops global model of a system using a fast, one-pass learning algorithm, and modest memory requirements. To illustrate the effectiveness of the approach, the paper considers actual time series forecasting applications concerning electricity load and stream flow forecasting.
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