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A novel adaptive noise reduction method for field natural gas pipeline defect detection signals

INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING 2022年 199卷

作者： Wu, Linyu Liang, Wei Sha, Duolin China Univ Petr Coll Safety & Ocean Engn Key Lab Oil & Gas Safety & Emergency Technol Minist Emergency Management Beijing 102249 Peoples R China

To suppress noise interference and improve defect identification accuracy, we propose a transfer learning-based adaptive wavelet neural network (AWNN) method for pipeline defect detection signal denoising. Simulated noisy signals and field detection signals are used to determine the network's initial structure and fine-tune its pa-rameters. The Meyer wavelet basis function is chosen as an excitation function based on the defect signal waveform. The pretrained structure is determined by a search algorithm. The network parameters are adaptively fine-tuned according to the sample entropy of the noise. The new genetic beetle antennae search strategy (GBA) optimization algorithm is proposed to improve the AWNN to avoid local optima. Compared with traditional noise reduction methods, the AWNN has excellent ability to recovery the pure denoised signal with a higher SNR and smaller RMSE. The AWNN can realize adaptive noise reduction of different field signals, providing reliable input for subsequent defect diagnosis.

关键词： Pipeline defects Noise reduction Wavelet neural network Transfer learning optimization algorithm

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An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images

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APPLIED SOFT COMPUTING 2022年 114卷

作者： Dey, Subhrajit Roychoudhury, Rajarshi Malakar, Samir Sarkar, Ram Jadavpur Univ Dept Elect Engn Kolkata India Jadavpur Univ Dept Comp Sci & Engn Kolkata India Asutosh Coll Dept Comp Sci Kolkata India

Early detection of Tuberculosis or TB can help in mitigating the chances of affecting the other body parts like the kidney, spine and brain, thereby reducing the death rate due to this disease. However, manual diagnosis by radiologists using Chest X-rays may include human error. Therefore, researchers have been trying hard to develop a computerized decision support system for efficient detection of TB from Chest X-ray images. In this work, we have proposed a model for screening TB using Chest X-ray images where the decisions from three base learners are combined using the type-1 Sugeno fuzzy integral based ensemble technique. Fuzzy measures required in this fuzzy integral based ensemble method are set experimentally in many state-of-the-art works. To overcome such manual tuning, we have used meta-heuristic optimization algorithms to set the fuzzy measures optimally during the training process of the model. The performance of the ensemble technique on the validation set is considered as the decider of the optimal fuzzy measures. Before applying the ensemble method we have extracted features from images using three state-of-the-art deep learning models, namely DenseNet121, VGG19 and ResNet50 pre-trained on imageNet dataset. With the above pre-trained models, the base learners are built using additional fully connected and softmax layers. We have evaluated the present work on a new and publicly available TB dataset consisting of Chest X-ray images. The obtained results (irrespective of the optimizer used) confirm that our method has outperformed state-of-the-art methods used for TB classification. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Tuberculosis Chest X-ray Deep learning Sugeno fuzzy ensemble optimization algorithm

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A Novel Framework for Multi-Objective optimization of Airfoils Using Invasive Weed optimization

A Novel Framework for Multi-Objective Optimization of Airfoi...

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AIAA Aviation Forum

作者： Pant, Pranshu Joseph, Joe Omkar, S. N. Indian Inst Sci Dept Aerosp Engn Bengaluru 560012 India

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Traditionally, airfoil design has been broadly limited to experimental and empirical methods. Over the years, computing power has grown exponentially and computational methods are becoming increasingly relevant. Still, generic airfoils continue to be used in most applications. Such airfoils yield sub-optimal performance and result in compromises in the overall design of the aircraft. With the advent of modern high-performance computing systems and metaheuristic optimization algorithms, optimizing airfoils for their specific use cases has become highly feasible. This paper presents a novel optimization framework for airfoils using the Invasive Weed Optimisation algorithm. The presented framework can be implemented using single or multiple objectives with the multi-objective functionality being realized through integration with NSGA-II. Additionally, this framework has the unique ability to operate in two fidelity modes in order to cater to a range of computational capabilities. The low fidelity mode is coupled with XFOIL while the high fidelity mode utilizes a RANS CFD solver on OpenFOAM. To depict the prowess of this framework, two test cases have been shown. The resulting optimized airfoils perform exceedingly well in their use case as compared to conventional airfoils, thereby validating the efficacy of this framework.

关键词： Airfoil Databases Computational Fluid Dynamics XFoil Reynolds Averaged Navier Stokes High Performance Computing Systems optimization algorithm Computational Methods Airfoil Geometry Multi Objective Evolutionary algorithms Genetic algorithm

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System-of-Systems Operational Analysis and optimization A Case-Study of Quadcopters fleet in Real-Time Delivery Mission

System-of-Systems Operational Analysis and Optimization A Ca...

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AIAA Aviation Forum

作者： Ross, Chana Rokitat, Tomer Rafael Adv Def Syst Ltd IL-3102102 Haifa Israel

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

This paper describes an operational analysis and optimization of a quad-copters fleet, designed for real-time package delivery. The fleet and its mission serve as a benchmark problem, nicknamed Rafazon, of a complex system-of-systems (SoS). First, a computational model was established using agent-based discrete-event-simulation (AB-DES). Next, the simulation was integrated into a multi-objective-optimization framework, in order to efficiently find the optimal fleet composition, which maximizes system efficiency, while minimizing overall cost. Finally, the optimization results are presented as a set of pareto optimal system designs, which reveals the SoS design trade-offs.

关键词： Discrete Event Simulation optimization algorithm Markov Decision Process Evolutionary algorithm Matching algorithm Unmanned Aerial Systems Systems Engineering Genetic algorithm Total Energy

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An Enhanced Pathfinder algorithm based MCSA for rotor breakage detection of induction motor

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年 64卷

作者： Halder, Sudip Dora, Bimal Kumar Bhat, Sunil Visvesvaraya Natl Inst Technol Nagpur India

This paper proposes a new broken rotor bar (BRB) finding technique based on a novel hybrid algorithm, namely the Enhanced Pathfinder algorithm (EPFA). Compared to existing optimization algorithms, this proposed approach provides more balanced exploration and exploitation. The main task regarding rotor fault detection is to identify sideband frequency components (SFCs) corresponding to the fault and in this context proposed method overcomes the disadvantages of traditional frequency domain analysis, such as short data length, effects of spectral leakage, and insufficient sampling frequency. To achieve the proposed diagnosis approach, a signal model for faulty current and a function of residual error are used for the formulation of a nonlinear least-square problem. The method is first tested on a synthetic fault signal, and finally, to validate the proposed technique, a Finite Element Model (FEM) of the induction motor is simulated in the ANSYS 2D workbench.

关键词： Broken rotor bar (BRB) fault optimization algorithm MCSA Squirrel cage induction motor (SCIM)

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Scheduling Home Appliances with Integration of Hybrid Energy Sources using Intelligent algorithms

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AIN SHAMS ENGINEERING JOURNAL 2022年第4期13卷

作者： Tantawy, El Sayed F. Amer, Ghada M. Fayez, Hanaa M. Benha Univ Fac Eng Dept Elect Engn Banha Egypt

This paper presents Home Energy Management System(HEMS) for smart homes. This system includes Photovoltaic Sources and Battery Storage Units(BSU)and scheduling Smart Home(SH) appliances with various kinds and types of residential users. In addition, a classification algorithm of energy response programs, which relies on the electricity prices, is introduced for scheduling appliances of single and multiple homes using combined the Time of Use(TOU) and Inclining Block Rate(IBR) to control the demand under the grid station capacity and energy efficacious integration of hybrid power sources in Smart Grid(SG). Four different optimization algorithms used and compared for scheduling appliances. The simulation results show that the proposed algorithm achieves the desired objectives, in which the electricity cost and the Peak Average Ratio(PAR) were lowered, waiting time to the user is minimized, reducing the electricity bill per month/year. In addition, the proposed system increases the stability and reliability of the grid and consumers satisfaction. (C) 2022 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University

关键词： Home Appliances Scheduling Hybrid energy system Renewable Energy Sources Demand Side Management optimization algorithm Home Energy Management System Smart Home

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Joint decision on service level of chain electricity retailers

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 173卷

作者： Yao, Jingyuan Shu, Lingli Jian, Xianzhong Xiao, Erliang Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Sichuan Univ Sch Business Chengdu 610000 Peoples R China

Nowadays, price competition in the electricity retailing industry is increasingly fierce. In the future, providing characteristic and suitable services to attract customers is an effective way. After gaining a certain market recognition by providing services, most decision makers will choose to establish subsidiary retailers to implement the chain operation. With the increase of subsidiary retailers, how to make joint decision on service level for all subsidiary retailers has become an issue faced by decision makers. To this end, this paper develops a model and designs a combined algorithm. Taking the Chinese electricity retailing as an example, we carry out parameter hypothesis and experimental simulation. Experiments verify that the proposed combined algorithm can effectively get the feasible and satisfactory price, service level and overall profit. The results can provide some theoretical guidance for decision-makers. Secondly, we find service levels significantly affected the overall profits of chain electricity retailer. The subsidiary retailers that provide the services can improve their own profits, and further can improve the overall profit. Through sensitivity analysis, we obtain some methods that can help decision makers adjust pricing and service levels in time to adapt to changes of uncertain parameters.

关键词： Electricity retailing Service level Chain operation optimization algorithm

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Optimal Operation Rules for Parallel Reservoir Systems with Distributed Water Demands

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2022年第6期148卷

作者： Meng, Weisa Wan, Wenhua Zhao, Jianshi Wang, Zhongjing Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Dongguan Univ Technol Sch Environm & Civil Engn Dongguan 523808 Guangdong Peoples R China

This paper addresses the doubts regarding the spatial characteristics of the commonly used rules for parallel reservoir system operation. The rules based on aggregation-decomposition determine the system total release first and then assign this release to individual reservoirs, without considering the water demand distribution in the river network. In this paper, a conceptual model for parallel reservoir systems with distributed water demands is proposed. Three specific optimality conditions are derived for determining the optimal analytical solution. A rigorous proof shows that the aggregation-decomposition-based rules are a special case of the derived rules. An efficient algorithm is then developed based on the optimality conditions and shortage allocation index (SAI), in which a larger SAI indicates taking a higher percentage of the system water shortage, as release or storage. Unlike traditional algorithms that modify the violated variables empirically, we propose a criterion in terms of relative deviation indicators to determine the crucial priority of variable modification. This criterion can effectively address constraint violations. The optimal rules along with the solution algorithm are then demonstrated by the operation of a parallel reservoir system in the Shiyang River Basin, China. The results show that the proposed rules and algorithm are more efficient and effective than traditional algorithms and aggregation-decomposition-based rules, especially in dry seasons with more binding constraints.

关键词： Hedging rule Marginal benefit Shortage allocation index (SAI) Variable modification optimization algorithm

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Least Squares Approximation-based Polynomial Chaos Expansion for Uncertainty Quantification and Robust optimization in Aeronautics

Least Squares Approximation-based Polynomial Chaos Expansion...

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AIAA Aviation Forum

作者： Mura, Riccardo Ghisu, Tiziano Shahpar, Shahrokh Univ Cagliari Ind Engn I-09123 Cagliari Italy Univ Cagliari Dept Mech Chem & Mat Engn I-09123 Cagliari Italy Rolls Royce PLC Aerothermal Design Syst Derby DE24 8BJ England

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

For many engineering problems, reliability and robustness are far more important than the nominal performance when it comes to the choice of a design over another. Availability of state-of-the-art simulation codes and trusted modeling approaches help in reducing the epistemic uncertainties, but what is needed for decision-making is ultimately the knowledge of the aleatory uncertainty for the system of interest, and methods capable of minimizing such uncertainty while limiting the impact on the expected performance. A reliable measure of performance is only achieved if all uncertainties affecting the system's geometry, physical properties and operational conditions can be propagated through the model equations to the quantities of interest. optimization in a deterministic sense, providing the best possible performance for nominal values of the design variables, is then replaced by Robust optimization, which aims at maximizing the expected performance while minimizing the probability of its degradation. Today, multi-physics problems and demanding high-fidelity simulations are common denominators in Aeronautics as well as in many other industrial fields. It is then necessary to employ techniques that are able to perform uncertainty quantification and propagation at reasonable costs, without requiring the development of dedicated solvers: Non-Intrusive Polynomial Chaos is a prime candidate, but not all of its variants are equally accurate and cost-effective. Efficiency and flexibility of Least Squares surrogates and gradient-enhanced Polynomial Chaos expansions for Uncertainty Quantification and Robust optimization are discussed in this paper employing an in-house framework. Advantages of the proposed methods over Collocation approaches are demonstrated by comparing the results for a selection of algebraic test cases and for the shape optimization of the NACA 0012 airfoil under transonic conditions.

关键词： Aeronautics NACA 0012 Optimal Control Theory Numerical Integration Computing Aerodynamic Shape optimization Probability Density Functions Robust Design Sensitivity Analysis optimization algorithm

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optimization of an air turbostarter of a launch system for gas turbine engine

Optimization of an air turbostarter of a launch system for g...

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AIAA Propulsion and Energy Forum

作者： Zubanov, Vasilii M. Popov, Grigorii M. Volkov, Andrei A. Egorov, Igor Marchukov, Evgenii Samara Natl Res Univ Res & Educ Ctr Gas Dynam Studies Samara 443086 Russia Samara Natl Res Univ Dept Aircraft Engine Theory Samara 443086 Russia Moscow Aviat Inst Moscow 125993 Russia

ISBN: (数字)9781624106026

ISBN: (纸本)9781624106026

The paper describes the process of optimizing the blades of a starter air turbine for a gas turbine engine launch system. This task was initiated by the necessity to use an existing turbine when starting a new engine. During the study, it was found that the turbine, although it ensures the conditions for the joint operation with the auxiliary power unit turbine and meet the strength constraints, it does not allow to start the engine within the required time. As a result of studies using mathematical optimization methods involving commercial programs, a variant was found to modernize the baseline turbine, which provides an acceptable value of torque on the shaft with an adequate start-up time at all operating modes with minimal design changes.

关键词： Launch System Gas Turbine Engines Auxiliary Power Unit Mathematical optimization Mass Flow Rate optimization algorithm Air Flowing Atmospheric Conditions Air Mass Flow Rates Numerical Modeling

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