Recently, the graph-accelerated non-intrusive polynomial chaos (NIPC) methods have been introduced to effectively address a variety of uncertainty quantification (UQ) challenges in multi-disciplinary and multi-point m...
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ISBN:
(数字)9781624107115
ISBN:
(纸本)9781624107115
Recently, the graph-accelerated non-intrusive polynomial chaos (NIPC) methods have been introduced to effectively address a variety of uncertainty quantification (UQ) challenges in multi-disciplinary and multi-point models. The essence of these graph-accelerated NIPC methods lies in leveraging tensor-grid input points for sampling the random space and using a computational graph transformation method, AMTC, to efficiently perform the tensor-grid evaluations by taking advantage of the inherent sparsity within the computational graph of the computational model. Despite their advancements, the effectiveness of these methods in tackling optimization under uncertainty (OUU) problems remains unexplored. This paper presents a detailed case study for a laser-beam-powered aircraft design problem. The focus is on applying the graph-accelerated NIPC methods to solve a large-scale multidisciplinary design optimization problem under uncertainty. This study not only compares the results of multidisciplinary optimization (MDO) and OUU but also highlights the effectiveness of the graph-accelerated NIPC method in tackling OUU challenges. The numerical results show that the OUU-optimized design is more robust under the variations of the flight conditions. Additionally, the AMTC method accelerates the optimization time by a factor of five, making the computational cost of the OUU problem only twice that of the MDO problem.
Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the ...
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Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or *** detection and intervention are vital for better ***,the diagnosis of schizophrenia still depends on clinical observation to *** reliable biomarkers,schizophrenia is difficult to detect in its early phase and hence we have proposed this *** this work,the EEG signal series are divided into non-linear feature mining,classification and validation,and t-test integrated feature selection *** this work,19-channel EEG signals are utilized from schizophrenia class and normal ***,the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s *** this process,1142 features of normal and schizophrenia class patterns can be *** other hand,157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential *** last,the Deep Learning(DL)technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network(DCNN)with mayfly optimization *** proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy,Signal to Noise Ratio(SNR),Mean Square Error,Normalized Mean Square Error(NMSE)and *** comparison,the proposed technique is proved to a better technique than other existing techniques.
The full 3-dimensional (3D) aerodynamic optimization of axial flow compressors is a typical high-dimensional and expensive optimization problem, which has multiple variables and long evaluation time. To address this c...
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The full 3-dimensional (3D) aerodynamic optimization of axial flow compressors is a typical high-dimensional and expensive optimization problem, which has multiple variables and long evaluation time. To address this challenge, this study first establishes a novel aerodynamic optimization platform for multistage axial compressors, which reduces the time of single sample evaluation by a dimensionality reduction method which adoptsquasi-3-dimensional aerodynamic calculation instead of full 3D numerical simulation. The optimization platform also adopts radial basis function (RBF) parameterization technique. It can control the deformation of 3D geometry by fewer control points, and thus the control variables of single blade and the dimension of the optimization problem are reduced. Then the aerodynamic performance of a 3.5-stage highly loaded compressor is optimized based on the optimization platform. The results show that after optimization, within the basically unchanged total pressure ratio and surge margin, the efficiency of design point is increased by a maximum of 2.2% and the optimized characteristics curves are in good agreementwith the 3Dcalculation results. Compared to the full 3D aerodynamic optimization, the optimization platform in this article can save more than 10 times the time. Thus, the effectiveness of the optimization platform in compressor aerodynamic optimization is verified.
Currently, grid forming inverters are used to support frequency and voltage in distribution networks. Hence, grid forming inverter is very important for active and reactive power optimization control. This paper first...
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ISBN:
(纸本)9798350365573;9798350365580
Currently, grid forming inverters are used to support frequency and voltage in distribution networks. Hence, grid forming inverter is very important for active and reactive power optimization control. This paper first introduces the virtual synchronous generator control method. The Successive Quadratic Programming (SQP) algorithm and particle swarm optimization (PSO) algorithm are respectively used to optimize the active and reactive power control parameters. The simulation results validate that the optimization algorithm can improve the frequency and voltage dynamic performance of the system.
This paper presents a novel method for approximate eigenanalysis of large linear systems, with a specific focus on unstable eigenvalues in the Lyapunov sense. The method utilizes residual vector analysis, principal co...
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ISBN:
(数字)9781624107115
ISBN:
(纸本)9781624107115
This paper presents a novel method for approximate eigenanalysis of large linear systems, with a specific focus on unstable eigenvalues in the Lyapunov sense. The method utilizes residual vector analysis, principal component analysis, and dynamic mode decomposition to identify unstable solution modes efficiently. Outlier detection models are employed to find the problematic cells on the unstructured mesh in the case of residual vector and principal component analysis while the dynamic mode decomposition points directly to the control volumes of interest without such help. By extracting and assembling corresponding rows of the Jacobian matrix, the large system is projected on a new matrix with significantly fewer degrees of freedom, leading to improved computational efficiency. The eigenvalue problem is then solved on this smaller matrix, and the obtained results are utilized for mesh optimization, enhancing the local stability of the dynamic system. By addressing the challenges of computational complexity and automation faced by previous methods, this novel approach offers a comprehensive and automated solution for the eigenanalysis of large linear systems. The potential impact of this method extends to various fields, providing a more efficient eigenanalysis process and opening new avenues for exploring and optimizing complex systems.
Future plans for deep-space exploration campaigns, targeting locations such as the Moon and Mars, feature a multitude of interdependent missions. For example, a crewed mission may require robotic pre-cursor scouting m...
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ISBN:
(数字)9781624107115
ISBN:
(纸本)9781624107115
Future plans for deep-space exploration campaigns, targeting locations such as the Moon and Mars, feature a multitude of interdependent missions. For example, a crewed mission may require robotic pre-cursor scouting missions, or supporting cargo delivery missions. As campaign complexity and mission inter-dependency increases, the potential knock-on effects and costs of launch delays also increase. Understanding the potential impacts of delays is an important part of architecture trade-offs in the early definition phase of a project, but quantifying those impacts for a large number of potential architecture solutions is difficult. This work aims to solve this issue by producing a method to both quantify the impact of launch delay and compare possible campaign launch schedules, in order to find robust and near-optimal solutions, by measuring the expected value of the optimization objective and expected probability of infeasibility due to the launch uncertainty. The method is applied specifically to potential lunar exploration architectures in a case study.
The growth of Wireless Sensor Networks(WSNs)has revolutionized thefield of technology and it is used in different application *** edges and other critical locations can be monitored using the navigation sensor *** WSN...
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The growth of Wireless Sensor Networks(WSNs)has revolutionized thefield of technology and it is used in different application *** edges and other critical locations can be monitored using the navigation sensor *** WSN required low energy consumption to provide a high network and guarantee the ultimate *** main objective of this work is to propose hybrid energy optimization in local aware *** hybrid proposed work consists of clustering,optimization,direct and indirect communication and *** aim of this research work is to provide and framework for reduced energy and trusted communication with the shortest path to reach source to destination in WSN and an extending lifetime of wireless *** proposed Artificial Fish Swarm optimization algorithm is used for energy optimization in military applications which is simulated using Network Simulator(NS)*** work optimizes the energy level and the same is compared with various genetic algorithms(GA)and also the cluster selection process was compared with thefission-fusion(FF)selection *** results of the proposed work show,improvement in energy optimization,throughput and time delay.
Coarse-graining techniques of large-scale complex networks have been an important approach to reduce network size, which merge nodes who share the same or similar properties while preserving some significant functions...
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Coarse-graining techniques of large-scale complex networks have been an important approach to reduce network size, which merge nodes who share the same or similar properties while preserving some significant functions or properties of the original networks. However, reducing network size is often at the cost of worsening network performance. Thus, there is a trade-off between the coarse-grained network sizes and network performance. To find the balance between the two sides and based on the spectral coarse-graining approach (SCG), we propose two optimization algorithms, which are called variable step size optimization algorithm (VSSOA) and variable scale optimization algorithm (VSOA). The two algorithms can calculate the optimal coarse-grained step size and the optimal scale to reduce the share size of the network. The two algorithms are applied to the coarse-graining of several typical networks. And the feasibility and validity of the proposed algorithms are further verified by phase synchronization of coupled Kuramoto oscillators on typical networks. The related investigation provides a deep insight to the coarse-graining of larg-escale complex networks. (C) 2018 Elsevier B.V. All rights reserved.
Multidisciplinary design optimization (MDO) models are built by assembling sub-models that represent varying disciplines and design conditions. Consequently, such models are often computationally expensive, requiring ...
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ISBN:
(数字)9781624107115
ISBN:
(纸本)9781624107115
Multidisciplinary design optimization (MDO) models are built by assembling sub-models that represent varying disciplines and design conditions. Consequently, such models are often computationally expensive, requiring their evaluation in a parallel computing environment to decrease solution time. MDO modeling frameworks, which are software tools to allow construction of MDO models, generally require manual user input to specify where to parallelize their code. However, this process can be tedious if the model is large or complex. In this paper, we propose a method heavily inspired by scheduling theory that aims to fully automate parallelization of MDO models. We use static task scheduling to partition mathematical operations in the model to different processors prior to model execution. Additionally, our approach supports adjoint-based derivative computation to enable the use of gradient-based optimization algorithms. Our results show a significant performance improvement when applying our method to several MDO applications. We believe our approach enables MDO modelers to solve computationally expensive problems in a more efficient manner.
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