Characterization of residual stresses from cold expansion using spatial statistics

Residual stress fields from cold expansion have been widely used to extend the fatigue life of aircraft structures. However, the spatial statistical character of these residual stress fields has not been established and has not been incorporated in current analysis methods. The objective of this study was to establish a spatial statistical method to quantify the residual stress field around a cold expanded hole. A framework called the Spatial Analysis of Residual Stress (SpARS) was developed utilizing spatial correlation, response surface modelling techniques and statistical resampling methods to characterize the residual stress field. Our results showed that tolerance bounds on residual stress can be quantified using this method. We also demonstrated the SpARS method using recently published round robin case studies. The newly developed model will be useful for aircraft structural fatigue crack growth analyses to incorporate residual stress fields for extending inspection intervals for fatigue and fracture critical structures.     

Design of High Speed AWGN Communication Channel Emulator

This paper presents a method for designing a high accuracy white gaussian noise generator suitable for communication channel emulation. The proposed solution is based on the combined use of the Box-Muller method and the central limit theorem. The resulting architecture provides a high accuracy AWGN with a low complexity architecture for a digital implementation in FPGA. The performance is studied by means of MATLAB simulations and various complexity figures are given.     

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

Wireless Personal Communications - In this paper, we propose a new reputation approach, called I-WD (improved WatchDog). We attempt to eliminate selective dropping attack that ensue when malicious...     

Bulk geochemical characteristics and carbon isotope composition of oils from the Sayhut sub-basin in the Gulf of Aden with emphasis on organic matter input,age and maturity

The Sayhut sub-basin is undergoing hydrocarbon exploration province in the Gulf of Aden, South Yemen. In this study, geochemical analyses were performed on three oil samples from two exploration wells in the Sayhut sub-basin. The results were used to describe the source organic matter input, age and maturity and to correlate between crude oils from different pay zones.The high saturated hydrocarbon values of more than 70% indicate that the analysed oils are normal crude oils and not degraded oils. This is supported by a complete suite of their normal alkanes and acyclic isoprenoids. The hydrocarbon distributions of normal alkane and isoprenoid with bulk carbon isotope data also suggest that the analysed oils are grouped into two genetic families and were generated from marine-source rock. The family A presents by one oil sample representing Harshiyat reservoir rock and characterized by relatively high Ph/Ph ratio > 2 and δ¹³C values of their saturated and aromatic hydrocarbon fractions range from ?26.1‰ to ?24.8‰. This oil family was derived from mixed organic matter with high contribution of a terrigenous organic matter input. The family A was deposited under suboxic conditions during the Late Cretaceous age. The family B presents by two oil samples representing Ghaydah and Habshiyah reservoir rocks and characterized by relatively low Ph/Ph ratio < 2 and carbon isotope less than ?23 for their saturated and aromatic hydrocarbon fractions. These oils were generated from source rock containing high contributions of marine organic matter (e.g., algal and microbial) with minor amount of land plant source inputs that was deposited in more reducing conditions. The family B oils are consistent with those of the Paleogene Umm Er Radhuma source rock.The hydrocarbon distribution and oil composition data also indicate that the analysed oils were generated from mature source rocks with a peak oil-window maturity.     

Impact of interconnection photovoltaic/wind system with utility on their reliability using a fuzzy scheme

Reliability analysis has been considered as an important step in any system design process. A reliable electrical power system means a system which has sufficient power to feed the load demand during a certain period or, in other words, has small Loss of Load Probability (LOLP). LOLP is defined as an expected fraction of load not met by its power needs from electrical power system during its lifetime. Photovoltaic (PV)/Wind Energy System (WES) Hybrid Electric Power System (PV/WES HEPS) differs considerably from the Utility Grid (UG) in its performance and operating characteristics. With the interconnection of PV/WES as a HEPS into the UG, the fluctuating nature of the energy produced by these systems has a different effect on the overall system reliability than that of the fluctuating nature of energy produced by UG. Therefore, this paper presents a complete study, from reliability point of view, to determine the impact of interconnecting PV/WES HEPS into UG. Four different configurations of PV/WES/UG have been investigated and a comparative study between these four different configurations has been carried out. The overall system is divided into three subsystems, containing the UG, PV and WES. The generation capacity outage table has been built for each configuration of these subsystems. These capacity outage tables of UG, PV/UG, WES/UG and PV/WES/UG are calculated and updated to incorporate their fluctuating energy production. This paper also presents a fuzzy logic technique to calculate and assess the reliability index for each HEPS configuration under study.     

RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers

Wireless Personal Communications - The position of mobile devices is determined by Real Time Differential Global Positioning System (RTDGPS). This system is composed of fixed and mobile station....     

Feasibility and performance evaluation of a 6LoWPAN‐enabled platform for ubiquitous healthcare monitoring

Technology has revolutionized medical practices by enabling more convenient and non‐intrusive monitoring of patient's health, leading to next generation ubiquitous healthcare (u‐healthcare). The exploitation of the Internet protocol version 6 addressing space along with the miniaturization of electronic devices has fostered providing interoperability and connectivity of wearable sensor devices in wireless body area networks to the Internet of Things. In this paper, we propose to integrate the IPv6 over low power wireless personal area network (6LoWPAN) to the u‐healthcare monitoring system architecture. The main objective is to study the feasibility of the 6LoWPAN‐enabled platform in real‐world scenarios dealing with medical data. The performance evaluation of this platform is carried out initially through simulations using OMNet++ and then supported by an experimental study using sensor motes and a customized micro‐computing unit. Performance metrics such as throughput, end‐to‐end delay, packet error rate, and energy consumption are investigated under acute health conditions, where patient's health information has to be sent continuously and at maximum rate to the care provider. The obtained results show that the proposed 6LoWPAN solution fulfills the main quality of service requirements of u‐healthcare applications. Copyright © 2015 John Wiley & Sons, Ltd.     

Misalignment controller in wireless battery charger for electric vehicle based on MPPT method and metaheuristic algorithm

This paper focuses on applying a wireless power transfer WPT technology in an electric vehicle battery charger. The wireless charger system is based on the principle of resonance inductive coupling power through a coreless transformer. The WPT system is considered as a multi parameter and multi constrained nonlinear system. The main contribution in this paper is the use of PSO and GA metaheuristic algorithms in the optimization of a transformer design regarding the impact of a lateral misalignment and the separation distance between the primary and secondary coils. To find the best global solution which is considered as the maximum efficiency in the complex system, both algorithms are compared. A perturbation-and-observation-based tracking system is developed through an efficiency sensing system to act on the misalignment issue and the car position. An additional PSO controller is performed to control the duty cycle of the boost converter in order to follows the maximum efficiency operating points of a WPT system. The discrepancy is the use of the resonant inductive coupling as a source of the MPPT so as to perturb the car position and observe the transferred. Furthermore, the modeling of a contactless transformer is optimized using metaheuristic algorithm.     

Compact all-diode-pumped femtosecond laser source based on chirpedpulse optical parametric amplification in periodically poledKTiOPO4

1 kHz chirped pulse optical parametric amplification in periodically poled KTiOPO₄ achieving 20 μJ amplified signal pulse energy at 1.573 μm and 320 fs pulse duration after recompression is reported     

Hilbert Transform and Statistical Analysis for Channel Selection and Epileptic Seizure Prediction

This paper is concerned with Electroencephalography (EEG) seizure prediction, which means the detection of the pre-ictal state prior to ictal activity occurrence. The basic idea of the proposed approach for EEG seizure prediction is to work on the signals in the Hilbert domain. The operation in the Hilbert domain guarantees working on the low-pass spectra of EEG signal segments to avoid artifacts. Signal attributes in the Hilbert domain including amplitude, derivative, local mean, local variance, and median are analyzed statistically to perform the channel selection and seizure prediction tasks. Pre-defined prediction and false-alarm probabilities are set to select the channels, the attributes, and bins of probability density functions (PDFs) that can be useful for seizure prediction. Due to the multi-channel nature of this process, there is a need for a majority voting strategy to take a decision for each signal segment. Simulation results reveal an average prediction rate of 96.46%, an average false-alarm rate of 0.028077/h and an average prediction time of 60.1595 min for a 90-min prediction horizon.