A FPC-ROOT Algorithm for 2D-DOA Estimation in Sparse Array

To improve the performance of two-dimensional direction-of-arrival (2D DOA) estimation in sparse array, this paper presents a Fixed Point Continuation Polynomial Roots (FPC-ROOT) algorithm. Firstly, a signal model for DOA estimation is established based on matrix completion and it can be proved that the proposed model meets Null Space Property (NSP). Secondly, left and right singular vectors of received signals matrix are achieved using the matrix completion algorithm. Finally, 2D DOA estimation can be acquired through solving the polynomial roots. The proposed algorithm can achieve high accuracy of 2D DOA estimation in sparse array, without solving autocorrelation matrix of received signals and scanning of two-dimensional spectral peak. Besides, it decreases the number of antennas and lowers computational complexity and meanwhile avoids the angle ambiguity problem. Computer simulations demonstrate that the proposed FPC-ROOT algorithm can obtain the 2D DOA estimation precisely in sparse array

Fault diagnosis method for spherical roller bearing of wind turbine based on variational mode decomposition and singular value decomposition

For the non-stationary characteristics of the vibration signal of wind turbine’s roller bearing in fault condition, a bearing fault diagnosis method based on variational mode decomposition (VMD) and singular value decomposition (SVD) is proposed. The VMD method is used to decompose wind turbine’s roller bearing’s fault vibration signal into several components. These components are regard as initial feature vector matrix. The singular value decomposition of the matrix is done. The obtained singular value is used as the extracted bearing fault feature vectors. The probabilistic neural network is used as pattern recognition classifier to determine the working state and fault type of wind turbine roller bearings. The result of case study showed that the proposed method can effectively identify the working state and fault type of wind turbine roller bearings

Fault diagnosis of rotor using EMD thresholding-based de-noising combined with probabilistic neural network

De-noising of signal processing is crucial for fault diagnosis in order to successfully conduct feature extraction and is an efficient method for accurate determination of cause. In this paper, the empirical mode decomposition (EMD) thresholding-based de-noising method and probabilistic neural network (PNN) are respectively used in the de-noising of the vibration signal and rotor fault diagnosis and compared with wavelet thresholding-based de-noising technology and back propagation neural network (BPNN). The results show that the clear iterative EMD interval thresholding performs better than wavelet thresholding in the de-noising of the vibration signal, and avoids the determination of wavelet basis and decomposition level. In addition, the PNN created by feature samples does not require training and has a higher accuracy than BPNN

A Kind of Risk-Sensitive Group Decision-Making Based on MDP

Abstract. One-switch utility function is used to describe how the risk attitude of a decision maker changes with his wealth level. In this paper additive decision rule is used for the aggregation of decision member's utility which is represented by one-switch utility function. Based on Markov decision processes (MDP) and group utility, a dynamic, multi-stages and risk sensitive group decision model is proposed. The proposed model augments the state of MDP with wealth level, so the policy of the model is defined as an action executed in a state and a wealth level interval. A backward-induction algorithm is given to solve the optimal policy for the model. Numerical examples show that personal risk attitude has a great influence on group decision-making when personal risk attitudes of members are different, while the weights of members play a critical role when personal risk attitudes of members are similar

Balancing performance and effort in deep learning via the fusion of real and synthetic cultural heritage photogrammetry training sets

Cultural heritage presents both challenges and opportunities for the adoption and use of deep learning in 3D digitisation and digitalisation endeavours. While unique features in terms of the identity of artefacts are important factors that can contribute to training performance in deep learning algorithms, challenges remain with regards to the laborious efforts in our ability to obtain adequate datasets that would both provide for the diversity of imageries, and across the range of multi-facet images for each object in use. One solution, and perhaps an important step towards the broader applicability of deep learning in the field of digital heritage is the fusion of both real and virtual datasets via the automated creation of diverse datasets that covers multiple views of individual objects over a range of diversified objects in the training pipeline, all facilitated by closerange photogrammetry generated 3D objects. The question is the ratio of the combination of real and synthetic imageries in which an inflection point occurs whereby performance is reduced. In this research, we attempt to reduce the need for manual labour by leveraging the flexibility provided for in automated data generation via close-range photogrammetry models with a view for future deep learning facilitated cultural heritage activities, such as digital identification, sorting, asset management and categorisation

Post-impoundment biomass and composition of phytoplankton in the Yangtze River

Damming, and thus alteration of stream flow, promotes higher phytoplankton populations and encourages algal blooms (density &gt; 10(6) cells L-1) in the Three Gorges Reservoir (TGR). Phytoplankton composition and biomass were studied in the Yangtze River from March 2004 to May 2005. 107 taxa were identified. Diatoms were the dominant group, followed by Chlorophyta and Cyanobacteria. In the Yangtze River, algal abundance varied from 3.13 x 10(3) to 3.83 x 10(6) cells L-1, and algal biomass was in the range of 0.06 to 659 mg C m(-3). Levels of nitrogen, phosphorus and silica did not show consistent longitudinal changes along the river and were not correlated with phytoplankton parameters. Phytoplankton abundance was negatively correlated with main channel discharge (Spearman r = -1.000, P &lt; 0.01). Phytoplankton abundance and biomass in the Yangtze River are mainly determined by the hydrological conditions rather than by nutrient concentrations.Damming, and thus alteration of stream flow, promotes higher phytoplankton populations and encourages algal blooms (density > 10(6) cells L-1) in the Three Gorges Reservoir (TGR). Phytoplankton composition and biomass were studied in the Yangtze River from March 2004 to May 2005. 107 taxa were identified. Diatoms were the dominant group, followed by Chlorophyta and Cyanobacteria. In the Yangtze River, algal abundance varied from 3.13 x 10(3) to 3.83 x 10(6) cells L-1, and algal biomass was in the range of 0.06 to 659 mg C m(-3). Levels of nitrogen, phosphorus and silica did not show consistent longitudinal changes along the river and were not correlated with phytoplankton parameters. Phytoplankton abundance was negatively correlated with main channel discharge (Spearman r = -1.000, P < 0.01). Phytoplankton abundance and biomass in the Yangtze River are mainly determined by the hydrological conditions rather than by nutrient concentrations

Hypoxia-ameliorated photothermal manganese dioxide nanoplatform for reversing doxorubicin resistance

Drug resistance is a huge hurdle in tumor therapy. Tumor hypoxia contributes to chemotherapy resistance by inducing the hypoxia-inducible factor-1α (HIF-1α) pathway. To reduce tumor hypoxia, novel approaches have been devised, providing significant importance to reverse therapeutic resistance and improve the effectiveness of antitumor therapies. Herein, the nanosystem of bovine serum albumin (BSA)-templated manganese dioxide (MnO2) nanoparticles (BSA/MnO2 NPs) loaded with doxorubicin (DOX) (DOX-BSA/MnO2 NPs) developed in our previous report was further explored for their physicochemical properties and capacity to reverse DOX resistance because of their excellent photothermal and tumor microenvironment (TME) response effects. The DOX-BSA/MnO2 NPs showed good biocompatibility and hemocompatibility. Meanwhile, DOX-BSA/MnO2 NPs could greatly affect DOX pharmacokinetic properties, with prolonged circulation time and reduced cardiotoxicity, besides enhancing accumulation at tumor sites. DOX-BSA/MnO2 NPs can interact with H2O2 and H+ in TME to form oxygen and exhibit excellent photothermal effect to further alleviate hypoxia due to MnO2, reversing DOX resistance by down-regulating HIF-1α expression and significantly improving the antitumor efficiency in DOX-resistant human breast carcinoma cell line (MCF-7/ADR) tumor model. The hypoxia-ameliorated photothermal MnO2 platform is a promising strategy for revering DOX resistance