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For more spacious place where the human bodies are sparse and scattered, it is feasible and convenient to use algorithm of frames subtraction or background elimination which can extract the human bodies. But for the places where the human bodies are intensive such as on the buses, it is difficult to segment the bodies using this algorithm. Since the heads are more scattered than the bodies, an algorithm based on several regional growths and feature extraction is studied to detect the heads in this paper. If the clothes gray is similar to the head gray, morphologic operation is used to extract the head again. Also this paper shows the flow of the algorithm in detail. Many images taken by the bus camera were used for the experiment. The result shows that the bus passengers could be detected exactly which verified the effectiveness of the algorithm.

To prepare curcumin nanoparticles and evaluate the in vitro release of curcumin.The chitosan-graft-vinyl acetate copolymers were synthesized by free radical polymerization. Curcumin nanoparticles were synthesized by ultrasonic irradiation. The encapsulation efficiency of the nanoparticles and the in vitro release of curcumin were studied.The nanoparticles were discrete and uniform spheres, covered with positive charges. The encapsulation efficiency of nanoparticles was up to 91.6%. The in vitro release profile showed the slower release rate of curcumin.The methods is simple. The nanoparticles possess good physical performance and sustained release character in vitro.

The beam direction is one of the important items of headlamp detection, but lots of internal detection machines can not give it an accurate detection. This article analyses the factors which affect the detection result, then gives a new accurate solution. It uses an automobile position fixing system and data correction algorithm. Firstly, the beam direction angle and the automobile's laying deviation angle can be got by using computer vision technique and image processing technique. Secondly, the beam direction angle can be corrected according to the automobile's laying deviation angle. This new method can efficiently find accurate optical axis direction of low beam and high beam. So it tells people whether the automobile works in good condition. It solves the detection problems of improper parking distance and improper laying position, decreases detection error and increases precision. What's more, it solves the internal actuality of inaccurate detection.

According to the features of human head which is taken by camera shooting from the top down, this paper puts forward a new and rapid algorithm for detecting human head. First, the image is binarized by threshold, then searches diameter of circular-like head area in binary image. If the diameter is in conformity with the feature of human head, then identifies the possible head further in other directions. This algorithm avoids complex concepts and mathematical formulas. Its time-space complexity is much lower than other common methods used in detecting circular-like targets. This paper compares this algorithm with Hough Transform (HT) which is typical in detecting circular-like targets. It is several tens of times faster than HT, and the memory this algorithm needs is several tens of times less than HT. At the end of this paper, practice proves its good effect.

The corners are the key points of light shape of automotive low-beam headlamp. This paper explains, in detail, the principle of detecting the corners of light shape of automotive low-beam headlamp with Inverse Hough Transform. There are three major steps. Firstly, gradient method is used to obtain the dividing line between brightness and darkness in the light shape of low-beam headlight. Secondly, the straight lines are detected by Hough Transform. Thirdly, the cross-points of straight lines are obtained by Inverse Hough Transform. Then according to the reasonability of cross-points, unreasonable cross-points are removed, thus the corners of light shape are obtained. Experiments are used to prove that this method can resist noise and work on real time. It realizes the transformation from manual detection to automatic detection in detecting optical axes of vehicle lamp.

Short-term load forecasting is very important for decision making in power system operation and planning. During the last several years, kernel machines have been widely employed for short-term forecasting. Owing to the inherent limitations, the corresponding forecasting accuracy can be impaired. To overcome the limitations, this paper develops a novel kernel machine, hereafter called learnable kernel machine, for shortterm load forecasting. The proposed method possesses several appealing properties. First, like all other kernel machines, it handles nonlinearity in a disciplined manner that is also computationally attractive; second, by incorporating both kernel learning and regularization parameter learning, it effectively enhances the overall performance; third, as the optimality criterion, it employs the leave-one-out error, leading to an almost unbiased estimator of the generalization error; forth, using the leave-one-out error as the optimality criterion, it can be also expressed in closed form, making it computationally feasible in practice; fifth, the computational cost to optimize the leave-one-out error can be further reduced by matrix technology. We present experimental results on real-world data sets to demonstrate the effectiveness.

The ability to accurately forecast the load plays an important role in electric power system planning and operating. In this paper, a novel approach was proposed for the electricity load forecasting by applying the manifold regularization learning methodology. Unlike traditional methods for load forecasting, the prediction method based on manifold regularization allows us to effectively exploit the geometric manifold structure of electricity load data in a semi-supervised learning setting. The effectiveness of the proposed approach is illustrated through an application to actual load data from the northwest China region.

Hyaluronan (HA) is a key molecule of the extracellular matrix that is thought to be critically involved in both atherosclerosis and restenosis. Recently, it has been demonstrated that the cyclooxygenase (COX) products, prostacyclin and prostaglandin E(2), induce HA synthesis in vitro by transcriptional up-regulation of HA-synthase 2 (HAS2) and HAS1. The relative roles in atherosclerotic and restenotic artery disease of tissue specifically expressed COX-1 and COX-2 are still under debate. Thus, the present study aimed to investigate the effect of COX isoform inhibition on HA-accumulation and regulation of HAS isoform expression in two models of pathologic artery remodelling in vivo. Firstly, ApoE-deficient mice were treated with a prototypic isoform non-selective inhibitor, indomethacin or with a prototypic COX-2 selective inhibitor, rofecoxib, for 8 weeks. Aortic HAS mRNA expression and HA-accumulation in atherosclerotic aortic root lesions were analyzed. Secondly, neointimal hyperplasia was induced by carotid artery ligation in ApoE-deficient mice on a high fat diet and the effects of the COX inhibitors were determined after 4 weeks of treatment. Intimal HA-accumulation was markedly reduced in both models by indomethacin and rofecoxib. This coincided with a strong inhibition of HAS1 mRNA expression in both models and with decreased HAS2 mRNA in the aorta of ApoE-deficient mice. HAS3 was not affected. The repression of HA-accumulation by both COX-2 selective and non-selective COX inhibition implicates COX-2 in the regulation of HA synthesis via stimulation of HAS1 and HAS2 expression in vivo. Modulation of vascular HA-accumulation might play a role in chronic effects of COX inhibitors on the progression of atherosclerosis.

In this paper a new signal processing method of Hilbert-Huang transform is applied to analyze AE lead-breaking signals and loaded concrete AE signals. Empirical model of decomposition (EMD) and cubic interpolation were used in AE signals analysis which was based on the local characteristic time scale. After the decomposition inherent model function (IMF) can effectively preserve non-linear and non-stationary features of signals during the processing. Meanwhile, noises of signals were eliminated by using multi-dimension filter property of EMD. The above two signal’s IMF can be clearly shown on a time-frequency vs. energy distribution spectrum and marginal spectrum by Hilbert-Huang transformation. The results show that HHT can efficiently reflecting the intrinsic properties of signals. Besides, HHT method, which is more adaptive than other methods, had a bigger superiority in adaptability and frequency concentration, and it has batter localization property and visual result of the time-frequency domain.Copyright © 2009 by ASME

In this paper, we analyze the transformer of X-ray high-voltage high-frequency generators and, have designed and implemented a high-voltage insulation testing system for its oil tank using full-bridge series resonant soft switching PFM DC-DC converter.

The fusion techniques have been developed quickly in recent years and become an important remote sensing research topic. This paper systematically discusses the technique used in pixel level image fusion including IHS transform, YIQ transform, HLS transform, HSV transform, PCA transform, HPF transform and wavelet transform image fusion method. Then a fusion experiment of IKONOS image is made to compares the different merging methods from spectral quality and the spatial quality in order to choose the suitable method for the high resolution image.

As one of the popular and advanced statistical learning algorithms, support vector machine (SVM) has been the new hot study area of pattern recognition and machine learning in recent years. SVM has such advantages as suitableness to high dimensional data, requirement of few samples and robustness to uncertainty, so it can be used to hyperspectral remote sensing image classification effectively. Based on the theory of SVM, a new approach for information classification on hyperspectral sensor has been developed by the experimental case of spatial information classification in central area of Shanghai city with PHI image. The algorithm is synthetically compared with the traditional classification methods. The experiment results confirm the effectiveness of the proposed method, which results in higher classification accuracy than the traditional methods.

An efficient and practical procedure for the Mannich reaction of secondary amine, formaldehyde, and indole or N-methylindole employing zinc chloride is presented. A series of gramine derivatives were obtained in 58-98% yields at room temperature in EtOH by using this method.

We present a subspace learning method, called local discriminant embedding with tensor representation (LDET), that addresses simultaneously the generalization and data representation problems in subspace learning. LDET learns multiple interrelated subspaces for obtaining a lower-dimensional embedding by incorporating both class label information and neighborhood information. By encoding each object as a second- or higher-order tensor, LDET can capture higher-order structures in the data without requiring a large sample size. Extensive empirical studies have been performed to compare LDET with a second- or third-order tensor representation and the original LDE on their face recognition performance. Not only does LDET have a lower computational complexity than LDE, but LDET is also superior to LDE in terms of its recognition accuracy.

Aminosulfonic acid is an environmentally friendly catalyst. Grinding a mixture of aromatic aldehydes, barbituric acid, and H2NSO3H at room temperature (without any solvent) gave 5‐arylidene barbituric acid in high yields, providing a simple and efficient route to synthesis of these compounds.

Based on the rate equation of four-level system and numerical simulation, the phase-matching characteristic of laser-diode-pumped Nd:GdVO4/KBe2BO3F2(KBBF) solid-state laser has been studied. The influences of pumping and oscillating spot size to output power and laser threshold with different gain medium are studied. Compared to CsLiB6O10 (CLBO) and β-BaB2O4 (BBO), the phase-matching characteristics of the nonlinear optical crystal KBBF are carried out through numerical simulation. For type I and type II phase-matching condition, KBBF which is more easy to realize ultraviolet laser output has shorter transmission bandwidth and broader tunable phase-matching angle than CLBO and BBO. It is useful to optimize and select the parameters of such systems.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

During the past few years, study of the all-solid-state blue laser has been focused on laser field applications in many fields and potential value of commerce. There has been much work done in order to obtain an efficient and simple solid-state blue laser source, this device being of interest for applications such as display technologies, production of high-density optical disk systems, high-resolution printing, or medical diagnostics. This paper discusses three means to realize all-solid-state blue lasers, including blue-emitting diode laser, direct frequency doubling of infrared laser diode (LD), diode-laser pumped all-solid-state blue lasers, respectively. However, direct emitters based on II - VI semiconductors are limited by the lifetime of laser diode. A practical and the most used way is the frequency-doubling of the 946-nm in Nd:YAG. In the field of nonlinear frequency conversion, we compare some different frequency-doubling crystals with improved optical characteristics, including higher nonlinear coefficient, wider transmission range, and more flexible phase-matching (PM) properties. Some nonlinear optical crystals usually used in solid-state laser are analyzed and compared, including KNbO 3 , LBO, BBO, BiBO, CBO (CsB 3 O 5 ), KBBF (KBe 2 BO 3 F 2 ). The recent progress on solid-state blue laser has resulted research in from gain media, frequency-doubled crystals, and configurations of the cavities. Two difficulties which are the coating techniques and the blue noise problem (the fluctuation of the laser output power) in the development of solid-state lasers are pointed out, and the techniques of solving blue noise problem that have been usually used in the past research are presented.

The output performance characteristics of all-solid-state lasers end-pumped by a diode laser have been theoretically analyzed. Furthermore, the optic properties of laser crystals and nonlinear optical materials employed in all-solid-state lasers have been studied and compared by computer numerical simulation. The theoretical analysis and numerical simulation of optical parameters of laser-diode-end-pumped Nd:YVO4 and Nd:YAG lasers were carried out on the basis of theory of crystal level structure and rate equation. The good output properties of high efficiency Nd:YVO4 solid-state laser end-pumped by a diode laser are superior to of Nd:YAG compared in the same conditions. Therefore, Nd:YVO4 crystal has been demonstrated as an ideal laser gain medium in solid-state lasers with account of its high laser gain and low pump threshold, etc. The conversion efficiency of 40% of Nd:YVO4 crystal has been presented with an input pump power of 15W and a crystal length of 1mm. Furthermore, on the basis of phase-matching for the nonlinear optical crystal, the angular tuning curves of KTiOAsO4 and RbTiOAsO4 crystals for type-Ι and type-ΙΙ phase-matching have been simulated by computer, respectively. The tunable wavelengths of RbTiOAsO4 crystal from 1.320μm to 5.484μm in type-Ι, from 1.324μm to 5.424μm in type-ΙΙ were both wider than that of KTiOAsO4 crystal in the mid-infrared bandwidth. Therefore, RbTiOAsO4 crystal is an excellent nonlinear optical crystal of which tuning range is wider than that of KTiOAsO4 crystal in the mid-infrared band. As new crystals, the emerging of RTA and KTA has shown a promise for nonlinear frequency generation throughout the 1-5μm spectral region. Consequently, all-solid-state lasers capable of generating high quality near mid-infrared light have strong use value.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The kernel fractional-stop nonlinear discriminant analysis (KF-NDA) method not only extends the fractional-step linear discriminant analysis (F-LDA) method to a nonlinear version, but also further improves the generalization ability of traditional kernel nonlinear discriminant analysis (K-NDA). On the other hand, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity, similar to those displayed by Gabor wavelets. Such characteristics produce salient local features that are most suitable for face recognition (FR). Hence, the augmented Gabor feature vector (AGFV) derived from a set of downsampled Gabor wavelet representations of face images is robust to the various of face images and simultaneously exhibits the more discriminatory information. Based on the AGFV and the KF-NDA, a robust feature extraction framework, i.e., the Gabor KF-NDA (GKF-NDA), is proposed for FR. In this framework, the KF-NDA method is directly applied to extract the robust nonlinear feature from the AGFV. Experimental results tested on the popular databases show that the GKF-NDA is more effective than oilier existing FR approaches.

Linear discriminant analysis (LDA) is a very effective tool used for dimensionality reduction and feature extraction in pattern recognition. However, the LDA is inadequate to describe complex and nonlinear patterns. To solve this problem, kernel nonlinear discriminant analysis (K-NDA) has been proposed. Although successful in many cases, classic K-NDA also suffers from the small sample size problem (SSSP) and loses some significant discriminatory information as same as classic LDA. In this paper, a novel K-NDA, i.e., the kernel generalized nonlinear discriminant analysis (KG-NDA) algorithm is introduced to effectively overcome these problems and it also views the optimal discriminant vectors as a global transform in the feature space to some extent. It not only deals with the nonlinear problem, but also effectively solves the SSSP. The KG-NDA is applied to the experiments on face recognition and the results tested on two popular databases demonstrate that this method is very effective.

Recently, a direct fractional-step linear discriminant analysis (DF-LDA) algorithm was proposed and successfully applied to face recognition (FR). However, the classification performance of DFT-LDA is degraded by the limitations of the direct linear discriminant analysis (D-LDA) used in DF-LDA. We describe a novel DF-LDA to solve this problem, and based on this novel DF-LDA, a novel Gabor DF-LDA (GDF-LDA), which directly applies the novel DF-LDA to the high-dimensional augmented Gabor feature vectors (AGFV) derived from the Gabor wavelet representation of face images, has been proposed for FR. The GDF-LDA not only is robust to facial variations, but also overcomes the limitations of the previous DF-LDA. The comparative results on the ORL database show that the GDF-LDA is more effective than existing FR methods.

The online pattern detection technology is an important part of the time series analysis, and some methods have been proposed, in which distance based window-sliding is popularly applied. For window-sliding, Euclidean distance and dynamic time warping (DTW) are always used as subsequence matching, but they have the drawbacks of sensitivity and expensive computational load respectively. Recently, the model based method is introduced into the field of online pattern detection, especially, the segmental semi-Markov model shows better performance than sliding methods in many aspects. However, the resolution of the model is limited. In this paper a hybrid online series pattern detection algorithm, which combines the distance based method and the model based method, is proposed. It is successfully demonstrated on real data sets, including financial and medical data.

On the one hand, the support vector machine (SVM) has the high performance in tackling small sample size and high-dimensional data, and has the good generalization ability too. On the other hand, Gabor wavelet exhibits strong characteristics of spatial locality, scale, and orientation selectivity, and the Gabor representations of face images can produce salient local features that are most suitable for face recognition. This paper proposes a new face recognition method based on independent Gabor features (IGF) and SVM. The proposed method has four steps as follows: 1) an augmented Gabor feature vector (AGFV) is derived from a set of downsampled Gabor wavelet representations of face images; 2) an IGF is obtained by applying the independent component analysis (ICA) to the AGFV; 3) To decrease the computational complexity and improve the recognition rate, Genetic Algorithms (GA) is used to select the optimal IGF set for classification; 4) the SVM is used to classify the optimal IGF. The experiments tested on the Yale database show that this method is very effective.

This paper introduces a novel Gabor direct generalized optimal discriminant vectors (GDG-ODV) method for face recognition (FR). This method can apply directly the generalized optimal discriminant vectors (G-ODV) method that views the optimal set of discriminant vectors as a global transform to the high-dimensional augmented Gabor feature vectors (AGFV) derived from the Gabor wavelet representation of face images. This method has three novelties: 1) it is robust to the facial variations; 2) it can efficiently solve the small sample size problem (SSSP), which widely exists in FR tasks; 3) it can consider the separability of optimal discriminant vectors (ODV) from a global viewpoint. The GDG-ODV method is compared, in terms of classification accuracy, to other common methods on the ORL database. The results indicate that this method is overall superior to those various FR methods.

Face recognition problem is challenging because face images can vary considerably in terms of facial expressions, lighting conditions and so on. This paper introduces a novel face recognition using support vector machines with the robust feature extracted by kernel principal component analysis (KPCA), which is robust to facial variations. This method derives firstly an augmented Gabor-face vector based on the Gabor wavelet transformation of face images using different orientation and scale local feature, which is robust to changes in facial expression and pose. KPCA is used to extract the feature of the augmented Gabor-face vector so that the principal components is computed within the space spanned by high-order correlation of input of augmented Gabor-face vector and produce a good performance. Finally, the support vector machine (SVM), which has high generalization capabilities and high performance in tackling small sample size in the pattern recognition task, is used to classify the feature. The comparative experiments in the ORL face database show that this algorithm is more effective than the previous methods.

Feature extraction techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of kernel-based Fisher discriminant analysis (KFDA) algorithms has attracted much attention due to their high performance. In this paper, the inherent limitations of those KFDA algorithms have been discussed and a novel algorithm is proposed to effectively overcome those limitations. Experimental results on face recognition suggest that this proposed algorithm is superior to the existing methods in terms of correct classification rate.

Synthesis of bis(indolyl)methanes via electrophilic substitution reactions of indoles with aromatic aldehydes and ketones catalyzed by aminosulfonic acid was carried out in 23–96% yield at 30–38 °C in EtOH aqueous solution under ultrasound irradiation.

It is difficult to use a single diagnosis theory or method to monitor and diagnose the whole condition of a large complicated system. To realize real-time condition monitoring and fault diagnosis of a large automatic production line in a steel factory, according to the structure and fault diagnosis characteristics of the production line system, a hybrid fault diagnosis expert system based on knowledge and neural network has been researched and built. This paper introduces the basic composition of the hybrid expert system, and gives the rule examples of the expert system based on knowledge, and emphatically introduces knowledge expression and knowledge acquisition and fault diagnosis inference of the fault diagnosis expert system based on neural network. Finally, fault diagnosis examples based on neural network are given. Research results show that a hybrid expert system is very effective to monitor and diagnose a large complicated modern production process.

Silica sulfuric acid catalyses the Michael addition of indole to α,β-unsaturated ketones under ultrasonic irradiation to afford the corresponding β-indolylketones in 50-85% yields at room temperature.