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Under the instruction of constructivism, this paper presents an innovative, interactive and independent instructional model based on innovative experiments. This advanced instructional model aims at cultivating students not only to grasp and use knowledge or skills but to discover problems and solve them in an active way, and to learn how to learn. Innovative experiment based on constructivism instructional...
The generating rule method is presented for incompatible and incomplete information of test data based on Bayesian theory. Firstly, the rule's conditional probability is calculated when the certainty (reliability) of the test data is the prior probability and the samples (supportability) is posterior probability. Then, Those rules whose conditional probability is bigger than a given threshold value...
Forecasting agriculture water demand is significant to optimize confirmation of water resources. In this study, we introduce a hybrid model which combines rough set theory and least square support vector machine to forecast the agriculture irrigation water demand. Through a certain district agriculture irrigation water demand dataset experiment, we have proved that the reduction feature set exacted...
Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. In this paper, a novel short-term cooling load forecasting approach is presented...
In order to predict blended coal's property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and weighted support vector machine (WSVM) was established. PCA was used to transform the high-dimensional and correlative influencing factors data to low-dimensional principal component subspace. These new features are then used as the inputs of WSVM to solve the...
Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Many forecasting approaches such as artificial neural network (ANN), support vector machine (SVM), autoregressive integrated moving average (ARIMA) and grey model, have been proposed in the field of building cooling load prediction. However, none of them has enough accuracy...
Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) is a powerful tool for modeling the inputs and output(s) of complex and nonlinear systems. However, parameters determination for a SVR model is competent to the forecasting accuracy. Several evolutionary algorithms, such as genetic algorithms and simulated annealing algorithms...
Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. So the accuracy of forecasting is influenced by many unpredicted factors...
In this paper, a novel building cooling load forecasting approach combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. The original inputs are firstly transformed into nonlinear principal components using KPCA. These new...
This study develops a novel methodology hybridizing genetic algorithms (GAs) and support vector regression (SVR) and implements this model in a problem forecasting hourly cooling load. The aim of this study is to examine the feasibility of SVR in building cooling load forecasting by comparing it with back-propagation neural networks (BPNN) and the autoregressive integrated moving average (ARIMA) model...
In this paper, a novel approach combining kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. KPCA is used as a preprocessor of LSSVM, which maps the original input...
Researches show that students learn better when they are actively engaged in the learning process. Therefore, the idea of motivating students into learning by setting challenges, problems and goals has received increasing attention recently. This inquiry learning educational method helps to cultivate all-round developed students. This paper probes into constructing a more interesting and efficient...
A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, a new hourly...
A novel approach to HVAC fan machinery fault diagnosis based on combination of artificial neuron network and D-S evidence theory is presented in this paper. Firstly, the system pre-processes the acquisition data from multi sensor, then makes pattern classifies observations based on BP neural network. Secondly, the output values of BPNN are directly taken as the basic probability assignment of the...
The varied-section tube heat exchanger (VSTHE) that enhances the shell-side and tube-side heat transfer simultaneously provides high heat transfer performance, cuts tube vibration failures and saves material since it does not require tube-support elements. Experiments were conducted on the baffle shell-and-tube heat exchanger (STHE) and VSTHE under different flow rates to compare the heat transfer...
This paper highlights the importance of protecting information security for industry management by investigating the multidimensional character of information security for industry management and the main industry management information risk sources. It points out that human is the most active and uncontrollable factor in information security. Human may be either the most vulnerable and dangerous...
Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Many forecasting techniques such as support vector machine (SVM), artificial neural network (ANN), autoregressive integrated moving average (ARIMA) and grey model, have been proposed in the field of air-conditioning load prediction. However, none of them has enough accuracy...
Based on the analysis on physical meaning of distribution parameters in simplified double-wave model for probability distribution of annual dry-bulb temperature, this paper creates the truncated normal distribution model, and puts forward a new method of determining distribution parameters in the case of unequal standard error in winter and summer temperature wave so that the model is supposed to...
In this paper, a novel HVAC fan machinery fault recognition method combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged...
Last decade saw a rapid development of science and technology, which provides better educational environment and conditions for both learners and teachers. Advanced educational technology attracts more and more interests. Mobile robotics provides a motivating and innovative platform for performing laboratory experiments regarding interdiscipline including mechatronics, electronics, microcomputer and...
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