In this study, two simple methods for the steady-state analysis of self-excited induction generators (SEIGs) are proposed. These methods neither require lengthy mathematical derivations nor any advanced optimisation t...
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In this study, two simple methods for the steady-state analysis of self-excited induction generators (SEIGs) are proposed. These methods neither require lengthy mathematical derivations nor any advanced optimisation techniques to solve the equivalent circuit of SEIGs. First method involves a simple linear search algorithm and the second method employs a binary searchalgorithm to find the operating frequency at any given rotor speed, leading to the performance predetermination of SEIGs. To start the search techniques, a systematic approach has been formulated for fixing the boundary values required for the unknown pu frequency. The efficacy of the proposed methods has been demonstrated by presenting the predetermined performance characteristics of a three-phase, 230V, four-pole, 50Hz SEIG. The same SEIG was also tested in the laboratory using a DC motor as the prime mover. The closeness observed between the predetermined values and the experimental results further confirms the validity of the searchalgorithms. It has also been shown that the proposed methods can be extended with the same simplicity, for carrying out the performance predetermination of the short-shunt configuration of SEIGs, used for obtaining improved voltage regulation with lagging power factor loads.
Background In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of ...
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Background In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. Results In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.
This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (ma...
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This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow fever and dengue) which have several similar signs and symptoms. The memory can distinguish a single infection from a polyinfection. Our model is a combination of the improved versions of the original linear quantum retrieving algorithm proposed by Ventura and the non-linear quantum searchalgorithm of Abrams and Lloyd. From the given simulation results, it appears that the efficiency of recognition is good when particular signs and symptoms of a disease are inserted given that the linearalgorithm is the main algorithm. The non-linearalgorithm helps confirm or correct the diagnosis or give some advice to the medical staff for the treatment. So, our QAMDiagnos that has a friendly graphical user interface for desktop and smart-phone is a sensitive and a low-cost diagnostic tool that enables rapid and accurate diagnosis of four tropical diseases. (C) 2017 Elsevier Ltd. All rights reserved.
PurposeThis paper aims at developing an improved method, based on binary searchalgorithm (BSA) for the steady-state analysis of self-excited induction generators (SEIGs), which are increasingly used in wind energy el...
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PurposeThis paper aims at developing an improved method, based on binary searchalgorithm (BSA) for the steady-state analysis of self-excited induction generators (SEIGs), which are increasingly used in wind energy electric conversion systems. The BSA is also compared with linear search algorithm (LSA) to bring out the merits of BSA over ***/methodology/approachAll the parameters of SEIG, including the varying core loss of the machine, have been considered to ensure accuracy in the predetermined performance values of the set up. The nodal admittance method has been adopted to simplify the equivalent circuit of the generator and load. The logic and steps involved in the formulation of the complete procedure have been illustrated using elaborate *** proposed approach is validated by the experimental results, obtained on a three-phase 240 V, 5.0 A, 2.0 kW SEIG, which closely match with the corresponding predicted performance values. The analysis is shown to be easy to implement with reduced computation ***/valueA novel improved and simplified technique has been formulated for estimating the per unit frequency (a), magnetizing reactance (Xm) and core loss resistance (Rm) of the SEIG using the nodal admittance of its equivalent circuit. The accuracy of the predetermined performance is enhanced by considering the SEIG's varying core loss. Only simple MATLAB programming has been used for adopting the algorithms.
Now a days English Text to Speech (TTS) Engines are available online as well as offline. There is very less work has been done in Devanagari TTS systems especially for Marathi text. However, in this paper a new method...
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ISBN:
(纸本)9781467365406
Now a days English Text to Speech (TTS) Engines are available online as well as offline. There is very less work has been done in Devanagari TTS systems especially for Marathi text. However, in this paper a new method is proposed for a Devanagari (Marathi) Text To Speech system. Here a new technique is proposed with mapper and combiner in order to build Marathi TTS system. The Marathi TTS system is designed using existing English TTS Engine. Marathi input text is mapped with the text present in the database using simple linear search algorithm then it is provided as input to the Existing English TTS. CUlTently Concatenative speech synthesis is the method mostly used in TTS systems. Main drawbacks in Concatenation Synthesis, such as glitches, reverberations, spectral mismatch and requirement for huge database are removed up to great extent. The proposed system introduces new concept, which is more feasible and easier than the earlier methods used for Marathi Text-To-Speech. Also the proposed method provides maximum accuracy for text mapping.
Selecting proper links for some indirect-conneted nodes to add in a current network can improve the system's robustness and livability. In this paper, we compare the basic thoughts and implementations of several m...
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Selecting proper links for some indirect-conneted nodes to add in a current network can improve the system's robustness and livability. In this paper, we compare the basic thoughts and implementations of several methods in solving this problem and, including the linear search algorithm method,the CRCS method and the Genetic algorithm method. We also propose the Simulated Annealing algorithm method,compare the running results of these methods on two instances,and finally,evaluate their performance separately.
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