Harbin Power Vocational Technology College

Harbin, China

Harbin Power Vocational Technology College

Harbin, China
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Xu Y.,Harbin Power Vocational Technology College | Zhu Q.-Y.,Harbin Institute of Technology
Dongli Gongcheng Xuebao/Journal of Chinese Society of Power Engineering | Year: 2011

CuO/AC desulphurizers were prepared using cocoanut shell activated carbon as the support and a fixed-bed flue gas desulfurization (FGD) reactor was set up, so as to study the influence of following factors on FGD performance of the desulphurizer, such as the calcination temperature, Cu loading capacity, desulphurizing temperature and gas component, etc. Results show that for higher FGD performance of CuO/AC desulphurizer, the calcination temperature would better be kept at about 250°C, Cu loading capacity in 5%-7.5%, desulphurizing temperature in 200-250°C and O 2 concentration in flue gas at a moderate degree. Regenerated desulphurizer would have better performance if the regeneration temperature is kept at 300°C, however, its desulphurization reactivity is worse than the original CuO/AC desulphurizer.


Chen G.,Harbin Institute of Technology | Meng F.,Harbin Institute of Technology | Yang Z.,Harbin Power Vocational Technology College | Guo Y.,Harbin Institute of Technology | Ye D.,Harbin Institute of Technology
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

In order to realize the space camera which on satellite optical axis pointing precision measurement, a monocular vision measurement system based on object-image conjugate is established. In this system the algorithms such as object-image conjugate vision models and point by point calibration method are applied and have been verified. First, the space camera axis controller projects a laser beam to the standard screen for simulating the space camera's optical axis. The laser beam form a target point and has been captured by monocular vision camera. Then the two-dimensional coordinates of the target points on the screen are calculated by a new vision measurement model which based on a looking-up and matching table, the table has been generated by object-image conjugate algorithm through point by point calibration. Finally, compare the calculation of coordinates offered by measurement system with the theory of coordinate offered by optical axis controller, the optical axis pointing precision can be evaluated. Experimental results indicate that the absolute precision of measurement system up to 0.15mm in 2m×2m FOV. This measurement system overcome the nonlinear distortion near the edge of the FOV and can meet the requirement of space camera's optical axis high precision measurement and evaluation. © 2016 SPIE.


Tao X.-M.,Harbin Engineering University | Liu F.-R.,Harbin Power Vocational Technology College | Liu Y.,Harbin Engineering University | Fu D.-D.,Harbin Engineering University
Kongzhi yu Juece/Control and Decision | Year: 2011

To deal with the problem of single-scale mutation, premature convergence and slow search speed, a clone selection algorithm(CSA) with directional multi-scale Gaussian mutation is proposed. To implement the share of information between antibodies, the directional evolution mechanism is utilized to induce the antibodies to evolve to the best solution region. The special multi-scale Gaussian mutation operators are introduced to make antibodies explore the search space more efficiently. The large-scale mutation operators can be utilized to quickly localize the global optimized space at the early evolution, while the small-scale mutation operators can implement local accurate minima solution search at the late evolution, which can make the algorithm explore the global and local minima thoroughly at the same time. The comparison of the performance of the proposed approach with other CSAs with different mutations is experimented. The experimental results show that the proposed method can not only effectively solve the premature convergence problem, but also significantly speed up the convergence and improve the stability.


Li A.,Harbin Power Vocational Technology College | She X.,Harbin Power Vocational Technology College | Sun Q.,Harbin Power Vocational Technology College
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

Saliency is an important feature of human visual attention. Salient regions of an image immediately attract our attention. Therefore, attention to salient regions is an important attribute to measure image qualities. A novel image quality metric is proposed in this paper, in which salient regions are extracted and the use of FSIM (Feature SIMilarity) in these regions is analyzed for image quality assessment. Experimental results for a set of intuitive examples with different distortion types demonstrate that the improved FSIM can achieve a better performance than the original form. © 2013 SPIE.


Tao X.-M.,Harbin Engineering University | Fu D.-D.,Harbin Engineering University | Liu F.-R.,Harbin Power Vocational Technology College | Liu Y.,Harbin Engineering University
Kongzhi yu Juece/Control and Decision | Year: 2012

A novel multi-scale parallel artificial immune clone algorithm for unsupervised clustering(MSPAICC) is presented, in which, evolutions of subgroups are performed in parallel with the different mutation strategies. The mutation capability of an individual is determined by the competition among subgroups and subgroup fitness value. The larger mutation operator is used to quickly localize the global optimal space at the early evolution, while the smaller mutation operator whose scale gradually reduces are adopted to improve the local search ability at the later evolution. The experimental results show the proposed method can improve clustering performance and the robustness compared with other clustering algorithms.


Tao X.-M.,Harbin Engineering University | Liu F.-R.,Harbin Power Vocational Technology College | Tong Z.-J.,Harbin Engineering University | Yang L.-B.,Harbin Engineering University
Zhendong yu Chongji/Journal of Vibration and Shock | Year: 2010

The performance of traditional support vector machine (SVM) drops significantly when it is applied to the problem of learning from unbalanced datasets where the normal instances heavily outnumbers the fault instances. To address this problem, a novel fault detection approach was proposed based on a variant of synthetic minority over-sample technique (SMOTE) combined with different error cost-sensitive SVM. As the SVM decision boundary is determined only by a small quantity of support vectors, consequently, based on SMOTE, a new minority over-sample method was presented, in which only the minority examples near the borderline are over-sampled. In order to solve the noise effect, the different error cost-sensitive SVM based on K-nearest neighbors (KNN) was adopted to remedy the problem of noise positive instances. The proposed algorithm was applied in bearings fault detection and its results were compared with those by the algorithms of traditional SVM, different classes of cost-sensitive SVMs (SVM-C) and SVM+SMOTE. The experimental results show the approach can achieve better detection performance than other methods.


Liu F.-R.,Harbin Power Vocational Technology College | Wang H.-W.,Harbin Power Vocational Technology College | Gao X.-Z.,Aalto University
Zhendong yu Chongji/Journal of Vibration and Shock | Year: 2010

A novel fuzzy clustering algorithm: PFCM was proposed based on the fusion of particle swarm optimization (PSO) and fuzzy c-means clustering (FCM). The conventional FCM has the two drawbacks of sensitivity to initialization and easily being trapped into local optima, due to the gradient descent approach used. With the features of global optimization and fast convergence, the hybrid algorithm presented can overcome these shortcomings and yield the optimal clustering performance. The new data clustering technique provided was also applied in the vibration fault diagnosis of steam turbine. Computer simulations demonstrate that compared with FCM, the proposed PFCM has a superior fault diagnosis capability.


Tao X.,Harbin Engineering University | Song S.,Harbin Engineering University | Liu F.,Harbin Power Vocational Technology College | Cao P.,Harbin Engineering University
Proceedings of the 30th Chinese Control Conference, CCC 2011 | Year: 2011

In bearings fault detection application, To solve the problems of difficultly obtaining labeled samples and exploiting a large amount of unlabeled samples, a novel semi-supervised Support vector machine fault detection model based on Laplacian regularization is presented in this paper. A smoothness penalty is introduced into the optimization function of regularization network which can exploit the clustering and manifold information of unlabeled samples. The comparisons with other Support vector machine,Fuzzy Support vector machine and Transductive Support vector machine fault detection algorithm are performed. The experiments show that the proposed approach can efficiently utilize the information provided by unlabeled samples to improve the performance of fault detection with labeled training samples of different sizes. The proposed fault detection methods with test samples and without test samples are compared. The results illustrate the investigated techniques with test samples as unlabeled samples can outperform the one without test samples as unlabeled samples. © 2011 Chinese Assoc of Automati.


Tao X.,Harbin Engineering University | Liu F.,Harbin Power Vocational Technology College
Proceedings of the 29th Chinese Control Conference, CCC'10 | Year: 2010

To solve the problems of difficultly obtaining abnormal samples in bearings fault detection application and overfitting of conventional classifications due to the abnormal data imbalanced, a novel one-class detection model based on AR model self-correlation's entropy characteristic is presented in this paper. The AR model is employed to extract the normal samples' parameter characteristics and consequently normal samples' AR sub-space is established. The self-correlation of errors generated by other samples' being projected onto the AR model space is calculated. The entropy of the previously calculated self-correlation is used as the metrics of similarity with the normal sub-space. The experiments show that the proposed approach can efficiently overcome the drawback of computational complexity and detection rate's sensitivity to the length of samples of conventional detection methods based on AR' parameters. The single fault detection and diagnosis schemes based on AR self-correlation's entropy also are proposed in this paper. The model's threshold value settings are also analyzed and the determination approach based on the Particle Swarm Optimization is investigated in this paper. The proposed detection scheme is compared against MLP and detection techniques based on AR parameters as features in the experiments. The results illustrate effectiveness of the investigated techniques with some concluding remarks.

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