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A modified hybrid algorithm based on black hole and differential evolution algorithms to search for the critical probabilistic slip surface of slopes

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COMPUTERS AND GEOTECHNICS 2021年 129卷 103902-103902页

作者： Ding, Jiayi Zhou, Jianfang Cai, Wei Zheng, Dingcong Hohai Univ Coll Mech & Mat Nanjing 211100 Peoples R China Hohai Univ Coll Mech & Elect Engn Changzhou 213022 Peoples R China

optimization algorithms play an important role in the reliability analysis of slopes to determine the critical slip surface. In this study, the original hybrid algorithm based on black hole and differential evolution algorithms (BHDE) is modified to speed up the convergence rate. Subsequently, the modified BHDE (MBHDE) combined with the subset simulation (SS) is firstly applied to search for the critical probabilistic slip surface of a slope where Bishop simplified method is employed to calculate the safety factor. Finally, four typical examples of soil slope are analyzed to illustrate the merits in convergence rate for MBHDE. The results achieved by MBHDE combined with SS also indicate its applicability and efficiency in the reliability analysis of slopes.

关键词： Slope stability Critical probabilistic slip surface Reliability optimization algorithm Subset simulation

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An Eddy-Current Testing Method for Measuring the Thickness of Metallic Plates

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Sardellitti, Alessandro Milano, Filippo Laracca, Marco Ventre, Salvatore Ferrigno, Luigi Tamburrino, Antonello Univ Cassino & Southern Lazio Dept Elect & Informat Engn I-03043 Cassino Italy Sapienza Univ Rome Dept Astronaut Elect & Energy Engn I-00185 Rome Italy Consorzio Nazl Interuniv Telecomunicaz CNIT I-43124 Pisa Italy Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

Thickness measurements of metallic plates are mandatory in many industrial scenarios. Methods based on eddy-current testing (ECT) are ideal for fast and accurate online contactless thickness measurements, making them very attractive in the Industry 4.0 scenario. This contribution is focused on a specific and robust ECT technique proposed in the past by the scientific community. The main limitation is its applicability to thin materials only, where the thickness of the material is much smaller than the overall size of the ECT probe. Extending the range of applicability to thicker materials introduces a progressive and severe degradation of the measurement accuracy. In this article, we analyze the theoretical foundation of this method with an entirely original approach based on the celebrated Buckingham p theorem. In doing this, we draw the complete theoretical picture of the method, providing a simple, clear, and rigorous view of its performance and intrinsic limitations. Moreover, we propose two solutions, one analytical and the other iterative, to accurately estimate the thickness of the materials from thin to thick values. Finally, a numerical analysis combined with an experimental campaign confirms the effectiveness of the proposed solutions, making the method suitable for industrial and other applications.

关键词： Eddy-current testing (ECT) nondestructive evaluation (NDE) nondestructive testing (NDT) optimization algorithm thickness measurement

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An Intelligent Secured Framework for Cyberattack Detection in Electric Vehicles' CAN Bus Using Machine Learning

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IEEE ACCESS 2019年 7卷 127580-127592页

作者： Avatefipour, Omid Al-Sumaiti, Ameena Saad El-Sherbeeny, Ahmed M. Awwad, Emad Mahrous Elmeligy, Mohammed A. Mohamed, Mohamed A. Malik, Hafiz Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA Khalifa Univ Adv Power & Energy Ctr Elect Engn & Comp Sci Abu Dhabi 127788 U Arab Emirates King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Adv Mfg Inst Riyadh 11421 Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya 61519 Egypt

Electric Vehicles' Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle network communication. Simplicity, robustness, and suitability for real-time systems are the salient features of CAN bus. Unfortunately, the CAN bus protocol is vulnerable to various cyberattacks due to the lack of a message authentication mechanism in the protocol itself, paving the way for attackers to penetrate the network. This paper proposes a new effective anomaly detection model based on a modified one-class support vector machine in the CAN traffic. The proposed model makes use of an improved algorithm, known as the modified bat algorithm, to find the most accurate structure in the offline training. To evaluate the effectiveness of the proposed method, CAN traffic is logged from an unmodified licensed electric vehicle in normal operation to generate a dataset for each message ID and a corresponding occurrence frequency without any attacks. In addition, to measure the performance and superiority of the proposed method compared to the other two famous CAN bus anomaly detection algorithms such as Isolation Forest and classical one-class support vector machine, we provided Receiver Operating Characteristic (ROC) for each method to quantify the correctly classified windows in the test sets containing attacks. Experimental results indicate that the proposed method achieved the highest rate of True Positive Rate (TPR) and lowest False Positive Rate (FPR) for anomaly detection compared to the other two algorithms. Moreover, in order to show that the proposed method can be applied to other datasets, we used two recent popular public datasets in the scope of CAN bus traffic anomaly detection. Benchmarking with more CAN bus traffic datasets proves the independency of the proposed method from the meaning of each message ID and data field that make the model adaptable with different CAN datasets.

关键词： Electric vehicles controller area network (CAN Bus) anomaly detection one-class support vector machine optimization algorithm

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Retrieval of Carbon Dioxide Using Cross-Track Infrared Sounder (CrIS) on S-NPP

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REMOTE SENSING 2021年第6期13卷 1163-1163页

作者： Zhang, Xinxin Zhang, Ying Bai, Lu Tao, Jinhua Chen, Liangfu Zou, Mingmin Han, Zongfu Wang, Zhibao Beijing Normal Univ Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Ulster Univ Sch Comp Belfast BT37 0QB Antrim North Ireland Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China Northeast Petr Univ Sch Comp & Informat Technol Daqing 163318 Peoples R China

The Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite is a spaceborne Fourier transform infrared spectrometer. The study aims to retrieve carbon dioxide (CO2) information (the CO2 profile and column-averaged dry-air mole fraction of XCO2) from June 2018 to December 2019 based on the The National Oceanic and Atmospheric Administration (NOAA)-Unique Combined Atmospheric Processing System (NUCAPS) Cloud-Cleared Radiances (CCRs) via the CrIS. The CCRs products for the CrIS with 2223 channels have been available since 22 May 2018. Characteristics of the CO2 weighting functions inform the choice of multiple channels that are around 15 mu m in size that differ by latitude and season to maximize retrieval sensitivity to CO2 and minimize sensitivity to other interfering atmospheric parameters. CO2 was retrieved from these channels using an adopted nonlinear optimization algorithm. The temperature, water vapor, and ozone profiles used in the inversion process were gathered from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Validations of CO2 concentrations as retrieved from CrIS showed the following conclusions: (1) The relative error of the retrieved CO2 concentrations, as compared to Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container (CARIBIC) in situ aircraft measurements, was less than 0.5%, and the root mean square errors (RMSE) were less than 0.7 ppmv (with correlation coefficients of 0.56-0.86);(2) the retrieved XCO2 from June 2018 to December 2019 correlated well with the ground-based Total Carbon Column Observing Network (TCCON) observations, and the differences were within +/- 0.2 ppmv. Further analysis of the temporal and spatial distribution of the retrieved CO2 at 300 hPa demonstrated a strong seasonal variation of CO2 in 0-60 degrees N in the Northern Hemisphere with the maximum values in June-August and larger amplitudes o

关键词： Cross-track Infrared Sounder Suomi National Polar-Orbiting Partnership CCRs optimization algorithm XCO2 and CO2 profile

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Optimal rule based fuzzy-PI controller for core power control of nuclear reactor

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ANNALS OF NUCLEAR ENERGY 2023年 194卷

作者： Acharya, Debasis Rai, Ankur Das, Dushmanta Kumar Natl Inst Technol Nagaland Dept Elect & Elect Engn Nagaland India

In this paper, an optimal rule based fuzzy proportional integral controller is proposed to control the core power of the molten salt breeder reactor (MSBR) model. Firstly, a nonlinear MSBR model is considered. It is then linearized using the perturbation linearizing approach, and the transfer function of the linearized MSBR system is established. Secondly, an optimal Fuzzy-PI control structure is formulated where a PI controller is set as the principal controller for the MSBR model and a fuzzy control system is used to adjust the PI controller parameters during any disturbance in the MSBR. The rule base system of the fuzzy controller is optimized with a reshaped class topper optimization algorithm. Finally, the optimized Fuzzy-PI controller tests under a variety of conditions. Furthermore, the Nyquist stability approach is used to analyze the stability of the closed loop MSBR model.

关键词： Molten salt breeder reactor Core power control PI control FLC Fuzzy membership function optimization algorithm

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Research on Optimal Design of Low Hysteresis Brush Seal Structure

World Scientific Research Journal

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World Scientific Research Journal 2020年第10期6卷 179-187页

作者： Bo Cheng Xinyang Wang Yifei Ren

The low hysteresis brush seal can effectively reduce the hysteresis effect and the increase in leakage caused by the basic brush seal during the radial deviation of the rotor. However, the structure is relatively more complicated. In order to achieve a low hysteresis brush seal engineering design, we establish the agent-based response surface model gas seals leak coefficient based on ISIGHT, algorithms including MIGA, ASA and POINTER are applied to the optimization design research of the low hysteresis brush seal structure. Research indicates that the established agent-based model based on experiment results can better match the low hysteresis brush seal, and it is concluded that the leakage of the decompression chamber is at its least when the size is 0.2949mm. In addition, the MIGA algorithm has an excellent performance with better optimization efficiency and optimization results.

关键词： Optimal design Low hysteresis Brush seal optimization algorithm

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Data-Driven Method for Response Control of Nonlinear Random Dynamical Systems

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JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME 2021年第4期88卷 041012页

作者： Tian, Yanping Jin, Xiaoling Wu, Lingling Yang, Ying Wang, Yong Huang, Zhilong Hangzhou Dianzi Univ Coll Mech Engn Hangzhou 310018 Peoples R China Zhejiang Univ Dept Engn Mech Hangzhou 310027 Peoples R China Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA

The response control of nonlinear random dynamical systems is an important but also difficult subject in scientific and industrial fields. This work merges the decomposition technique of feedback control and the data-driven identification method of stationary response probability density, converts the constrained functional extreme value problem associated with optimal control to an unconstrained optimization problem of multivariable function, and determines the optimal coefficients of preselected control terms by an optimization algorithm. This data-driven method avoids the difficulty of solving the stochastic dynamic programming equation or forward-backward stochastic differential equations encountered in classical control theories, the miss of the conservative mechanism in the nonlinear stochastic optimal control strategy, and the difficulty of judging the integrability and resonance of the controlled Hamiltonian systems encountered in the direct-control method. The application and efficacy of the data-driven method are illustrated by the random response control problems of the Duffing oscillator, van der Pol system, and a two degrees-of-freedom nonlinear system.

关键词： random response control nonlinear dynamical system data-driven method decomposition technique of feedback control optimization algorithm vibration

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Adaptive Wind Field Estimation Using an Empirical Bayesian Approach

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第11期47卷 2386-2396页

作者： Kiehn, Daniel Schultz, Julius Fezans, Nicolas Roemer, Ulrich German Aerosp Ctr Inst Flight Syst Dept Flight Dynam & Simulat DLR Lilienthalpl 7 D-38108 Braunschweig Germany Tech Univ Carolo Wilhelmina Braunschweig Inst Acoust & Dynam Fac Mech Engn Langer Kamp 19 D-38106 Braunschweig Germany

In lidar-based gust load alleviation, the wind profile ahead of the aircraft cannot be measured directly but has to be reconstructed (estimated) based on the acquired line-of-sight measurements. Such wind reconstruction algorithms typically include regularization in order to adequately handle the noise within the data. This paper presents an empirical Bayesian approach to choosing optimal regularization parameters for any given set of measurements. Using simulations of flight through turbulence, the Bayesian approach is compared with a former approach (based on engineering guess) and an omniscient optimizer, which yields the best achievable results for a constant set of parameters by using the full knowledge of the wind field. The Bayesian approach is shown to outperform the engineering guess and performs close to the omniscient optimizer while purely relying on the lidar measurement data.

关键词： Artificial Intelligence Light Detection and Ranging Loads Alleviation Power Spectral Density Gain Scheduling True Airspeed Atmospheric Conditions Probability Density Functions optimization algorithm Turbulence Intensity

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Computer-assisted design of transducers for ultrasonic sensor systems

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MEASUREMENT SCIENCE AND TECHNOLOGY 2009年第12期20卷 124012-124012页

作者： Henning, B. Rautenberg, J. Unverzagt, C. Schroeder, A. Olfert, S. Univ Gesamthsch Paderborn Measurement Engn Grp Fac Elect Engn Comp Sci & Math D-4790 Paderborn Germany

In this contribution, possibilities and methods for computer-assisted design of ultrasound transducers are described. These transducers are essential for an ultrasonic sensor design, e. g. for continuous non-invasive determination of quantities that are important in process technology. To achieve technical reliability and robustness, the precise determination of all acoustic properties of the used sensor materials is of great importance. Problem-oriented modeling, numerical simulation, special optimization algorithms and improved methods for the visualization of propagating waves offer new and promising possibilities for developing ultrasonic transducers with enhanced properties.

关键词： ultrasonic sensor design optimization algorithm

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Carbon price forecasting system based on error correction and divide-conquer strategies

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APPLIED SOFT COMPUTING 2022年第0期118卷 107935-107935页

作者： Niu, Xinsong Wang, Jianzhou Zhang, Lifang Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Carbon price forecasting is an important component of a sound carbon price market mechanism. The accurate prediction of carbon prices is an active topic of research. However, many previous studies have focused on the application of a single model, ignoring the application of combination strategies. In this study, a hybrid forecasting system that includes error correction strategy and divide-conquer strategy is designed to predict the carbon price series accurately. Specifically, the main framework of this article comprises four modules. Data preprocessing module of the divide and conquer strategy is proposed. Next, the optimization module uses a multi-objective grasshopper optimization algorithm to enhance the performance of the prediction module. Then, the error correction module predicts the error sequence and corrects the model results. To verify the performance of the established hybrid forecasting system, experiments were performed using two real carbon price series from China and European Union emissions trading schemes, and the results showed that the mean absolute percentage errors of the system were 2.7793% and 0.6720%, respectively, which are better than the other benchmark methods considered. Moreover, it was proved that the designed forecasting system provides a new, effective, and feasible solution for carbon price forecasting. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Artificial intelligence Carbon price forecasting Data preprocessing optimization algorithm Error correction

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