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In the face of the increasingly severe situation of air pollution, the research on evaluation technology and method of high-efficiency air pollution control equipment in China is being advanced. This paper discusses the background and significance of the research on high efficiency air cleaner evaluation techniques and methods, and introduces the General Ideas and principles of the research on high efficiency air cleaner evaluation techniques and methods, the technical methods of high efficiency air cleaner evaluation are established, including the evaluation object and scope of application, basic requirements of high efficiency air cleaner qualitative evaluation, quantitative evaluation requirements of key evaluation indicators, testing, calculation and evaluation methods, etc., the application of the research results of air cleaner will facilitate the production and consumption of air cleaner products.

Pt/AlGaN/AlN/GaN Schottky diodes are fabricated and characterized for hydrogen sensing. The Pt Schottky contact and the Ti/Al/Ni/Au ohmic contact are formed by evaporation. Both the forward and reverse currents of the device increase greatly when exposed to hydrogen gas. A shift of 0.3V at 300K is obtained at a fixed forward current after switching from N2 to 10%H2+N2. The sensor responses under different concentrations from 50ppm H2 to 10%H2+N2 at 373K are investigated. Time dependences of the device forward current at 0.5V forward bias in N2 and air atmosphere at 300 and 373K are compared. Oxygen in air accelerates the desorption of the hydrogen and the recovery of the sensor. Finally, the decrease of the Schottky barrier height and sensitivity of the sensor are calculated.

In dimensionality reduction, Isometric Mapping (Isomap) is a classical method with non-linear feature transform, but relies on minimum matrix distance function and assumptions. Maximization of Mutual Information (MMI) derives the effective dimensionality reduction transform from the Information Theory, but difficult to get the solution. We present a new method (ISO-MMI) for learning discriminative feature transforms, using mutual information between objective function and transformed feature, based on the Isomap algorithms, by complementary combination of these two methods. Numerous experiments on different data sets comparing with PCA, LDA, and Isomap, show the effectiveness of this proposed algorithm.

This paper introduces first the basic characteristics, mathematical model, and the computation process of the Kalman filtering algorithm, then analyzed the key problems in its applications. We explain the merits of the algorithm through two concrete application cases, and at lastly the prospects of applications are envisioned