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 ...
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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.
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 ...
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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.
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 ...
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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.
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) i...
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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
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 t...
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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.
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...
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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.
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-...
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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.
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 reconstructi...
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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.
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 ...
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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.
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 ...
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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.
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