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Artificial Neural Network Based Particle Swarm optimization for Microgrid Optimal Energy Scheduling

IEEE TRANSACTIONS ON POWER ELECTRONICS 2021年第11期36卷 12151-12157页

作者： Abdolrasol, Maher G. M. Mohamed, Ramizi Hannan, M. A. Al-Shetwi, Ali Q. Mansor, M. Blaabjerg, Frede Univ Kebangsaan Malaysia Bangi 43600 Malaysia Univ Tenaga Nas Dept Elect Power Engn Kajang 43000 Malaysia Fahad Bin Sultan Univ Tabuk 47721 Saudi Arabia Univ Tenaga Nas Kajang 43000 Malaysia Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

This letter proposes an enhancement for artificial neural network (ANN) using particle swarm optimization (PSO) to manage renewable energy resources (RESs) in a virtual power plant (VPP) system. This letter highlights the comparison of the ANN-based binary particle swarm optimization (BPSO) algorithm with the original BPSO algorithm. The comparison has been made upon searching the optimal value of the number of nodes in the hidden layers and the learning rate. These parameter values are used in ANN training for microgrid (MG) optimal energy scheduling. The proposed approach has been tested in the VPP system covering MGs involving RESs to minimize the power and giving priority to sustainable resources to participate instead of buying power from the utility grid. This model is tested using real load demand recorded for 24 h in Perlis state, the northern part of Malaysia. Besides, real weather condition data are recorded by Tenaga Nasional Berhad Research solar energy meteorology for a 1-h average (e.g., solar irradiation, wind speed, battery status data, and fuel level). The results show that ANN-PSO gives precise decision compared with BPSO algorithm, which in turn prove that the enhancement for the neural net reaches the optimum level of energy scheduling.

关键词： Training Microgrids Optimal scheduling Meteorology Job shop scheduling Search problems Schedules Artificial neural network (ANN) energy management (EM) microgrid (MG) optimization algorithm scheduling

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Free-Form Deformation of Parametric CAD Geometry via B-Spline Transformations

Free-Form Deformation of Parametric CAD Geometry via B-Splin...

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AIAA Aviation Forum

作者： Gomez, Marlena C. Galbraith, Marshall C. Haimes, Robert MIT Comp Sci & Engn Cambridge MA 02139 USA MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

This paper presents a method for local shape modification using free-form deformation to morph parametric geometry. The goal is analysis driven shape design which combines CAD-like parametric solid model geometry construction with free-form-like local deformation. A free-form deformation box is created and the geometry inside the box is deformed, where any analytic geometry inside the box which is not by default defined by a B-spline is converted to a B-spline. The free-form deformation is then used to move the surface B-spline control point net. This allows for the generation of smooth geometry, while keeping the number of degrees of freedom manageable for the optimizer. This method is demonstrated using optimization with the objective function minimizing the L-2-norm difference between a morphed and target shape. The set-up for a generalized geometric input is also presented, along with the computed sensitivity calculations which are necessary for shape design driven by analysis.

关键词： Gaussian Quadrature NACA 0012 Computational Fluid Dynamics Shape optimization OpenMDAO Design optimization Aspect Ratio Computing Finite Difference Scheme optimization algorithm

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Hybrid optimisation dependent deep belief network for lane detection

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2022年第2期34卷 175-187页

作者： Shirke, Suvarna Udayakumar, R. Bharath Inst Higher Educ & Res Dept Comp Sci & Engn Chennai 600073 Tamil Nadu India Bharath Inst Higher Educ & Res Dept Informat Technol Chennai Tamil Nadu India

Nowadays, in research introducing an advanced driver assistance system for improving driving is considered as the trending one. In this research, concentrate more on proposing lane detection model and assist in driving. This work develops a lane detection model through the deep learning scheme. The proposed scheme has two major phases, such as image transformation and lane detection. Initially, the proposed method obtains the multiple-lane images and transforms the image and this image transformation helps in classifier training. For detecting the lane from the bird's view image, this work considers the Deep Convolution Neural Network (DCNN) classifier. A novel optimisation algorithm, namely Earth Worm-Crow Search algorithm (EW-CSA), is developed in this work to assist the DCNN classifier with the optimal weights. The proposed algorithm is developed by modifying the Earth Worm Optimisation algorithm (EWA) with the properties of the Crow Search algorithm (CSA). The proposed system is compared with other existing methods, in which the proposed method offers maximum sensitivity 0.9925, the detection accuracy of 0.99512, and specificity of 0.995.

关键词： Driver assistance system lane detection model bird’ s eye view image image transformation optimization algorithm

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Calibration of the Transitional k-ω-γ-Reθt Turbulence Model

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AIAA JOURNAL 2022年第7期60卷 4140-4148页

作者： Barrouillet, Benjamin Laurendeau, Eric Yang, Hong Montreal Sch Technol Mech Engn Dept CP 6079 Montreal PQ H3C 3A7 Canada Bombardier Aviat Adv Aerodynam CP 6087 Dorval PQ H4S 1Y9 Canada

In the present paper, the gamma-Re-theta t turbulent transition model from Langtry and Menter ( "Correlation-Based Transition Modeling for Unstructured Parallelized Computational Fluid Dynamics Codes, " AIAA Journal, Vol. 47, No. 12, 2009, pp. 2894-2906) is implemented in the structured finite volume Reynolds-averaged Navier-Stokes code FANSC and coupled with the k-omega shear-stress transport turbulence model. The model is modified and recalibrated to better match experimental results. The local turbulent intensity is replaced with a more consistent turbulent fluctuation representation to avoid nonphysical solutions at the stagnation point that would contaminate the downstream boundary-layer predictions. A calibration is devised by customizing several constants involved in the equation for the intermittency gamma and the destruction term for the turbulent kinetic energy k. An optimization algorithm is thus employed to facilitate the calibration on well-known two-dimensional test cases, such as the flat plates of the T3 series and of Schubauer and Klebanoff ( "Contributions on the Mechanics of Boundary-Layer Transition, " NACA TN 3489, 1955), as well as the NACA 0012 airfoil. The calibrated gamma-Re-theta t model is then validated on the NLF-0416 and S809 airfoil cases that have not been involved in the optimization process.

关键词： Turbulence Models NLF(1) 0416 Laminar Turbulent Transition Reynolds Averaged Navier Stokes Aerodynamic Shape optimization optimization algorithm Kinetic Energy Shear Stress Computational Fluid Dynamics Code Lift Coefficient

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Space Debris Removal with Harpoon Assistance: Choice of Parameters and optimization

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第4期44卷 767-778页

作者： Sizov, Dmitry A. Aslanov, Vladimir S. Samara Natl Res Univ Dept Theoret Mech 34 Moscovskoe Shosse Samara 443086 Russia

This paper discusses three phases of the space debris removal with harpoon assistance: capture, tether deployment, and towing. The harpoon impact momentum is used to detumble the target. Equations of motion for each phase are given in dimensionless form, which significantly reduces the number of parameters describing the system. The aim of the paper is to propose algorithms for choosing optimal parameters for the capture and tether deployment phases. This optimization provides small amplitudes of the tether and debris oscillations during towing. The numerical simulations of the removal of a spent Ariane 4 upper stage H10 confirm the correctness of the proposed mathematical models and optimization algorithms.

关键词： Space Debris Numerical Simulation optimization algorithm Mathematical Models Phase Diagram Total Energy Kinetic Energy Angular Motion International Space Station Space Tethers

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The Dual-Fuzzy Convolutional Neural Network to Deal With Handwritten Image Recognition

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第12期30卷 5225-5236页

作者： Zhou, Wei Liu, Man Xu, Zeshui Yunnan Univ Finance & Econ Business Sch Kunming 650221 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

Subjective evaluation is a commonly used method in the real recognition process. Generally, two fuzziness can be found in evaluation information, namely what values should be given to fully describe the information and how to distinguish different values. The recently developed probabilistic hesitant fuzzy set could perfectly address these issues. In this article, we propose a dual-fuzzy convolutional neural network (DF-CNN) by fusing the hot neural network algorithm into the probabilistic hesitant fuzzy environment and then using it in a practical handwritten image recognition process. For this new DF-CNN, we provide the whole calculation process including the forward propagation, backward propagation, and parameter updating calculations. Also, the optimization algorithm of the DF-CNN is given to derive its optimal results. Finally, we apply the DF-CNN and its optimization algorithm to deal with a real issue, namely the handwritten numeral image recognition. The calculation process and the comparison fully demonstrate the feasibility and effectiveness of the proposed new model and algorithm.

关键词： Convolutional neural network (CNN) dual-fuzzy information handwritten image recognition optimization algorithm subjective evaluation

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UAV Collision Avoidance Using Mixed-Integer Second-Order Cone Programming

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第9期45卷 1732-1738页

作者： Zhang, Guoxu Liu, Xinfu Beijing Inst Technol Beijing 100081 Peoples R China Sch Aerosp Engn Beijing Peoples R China

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Application of artificial intelligence based on synchrosqueezed wavelet transform and improved deep extreme learning machine in water quality prediction

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第25期29卷 38066-38082页

作者： Song, Chenguang Yao, Leihua China Univ Geosci Beijing Sch Engn & Technol Beijing 100083 Peoples R China

Water quality prediction is the basis for the prevention and control of water pollution. In this paper, to address the problem of low prediction accuracy of existing empirical models due to the non-smoothness and nonlinearity of water quality series, a novel water quality forecasting model integrating synchrosqueezed wavelet transform and deep extreme learning machine optimized with the sparrow search algorithm (SWT-SSA-DELM) was proposed. First, the water quality series was denoised by SWT to reduce the non-stationarity and randomness of water quality series. Then, construct DELM by combining ELM and an autoencoder, and an innovative metaheuristic algorithm, SSA, was used to optimize the hyperparameters of the DELM. Finally, the constructed feature vector was used as the input of the DELM, and the proposed water quality prediction model SWT-SSA-DELM was trained and tested with the data sets of Xinchengqiao and Xiaolangdi in the Yellow River Basin, China. Models such as ELM and DELM alone, as well as their improved form based on ensemble learning, long short-term memory network (LSTM), autoregressive integrated moving average (ARIMA) were adopted as comparison models. The results make it evident that the model presented, linking the ability to ensure convergence to the global optima of the SSA with the nonlinear mapping of the DELM, outperforms similar models in terms of predictive performance, with average MAE, MAPE, and RMSE of 0.15, 2.02%, and 0.21 in the test stage, which is 72.82%, 72.88%, and 74.32% lower than the baseline ELM model, respectively.

关键词： SSA SWT Integrated model Data-driven model optimization algorithm

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Sliding mode control of interval type-2 T-S fuzzy systems with redundant channels

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NONLINEAR DYNAMICS 2022年第4期108卷 3579-3593页

作者： Zhang, Zhina Niu, Yugang Anhui Polytech Univ Sch Math Phys & Finance Energy Internet Engn Res Ctr Anhui Prov Dept Educ Wuhu 241000 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

This paper investigates the sliding mode control (SMC) of interval type-2 (IT2) T-S fuzzy systems. The measurement outputs are propagated via redundant channels for reducing the probability of packet loss and improving the reliability of data transmission. A key feature for the above problem is that the premise variables and the measurement signals may not be available by the controller, which brings difficulty to stabilize the nonlinear systems. Accordingly, a crucial issue is how to synthesize an implementable SMC law under the redundant channels. To this end, the characteristic of the redundant channels is firstly analyzed and the model of available measurement output signals is established. By employing these available measurements as the premise variables and utilizing the upper and lower bounds of the system membership functions (MFs), new MFs are constructed and the sliding mode controller is synthesized. By introducing some null terms carrying the information of MFs, sufficient conditions are derived in terms of nonlinear matrix inequalities to ensure the stochastically ultimate boundedness of the closed-loop system and the reachability of the specified sliding surface. Besides, a binary genetic algorithm (GA) is introduced to solve the nonlinear criteria via the objective function reflecting the control performance. Finally, a numerical example illustrates the effectiveness of the proposed methods.

关键词： Interval type-2 T-S fuzzy system Redundant channel transmission Sliding mode control optimization algorithm

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Fuzzy Intermittent Extended Dissipative Control for Delayed Distributed Parameter Systems With Stochastic Disturbance: A Spatial Point Sampling Approach

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第6期30卷 1734-1749页

作者： Ding, Kui Zhu, Quanxin Hunan Normal Univ Sch Math & Stat MOE LCSM Changsha 410081 Peoples R China Nanjing Normal Univ Sch Math Sci Nanjing 210023 Peoples R China Nanjing Normal Univ Inst Finance & Stat Nanjing 210023 Peoples R China Chengdu Univ Sch Informat Sci & Engn Chengdu 610106 Peoples R China

In this article, the extended dissipative performance of distributed parameter systems (DPSs) with stochastic disturbances and multiple time-varying delays is studied by using a new fuzzy aperiodic intermittent sampled-data control strategy. Different from the previous fuzzy sampled-data control results, the state sampling of the proposed sampled-data controller occurs only in space and is intermittent rather than continuous in the time domain. By introducing a novel multitime-delay-dependent switched Lyapunov functional to explore the dynamic characteristics of the controlled system, and by means of the famous Jensen's inequality with reciprocally convex approach, Wirtinger's inequality, the criterion of the system's mean square stabilization is established based on the LMI technique, which quantitatively reveals the relationship between the control period, the control length, and the upper bound of the control sampling interval. Especially, the optimal control gain is given by designing an optimized algorithm in the article, which greatly reduces the cost. Finally, two numerical examples are presented to demonstrate the effectiveness and superiority of the proposed approach.

关键词： Actuators Control systems Delays Time-varying systems Aerospace electronics Switches Distributed parameter systems Distributed parameter systems (DPSs) extended dissipative performance fuzzy aperiodic intermittent sampled-data control optimization algorithm stochastic disturbances

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