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Optimized reactive silencers composed of closely-spaced elongated side-branch resonators

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2019年第4期145卷 2210-2220页

作者： Cervenka, Milan Bednarik, Michel Groby, Jean-Philippe Czech Tech Univ Fac Elect Engn Technicka 2 Prague 16627 6 Czech Republic Le Mans Univ Lab Acoust LAUM UMR CNRS 6613 Univ Mans Ave Olivier Messiaen F-72085 Le Mans 9 France

This paper reports a theoretical study of the sound propagation in a rectangular waveguide loaded by closely-spaced elongated side-branch resonators forming a simple low-frequency broadband reactive silencer. Semi-analytical calculations account for the evanescent modes both in the main waveguide and side-branch resonators and for the viscothermal losses in the silencer elements. Reasonable accuracy is maintained in the evaluation of transmission, reflection, and absorption coefficients, while the calculation time is reduced by a few hundred times in comparison with the finite element method. Therefore, the proposed method is particularly suitable for optimization procedure. The lengths of the individual equally spaced side-branch resonators are optimized by a heuristic evolutionary algorithm that maximizes the minimum transmission loss (TL) over a pre-defined frequency range. Numerical results indicate that the minimum TL of the optimized silencers is reduced due to the destructive effect of the evanescent coupling from the resonators of the nearest side-branches. In the opposite, the TL increases linearly with the number of the side-branch resonators. (C) 2019 Acoustical Society of America.

关键词： Acoustical properties Acoustic signal processing optimization algorithms Finite-element analysis Speed of sound Cognitive science Thermodynamic properties Acoustic field Thermodynamic states and processes Optical absorption

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Simultaneous reconstruction of temperature and velocity fields using nonlinear acoustic tomography

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APPLIED PHYSICS LETTERS 2019年第10期115卷 104104-104104页

作者： Yu, Tao Cai, Weiwei Shanghai Jiao Tong Univ Key Lab Educ Minist Power Machinery & Engn Sch Mech Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

Acoustic tomography developed and investigated before can only recover either the temperature or the velocity field from the time-of-flight measurements along a single direction of sonic rays. However, the sonic propagation time is a nonlinear function of temperature and velocity;thus, in principle, the measurements allow us to reconstruct both simultaneously. In order to achieve this, we propose a modality of acoustic tomography based on the recently proposed concept of nonlinear tomography, which can take full advantage of the aforementioned nonlinear dependences. Proof-of-concept numerical demonstrations are presented using representative two-dimensional temperature and flow velocity phantoms. The obtained simulative results confirmed the feasibility of nonlinear acoustic tomography in recovering the distributions of temperature and velocity fields.

关键词： Acoustic signal processing optimization algorithms Computed tomography Flow visualization

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Optimized ensemble learning-based student's performance prediction with weighted rough set theory enabled feature mining

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第7期35卷 e7601-e7601页

作者： Sateesh, Nayani Rao, Pasupureddy Srinivasa Lakshmi, Davuluri Rajya Jawaharlal Nehru Technol Univ Kakinada Dept Comp Sci & Engn Kakinada India MVGR Coll Engn Dept Comp Sci & Engn Vizianagaram India Jawaharlal Nehru Technol Univ Kakinada Univ Coll Engn Vizianagaram Dept Comp Sci & Engn Kakinada India

Currently, educational data mining act as a major part of student performance prediction approaches and their applications. However, more ensemble methods are needed to improve the student performance prediction, and also which helps increase the learning quality of the Student's performance. The usage of an ensemble classifier with rule mining to predict students' academic success is proposed. In response to this need, this research mainly concentrated on an ensemble classifier with rule mining to predict students' academic success. The feature mining is performed using the weighted Rough Set Theory method, in which the proposed meta-heuristic algorithm optimizes the weight function. The variable optimization of the ensemble classifier is accomplished with the help of a combination of Harris Hawks optimization (HHO), and Krill Herd Algorithm (KHA) known as Escape Energy Searched Krill Herd-Harris Hawks optimization (EES-KHHO) for maximizing the prediction rate. Extensive tests are carried out on various datasets, and the findings show that our technique outperforms conventional approaches. Throughout the result analysis, the offered method attains a 92.77% accuracy rate, and also it attains a sensitivity rate of 94.87%. Therefore, the offered student performance prediction model achieves better effectiveness regarding various performance metrics.

关键词： feature mining optimization algorithms optimized ensemble learning student's performance prediction

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Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期212卷 110254-110254页

作者： Mehrad, Mohammad Ramezanzadeh, Ahmad Bajolvand, Mahdi Hajsaeedi, Mohammad Reza Shahrood Univ Technol Fac Min Petr & Geophys Engn Shahroud Semnan Province Iran

Shear-wave velocity (Vs) is a key petrophysical data for a wide spectrum of applications in the upstream oil industry. In many wells, however, the corresponding log cannot be acquired due to technical and/or cost-related issues. Appreciating the importance of this parameter, its relationship to other petrophysical logs has been extensively studied. For the most part, such studies focus on modes based on either rock physics, analytic equations, or artificial intelligence (AI). Inherent complexity of hydrocarbon reservoirs, especially carbonate ones, has made it difficult to build a comprehensive model of adequately high accuracy for various fields, keeping the research on novel models for such a purpose a still hot topic. This paper presents a high-accuracy high-generalizability model for predicting Vs from logging data. The required logs were acquired along three wells penetrating three carbonate reservoirs in SW Iran. In a preprocessing step, a robust regression technique was applied to identify and omit outliers. Subsequently, the data at two wells were used for training the model, with the data at the third well used for validating the trained model. Feature selection was performed by NSGA-II and five parameters were selected (Vp, Depth, RHOB, NPHI, and RT) as inputs to the model. For the first time, we employed the convolutional neural network (CNN) and multilayer extreme learning machine (MELM) in simple and hybrid forms with a few optimization algorithms, including particle swarm optimization (PSO), cuckoo optimization algorithm (COA), and genetic algorithm (GA) to build different models for predicting Vs from logging data. For the sake of comparison, we further applied the least-squares support-vector machine (LSSVM) in simple and hybrid forms with COA, PSO, and GA as well as a couple of popular analytic methods. Results of the training showed the superiority of the CNN, as measured by RMSE. Nevertheless, the MELM-COA model provided for much shorter learning

关键词： Extreme machine learning optimization algorithms Outlier detection Shear wave velocity estimation Deep learning

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OPTIMUM PROFILE MODIFICATIONS FOR THE MINIMIZATION OF STATIC TRANSMISSION ERRORS OF SPUR GEARS

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JOURNAL OF MECHANISMS TRANSMISSIONS AND AUTOMATION IN DESIGN-TRANSACTIONS OF THE ASME 1986年第1期108卷 86-95页

作者： TAVAKOLI, MS HOUSER, DR M. S. Tavakoli D. R. Houser Gear Dynamics and Gear Noise Research Laboratory The Ohio State University 206 W. 18th Avenue Columbus Ohio 43210

A procedure for computing static transmission errors and tooth load sharing was developed for low and high contact ratio internal and external spur gears. A suitable optimization algorithm was used to minimize any combination of the harmonics of gear mesh frequency components of the static transmission error. Different combinations of tip and root relief may be used to achieve optimization. These include varying the starting point of relief and varying the magnitude of relief, and selecting the gear and/or the pinion teeth to be tip and/or root-relieved. Also, there exists an option for using either linear or parabolic relief. In addition to the presentation of optimal profile modifications, the effects of off-design loads, nonoptimum modifications, and random spacing errors are presented.

关键词： Gears Errors Spur gears Stress Design optimization optimization algorithms

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Guided Intelligent Hyper-Heuristic Algorithm for Critical Software Application Testing Satisfying Multiple Coverage Criteria

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第2期33卷 2450029-2450029页

作者： Rani, S. Alagu Akila, C. Raja, S. P. Anna Univ Reg Campus Dept Comp Sci & Engn Tirunelveli 627007 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods.

关键词： optimization algorithms hyper-heuristic artificial intelligence software testing fault detection

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FRED: The Flexible Renewable Energy System Dispatch Optimizer

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2019年第5期141卷 1-17页

作者： Burghi, Ana Carolina do Amaral Hirsch, Tobias Pitz-Paal, Robert German Aerosp Ctr DLR Inst Solar Res Wankelstr 5 D-70563 Stuttgart Germany German Aerosp Ctr DLR Inst Solar Res D-51147 Cologne Germany

Due to their thermal storage capability, concentrated solar power (CSP) plants have flexibility on electricity dispatch, being able to participate in balancing power markets. The development of an optimum electricity delivery schedule should be fast to react to updated forecast and the dynamic electricity markets, apart from considering best operational practices, as it brings significant cost reductions and improvement in plant performance. Therefore, dispatch optimization tools should combine financial and operational optimization with acceptable computational time. In this context, an innovative dispatch optimization algorithm used to derive a CSP plant operation schedule is presented. FRED is a heuristic rule-based algorithm used to optimize financial income while considering plant best operational practices. Simulations performed with a CSP plant tower model following FRED optimization strategy show the possibility of improved financial results with fast dispatch planning, ensuring the importance of this technology in the pathway to a highly renewable energy mix.

关键词： optimization Simulation Solar energy Storage algorithms Renewable energy optimization algorithms

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Prediction of ultra-short-term wind power based on BBO-KELM method

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JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 2019年第5期11卷 056104-056104页

作者： Li, Jun Li, Meng Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Peoples R China

For ultrashort-term wind power prediction, an optimized extreme learning machine method based on biogeography -based optimization (BBO-KELM) is proposed. The kernel extreme learning machine (KELM) method only uses the kernel function to represent the unknown nonlinear feature map of the hidden layer and does not need to select the number of nodes of the hidden layer. Meanwhile, the output weight of the network is calculated by the regularized least squares algorithm. The BBO algorithm, which is a new evolutionary algorithm (EA) motivated by biogeography, which is the study of the distribution of biological species through time and space, is efficient in solving high dimensional, multiobjective optimization problems. In this paper, the KELM method is optimized using the BBO algorithm to optimize the selection of input variable sets, the parameters of the kernel function, and the Tikhonov regularization coefficient, so as to further improve the learning performance of the KELM method. To verify the effectiveness of the BBO-KELM method proposed in this paper, the BBO-KELM method is applied to ultrashort-term wind power prediction research in different regions and is compared with benchmark methods such as persistence, neural networks, support vector machine, extreme learning machine (ELM), and other optimized ELM (0 -ELM) or KELM (0-KELM) methods such as BBO-ELM, particle swarm optimization (PSO)-ELM, differential evolution-KELM, simulated annealing-KELM, and PSO-KELM, under the same conditions. Experimental results show that the BBO-KELM methods with cosine migration can give better prediction accuracy;in addition, in the proposed method, the parameters of the kernel function do not need to be selected by trial-and -error and the relevant input variables can be automatically selected, improving the generalization capability.

关键词： Prediction theory Artificial intelligence Evolutionary computation optimization algorithms Regression analysis Decision theory Machine learning Artificial neural networks Sustainable energy Wind energy

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Gene selection and classification of microarray data method based on mutual information and moth flame algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 166卷 114012-114012页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mokhtari, Rabah Abderrahmane Mira Univ Dept Comp Sci Fac Exact Sci Bejaia Algeria Lille Univ Fac Sci & Technol Lille France Mohamed Boudiaf Univ Dept Comp Sci Fac Math & Comp Sci Msila Algeria Res Ctr Comp Sci Signal & Automat Control Lille C Lille France Lab Med Comp LIMED Msila Algeria Lab Informat & Its Applicat Msila LIAM Msila Algeria

Several techniques or methods may help in detecting diseases and cancer. Creating an effective method for extracting disease information is one of the major challenges in the classification of gene expression data as long as there is (in the presence) a massive amount of redundant data and noise. Bio-inspired algorithms are among the most effective when used for solving gene selection. Moth Flame optimization Algorithm (MFOA) is computationally less expensive and can converge faster than other methods. In this paper, we propose a new extension of the MFOA called the modified Moth Flame Algorithm (mMFA), the mMFA is combined with Mutual Information Maximization (MIM) to solve gene selection in microarray data classification. Our approach Called Mutual Information Maximization - modified Moth Flame Algorithm (MIMmMFA), the MIM based pre-filtering technique is used to measure the relevance and the redundancy of the genes, and the mMFA is used to evolve gene subsets and evaluated by the fitness function, which uses a Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV) classifier and the number of selected genes. In order to test the performance of the proposed MIM-mMFA algorithm, we compared the MIM-mMFA algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on sixteen benchmark datasets either binary-class or multi-class, confirm that MIM-mMFA algorithm provides a greater classification accuracy.

关键词： Gene expression Feature selection Microarray Cancer classification Moth Flame Algorithm Mutual information maximization Bio-inspired algorithms Bioinformatics optimization algorithms Evolutionary algorithm Molecular biology Swarm intelligence

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Hybrid Optimal Control of a Flying+Sailing Drone

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ASME Letters in Dynamic Systems and Control 2023年第3期3卷 031008页

作者： Yasini, Taha Pakniyat, Ali Department of Mechanical Engineering University of Alabama Tuscaloosa AL 35401

This paper studies the combined maneuver of flying and sailing for a robotic system which is referred to as a flying+sailing drone. Due to the emergence of hybrid systems behavior in tasks which involve both the flying and sailing modes, a hybrid systems formulation of the robotic system is presented. Key characteristics of the system are (i) changes in the dimension of the state space as the system switches from flying to sailing and vice versa and (ii) the presence of autonomous switchings triggered only upon the landing of the drone on the water surface. For the scenario in which the drone’s initial state is given in the flying mode and a fixed terminal state is specified in the sailing mode, the associated optimal control problems are studied within the vertical plane passing through the given points, hence the dynamics of the drone in the flying mode are represented in a five-dimensional state space (associated with three degrees-of-freedom) and in a three-dimensional state space in the sailing mode (associated with two degrees-of-freedom). In particular, the optimal control problems for the minimization of time and the minimization of the control effort are formulated, the associated necessary optimality conditions are obtained from the hybrid minimum principle (HMP), and the associated numerical simulations are presented.

关键词： Optimal control Unmanned aerial vehicles Robotics algorithms Dynamics (Mechanics) optimal controls nonlinear systems robotics switched systems Computer simulation optimization algorithms vehicle dynamics and control hybrid systems nonlinear control

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