Membrane-hydration-state detection in proton exchange membrane fuel cells using improved ambient-condition-based dynamic model

Proton-exchange-membrane fuel cells (PEMFCs) are a popular source of alternative energy because of their operational reliability and compactness. This paper presents an improved model to represent the semi-empirical voltage of PEMFCs to overcome the limitations of existing models. The proposed model considers variations in ambient conditions, such as the ambient temperature and relative humidity, to obtain the accurate output voltage that corresponds to variations in dynamic and static loads. The proposed model was developed by conducting several experiments on the Horizon PEMFC system under normal, humid, and dry ambient conditions. Subsequently, the model parameters corresponding to each case were optimised using the quantum lightning search algorithm (QLSA). Parameters demonstrating significant variations with ambient conditions were finally represented as a function of the ambient temperature and relative humidity via statistical regression analysis. The voltage obtained using the modified model was verified by conducting experiments on both the Horizon and NEXA PEMFC systems by varying the ambient temperature and relative humidity with root mean square error (RMSE) less than 0.5. As observed, the results we obtained using the modified model closely approximated those obtained using PEMFCs under various operating conditions, and in both cases, the PEMFC voltage was observed to vary with the ambient and load conditions. The inherent advantages of the proposed PEMFC model include its ability to determine the membrane-water content and water pressure inside PEMFCs. The membrane-water content provides clear indications regarding the occurrence of drying and flooding faults. Under normal conditions, this membrane water content ranges from 11 to 7 for both the Horizon and NEXA PEMFC system. The simulation results suggested using the threshold membrane-water-content level as a possible indicator of fault occurrence under extreme ambient conditions. The limits of the said threshold were observed to be useful for fault diagnosis within PEMFC systems.     

A Robust Approach for Locating and Assessing Mechanical Faults in an Actual Transformer Winding Using the State Space of Its Lumped Equivalent Model

Russian Journal of Nondestructive Testing - Transfer function and state-space model are two powerful complementary tools have been used to monitoring the physical system. These two ways can be very...     

Infective endocarditis in adolescents. Analysis of risk factors for hospital mortality

PURPOSE: To study the epidemiological, clinical, therapeutic and evolutive aspects of endocarditis in a group of patients aging 12 to 20 years-old (mean 15.5). METHODS: Thirty-three consecutive patients (14 males, 19 females) admitted with infective endocarditis were retrospectively studied. RESULTS: Infective endocarditis mortality was 42%. Rheumatic heart disease was the predominant underlying condition in 63% of patients. Congenital heart disease (24%) and cardiac prosthesis (12%) were the other affections involved. The majority of patients (78%) were in functional class III and IV, with more deaths than the 22% who were in functional class I and II (p = 0.01). Staphylococcus aureus was the most frequently isolated agent (42% of the positive blood cultures, followed by Staphylococcus viridans, 21%). Multivariate analysis identified total leukocyte count above 10,000/mm3 and functional class, both at admission (p = 0.01 and p = 0.004, respectively), and the occurrence of embolic complications (p = 0.03) as independent predictors of in-hospital mortality. CONCLUSION: Rheumatic heart disease remains, as in adults, the main predisposing factor for infective endocarditis in adolescents, and S. aureus is, like in children, the leading agent. Mortality is high and functional class at hospital admission, embolic complications and leukocytosis are independent predictors of in-hospital mortality.     

Correction to: SPMI: Single Phase Multiple Initiator Protocol for Coverage in Wireless Sensor Networks

One of the complex problems nowadays in communication systems is the lack of frequency spectrum. To solve this problem, cognitive radio is considered the best candidate that can opportunistically exploit the spectrum. The periodogram based spectrum sensing technique can be used to detect the spectrum in cognitive radio. It is a useful technique since does not need to prior information about the primary signal. In this paper, a new periodogram is presented using the Discrete Cosine Transform (DCT). Results are analyzed and compared with the current raw periodogram. It is observed that the DCT periodogram outperforms the raw technique in terms of probabilities of false alarm and detection, variance, and complexity. In addition, the lowest power of DCT coefficients can be removed without compromising the sensing performance. The proposed system shows high probability of detection with low probability of false alarm even in the case of low Signal-to-Noise Ratio (SNR).     

E-banking Overview: Concepts,Challenges and Solutions

Wireless Personal Communications - The expansion of information technology has led to a new form of banking. Traditional banking, based on the physical presence of the customer, is only a part of...     

A sensitivity analysis method aimed at enhancing the metaheuristics for continuous optimization

An efficient covering of the search space is an important issue when dealing with metaheuristics. Sensitivity analysis methods aim at evaluating the influence of each variable of a problem on a model (i.e. objective function) response. Such methods provide knowledge on the function behavior and would be suitable for guiding metaheuristics. To evaluate correctly the dimensions influences, usual sensitivity analysis methods need a lot of evaluations of the objective function or are constrained with an experimental design. In this paper, we propose a new method, with a low computational cost, which can be used into metaheuristics to improve their search process. This method is based on two global sensitivity analysis methods: the linear correlation coefficient technique and Morris’ method. We propose to transform the global study of a non linear model into a local study of quasi-linear sub-parts of the model, in order to evaluate the global influence of each input variable on the model. This sensitivity analysis method will use evaluations of the objective function done by the metaheuristic to compute a weight of each variable. Then, the metaheuristic will generate new solutions choosing dimensions to offset, according to these weights. The tests done on usual benchmark functions of sensitivity analysis and continuous optimization (CEC 2013) reveal two issues. Firstly, our sensitivity analysis method provides good results, it correctly ranks each dimension’s influence. Secondly, integrating a sensitivity analysis method into a metaheuristic (here, Differential Evolution and ABC with modification rate) improves its results.     

Spintronics based random access memory: a review

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High Field Magnetization Investigation and Mean Field Theory in Amorphous Co<Subscript>75</Subscript>Er<Subscript>17</Subscript>B<Subscript>8</Subscript> Ribbons

Amorphous Co₇₅Er₁₇B₈ ribbons were prepared by the melt spinning technique, and their magnetic properties were studied. Mean field theory was used to describe the temperature dependence of magnetization. High-field magnetization studies performed in magnetic fields up to 15 T have revealed a magnetic behavior typical of a non-collinear magnetic structure of Er and Co sublattices. The simulated magnetization curves show the existence of two critical fields at H _cri1 =?9.5 T and H _cri2 =?94.2 T, corresponding to collinear ferrimagnet, and collinear field-forced ferromagnetic behaviors. The high value of H _cri2 highlights the strong antiferromagnetic interaction between Er and Co sublattices. From the non-collinear regime, the inter-subnetwork molecular field coefficients of the ferrimagnetic alloy were accurately evaluated. In addition, it is shown that the region of canted moments can be satisfactorily described by a phase diagram in the H-T plane.     

Combining Data and Computation Distribution Directives for Hybrid Parallel Programming : A Transformation System

This paper describes dSTEP, a directive-based programming model for hybrid shared and distributed memory machines. The originality of our work is the definition and an implementation of a unified high-level programming model addressing both data and computation distributions, providing a particularly fine control of the computation. The goal is to improve the programmer productivity while providing good performances in terms of execution time and memory usage. We define a generic compilation scheme for computation mapping and communication generation. We implement the solution in a source-to-source compiler together with a runtime library. We provide a series of optimizations to improve the performance of the generated code, with a special focus on reducing the communications time. We evaluate our solution on several scientific kernels as well as on the more challenging NAS BT benchmark, and compare our results with the hand written Fortran MPI and UPC implementations. The results show first that our solution allows to make explicit the non trivial parallel execution of the NAS BT benchmark using the dSTEP directives. Second, the results show that our generated MPI+OpenMP BT program runs with a 83.35 speedup over the original NAS OpenMP C benchmark on a hybrid cluster composed of 64 quadricores (256 cores). Overall, our solution dramatically reduces the programming effort while providing good time execution and memory usage performances. This programming model is suitable for a large variety of machines as multi-core and accelerator clusters.