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Modeling and optimization of nano-rod plasmonic sensor by adaptive neuro fuzzy inference system (ANFIS)

OPTICAL AND QUANTUM ELECTRONICS 2021年第2期53卷 88-88页

作者： Ganji, Jabbar Kaboli, Milad Tabatabaee, Seyed Sajjad Shabani, Pejman Islamic Azad Univ Dept Elect Engn Mahshahr Branch Mahshahr Iran

Predicting the behavior of photonic and Plasmon nanostructures has always been an attractive topic for photonic sciences researchers. One of the methods for estimating the behavior of devices in engineering sciences is adaptive neuro fuzzy inference system. Modeling and designing of photonic and plasmonic nanostructures and their optimization strongly relies on the timing of unstructured electromagnetic response simulations and optimal design requires a high number of simulations and if the optimal answer is reached, other simulation results will be wasted. Deep learning method which has been created by ANFIS networks provides a powerful and efficient device for creating accurate relationship between plasmonic geometric parameters and resonance spectrums. Millions of different nanostructures can be obtained without the need for any costly simulations and it costs only one investment to obtain training data. In this study, we use of deep learning based on ANFIS for predicting optic sensors' answer by using plasmonic nano rods. In addition to prediction, this method can approximate a special answer based on photonic structure. In this study, ANFIS networks, at first, model a 5*5 array of plasmonic nanostructures based on obtained experimental data from optic absorption. In the following, based on obtained model, it designs nanoparticles geometry so that we achieve a relatively narrow absorption spectrum with high sensitivity in relation to change air refractive index. This method can be applied for other similar types of nano-photonic systems which can help to destroy simulation procedure and acceleration photonic sensor design trend.

关键词： Plasmonic nano particles Neural network optimization algorithm

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Distributed Quadrotor UAV Tracking using a Team of Unmanned Ground Vehicles

Distributed Quadrotor UAV Tracking using a Team of Unmanned ...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Omotuyi, Oyindamola Pokhrel, Sameer Sharma, Rajnikant Univ Cincinnati Dept Mech & Mat Engn Cincinnati OH 45221 USA Univ Cincinnati Dept Aerosp Engn & Engn Mech Cincinnati OH USA Univ Cincinnati Aerosp Engn Cincinnati OH USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

In this paper, we present an algorithm to optimally track a non-cooperative quadrotor using a team of Unmanned Ground Vehicles (UGVs) which are equipped with GPS as well as radar capable of sensing range, azimuth angle and elevation angle measurements. First, a distributed information filter algorithm is developed for each UGV to locally estimate the position and velocity of the quadrotor using its information and information from neighboring UGVs. We then fuse the information from various UGVs using a Consensus Filter such that their estimate of the state of the quadrotor converges. However, the accuracy of the estimation by the consensus filter depends upon the relative position of the sensors. Hence, it is necessary to design a control policy that leads to optimum configuration of UGVs in order to maximize the utility of measurement. A Model Predictive Controller(MPC) is developed to optimally control the UGVs to improve the state estimation. By defining a proper cost function and optimization algorithm in MPC, we are able to control the path of UGVs to increase the accuracy of estimating the position and velocity of a non-cooperative quadrotor.

关键词： Quadcopter Unmanned Ground Vehicle Unmanned Aerial Vehicle Model Predictive Control Track algorithm optimization algorithm Robots Optimal Control Path Planning Consensus algorithm

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Stability analysis of anti-dip bedding rock slopes using a limit equilibrium model combined with bi-directional evolutionary structural optimization (BESO) method

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COMPUTERS AND GEOTECHNICS 2021年 134卷 104116-104116页

作者： Liu, Tingting Ding, Luyang Meng, Fei Li, Xinping Zheng, Yun Wuhan Univ Technol Key Lab Roadway Bridge & Struct Engn Wuhan 430070 Hubei Peoples R China Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Hubei Peoples R China

Anti-dip bedding rock slope (ABRS) is a very common type of rock slopes in nature and engineering construction. However, the stability assessment of ABRSs is still a hard problem for geotechnical engineers due to the difficulty in finding the critical failure surface. In this work, a new method called LEM-BESO was proposed to tackle this problem based on limit equilibrium theory and bi-directional evolutionary structural optimization method. Two model tests reported in previous studies were employed to verify the correctness of the LEM-BESO method. Moreover, a comparative study of the LEM-BESO method and the genetic algorithm was also conducted. The highly consistent results demonstrate that using the LEM-BESO method can accurately predict the failure surface of the slope. Then, the LEM-BESO method was extended to study the influence of two key parameters (i.e. friction angle of the joints and tensile strength of the rock layers) on the failure mechanism of anti-dip bedding rock slopes. The results show that these two parameters have a significant influence on the stability of the slope. However, the failure surface is insensitive to the tensile strength of the rock layers. The LEM-BESO method provides an effective means for the stability evaluation and engineering design of ABRSs.

关键词： Anti-dip bedding rock slope (ABRS) Toppling failure Failure surface optimization algorithm Bi-directional evolutionary structural optimization (BESO)

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Data-Driven Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

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SN Computer Science 2021年第1期2卷 40页

作者： Zhu, Xianglin Rehman, Khalil Ur Bo, Wang Shahzad, Muhammad Hassan, Ahmad School of Electrical and Information Engineering Jiangsu University Jiangsu Zhenjiang 212013 China School of Computer and Information Engineering Jiangxi Normal University Nanchang 330022 China

Fermentation process is a time-varying, nonlinear and multivariable dynamic coupling system. Therefore, it is difficult to directly measure the key biological variables using traditional physical sensors during the process of fermentation, which makes the monitoring and real-time control impossible. To resolve this problem, a data-driven soft sensor modeling method based on deep neural network (DNN) is proposed in this paper. This method is suitable for large amount of data and it enjoys high efficiency and robustness. At the same time, an adaptive moment estimation (Adam) algorithm is used to optimize the hyper-parameters of the DNN model, which is a technique for efficient stochastic optimization that only requires first-order gradients with little memory requirement. The consistent correlation method is used to determine the auxiliary variables of the soft sensor model. The penicillin and l-lysine fermentation processes are taken as the research object, substrate concentration, cell concentration, and product concentration are selected as a target variable. The performance of established soft sensor model is evaluated through the indexes of mean square error (MSE), root-mean-square error (RMSE), and mean absolute error (MAE). The simulation results show that the prediction performance of the soft sensor model based on DNN-Adam is good and compared with model based on stochastic gradient descent (SGD) with momentum optimization algorithm. It is verified that the proposed method can make a more accurate real-time prediction of quality variables in the fermentation process, and it has higher prediction accuracy than DNN-SGD method. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning Fermentation process optimization algorithm Quality prediction Soft sensor model

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Improving Courier Service Network Efficiency through Consolidations

Improving Courier Service Network Efficiency through Consoli...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Zhang, Wenxin Payan, Alexia P. Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn Aerosp Syst Design Lab 270 Ferst Dr Atlanta GA 30332 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Service network design is a significant consideration for courier companies because an efficient design reduces operating costs while maintaining service quality. While companies typically rely on subject-matter experts knowledge to modify their service network design on a regular basis based on changes in demand, some of them have also developed an optimization-driven approach to improve the design of their service network in the long-term. Typically, service networks are based on a hub-and-spoke design. However, operating costs may be reduced by adding consolidations on the pickup and/or the delivery routes into and out of hubs. Consolidations are locations where packages can be aggregated from multiple spokes to go into a hub or can be disaggregated to be delivered to multiple destinations from a hub. This service network design feature ultimately reduces the number of aircraft used on each route and therefore decreases the operating costs. In this study, we use Integer Programming with hierarchical objectives to generate consolidation options. The proposed algorithm accounts for network-wide demand considerations and aims at reducing costs from operating several modes of transportation by minimizing the number of consolidation locations while ensuring that every package is served and gets delivered on time at its intended destination. The algorithm is being implemented on the entire domestic U.S. market and has the flexibility to generate one or more consolidation options for each group of packages going from a given origin to a given destination. Results from the optimization are compared to solutions from a heuristic approach based on a series of geographical and operational rules. Results show that the optimization approach is able to generate better consolidation options compared to the heuristic approach. In particular, allowing packages to consolidate at a maximum of three consolidation locations results in a two percent reduction in the total costs over i

关键词： Operating Costs Aeroplane Mathematical optimization Hub Airports optimization algorithm Aircraft Sizing

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Self-scaling Collocation Methods Preserving Constants of Motion

Self-scaling Collocation Methods Preserving Constants of Mot...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Sagliano, Marco Hoelzel, Matthew German Aerosp Ctr GNC Dept D-28359 Bremen Germany Teraki GmbH D-10785 Berlin Germany

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

We present a class of self-scaling collocation integrators which, unlike explicit Runge-Kutta integrators, will automatically preserve quadratic constants of motion such as energy, linear momentum, and angular momentum, without the need for small stepsizes. However, collocation integrators in general require numerical optimization to solve an implicit set of equations which are difficult and computationally expensive to solve when the problem's Jacobian becomes ill-conditioned. Therefore, the integrators we present automatically scale the collocation conditions using a generalized eigenvalue problem (GEVP) approach, and exploit the Jacobian's structure to improve the optimization algorithm's precision and speed. Numerical results show that the presented integrators preserve invariants in a broader class of integration problems, with an improvement of approximately one order of magnitude over traditional Runge-Kutta 4(5) schemes.

关键词： Dormand Prince Method Numerical optimization optimization algorithm Runge Kutta Integration Satellites Quaternions Orbital Period Taylor Series Earth Attitude Dynamics

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Frequency control of PV-connected micro grid system using fuzzy logic controller

Frequency control of PV-connected micro grid system using fu...

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International Conference on Advances in Materials Research (ICAMR)

作者： Rajesh, T. Gunapriya, B. Sabarimuthu, M. Karthikkumar, S. Raja, R. Karthik, M. Malla Reddy Engn Coll Dept Elect & Elect Engn Secunderabad India New Horizon Coll Engn Dept Elect & Elect Engn Bangalore Karnataka India Kongu Engn Coll Dept Elect & Elect Engn Perundurai 638060 Erode India Jai Shriram Engn Coll Dept Elect & Elect Engn Tirupur 638660 India Muthayammal Engn Coll Dept Elect & Elect Engn Namakkal Rasipuram 637408 India Sri Ramakrishna Engn Coll Dept Elect & Elect Engn Coimbatore 641022 Tamil Nadu India

Frequency is a variable parameter in a power system, and it denotes the balance among the generation and the requirement. The operator of the system has to maintain the frequency within certain acceptable limits. The tediousness of integrating Distributed Generators (DGs), like solar photovoltaic (PV) system or wind turbines is the intermittency in power system that balances the power and regulates the frequency and voltage. If a number of electric motors are connected along with PV, the frequency of rotor speed will be varied. Hence, this paper intends to propose a Fuzzy Logic Controller (FLC) for controlling the frequency of rotor speed to enhance the power system performance. Hence, the major objective of the proposed model is to provide control over the output signal by reducing the error between the reference signal and control signal. The performance evaluation of the proposed model is done and proved over other controllers with respect to switching time, and the performance metrics, namely settling time, rise time, and percentage overshoot. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research-2019.

关键词： Wind turbines Distributed generators Solar photovoltaic system Frequency optimization algorithm

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One Shot optimization with Generalized Constraints

One Shot Optimization with Generalized Constraints

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Munguia, Brian C. Alonso, Juan J. Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

One-shot optimization is an efficient method for simultaneously solving and optimizing a problem, such as problems in aerodynamic shape optimization. The proposed one-shot framework uses a preconditioned design update in order to ensure convergence of the coupled primal, adjoint, and design solvers. This paper builds upon previous work which applied one-shot optimization to problems with equality constraints on top of the state and co-state equations. In particular, this work proposes a one-shot optimization framework for design problems with inequality constraints.

关键词： Aerodynamic Shape optimization NACA 0012 Python optimization algorithm Turbulence Models Renewable Energy Lift Coefficient Optimal Control Aircraft Design Software Tool

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Teaching-Learning-Based optimization of Neural Networks for Water Supply Pipe Condition Prediction

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WATER 2021年第24期13卷 3546页

作者： Elshaboury, Nehal Abdelkader, Eslam Mohammed Al-Sakkaf, Abobakr Alfalah, Ghasan Housing & Bldg Natl Res Ctr Construct & Project Management Res Inst Giza 12311 Egypt Cairo Univ Fac Engn Struct Engn Dept Giza 12613 Egypt Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ H3G 1M8 Canada Hadhramout Univ Coll Engn & Petr Dept Architecture & Environm Planning Mukalla 50512 Yemen King Saud Univ Coll Architecture & Planning Dept Architecture & Bldg Sci Riyadh 11421 Saudi Arabia

The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery's water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickness, diameter, material, lining and coating, surface type, traffic distribution, cathodic protection, flow velocity, and c-factor. For the Shaker Al-Bahery network, the data gathered include length, material, age, diameter, depth, and wall thickness. Three assessment criteria are used to evaluate the suggested machine learning models, namely index of agreement (IOA), correlation coefficient (R), and root mean squared error (RMSE). The results reveal that coupling FFNN with the TLBO algorithm outperforms other prediction models. Therefore, the FFNN-TLBO model can be a valuable tool for simulating the water network pipe condition. This study could help the water municipality allocate the available budget effectively and plan the required maintenance and rehabilitation actions.

关键词： teaching-learning-based optimization optimized neural network machine learning optimization algorithm condition prediction

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Landing performance optimization algorithm of civil aircrafts

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2nd International Conference on Civil Engineering and Transportation (ICCET 2012)

作者： Chen, Hongying Luo, Wentian Civil Aviat Flight Univ China Flight Technol Coll Guanghan City Si Chuan Peoples R China

ISBN: (纸本)9783037855645

This paper explains and demonstrates how to calculate and optimize landing performance. According regulation requirement, it needs to consider the different requirements [1], including landing field length, missed approach climb gradient, brake energy and tire speed. Weights limited by the different requirements are calculated, the minimum of these weights limited are determined as the actual allowed maximum landing weight.

关键词： Landing performance optimization algorithm Operating landing distance Landing speed Landing weight

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