A new visual monitoring communication algorithm model is described in this paper which can improve the communication efficiency between the PLC(programmable logic controller) and its programming software. The model co...
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ISBN:
(纸本)9781784660468
A new visual monitoring communication algorithm model is described in this paper which can improve the communication efficiency between the PLC(programmable logic controller) and its programming software. The model consists of dynamic packaging algorithm and scrolling view algorithm aiming at meeting real-time visual monitoring. It is described in detail that how to adjust the length of communication data frame and refresh the data of current scrolling view of the programming software concerning the algorithm model. The experimental results show that the communication efficiency and the interface refreshing properties of visual monitoring will be improved with the increase of communication traffic load which proves that the algorithm modelling is effective.
We propose a novel regression, which is called Twin Support Vector Regression(TSVR) to improve the precision of indoor positioning. Similar as Support Vector Regression(SVR), there are 6 parameters to be identified. H...
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ISBN:
(纸本)9781479937097
We propose a novel regression, which is called Twin Support Vector Regression(TSVR) to improve the precision of indoor positioning. Similar as Support Vector Regression(SVR), there are 6 parameters to be identified. However, compared with SVR, less computation time and approximate performance can be achieved with TSVR. Genetic Algorithm(GA) is used to avoid local optimum in indoor positioning to get proper parameters in TSVR. Experimental example is shown to illustrate the effectiveness of the proposed methods.
Low cycle fatigue life of a steam turbine rotor is predicted using a new CDM model. A simulation experiment is made to compute the damage accumulation of a 300 MW steam turbine rotor under cold start. Adopting the cyc...
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ISBN:
(纸本)9781479947249
Low cycle fatigue life of a steam turbine rotor is predicted using a new CDM model. A simulation experiment is made to compute the damage accumulation of a 300 MW steam turbine rotor under cold start. Adopting the cyclic stress-strain relation,this new continuum damage mechanics model exhibits a conservative character. Comparison with result of the linear accumulation theory and practical test data indicates that present new nonlinear CDM model describes the damage accumulation more precisely and reasonably in engineering.
The purpose of HVAC system is to make occupants comfortable by adjusting the indoor thermal environment. The predicted mean vote (PMV) index is widely used to evaluate the indoor thermal comfort. However, PMV is diffi...
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The purpose of HVAC system is to make occupants comfortable by adjusting the indoor thermal environment. The predicted mean vote (PMV) index is widely used to evaluate the indoor thermal comfort. However, PMV is difficult to calculate in real time as its complicated mathematical functions. Meanwhile, the physical conception of the model and the impact on the output of model by the human conditions are often neglected by PMV modeling in previous literatures. In this paper, all the six variables of PMV are considered. The prior knowledge about the current working conditions are used to build the initial T-S fuzzy model. Then the ANFIS is used to train and adjust the parameters of the fuzzy model through the existing dataset. Simulation results show that this ANFIS method which is based on prior knowledge not only keeps the physical means of this fuzzy model but also improves the accuracy. Moreover it is superior to the model which does not consider the human variables in accuracy of model. The proposed method is effective and accurate.
This paper establishes the prediction model of low cycle fatigue damage of steam turbine rotor using the rich data from finite element analysis. In order to monitor the damage, a multiple regression analysis of input ...
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This paper establishes the prediction model of low cycle fatigue damage of steam turbine rotor using the rich data from finite element analysis. In order to monitor the damage, a multiple regression analysis of input data/output data with high correlation is made via dynamic PLS. The variation of the process parameters is extracted and it restrains the multiple dependency of the several parameters in different time series. Finally, a simulation of rolling process of a domestic 300MW turbine unit validates the effectiveness and accuracy of the prediction model based on dynamic PLS.
In the combustion system and ash fouling system of boiler,the furnace exit gas temperature(FEGT)is the key parameter for ensuring high *** it is hard to achieve satisfactory performance through conventional control st...
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In the combustion system and ash fouling system of boiler,the furnace exit gas temperature(FEGT)is the key parameter for ensuring high *** it is hard to achieve satisfactory performance through conventional control strategy,the control problem of FEGT has become critical and significant in coal-fired boiler *** this paper,a new predictive control scheme based on particle swarm optimization(PSO)and CM-LSSVM-PLS model is *** the proposed control scheme,a new CM-LSSVM-PLS method is proposed and used as the predictive model to predict the future *** the process of CM-LSSVM-PLS method,c-means cluster(CM)algorithm is used to partition the training data into several different subsets by considering the characteristics of operational *** sub-models are subsequently developed in the individual subsets based on least squares support vector machine(LSSVM).Then,partial least squares algorithm(PLS)is employed as the combination ***,PSO is used as the receding optimization *** proposed control is verified through operation data of a 300MW generating *** simulation results show that the effectiveness of our proposed control scheme.
This paper mainly studies the hourly water demand forecasting performances of water supply system in shanghai with LS-SVM. The teaching-learning-based optimization(TLBO) is adopted to adjust the hyper-parameters of le...
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This paper mainly studies the hourly water demand forecasting performances of water supply system in shanghai with LS-SVM. The teaching-learning-based optimization(TLBO) is adopted to adjust the hyper-parameters of least squares support vector machine(LS-SVM).To improve the forecast accuracy, An ameliorated TLBO algorithm called ATLBO is introduced. The experimental results show that the model of water demand forecasting with ATLBO has better regression precision than grid search, particle swarm optimization(PSO) and TLBO.
In recent years, more and more plug-in hybrid electric vehicles (PHEVs) have been put to use in smart grid. In this paper, we consider a dynamic aggregator-PHEV system, where the aggregator convinces the PHEVs to use ...
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In recent years, more and more plug-in hybrid electric vehicles (PHEVs) have been put to use in smart grid. In this paper, we consider a dynamic aggregator-PHEV system, where the aggregator convinces the PHEVs to use electricity rather than gas by setting an appropriate charging price dynamically. We propose a payoff-maximizing algorithm for the aggregator to decide not only the charging price but also the electricity amount purchased from real-time power market based on Lyapunov optimization. Furthermore, we transform the power purchase problem into the energy allocation problem among all the PHEVs. The proposed algorithm operates in real time and does not require any prior knowledge of the statistical information of the system. Theoretically, we demonstrate the proposed algorithm can guarantee system stability and achieve a result that is away from the optimum by O(1/V ), where V is a control parameter. The effectiveness and robustness of the algorithm is validated through simulation results.
Monitoring system of furnace ash fouling is the foundation of the soot-blowing operation on furnace *** furnace exit gas temperature(FEGT) is the key parameter in monitoring system,a new CM-LSSVM-PLS method is propose...
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Monitoring system of furnace ash fouling is the foundation of the soot-blowing operation on furnace *** furnace exit gas temperature(FEGT) is the key parameter in monitoring system,a new CM-LSSVM-PLS method is proposed to predict *** the process of CM-LSSVM-PLS method,considering the characteristics of operational data,c-means(CM) cluster algorithm is used to partition the training data into several different *** are subsequently developed in the individual subsets based on least squares support vector machine(LSSVM).Finally,partial least squares(PLS) algorithm is employed as the combination *** single LSSVM is established to make a comparison with CM-LSSVM-PLS *** proposed model is verified through operation data of a 300 MW generating *** comparison result shows that the new CM-LSSVM-PLS method can predict FEGT accurately while the time consumed in modeling decrease drastically.
The increasing demands on the indoor location service inspire the wide attentions to investigate the indoor position algorithms. Access point(AP) selection is critical important for increasing the estimation accuracy ...
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The increasing demands on the indoor location service inspire the wide attentions to investigate the indoor position algorithms. Access point(AP) selection is critical important for increasing the estimation accuracy of the indoor location. In this paper, the key features in influencing the accuracy of indoor location systems are investigated. Base on the analysis results, we present an AP selection strategy for indoor location by proposing a novel AP selection index for test point. By using the experiment data, the K-Nearest Neighbor(KNN) and weighted-KNN(WKNN) indoor location methods are carried out to illustrate the performance proposed AP selection strategy. The performance of our AP selection strategy is validated by comparing with the exhaustive AP selection strategy, the fisher AP selection strategy and the largest RSSI strength AP selection strategy. The experiment results show that the proposed AP selection strategy can improve the location accuracy of indoor location with Wi-Fi.
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