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Indium tin oxide (ITO) nano-polycrystalline powders were prepared by ultrasonic spray pyrolysis (USP) method using a precursor solution of indium and tin chlorides in a simple one-step process without any post-heat treatment, additives and other complex operations. The morphology and crystal structure of ITO powders were studied by thermogravimetric and differential thermal analysis (TG–DTA), X-ray diffraction (XRD), energy-dispersive spectrometer (EDS), scanning electron microscopy (SEM), laser particle size analyzer (LPSA), transmission electron microscope (TEM), high-resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED), respectively. The results indicate that pure cubic ITO solid solution could be obtained at and above the pyrolysis temperature of 600 °C. Through adjusting the size of atomized precursor droplets, the particles with regular spherical shape and smooth surface are also obtained. The spherical particles have a narrow size distribution with a dominant diameter size in the range of 0.52–1.28 μm, and the average value is 1.01 μm. The clear lattice fringes in HRTEM image confirm a polycrystalline structure of ITO particles with the growth direction along (222) direction.

Artificial neural networks and support vector machine (SVM), as two important tools, have widely applied in artificial intelligence and pattern recognition. In this paper, a comparative study has been done for making an analysis on their performances, when they are used in pattern recognition. Through theoretical analysis and confirmed by experimental results, a conclusion can be drawn that support vector machines have obvious advantages over those of traditional neural networks.

Building energy research has been focused on the study of improving the efficiency of equipment operation and operating control mode, rather than the management of central air conditioning system terminal. In fact, large amounts of energy can be conserved through that study. And terminal temperature of the central air conditioning with automatic detection is necessary to fulfill the energy-saving with the management of central air conditioning system terminal. An automatic terminal temperature detection of central air conditioning system based on wireless communication mode has been designed. It can transfer data to the monitoring center to provide reference data to fulfill the energy-saving.

Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVR machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave preparing partially stabilized zirconia (PSZ) and built the stability prediction model, the better prediction accuracy and the better fitting results are verified and analyzed. This is conducted to elucidate the good generalization performance of SVMs, specially good for dealing with nonlinear data.

This paper presents a predictive control method of heating system of heating power station. Firstly, the forecast of heating load is introduced using time series analysis, and the obtained result is used as an energy-saving initial value of predictive control system. Secondly, model simplification method is given and immediate control law is derived, the predictive model order is decreased from N to n. Simplification model satisfies the demand of real-time property of the control system. Thirdly, predictive error correction is used to replace error correction to implement the correction of optimum control of the system, which can improve adaptability and robustness of the system. Finally, simulation of heating system of heating power station is conducted and the results prove that the algorithm is effective in ensuring real-time control, improving tracking and robustness property.

On the basis of brief analysis of the deficiencies of the existing traffic light control system. According to the actual needs of the traffic light, a traffic light control system with remote monitoring by using GPRS wireless module is designed. ForceControl configuration software is used as the monitoring software in the control center. To achieve traffic light control and running test, it exchanges information with each intersection of the field control system through GPRS wireless and communication network. The system realizes wireless remote monitoring of traffic lights, retrenches monitoring cost, improves the system stability and reliability, and facilitates the transformation of the system and the increase of traffic intersections.

The stability is a key product performance index, which can directly determine the quality of the partially stabilized zirconia (PSZ), so how to predict the stability of PSZ accurately, quickly and easily in the preparation process is very important. In this paper, a new mathematical model to predict the stability of PSZ prepared by microwave heating was proposed, based on statistical theory (SLT) and support vector machine (SVM) theory, which relates the stability of PSZ and the influence factors, such as the holding temperature, rising rate of temperature, holding time, decreasing rate of temperature and hardening temperature. Typical data collected from 58 experiments were used for the training samples and test samples. Then testing and analyzing was done. The results showed that the SVM model is reasonable and it is accurate and reliable to predict the stability of the partially stabilized zirconia prepared by microwave heating by SVM model. Besides, multiple influence factors can be comprehensively considered in the SVM model, thus a new highly effective method for predicting the stability of PSZ prepared by microwave heating is provided for future application, which is of great significance to theory and practice.

The stability is one most important product performance index, which can directly determine the quality of the partially stabilized zirconia (PSZ), and the stability of PSZ is always fluctuating in the commercial process, so how to accurately, quickly and easily predict the stability of PSZ in the preparation process is very important. In the present paper, a new mathematical model to predict the stability of PSZ was proposed, based on statistical theory (SLT) and support vector machine (SVM) theory, which relates the stability of PSZ and the influence factors, such as the holding temperature, rising rate of temperature, holding time, decreasing rate of temperature and hardening temperature. Typical data collected from commercial process were collected for the training samples and test samples. Then testing and analyzing was done. The results showed that the max relative error was 1.80%, the least relative error was 0%, and the average relative error was 0.58%. It is accurate and reliable to predict the stability of PSZ by SVM model. Besides, multiple influence factors can be comprehensively considered in the SVM model, thus a new highly effective method for predicting the stability of PSZ is provided for commercial application.