In this study, a control scheme for the stand-alone generation system based on brushless doubly-fed machine (BDFM) is presented. The output voltage amplitude and frequency are kept constant under variable rotor speed and load by regulating the amplitude and frequency of the control winding (CW) current of BDFM appropriately. The control scheme utilises a CW current vector controller as the inner loop, and a power winding voltage amplitude controller and a frequency controller as the outer loop. The proposed control scheme has been implemented on a prototype BDFM designed for the variable-speed stand-alone ship shaft generation application. Moreover, the satisfactory dynamic performance and low total harmonic distortion of the output voltage in the proposed stand-alone generation system is verified by experiments with two kinds of typical loads: three-phase induction motor load and three-phase RL inductive load.
In a worldwide network of suppliers, factories, warehouses, distribution centres and retailers, the supply chain plays a very important role in the acquisition, transformation, and delivery of raw materials and products. One of the most important characteristics of agile supply chain is the ability to reconfigure dynamically and quickly according to demand changes in the market. In this paper, concepts and characteristics of an agile supply chain are discussed and the agile supply chain is regarded as one of the pivotal technologies of agile manufacture based on dynamic alliance. Also, the importance of coordination in supply chain is emphasised and a general architecture of agile supply chain management is presented based on a multi-agent theory, in which the supply chain is managed by a set of intelligent agents for one or more activities. The supply chain management system functions are to coordinate its agents. Agent functionalities and responsibilities are defined respectively, and a contract net protocol joint with case-based reasoning for coordination and an algorithm for task allocation is presented.
Background: Identifying molecular subtypes of ovarian cancer is important. Compared to identify subtypes using single omics data, the multi-omics data analysis can utilize more information. Autoencoder has been widely used to construct lower dimensional representation for multi-omics feature integration. However, learning in the deep architectures in Autoencoder is difficult for achieving satisfied generalization performance. To solve this problem, we proposed a novel deep learning-based framework to robustly identify ovarian cancer subtypes by using denoising Autoencoder. Results: In proposed method, the composite features of multi-omics data in the Cancer Genome Atlas were produced by denoising Autoencoder, and then the generated lowdimensional features were input into k-means for clustering. At last based on the clustering results, we built the light-weighted classification model with L1-penalized logistic regression method. Furthermore, we applied the differential expression analysis and WGCNA analysis to select target genes related to molecular subtypes. We identified 34 biomarkers and 19 KEGG pathways associated with ovarian cancer. Conclusions: The independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results.
This paper presents a stand-alone variable speed constant frequency (VSCF) ship shaft generator system based on a brushless doubly-fed machine (BDFM). In this system, the output voltage amplitude and frequency of the BDFM are kept constant under a variable rotor speed and load by utilizing a well-designed current vector controller to regulate the control winding (CW) current. The control scheme is proposed, and the hardware design for the control system is developed. The proposed generator system is tested on a 325 TEU container vessel, and the test results show the good dynamic performance of the CW current vector controller and the whole control system. A harmonic analysis of the output voltage and a fuel consumption analysis of the generator system are also implemented. Finally, the total efficiency of the generator system is presented under different rotor speeds and load conditions.
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