Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine

A new method of transformer fault diagnosis based on relevance vector machine (RVM) is proposed. Bayesian estimation is applied to support vector machine (SVM) in the novel algorithm, which made fault diagnosis system work more effectively. In the paper, the analysis model is presented that the solutions of RVM have the feature of sparsity and RVM can obtain global solutions under finite samples. The process of transformer fault diagnosis for four working statuses is given in experiments and simulations. The results validated that this method has obvious advantages of diagnosis time and accuracy compared with backpropagation (BP) neural networks and general SVM methods

A Joint Doppler Frequency Shift and DOA Estimation Algorithm Based on Sparse Representations for Colocated TDM-MIMO Radar

We address the problem of a new joint Doppler frequency shift (DFS) and direction of arrival (DOA) estimation for colocated TDM-MIMO radar that is a novel technology applied to autocruise and safety driving system in recent years. The signal model of colocated TDM-MIMO radar with few transmitter or receiver channels is depicted and “time varying steering vector” model is proved. Inspired by sparse representations theory, we present a new processing scheme for joint DFS and DOA estimation based on the new input signal model of colocated TDM-MIMO radar. An ultracomplete redundancy dictionary for angle-frequency space is founded in order to complete sparse representations of the input signal. The SVD-SR algorithm which stands for joint estimation based on sparse representations using SVD decomposition with OMP algorithm and the improved M-FOCUSS algorithm which combines the classical M-FOCUSS with joint sparse recovery spectrum are applied to the new signal model’s calculation to solve the multiple measurement vectors (MMV) problem. The improved M-FOCUSS algorithm can work more robust than SVD-SR and JS-SR algorithms in the aspects of coherent signals resolution and estimation accuracy. Finally, simulation experiments have shown that the proposed algorithms and schemes are feasible and can be further applied to practical application

Augmented Lagrange Based on Modified Covariance Matching Criterion Method for DOA Estimation in Compressed Sensing

A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is presented, in which DOA estimation is considered as the joint sparse recovery from multiple measurement vectors (MMV). The proposed method is obtained by minimizing the modified-based covariance matching criterion, which is acquired by adding penalties according to the regularization method. This minimization problem is shown to be a semidefinite program (SDP) and transformed into a constrained quadratic programming problem for reducing computational complexity which can be solved by the augmented Lagrange method. The proposed method can significantly improve the performance especially in the scenarios with low signal to noise ratio (SNR), small number of snapshots, and closely spaced correlated sources. In addition, the Cramér-Rao bound (CRB) of the proposed method is developed and the performance guarantee is given according to a version of the restricted isometry property (RIP). The effectiveness and satisfactory performance of the proposed method are illustrated by simulation results

Nonlinear Model Predictive Control with Terminal Invariant Manifolds for Stabilization of Underactuated Surface Vessel

A nonlinear model predictive control (MPC) is proposed for underactuated surface vessel (USV) with constrained invariant manifolds. Aimed at the special structure of USV, the invariant manifold under the given controller is constructed in terms of diffeomorphism and Lyapunov stability theory. Based on MPC, the states of the USV are steered into the constrained terminal invariant manifolds. After the terminal manifolds set is reached, a linear feedback control is used to stabilize the system. The simulation results verified the effectiveness of the proposed method. It is shown that, based on invariant manifolds constraints, it is easy to get the MPC for the USV and it is suitable for practical application

Modeling Analysis of DC Magnetic Bias of Iron Core Reactor of APF

As one of the main power devices of active power filter (APF), iron core reactor DC magnetic bias would affect the performance of APF. Based on the study of DC magnetic bias mechanism of APF iron core reactor, the data model was established in this paper. The performance of APF device impacted by iron core reactor DC magnetic bias was analysed through the simulation in different DC current conditions, and optimization scheme was proposed to reduce DC magnetic bias to improve working performance of APF. To reduce DC magnetic bias, main circuit parameters and control characteristics were uniform, and reluctance of iron core was increased. Results of the simulations and experiments validated that the improved method could restrain reactor DC magnetic bias to reduce even harmonic current in APF output current, which could greatly optimize APF performance

A Discrete Geese Swarm Algorithm for Spectrum Assignment of Cognitive Radio

In order to solve spectrum assignment problem, this paper proposes a discrete geese swarm algorithm (DGSA) based on particle swarm optimization and quantum particle swarm optimization, and we evaluate the performance of the DGSA through some classical benchmark functions. The proposed DGSA algorithm applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization. We also use it to solve cognitive radio spectrum assignment problem. The new spectrum allocation method has the ability to search global optimal solution under different network utility functions. Simulation results for cognitive radio system are provided to show that the designed spectrum allocation algorithm is superior to some previous spectrum allocation algorithms

The Potential Role of ORM2 in the Development of Colorectal Cancer

Colorectal cancer (CRC) is the third most common malignancy in the world. The risk of death is closely correlated to the stage of CRC at the time of primary diagnosis. Therefore, there is a compelling need for the identification of blood biomarkers that can enable early detection of CRC. We used a quantitative proteomic approach with isobaric labeling (iTRAQ) to examine changes in the plasma proteome of 10 patients with CRC compared to healthy volunteers. Enzyme-Linked Immunosorbnent Assay (ELISA) and Western blot were used for further validation. In our quantitative proteomics analysis, we detected 75 human plasma proteins with more than 95% confidence using iTRAQ labeling in conjunction with microQ-TOF MS. 9 up-regulated and 4 down-regulated proteins were observed in the CRC group. The ORM2 level in plasma was confirmed to be significantly elevated in patients suffering from CRC compared with the controls. ORM2 expression in CRC tissues was significantly increased compared with that in corresponding adjacent normal mucous tissues (P<0.001). ITRAQ together with Q-TOF/MS is a sensitive and reproducible technique of quantitative proteomics. Alteration in expression of ORM2 suggests that ORM2 could be used as a potential biomarker in the diagnosis of CRC