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Search Results: 1 - 10 of 26428 matches for " Peng Kang "
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Entrepreneurial Intentions and Its Influencing Factors: A Survey of the University Students in Xi’an China  [PDF]
Zhengxia Peng, Genshu Lu, Hui Kang
Creative Education (CE) , 2012, DOI: 10.4236/ce.2012.38B021
Abstract: Based on a survey of 2,010 senior university students from nine universities in Xi’an, China, this paper analyzes the student’s entrepreneurial intention level and its influencing factors. The results show that the perceived subjective norm of university students has significantly positive influence on their entrepreneurial attitude and the entrepreneurial self-efficacy while all these factors influence their entrepreneurial intentions significantly. This paper also examines the influence of other factors such as individual/psychological factors, family background factors and social environment factors, and further discusses its policy implications.
Neural Network Sliding Mode based Current Decoupled Control for Induction Motor Drive
Peng Kang,Zhao Jin
Information Technology Journal , 2010,
Abstract: This study deals with the problem of stator currents coupling effect in induction motor drives. In field oriented control, d-axis stator current and q-axis stator current should be regulated independently. However, the stator currents in d-q synchronously rotating reference frame are not naturally decoupled, therefore, a new control scheme for current control is proposed that utilizes Sliding Mode Control (SMC) and Radial Basis Function Neural Network (RBFNN) to achieve the decoupling without considerable chattering. In this control strategy, a RBFNN controller replaces the discontinuous part of the sliding mode controller to eliminate undesired chattering of conventional sliding mode controller. The decoupling method in this study uses two RBFNN sliding mode controllers to regulate d-axis stator current and q-axis stator current, respectively. Finally, simulation results of the proposed scheme have presented perfect performances, such as perfect decoupling, strong robustness and reduced chattering, in comparison with proportional-integral controller and sliding mode controller.
Research on Extraction Process of Gallic Acid from Penthorum chinense Pursh by Aqueous Ethanol  [PDF]
Luping Kang, Shanbin Yang, Yan Peng, Jiao Dai, Xingchun Ying
Green and Sustainable Chemistry (GSC) , 2015, DOI: 10.4236/gsc.2015.52009
Abstract: Penthorum chinense Pursh is rich in gallic acid, which has antioxidant, anti-inflammatory, anti-fungal and antitumor activities. In order to optimize their extraction conditions, various extraction parameters were chosen to identify their effects on gallic acid extraction. With extraction amount of gallic acid as index, based on single factor analysis, influence of solid/liquid ratio, ethanol concentration, fetch time and extraction temperature on extraction technology were investigated by orthogonal test. The optimization conditions for gallic acid extraction were determined as follows: ethanol concentration 60%, extraction time 2.5 h, temperature 90°C and solid/liquid ratio 1:30. The corresponding gallic acid content was 4.85%. This optimized extraction process was stable and feasible.
Erosion Characteristics of Hydraulic Turbine Guide-Vane End Clearance in Sediment Water Flow: A Simplified Model Analysis  [PDF]
Wei Han, Jie Wang, Jingbo Kang, Lianyuan Li, Guoyi Peng
Journal of Flow Control, Measurement & Visualization (JFCMV) , 2017, DOI: 10.4236/jfcmv.2017.54009
Abstract: The effect of clearance flow on the erosion characteristics of a circular cylinder with a backward facing step in sediment-laden water flow is analyzed numerically with the mixture model and the re-normalization group (RNG) k-ε turbulence model. Thirty-six monitoring points are set up on different stream surfaces to collect information on the impact erosion under different flow conditions, where the Initial Sediment Volume Fraction (ISVF) is set to 0.05, 0.075, 0.1, 0.125, and 0.15; particle diameter is set to 0.05 mm, 0.15 mm, 0.25 mm, 0.35 mm, and 0.45 mm respectively. The distribution of particle velocity and Local Solid-Phase Volume Fraction (LSVF) along different stream surfaces are calculated, based on which the trend of erosion is qualitatively evaluated. ISVF and particle diameter play different roles on the impact erosion index parameter (\"\") on the different wetted walls. Relative wear rate of numerical estimation agrees well with the practical one under the same working condition. Numerical analysis demonstrates that guide vane with a negative curvature end surface (concave surface) can decrease erosion damage effectively, which may provide a reference for optimal design and maintenance of hydraulic turbine.
Qiu He,Kang An,Peng Wang,Peng Yu
Acta Crystallographica Section E , 2010, DOI: 10.1107/s160053681002221x
Abstract: The title compound, C16H12FN3OS, was synthesized by the reaction of 5-(4-methoxyphenyl)-1,3,4-thiadiazol-2-amine and 4-fluorobenzaldehyde. An intramolecular C—H...S hydrogen bond results in the formation of two five-membered rings. In the crystal structure, intermolecular C—H...N hydrogen bonding links the molecules, forming a two-dimensional network.
Identification of circulating miRNA biomarkers based on global quantitative real-time PCR profiling
Kang Kang, Xiao Peng, Jun Luo, Deming Gou
Journal of Animal Science and Biotechnology , 2012, DOI: 10.1186/2049-1891-3-4
Abstract: MicroRNAs (miRNAs), a class of 18 to 25 noncoding nucleotides, are capable of regulating gene expression through messenger RNA degradation or translational repression and are involved in various biological processes, such as proliferation, differentiation, development, and apoptosis [1,2]. Recently, the presence of miRNAs in the blood circulation has been reported [3]. Interestingly, deregulation of circulating miRNAs has been associated with a variety of human diseases, including cancer [4,5] and cardiovascular diseases [6,7], indicating that miRNAs could be used as biomarkers for cancer and other diseases.Several methods, such as northern blot [8], bead-based flow cytometry [9], microarray [10,11], quantitative real-time PCR (qRT-PCR) [12-14], and deep sequencing [15,16] have been developed to measure miRNA expression [17]. Of these methods, qRT-PCR is superior due to its high sensitivity, specificity and reproducibility. While other methods, such as microarray, require a larger amount of RNA sample (usually more than 1 μg), qRT-PCR requires less RNA input, where even as little as a single cell can be used for profiling [18,19]. Since the expression levels of circulating miRNAs are very low, qRT-PCR is well adapted for analyzing circulating miRNAs profiles because of its sensitivity. In addition, approximately 1,900 mature miRNAs have been found in human genome (miRbase 18, released on November 3, 2011) [20]. As qRT-PCR is easily adapted to 384-well plates, it is possible to carry out high-throughput screening. Here, we describe a procedure for the identification of circulating miRNA biomarkers by qRT-PCR profiling that is composed of four steps: (1) sample collection and preparation; (2) global miRNA profiling using qRT-PCR; (3) data normalization and analysis; (4) selection and validation of miRNA biomarker(s).Blood samples can be collected after obtaining the approval of relevant ethics committees and informed consents of donors. All information collected from
The effect of Strong Magnetic Field On the Standard Model of Quasars and AGNs
Qiuhe Peng,Chih-Kang Chou
Physics , 2015,
Abstract: Recent observational evidence indicates that the center of our Milky Way harbours a super-massive object with ultra-strong radial magnetic field (Eatough et al., 2013). Here we demonstrate that the radiations observed in the vicinity of the Galactic Center (GC) (Falcke and Marko 2013) cannot be emitted by the gas of the accretion disk since the accreting plasma is prevented from approaching to the GC by the abnormally strong radial magnetic field. These fields obstruct the infalling accretion flow from the inner region of the disk and the central massive black hole in the standard model. It is expected that the observed radiations near the Galactic Center cannot be generated by the central black hole. We also demonstrate that the observed ultra-strong radial magnetic field near the Galactic Center ( Eatough et al., 2013) cannot be generated by the - turbulence dynamo mechanism of Parker since preliminary qualitative estimate in terms of this mechanism gives a magnetic field strength six orders of magnitude smaller than the observed field strength at . However, both these difficulties or the dilemma of the standard model can be overcome if the central black hole in the standard model is replaced by a supper-massive stellar object containing magnetic monopoles ( SMSOMM, Peng and Chou, 2001). The observed power peaking of the thermal radiation is essentially the same as our theoretical prediction. In addition, the discovery of the ultra-strong radial magnetic field near the Galactic Center can be naturally explained and is consistent with the prediction of our model( Peng and Chou 2001). Furthermore, the observed ultra-strong radial magnetic field in the vicinity of the Galactic Center may be considered as the astronomical evidence for the existence of magnetic monopoles as predicted by the Grand Unified Theory of particle physics.
Robust Subspace Clustering via Smoothed Rank Approximation
Zhao Kang,Chong Peng,Qiang Cheng
Computer Science , 2015, DOI: 10.1109/LSP.2015.2460737
Abstract: Matrix rank minimizing subject to affine constraints arises in many application areas, ranging from signal processing to machine learning. Nuclear norm is a convex relaxation for this problem which can recover the rank exactly under some restricted and theoretically interesting conditions. However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum. To seek a solution of higher accuracy than the nuclear norm, in this paper, we propose a rank approximation based on Logarithm-Determinant. We consider using this rank approximation for subspace clustering application. Our framework can model different kinds of errors and noise. Effective optimization strategy is developed with theoretical guarantee to converge to a stationary point. The proposed method gives promising results on face clustering and motion segmentation tasks compared to the state-of-the-art subspace clustering algorithms.
Robust PCA via Nonconvex Rank Approximation
Zhao Kang,Chong Peng,Qiang Cheng
Computer Science , 2015,
Abstract: Numerous applications in data mining and machine learning require recovering a matrix of minimal rank. Robust principal component analysis (RPCA) is a general framework for handling this kind of problems. Nuclear norm based convex surrogate of the rank function in RPCA is widely investigated. Under certain assumptions, it can recover the underlying true low rank matrix with high probability. However, those assumptions may not hold in real-world applications. Since the nuclear norm approximates the rank by adding all singular values together, which is essentially a $\ell_1$-norm of the singular values, the resulting approximation error is not trivial and thus the resulting matrix estimator can be significantly biased. To seek a closer approximation and to alleviate the above-mentioned limitations of the nuclear norm, we propose a nonconvex rank approximation. This approximation to the matrix rank is tighter than the nuclear norm. To solve the associated nonconvex minimization problem, we develop an efficient augmented Lagrange multiplier based optimization algorithm. Experimental results demonstrate that our method outperforms current state-of-the-art algorithms in both accuracy and efficiency.
Robust Subspace Clustering via Tighter Rank Approximation
Zhao Kang,Chong Peng,Qiang Cheng
Computer Science , 2015, DOI: 10.1145/2806416.2806506
Abstract: Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict its capability in dealing with many practical problems. In this paper, an arctangent function is used as a tighter approximation to the rank function. We use it on the challenging subspace clustering problem. For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method. Extensive experiments on face clustering and motion segmentation show that the proposed method is effective for rank approximation.
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