This paper proposes a novel transverse flux permanent magnet motor (TFPMM) with double-C stator hoops. The proposed TFPMM carries significantly increased power density due to higher coefficient of utilization of the v...
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ISBN:
(纸本)9781479957774
This paper proposes a novel transverse flux permanent magnet motor (TFPMM) with double-C stator hoops. The proposed TFPMM carries significantly increased power density due to higher coefficient of utilization of the volume compared with the traditional double-side counterparts. The topology, principle and parametric calculation of the TFPMM are described elaborately. The magnetic field and characteristics analysis are conducted by three-dimensional finite element method (3D FEM) including flux density, EMF and winding inductance, and cogging torque are obtained to verify the feasibility of proposed TFPMM design. The mechanism of cogging torque of the proposed double-C stator hoops TFPMM is also analyzed. The results of transient simulation of power density well align with the theoretical analysis, which confirm that the power density is significantly increased due to its higher volumetric utilization ratio.
Due to its ability to tolerate high channel loss, decoy-state quantum key distribution (QKD) has been one of the main focuses within the QKD community. Notably, several experimental groups have demonstrated that it is...
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Due to its ability to tolerate high channel loss, decoy-state quantum key distribution (QKD) has been one of the main focuses within the QKD community. Notably, several experimental groups have demonstrated that it is secure and feasible under real-world conditions. Crucially, however, the security and feasibility claims made by most of these experiments were obtained under the assumption that the eavesdropper is restricted to particular types of attacks or that the finite-key effects are neglected. Unfortunately, such assumptions are not possible to guarantee in practice. In this work, we provide concise and tight finite-key security bounds for practical decoy-state QKD that are valid against general attacks.
In this paper, we propose a clustering based multiobjective evolutionary algorithm (CLUMOEA) to deal with the multiobjective optimization problems with irregular Pareto front shapes. CLUMOEA uses a k-means clustering ...
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In this paper, we propose a clustering based multiobjective evolutionary algorithm (CLUMOEA) to deal with the multiobjective optimization problems with irregular Pareto front shapes. CLUMOEA uses a k-means clustering method to discover the population structure by partitioning the solutions into several clusters, and it only allows the solutions in the same cluster to do the reproduction. To reduce the computational cost and balance the exploration and exploitation, the clustering process and evolutionary process are integrated together and they are performed simultaneously. In addition to the clustering, CLUMOEA also uses a distance tournament selection to choose the more similar mating solutions to accelerate the convergence. Besides, a cosine nondominated selection method considering the location and distance information of the solutions are further presented to construct the final population with good diversity. The experimental results show that, compared with some state-of-the-art algorithms, CLUMOEA has significant advantages on dealing with the given test problems with irregular Pareto front shapes.
Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop a...
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Considering the under-actuated characteristic of the carangiform robotic fish whose forward movement and turning are carried out by its single caudal fin, this paper investigates its shortest path planning problem sub...
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Considering the under-actuated characteristic of the carangiform robotic fish whose forward movement and turning are carried out by its single caudal fin, this paper investigates its shortest path planning problem subjected to the terminal constrains. First, a simplified kinematics model is proposed in view of its locomotion characteristic. With this model, the shortest path planning is transformed into the shortest time optimal control problem and the maximum principle is employed to obtain the necessary condition. Furthermore, based on the analysis on the relationship between the length of all feasible paths and the terminal condition, the sufficient and necessary conditions are derived for the shortest path under any terminal constrains. Finally, a program developed with Matlab verifies the effectiveness of the proposed results.
Liquid scintillation counter (LSC) has been used as a very efficient technique for a quantitative measurement of radioactivity. The LSC has been the most powerful tool in the fields of low level environmental radioact...
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ISBN:
(纸本)9781479960989
Liquid scintillation counter (LSC) has been used as a very efficient technique for a quantitative measurement of radioactivity. The LSC has been the most powerful tool in the fields of low level environmental radioactivity monitoring and the detection of low energy beta decay events of radioactivity. In LSC the sample is dissolved in a liquid scintillation cocktail in a sample vial. In order to quantify the nuclear decay in terms of activity, the LSC counts the number of flashes of scintillation lights. However, the sample's count rate varies with the detection efficiency that depends on the energy conversion from the released beta ray energy in nuclear decay to light flashes. Since the sample solution absorbs part of nuclear decay energy and photons of scintillation lights, which is known as quench, the observed count rate becomes less than the expected rate from actual energy. Therefore the compensation of count rate loss is essential to determine the sample's activity. There are many techniques to determine the efficiency, however, it is known that those techniques lead different results especially in low energy beta decay radionuclides such as ~3H. In order to investigate the influence of quench phenomena in LSC, we developed a Geant4 based Monte Carlo simulation and validated the beta-ray scintillation spectra in LSC. The simulation takes into account radioactive decay, flashes of scintillation lights, and transportation of optical photons including reflection and refraction characteristics at material boundaries. In this paper, we report on the detail of our simulation and the comparison of beta ray spectra detected in LSC between the simulation and measurements.
This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driv...
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This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)-based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a nonlinear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.
This paper presents the design of a scalable, high power battery system for pulsed power operations. The battery system is modular in design, with each module containing four Lithium Ion Polymer (LiPo) cells and a cus...
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This paper presents the design of a scalable, high power battery system for pulsed power operations. The battery system is modular in design, with each module containing four Lithium Ion Polymer (LiPo) cells and a custom designed cell management board that actively monitors the voltage and temperature of each cell and also provides cell balancing functionality. The system is designed to be scalable by adding up to 25 modules in a series configuration. While the battery management system should be compatible with any lithium ion cells, this implementation uses 8 Ah capacity dual-core LiPo cells, rated for a 150 C discharge rate; allowing for a peak current output of 1,200 A. With 25 modules (96 LiPo cells) the system would have an open circuit voltage of 385 V and be capable of providing up to 1,200 A at 355 V for a peak output power of 420 kW. Special attention has been placed on safety features including overvoltage, undervoltage and temperature monitoring of every cell in the system. The charging/balancing system is capable of automatically shutting down if any of the voltages or temperatures exceeds established limits. The management circuitry is designed to have a low off-state power draw in order to maximize battery life when the system is not in use.
This paper discusses randomly placed antenna array elements for direction of arrival and adaptive beamforming determination. Random array structures are discussed with various configurations. This work can be extended...
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This paper discusses randomly placed antenna array elements for direction of arrival and adaptive beamforming determination. Random array structures are discussed with various configurations. This work can be extended to space applications using a cluster of small satellites that are not aligned together in a standard linear or planar geometrical configuration but in some random fashion.
We present a multi-observer approach for the parameter and state estimation of continuous-time nonlinear systems. For nominal parameter values in the known parameter set, state observers are designed with a robustness...
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ISBN:
(纸本)9781467360890
We present a multi-observer approach for the parameter and state estimation of continuous-time nonlinear systems. For nominal parameter values in the known parameter set, state observers are designed with a robustness property. At any time instant, one observer is selected by a given criterion to provide its state estimate and its corresponding nominal parameter value. Provided that a persistency of excitation condition holds, we guarantee the convergence of state and parameter estimates up to a given margin of error which can be reduced by increasing the number of observers. The potential computational burden of the scheme is eased by introducing a dynamic parameter re-sampling technique, where the nominal parameter values are iteratively updated using a zoom-in procedure on the parameter set. We illustrate the efficacy of the algorithm on a model of neural dynamics.
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