Probabilistic methods are growing more important in the aerospace industry due to the ability to describe the behaviour of complex systems in the presence of input parameter variance. Sensitivity analysis based on met...
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ISBN:
(纸本)9780791886076
Probabilistic methods are growing more important in the aerospace industry due to the ability to describe the behaviour of complex systems in the presence of input parameter variance. Sensitivity analysis based on meta models can be utilized for this purpose. The reliability of the results is dependent on the surrogate model quality, which in turn depends on the available data. A priori the appropriate meta model type is not known. An approach to automatically select the best fitting model for a given data set is presented in this paper. For comparison, polynomial regression with least squares fitting, moving least squares, radial basis functions, and support vector regression are used as candidate types. The selection of the best meta model type is based on two quality criteria utilizing a cross-validation (CV) scheme. The developed approach is demonstrated on the sensitivity analysis of a cooled turbine blade.
Extra virgin olive oils (EVOO) are typically categorized in accordance with the nature of their fruitiness (green or ripe) and the intensity of their fruitiness (delicate, medium, or robust) in accordance with the pro...
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Extra virgin olive oils (EVOO) are typically categorized in accordance with the nature of their fruitiness (green or ripe) and the intensity of their fruitiness (delicate, medium, or robust) in accordance with the proposal of the competitor (producers) or a chemical analysis certificate established by an official panel. Depending on the category in which EVOO participates, its categorization may either disadvantage or benefit its rating. To eliminate this ambiguity and maximize an EVOO's probability of winning, the jury of the International "Word Edible Oils" competition use an innovative method to assess the EVOOs using a restricted amount of sensory descriptors (aromatic maturity, structure and fruitiness). Independent of category, the best EVOOs with comparable organoleptic properties have been identified using a statistical processing of the scores of the three descriptors. This is an iterative version of the technique developed by Wootton, Sergent, and Phan-Tan-Luu (iWSP), which generates subspaces that enable a local selection of the best EVOOs in a 2D aromatic maturity vs structure plan. In each subspace, their ranking is determined by their fruitiness score. An iterative approach that takes into consideration the changing sequence of subspace formation and the varying size of the subspace enables the selection of the best EVOOs. The development of the iWSP algorithm enabled the elimination of category constraints, (ii) the selection of the best EVOOs among those with comparable organoleptic features based on a large number of simulations, and (iii) the creation of a ranking to aid the jury's final judgment.
Service Function Chaining (SFC) is used to steer the traffic to a specific set of Network Functions (NFs) (such as load balancer, proxy, firewall, etc.) based on the type of traffic and operator policy. Handling the m...
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ISBN:
(纸本)9781538693766
Service Function Chaining (SFC) is used to steer the traffic to a specific set of Network Functions (NFs) (such as load balancer, proxy, firewall, etc.) based on the type of traffic and operator policy. Handling the massive amount of user traffic envisioned in the next generation networks using traditional techniques is costly and tedious. By leveraging advanced technologies such as Network Functions Virtualization (NFV) and Software Defined Networking (SDN), NFs can be deployed as software instances on Virtual Machines (VMs) (also called as Virtual Network Function (VNF)). Network operators widely place different types of VNFs at different locations to meet the user traffic demands. Multiple VNF instances on the same physical server compete for common resources such as network I/O bandwidth, CPU cycles, cache memory, and main memory which can lead to severe performance interference, which is ignored in existing NF selection mechanisms. However, increasing the SFC acceptance rate of SFC requests with an effective selection of required VNFs under the constraint of end-to-end latency is still an open problem. Since this problem is NP-Hard, we propose a heuristic algorithm based on dynamic programming which efficiently selects the required VNFs and steers the traffic by considering the interference effect. Results show that the proposed algorithm improves the average SFC acceptance rate by 29% as compared with existing methods.
In the last two decades, there has been a rapid growth in the use of the Internet and Communication MANET consists of number of nodes in which it communicates with each other. One of the main design issues in MANET is...
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ISBN:
(数字)9781728106663
ISBN:
(纸本)9781728106670
In the last two decades, there has been a rapid growth in the use of the Internet and Communication MANET consists of number of nodes in which it communicates with each other. One of the main design issues in MANET is that they are power constrained. To spare the vitality, the hubs can be assembled as groups. Cluster heads are having more duties, for example, to limit the re-connection, topology changes, and the strength of the on request organizes. Clustering is system to limit the multifaceted nature by solving the issue and to make smaller groups called clusters. To achieve this efficient cluster head should be elected. In this paper “A coherent Cluster head selection algorithm based on pinnacle weight for MANET” is proposed from ECAM with additional parameter.
Recently, the development of Unmanned Aerial Vehicle (UAV) has been nearly matured and widely used in various fields. The combination of UAV and communication technologies, such as UAV Base Station (UAV-BS), can signi...
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ISBN:
(数字)9781728138930
ISBN:
(纸本)9781728138947
Recently, the development of Unmanned Aerial Vehicle (UAV) has been nearly matured and widely used in various fields. The combination of UAV and communication technologies, such as UAV Base Station (UAV-BS), can significantly increase the flexibility and scalability of the overall communication networks to provide more efficient communication services. While the UAV-BS improves the network service efficiency, the quality of services (QoS) in the air-to-ground communication link is highly affected unless the right users are unknown. In this paper, we propose the learning-based downlink user selection algorithm. The 3D downlink channel can be fast identified to judiciously select the users subset. In our proposed framework, we combine the k-means clustering and Convolutional Neural Network (CNN) that can increase the estimation accuracy of 3D wireless channels to enhance the communication service efficiency of the UAV-BS network. The field measurement results show that proposed method can achieve an average bit error rate (BER) of 3.56x10 -7 , which is better than the distance-based selection scheme that has an average of BER 2.88x10 -3 . The feasibility and effectiveness of the proposed method in real environment are proved, experimentally.
Finding the k smallest/largest element of a large array, i.e., k-selection is a fundamental supporting algorithm in data analysis. Due to the fact that big data born in geo-distributed environments, it especially requ...
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Finding the k smallest/largest element of a large array, i.e., k-selection is a fundamental supporting algorithm in data analysis. Due to the fact that big data born in geo-distributed environments, it especially requires communication-efficient distributed k-selection, besides typical computation and memory efficiency. Moreover, sensitive organizations make data privacy a rigorous precondition for their participation in such distributed statistical analysis for common profit. To this end, we propose a Distributed Privacy-Aware Median (DPAM) selection algorithm for median selection in distributed large-scale data while preserving local statistics privacy, and extend it to arbitrary k-selection. DPAM utilizes mean to approximate median, via contraction of the standard deviation. It is the theoretical fastest with a worst computation complexity of O(N), and also highly efficient in communication overhead (in logarithm of data range). To preserve is an element of-differential privacy of local statistics, DPAM randomly adds dummy elements (the number follows a rounded Laplacian distribution) to local data. The noise does not degrade the estimation precision or convergence rate. Performance of DPAM is compared with centralized/distributed quick select and optimization, in terms of complexity and privacy preserving ability. Extensive simulation and experiment results show the higher efficiency of DPAM.
An optimal transmission channel selection algorithm based on AHP (Analytical Hierarchy Process) aiming at fulfilling the demand of emergency communication scenario is proposed in this paper. In the process of the algo...
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ISBN:
(数字)9781728158570
ISBN:
(纸本)9781728158587
An optimal transmission channel selection algorithm based on AHP (Analytical Hierarchy Process) aiming at fulfilling the demand of emergency communication scenario is proposed in this paper. In the process of the algorithm, a Specifications Vector Library is set up based on communication requirement presetting, meanwhile, a Weight Vector Library is set up according to the definition of the importance of the specifications. By matching the available network channel resources and the two Vector Libraries, a communication link scheme is produced with an AHP based decision making process to strike a balance between transmission efficiency and economy under the restraint of the available communication network channel resource condition.
A new service can be constructed(1) by composing web services in service-oriented architecture (SOA). Although many selection algorithms have been proposed, a universal one cannot be determined because each algorithm ...
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ISBN:
(纸本)9781450365123
A new service can be constructed(1) by composing web services in service-oriented architecture (SOA). Although many selection algorithms have been proposed, a universal one cannot be determined because each algorithm has different characteristics and each situation is unique. Consequently, choosing an algorithm according to the user's requirements leads to selecting the optimum web service. This research develops recommendations of selection algorithms by a quantitative score comparison. We define the quality of algorithm (QoA), which expresses the characteristics of the algorithm, and propose a method to recommend algorithms that satisfy the user's requirements. Our method can compare and select algorithms quantitatively by merely inputting the desired characteristics into the system, reducing the costs of selecting services and improving the user's satisfaction.
The classification of microarray data has positive significance for the judgment of cancer and the determination of clinical programs. However, the high dimensionality and small sample characteristics of the microarra...
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ISBN:
(纸本)9781728137216
The classification of microarray data has positive significance for the judgment of cancer and the determination of clinical programs. However, the high dimensionality and small sample characteristics of the microarray data has brought classification a difficult problem. Aiming at the feature selection problem in microarray data classification, a feature selection algorithm based on artificial bee colony algorithm and genetic algorithm is proposed to solve the dimensionality disaster problem in microarray data classification. Finally, the feature subsets obtained by the algorithm are combined with the SVM (Support Vector Machine) classifier to apply to the six published microarray datasets. The experimental results show that the feature subsets obtained based on the proposed scheme can significantly improve the classification performance.
Based on the principle of the quasi-optimal satellite selection algorithm and the fast satellite selection algorithm, this paper gives a combined satellite selection algorithm. The algorithm introduces a new satellite...
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ISBN:
(纸本)9781728105529
Based on the principle of the quasi-optimal satellite selection algorithm and the fast satellite selection algorithm, this paper gives a combined satellite selection algorithm. The algorithm introduces a new satellite selection strategy by considering different distributions of the similar satellite group. The computation amount of the combined algorithm is lower than quasi-optimal satellite selection algorithm. The experiment results show this algorithm can get a lower GDOP value than quasi-optimal satellite selection algorithm and close to the minimal GDOP.
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