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This paper addresses a new classification technique: Partially Supervised Classification (PSC), which is used to identify a specific land-cover class of interest from a remotely sensed image using unique training samples that belongs to a specified class. This paper also presents and discusses a newly proposed novel Support Vector Machine (SVM) algorithm for PSC. Accordingly, its training set includes labeled samples that belong to the class of interest and unlabeled samples of all classes randomly selected from a remotely sensed image. Moreover, all unlabeled samples are assumed to be training samples of other classes and each of them is assigned a weight factor indicating the likelihood of this assumption; hence, the algorithm is called ‘Weighted Unlabeled Sample SVM’ (WUS-SVM). Based on the WUS-SVM, a PSC method is proposed. Experimental results with both simulated and real datasets indicate that the proposed PSC method can achieve encouraging accuracy and is more robust than the 1-SVM and the Spectral Angle Mapping (SAM) method.

Vapor pressures of dimethyl ether were measured at temperatures from (233 to 399) K and at pressures from (54 to 5146) kPa. The uncertainties of measurements are within ±5 mK on the ITS-90 scale for temperature and are within ±0.7 kPa for pressure, respectively. With the new experimental data, a Wagner type vapor pressure equation of dimethyl ether was fitted. The absolute average deviation between the experimental data and the new Wagner equation is 0.043%, and the maximum deviation is 0.096%. The new vapor pressure equation of dimethyl ether was also compared with the published data.

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

Measurements of the viscosity of saturated liquid dimethyl ether are reported over the temperature range from (227 to 343) K along the saturation line made with a calibrated capillary viscometer. The results were correlated as a function of temperature. The standard deviation and the maximum deviation of the experimental results from the correlation equation are 0.5% and 1.3%, respectively.