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ZnO films with different oxygen gas flow rate are deposited on copper nitride substrates by radio frequency magnetron sputtering. All samples are annealed in vacuum at 350 °C for 1h. Results indicates that Cu3N diffraction phase is observed in the XRD pattern of copper nitride substrates. As ZnO layer is deposited on copper nitride substrates, Cu3N diffraction peak disappears. PL spectra of all samples shows two blue emission peaks and a green emission. Under the unbalanced stoichiometric ratio, the blue peaks are enhanced. Especially, at a low oxygen gas flow rate of 5:25 sccm, two blue peak intensity reach maximum. In this case, there are overabundance Zn atoms due to a poor oxygen atmosphere. Both interstitial Zn and various substitution defects will appear in the deposited films, thus causing the intensity increment of visible emission peaks.

This paper proposes a novel approach to simultaneously estimate speaker identity and the other two traits of the speaker, specifically, emotion and gender. This simultaneous process is named as multi-dimensional speaker information recognition. We choose i-vector to represent the utterance with different duration as fixed length vector. In our paper, we first build individual neural networks (NN) for each single recognition task. In this single task, the input is i-vector of one speaker and the output is the corresponding one-hot vector. We then design a multi-task learning (MTL), the difference with the single task is the NN output, which contains three parallel one-hot vectors for MTL. The one-hot vectors denote the identity, emotion and gender information, respectively. The proposed approach is conducted on the KSU-Emotions corpus, which was recorded mainly for emotional recognition. Experimental results show that the best multi-task mechanism recognition accuracy is 4.6% better than the single task.

The paper mainly aims at cross sensitivity problems in the measurement process with pressure sensor. Cross sensitivity problems are mainly manifested in the following aspects: pressure measurement is affected by target quantity and interfered by non-target quantity synchronously, such as temperature and other factors. New thought is provided for sensor data fusion due to continuous development of artificial neural network technology. In the paper, multi-sensor data fusion algorithm based on PSO-RBF neural network is proposed on the basis of technology for studying commonly used data fusion methods. PSO Particle Swarm Optimization is utilized for optimizing weight and base width of RBF neural network. Influence of non-target quantity can be eliminated through RBF neural network algorithm under the precondition of fully considering non-target quantities, such as temperature, etc. thereby improving measurement precision of pressure sensor.

In this paper, polyimide/multi-walled carbon nanotubes(PI/MWNTs) triple hybrid films were fabricated through thermal imine process of pure polyamic acid and polyamic acid hybrid solution. Polyamic acid (PAA) hybrid solution was synthesized via in situ polymerization with 4, 4-diamino diphenyl ether (ODA) and pyromellitic acid dianhydride (PDMA) as monomers in DMAc solvent with MWNTs modified by mixed acid treatment under ultrasonic. The dispersibility of MWNTs in solvent was characterized before and after functionalization; the morphology of the PI/MWNTs hybrid films were investigated by SEM; the tensile strength and elongation at break of triple hybrid films were tested. The results showed that the dispersibility of modified MWNTs in DMAc had been obviously improved and the mechanical properties of the triple hybrid films were also improved.

In this paper, The polyamic acid/MWNTs (PAA/MWNTs) hybrid solution were synthesized by polymerization process with 4,4′-Oxydianiline(ODA) and Pyromellitic dianhydride (PMDA) in N,N-dimethylacetamide (DMAc) in which MWNTs (modified by Fenton reagents) had been dissolved. After the imidization, PI/MWNTs ternary hybrid films were obtained. Fourier transform infrared spectroscopic (FTIR) spectra of modified MWNTs indicated that hydroxy and carboxy were introduced to MWNTs after modification. The surface and cross-section morphologies of the films were investigated by scanning electron microscopy (SEM). Dielectric properties of PI/MWNTs ternary hybrid films were studied including dielectric permittivity (ξ R ), dielectric loss and volume resistivity. The results indicated that the dielectric permittivity (ξ R ) and loss were enhanced in PI/ MWNTs ternary hybrid films as compared to the PI films.

This paper proposes a method for classification of incomplete data using neural network ensembles. In the method, the incomplete data set is analyzed and projected into a group of complete data subsets that give a full description of the known values in the data set by joining together. Those complete data subsets are then used as the training sets for the neural networks. Base classifiers are selected and integrated according to their classification accuracies and the support degrees of their training data sets to give the final predication. Compared with other methods dealing with missing data in classification, the proposed method can utilize all the information provided by the incomplete data, maintain maximum consistency of the incomplete data set and avoid the dependency on distribution or model assumptions. Experiments on two UCI datasets showed the superiority of the algorithm to other two typical treatments of missing data in ensemble learning.

The importance of incremental learning in changing environments has been acknowledged in recent years. In this paper we present an ensemble learning method for supervised learning with drifting concepts. The method employs hypothesis test as mechanism for detecting concept drift and learns a base classifier for each new training data chunk. Former classifiers deemed as usable by the hypothesis test mechanism and the new classifiers are integrated to form the final classifiers ensemble for prediction. The main focus of the work is to identify the usability of base classifiers that representing the same or similar concept with the current one, make full use of the older valid information together with the newer examples to improve classification accuracy, and avoid the interference of classifiers representing conflictive concepts with the current one. Experiments with simulated concept drift scenarios compared the proposed method with other approaches. The results showed that the method could consistently recognize different types of drift, adapt quickly to these changes to maintain its performance level, and utilize the former knowledge to improve its performance for recurring context.

Undoped and Ti-doped ZnO films were deposited using radio frequency reactive magnetron sputtering at various sputtering powers. The crystal structures, surface morphology, chemical state of titanium, zinc, and oxygen and optical properties in Ti-doped ZnO films were systematically investigated via X-ray diffraction (XRD), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and ultraviolet visible (UV-Vis)spectrophotometer. ZnO films showed a stronger preferred orientation toward c-axis and became smoother after Ti doping. The intensity of Ti-doped ZnO (002) peak further increased and surface showed more lattice defects as sputtering power increased from 100 W to 150 W. The XPS spectrum confirmed that Zn exists only in the oxidized state and the sample is a mixture of ZnO and TiO 2 . In addition, the UV absorption edge of ZnO films shifted to a longer wavelength as sputtering power increased.

In the paper, polyimide/Multi-walled carbon nano-tubes hybrid films were prepared from polyamide acid precursor and Multi-walled carbon nano-tubes (MWNTs) through ultrasonic blending and in-situ polymerization. The surface morphology of the films was characterized by SEM. The influence of MWNTs on the electrical property was researched, which showed that with the increasing content of MWNTs, the volume resistivity declined. The corona-resistance of the hybrid films was higher than the pure polyimide films which time was 4h. When the doping content of MWNTs was near to 0.34wt%, the time of corona-resistance reached the maximum of 7.75h.

Aiming at causing frequency traffic accidents by the drunken driving, the paper puts forward an automatic locked control system of vehicle drunken driving as a smart solution to prevent the phenomenon. And designs a automatic locked control system based on PIC16F877A. The system hardware modules and software system are elaborated in detailed. At last through the experiment, it verifies that the automatic control system has reached requirement.

A water-soluble polysaccharide (IOPS) was isolated from the Inonotus obliquus (Fr.) Pilat, which was a medicinal mushroom with many health-benefiting functions. The chemical composition and acute toxicity of IOPS was investigated in order to determine the possibility of using them for human consumption. Chemical analysis of IOPS showed that it was a nonstarch protein bounded acidic polysaccharide. The protein, neutral sugar, uronic acid contents of the polysaccharide IOPS were 42.50%, 18.50% and 6.10%, respectively. The neutral sugars of IOPS were composed of Rha (D-rhamnose), Xyl (D-xylan), Gal (D-galactose) and Glc (D-glucose). The contents of thirteen mineral elements in IOPS were measured by inductivity coupled plasma-optical emission spectrometry (ICP-OES). Results showed that the concentrations of the elements in IOPS were much higher than that of Inonotus obliquus powder. IOPS, when orally administrated to mice at the single dose of 5000 mg/kg body weight, was found no toxicity to the liver, kidney, heart, thymus or spleen of the mice and none of the mice died throughout the period of the experiment. Based on the study, IOPS could be classified either as a very low toxicity substance, i.e. GHS Category 5 (globally harmonized system) or unclassified when orally administrated to mice and it might be a candidate of dietary supplements.

A lot of research has been made on space vector modulation (SVM) strategy of matrix converter (MC), but research on over modulation strategy of MC is almost blank. This paper analyzed conventional control theory of dual-bridge MC, derived voltage transmission characteristic, then presented an over modulation method. At first, the rectifier is over modulated to get the greatest DC voltage, then the inverter is modulated from linear region to six-step mode seamlessly, which effectively improve the voltage utilization. Voltage transfer ratio under different conditions is emphatically analyzed with Fourier transform. Finally, different modulation method is simulated with MATLAB. Both math analyses and simulation results verify that the over modulation strategy is very useful to improve voltage utilization, which provide a theoretical basis for further research.

A novel over modulation strategy for matrix converter (MC) is put forward on the basis of conventional AC/DC/AC MC control theory. First, fictitious AC/DC rectifier is over modulated to get the greatest dummy DC voltage, then a novel over modulation strategy was invented for the inverter side, which effectively enhanced voltage transfer ratio to 1.05 with good linearity. At last, the method was extensively investigated with MATLAB under both output voltage and spectrum wave. Simulation results showed that the over modulation methodology proposed in this paper was feasible and liable, which provide a theoretical basis for further research.

This paper proposes a neural network ensemble model for classification of incomplete data. In the method, the incomplete dataset is divided into a group of complete sub datasets, which is then used as the training sets for the neural networks. Compared with other methods dealing with missing data in classification, the proposed method can utilize all the information provided by the data with missing values, maintain maximum consistency of incomplete data and avoid the dependency on distribution assumption. Experiments on two UCI datasets show the superiority of the algorithm to other two typical treatments of missing data in ensemble learning

We simultaneously observe the probe transmission, four-wave mixing (FWM) and fluorescence signals under dressing effects in a four-level -type atomic system for the first time. The physical mechanisms of three types of signals are explored by changing the frequency detuning and power of incident laser beams. Especially, the ultranarrow fluorescence signals have been experimentally obtained due to twice shearing by electromagnetically induced transparency (EIT) window, which are much narrower than the Doppler-free EIT window. Such ultranarrow fluorescence signals with very high coherence and monochromaticity have potential applications in the quantum correlation and narrow linewidth laser. Studying the FWM processes can find applications in optical switch and optical communication.