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The parameter extraction method of De Soto is implemented in the single diode model and evaluated against measured data at varying temperatures and irradiances. The simulated current-voltage-curves (IV-curves) of the method can be seen as acceptable at reference conditions, but they result in a different IV-curve, not matching the measured IV-curve, at conditions that differ from the reference conditions...
This paper proposes a hybrid compressor model for the purpose control and optimization of vapor compression systems. Unlike those existing models, this model is determined by only the inlet and outlet conditions of compressor without requiring detailed geometric specifications, and only the variables responsible to the system performance, which can be measured and controlled, are selected as the input/output...
Data fusion based on feed-forward natural network can get the goal of minimum error, as well as reduce redundant data to transmit and save energy consumption. BP neutral network is considered as one of the most mature algorithm, but the traditional BP algorithm converges slowly and easy traps in a local minimum value, so new improved BP algorithm named algebraic algorithm is put forward. The algebraic...
This paper presents a steepest descent based algorithm for the distributed optimization towards to data regression modeling in wireless sensor networks (WSNs). In doing this, the junction tree based routing structure is employed to organize the nodes in coordination to accomplish the in-network implementation of the distributed iterative scheme. Experimental results are reported to demonstrate the...
Top-k monitoring is a noteworthy query that recently has been put into practice in wireless sensor networks (WSN). In top-k monitoring (i.e., an instance of continuous distributed monitoring), base station or coordinator continuously monitors k sensors with the highest (or lowest) values. Since the goal in such applications is to perform monitoring task while incurring minimum communication (data...
Intensive research has focused on redundance reduction in wireless sensor networks among sensory data due to the spatial and temporal correlation embedded therein. In this paper, we propose a novel approach termed asynchronous sampling that complements existing study. The key idea of asynchronous sampling is to spread the sampling times of the sensor nodes over the time line instead of performing...
Three applications in wireless networks where model-free stochastic learning is applicable, are discussed. The learning based optimization problems are formulated and simulation results are presented. Some open issues are also discussed.
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