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21.
Maureen Hany Mahmoud A. Mahmoud 《Quality and Reliability Engineering International》2016,32(5):1825-1835
We evaluate the performance of the Crosier's cumulative sum (C‐CUSUM) control chart when the probability distribution parameters of the underlying quality characteristic are estimated from Phase I data. Because the average run length (ARL) under estimated parameters is a random variable, we study the estimation effect on the chart performance in terms of the expected value of the average run length (AARL) and the standard deviation of the average run length (SDARL). Previous evaluations of this control chart were conducted while assuming known process parameters. Using the Markov chain and simulation approaches, we evaluate the in‐control performance of the chart and provide some quantiles for its in‐control ARL distribution under estimated parameters. We also compare the performance of the C‐CUSUM chart to that of the ordinary CUSUM (O‐CUSUM) chart when the process parameters are unknown. Our results show that large number of Phase I samples are required to achieve a quite reasonable performance. Additionally, the performance of the C‐CUSUM chart is found to be superior to that of the O‐CUSUM chart. Finally, we recommend the use of a recently proposed bootstrap procedure in designing the C‐CUSUM chart to guarantee, at a certain probability, that the in‐control ARL will be of at least the desired value using the available amount of Phase I data. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
22.
Mahmoud Ragab Mohammed W. Al-Rabia Sami Saeed Binyamin Ahmed A. Aldarmahi 《计算机、材料和连续体(英文)》2023,74(2):2889-2904
With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches. 相似文献
23.
24.
In wireless communication reception, the reliability of orthogonal frequency division multiplexing (OFDM) is limited because of the time-varying nature of the channel. This causes inter-carrier interference (ICI) and increases inaccuracies in channel tracking. This can effectively be avoided at the cost of power loss and bandwidth expansion by inserting a cyclic prefix guard interval before each block of parallel data symbols. However, this guard interval decreases the spectral efficiency of the OFDM system as the corresponding amount. Recently, it was found that based on Haar-orthonormal wavelets, discrete wavelet-based OFDM (DWT-OFDM) is capable of reducing the inter symbol interference (ISI) and ICI, which are caused by the loss in orthogonality between the carriers. DWT-OFDM can also support much higher spectrum efficiency than discrete Fourier-based OFDM (DFT-OFDM). In this paper the DFT-OFDM is replaced by Multiwavelets OFDM (DMWT-OFDM) in order to further reduce the level of interference and increase spectral efficiency. It is found that proposed Multiwavelet design achieves much lower bit error rates, increases signal to noise power ratio (SNR), and can be used as an alternative to the conventional OFDM. The proposed OFDM system was modeled tested, and its performance was found under different channel conditions. 相似文献
25.
In this article, two cascaded defected microstrip structures (CDMSs) are proposed to increase the effective inductance. So the cutoff frequencies of filters made by CDMSs can be controlled. The proposed CDMSs have wider stopband characteristic and sharper transition from passband to stopband than defected microstrip structure (DMS). To show the behavior variations of the effective inductance, the DMS and CDMSs have been analyzed, compared, and their design parameters are formulated. These parameters are necessary for a fast filter design procedure. Also, the models of DMS and CDMSs using the very simple lumped element forms are presented according to the physical parameters. The results of the proposed models have been compared with those obtained by full wave analysis. To verify the proposed DMS model, the comparison in response not only has been considered in amplitude but also in phase of all S‐parameters of proposed model and full wave analysis. It is obvious that the stopband is enhanced by periodically cascading the DMS. Surviving of EMC aspects between DMS, DGS, and CDMS is considered and compared. Finally, the theoretical results are validated by comparison with simulations of the structures and experimental results. The simulations from modeling results are found to be in good agreement with the full wave analysis and measurement results in the range of 0.5–35 GHz. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010. 相似文献
26.
While several service discovery protocols and standards have been proposed for supporting service discovery from mobile devices,
this remains a challenging problem. In many cases, mobile clients may discover services which they consider relevant but soon
realize that such services are not completely usable on their mobile devices due to compatibility and interoperability issues.
Without integrating device capabilities into the discovery process, or a device-aware mobile service discovery, it becomes
extremely difficult to determine whether discovered services may or may not function properly within the device’s constraints.
This paper introduces a solution known as MobiEureka, a mobile device-aware system for enhancing the discovery of mobile web
services from mobile devices. Experimental validation, results, and analysis of the introduced ideas are discussed. 相似文献
27.
Packages are important high-level organizational units for large object-oriented systems. Package-level metrics characterize the attributes of packages such as size, complexity, and coupling. There is a need for empirical evidence to support the collection of these metrics and using them as early indicators of some important external software quality attributes. In this paper, three suites of package-level metrics (Martin, MOOD and CK) are evaluated and compared empirically in predicting the number of pre-release faults and the number of post-release faults in packages. Eclipse, one of the largest open source systems, is used as a case study. The results indicate that the prediction models that are based on Martin suite are more accurate than those that are based on MOOD and CK suites across releases of Eclipse. 相似文献
28.
In this paper we present a technique for the automatic recognition of Arabic (Indian) bank check digits based on features
extracted by using the Log Gabor filters. The digits are classified by using the K-Nearest Neighbor (K-NN), Hidden Markov
Models (HMM) and Support Vector Machines (SVM) classifiers. An extensive experimentation is conducted on the CENPARMI data,
a database consisting of 7390 samples of Arabic (Indian) digits for training and 3035 samples for testing extracted from real
bank checks. The data is normalized to a height of 64 pixels, maintaining the aspect ratio. Log Gabor filters with several
scales and orientations are used. In addition, the filtered images are segmented into different region sizes for feature extraction.
Recognition rates of 98.95%, 98.75%, 98.62%, 97.21% and 94.43% are achieved with SVM, 1-NN, 3-NN, HMM and NM classifiers,
respectively. These results significantly outperform published work using the same database. The misclassified digits are
evaluated subjectively and results indicate that human subjects misclassified 1/3 of these digits. The experimental results,
including the subjective evaluation of misclassified digits, indicate the effectiveness of the selected Log Gabor filters
parameters, the implemented image segmentation technique, and extracted features for practical recognition of Arabic (Indian)
digits. 相似文献
29.
Mostafa Sabzekar Hadi Sadoghi Yazdi Mahmoud Naghibzadeh 《Neural computing & applications》2011,20(5):671-685
Real-world data collected for computer-based applications are frequently impure. Differentiation of outliers and noisy data
from normal ones is a major task in data mining applications. On the other hand, elimination of noisy and outlier data from
training samples of a dataset may lead to over-fitting or information loss. A fuzzy support vector machine (FSVM) provides
an effective means to deal with this problem. It reduces the effect of the noisy data and outliers by using a fuzzy membership
functions. In this paper, a new formation for SVMs is introduced that considers importance degrees for training samples. The
constraints of the SVM are converted to fuzzy inequalities. The proposed method, RSVM, shows better efficiency in the classification
of data in different domains. Especially, using the proposed RSVM for multi-class classification of arrhythmia disease is
presented at the end of this paper as a practical case study to show the effectiveness of the proposed system. 相似文献
30.
Tarek Sheltami Anas Al-Roubaiey Elhadi Shakshuki Ashraf Mahmoud 《Multimedia Systems》2009,15(5):273-282
Mobile Ad-hoc NETworks (MANET) are infrastructureless networks where self-configuring mobile nodes are connected by wireless
links. Because of its decentralized operation, these nodes rely on each other to store and forward packets. Video transmission
over MANETs is more challenging than over conventional wireless networks due to rapid topology changes and lack of central
administration. Most of the proposed MANET protocols assume that all nodes are working within a cooperative and friendly network
context. However, misbehaving nodes that exhibit abnormal behaviors can disrupt the network operation and affect the network
availability by refusing to cooperate to route packets due to their selfish or malicious behavior. In this paper, we examine
the effect of packet dropping attacks on video transmission over MANETs. We also study the effects of mitigation using intrusion
detection systems to MANET in presence of video traffic. To the best of our knowledge, this is the first attempt to study
multimedia over such environments. We propose a novel intrusion detection system, which is an adaptive acknowledgment scheme
(AACK) with the ability to detect misbehaved nodes and avoid them in other transmissions. The aim of AACK scheme is to overcome
watchdog weaknesses due to collisions and limited transmission power and also to improve TWOACK scheme. To demonstrate the
performance of our proposed scheme, simulation experiments are performed. The results of our experiments show that MPEG4 is
more suitable for our simulation environment than H264 video traffic. The simulation results show that AACK scheme provides
better network performance with less overhead than other schemes; it also shows that AACK outperforms both TWOACK and watchdog
in video transmission applications in the presence of misbehaving nodes. 相似文献