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.
As a result of contamination intrusion's inherent vulnerability, water quality security has been an important issue within water distribution systems (WDSs). In order to detect (un)intentional intrusion events in ...
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As a result of contamination intrusion's inherent vulnerability, water quality security has been an important issue within water distribution systems (WDSs). In order to detect (un)intentional intrusion events in a timely/effective manner, intensive studies have been undertaken to identify leakage detection and localization methodologies. It is possible to detect and localize leaks in water distribution systems using models-based methodologies. The purpose of this paper is to propose a novel leakage detection and leakage localization model that is based on the network hydraulic model. In order to solve the leakage problem, all hydraulic relationships have been modified and a new model has been developed. This model is developed based on head variation of network sensor nodes. This study utilized mathematical modeling based on the response surface methodology to detect leakages, in addition to detecting leaks;this method can also be used to assess the location of sensors. Consequently, in addition to developing a novel model, a new method is presented for assessing sensor placement in the present study. A leakage diagnosis benchmark dataset is used to demonstrate the proposed methodology and evaluate its effectiveness. Based on the final results, the presented method performed well and was highly accurate.
The purpose of this article is to design an optimal fuzzy type-2 proportional integral derivative (FT2PID) controller to enhance the pressure tracking capability of an artificial respiratory system. A patient-hose blo...
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The purpose of this article is to design an optimal fuzzy type-2 proportional integral derivative (FT2PID) controller to enhance the pressure tracking capability of an artificial respiratory system. A patient-hose blower-driven mechanical ventilator (MV) operated in pressure-controlled mode is examined with the proposed controller structure. The error between the desired airway pressure and the ventilator pressure is used as an input to the fuzzy type-2 controller. Another fuzzy input is the change in error. The output variables of the fuzzy inference system (FIS) of the fuzzy controller in the proposed control structure are the parameters of a PID controller. The ranges and points of a triangular-shaped fuzzy type-2 inference system are optimized for the ventilator model with a newly introduced optimizer named the human conception optimizer (HCO) algorithm. With the optimized fuzzy type-2 controller, the parameters of the PID controller are adjusted automatically during any external disturbance or in the presence of any parametric uncertainties in the system. The inherent features of handseling uncertainties of the fuzzy type-2 controller are verified with the PID controller for the ventilator model under different scenarios. With the proposed control scheme, the pressure tracking profile of the ventilator is improved in terms of response time, settling time, and overshoot as compared to the existing results.
In order to address the challenge of stability control of surrounding rock in roadways under complex geological conditions such as deep high-stress inclined strata and soft-hard interbedding, a comprehensive approach ...
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In order to address the challenge of stability control of surrounding rock in roadways under complex geological conditions such as deep high-stress inclined strata and soft-hard interbedding, a comprehensive approach combining similarity model tests and complex variable function analysis with the Adam optimization algorithm was employed. This approach was utilized to obtain the stress and failure characteristics of surrounding rock in roadways under inclined strata and soft-hard interbedding conditions, elucidating the stability failure mechanism of surrounding rock in roadways. The research results indicate that: the internal stress direction in inclined strata shows obvious orientation, tending to be perpendicular to the stratigraphic boundary;the stress distribution in roadway surrounding rock varies with the inclination direction of the strata. Specifically, when the strata inclination angle is clockwise, stress concentration tends to occur on the left sidewall and right floor of the roadway;conversely, when the strata inclination angle is counterclockwise, stress concentration tends to occur on the right sidewall and left floor of the roadway. The use of the Adam optimization algorithm for solving mapping functions demonstrates superiority, with a solution time of only 1.25 s and high accuracy. When the number of coefficient terms in the mapping function is 9, the average error is only 0.17%, which is 0.34 times lower compared to other optimization methods, with the required number of iterations reduced by 0.64, significantly reducing subsequent computational pressure. For cross-layer roadways with soft-hard interbedding surrounding rock, the deformation and failure of roadways are closely related to the interface position between the rock layers, with deformation more likely to occur in the soft rock area and with larger deformations. Under conditions of high stress, when facing both the soft-hard interbedding and inclined strata issues simultaneously, priorit
This study aims to predict the melt pool depth and width of 316L stainless steel welds during laser welding using a multilayer feed-forward neural network (MLFNN). The Taguchi method was employed to design the laser w...
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This study aims to predict the melt pool depth and width of 316L stainless steel welds during laser welding using a multilayer feed-forward neural network (MLFNN). The Taguchi method was employed to design the laser welding parameters and generate experimental data on melt depth and width. This allowed for an in-depth investigation of the effects of these parameters on the melt pool characteristics of 316L stainless steel. The results demonstrate that the MLFNN model, with a 3-10-10-10-2 structure, exhibits high accuracy and stability across training, validation, and testing phases. The correlation coefficient R-value between predicted and experimental results is 0.99995, indicating an excellent fit to the experimental data. The model's predictions can effectively reduce defects in 316L stainless steel during laser welding, significantly enhancing weld quality.
Appropriate selection of a satellite constellation design framework for a particular mission set requires a priori knowledge about the relative merits and shortcomings of different frameworks. Symmetric satellite cons...
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Appropriate selection of a satellite constellation design framework for a particular mission set requires a priori knowledge about the relative merits and shortcomings of different frameworks. Symmetric satellite constellation frameworks exhibit good properties for missions requiring continuous global coverage, whereas asymmetric satellite constellation frameworks benefit missions focusing on regional coverage. This research compares the performance of an asymmetric "string-of-pearls" common repeating ground track constellation design framework against a Walker constellation design framework for maintaining continuous connectivity between regions of interest. Several examples illustrate that the asymmetric string-of-pearls constellation framework appears to require an average of approximately 1.25 fewer satellites than the Walker constellation, whereas the Walker constellation appears approximately 7.86% more robust to satellite failures.
Digital real-time fault diagnosis is an effective way to ensure the reliable long-term operation of the diesel engine, but there is still a lack of systematic methods with high integrity and practicability. Therefore,...
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Digital real-time fault diagnosis is an effective way to ensure the reliable long-term operation of the diesel engine, but there is still a lack of systematic methods with high integrity and practicability. Therefore, a digital twindriven diesel engine fault diagnosis method based on the combination of the classification algorithm and the optimization algorithm is proposed and a case study of fuel injection system fault diagnosis is used to illustrate and verify the proposed method. This method closely links the physical system, virtual model, database, and diagnosis system through data transmission and the diagnostic process consists of three parts: classification, diagnosis, and decision. The fault classification part can preliminarily lock the possible types and degrees of faults, and point out the key classification features for each fault type by using classification algorithms such as Random Forest. The fault diagnosis part can diagnose and reproduce the diesel engine faults by using an optimization-simulation joint calculation model, where the virtual model variables and optimization algorithm are determined according to the possible fault types, and the optimization target depends on the importance of classification features. Then the maintenance decision can be made according to the fault detailed information. The proposed method reduces the requirement of covering the fault degree of the database, and the obtained fault model provides the possibility for subsequent online optimization and also facilitates the development of intelligent engine management.
Underwater acoustic signal (UAS) denoising is base and prerequisite for UAS detection, recognition and classification. In order to perform UAS denoising effectively, a new UAS denoising method based on modified unifor...
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Underwater acoustic signal (UAS) denoising is base and prerequisite for UAS detection, recognition and classification. In order to perform UAS denoising effectively, a new UAS denoising method based on modified uniform phase empirical mode decomposition (MUPEMD), hierarchical amplitude -aware permutation entropy (HAAPE), and improved wavelet threshold denoising (IWTD) method optimized by sand cat swarm optimization (SCSO) (SIWTD), named MUPEMD-HAAPE-SIWTD, is proposed. Firstly, decompose original signal into a battery of intrinsic mode functions (IMFs) by MUPEMD. Secondly, determine the double threshold by HAAPE to classify signal into pure signal, mixed signal and noisy signal. Then, optimize IWTD by SCSO, so that it can adaptively select the optimal wavelet basis function to achieve the denoising of mixed signal. Finally, the denoised mixed signal is reconstructed with pure signal to obtain ultima denoised signal. The denoising experiments on Lorenz signal and Duffing signal demonstrate that signal-to-noise ratio can be improved by 9 dB-13 dB by the proposed method. The denoising experiments on simulating ship radiated noise signals and four kinds of actual ship radiated noise signals (https://***/, accessed on June 1, 2023) demonstrate that the proposed method makes phase diagram smoother and clearer, and makes the ability of suppressing noise higher.
Denoising of underwater acoustic signal (UAS) has vital academic significance and practical value. To achieve effective denoising of UAS, a novel approach for UAS denoising based on improved time-variant filtered empi...
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Denoising of underwater acoustic signal (UAS) has vital academic significance and practical value. To achieve effective denoising of UAS, a novel approach for UAS denoising based on improved time-variant filtered empirical mode decomposition and weighted fusion filtering is proposed. Firstly, to improve decomposition efficiency, time-variant filtered empirical mode decomposition (TVFEMD) based on an improved walrus optimization algorithm (IWaOA) (IWTVFEMD) is proposed. It decomposes signal into some intrinsic mode functions (IMFs), and IMFs are classified into high frequency IMFs and low frequency IMFs by energy analysis. Secondly, Gaussian-weighted moving average filtering (GWMAF) is used to filter boundary low frequency IMF and remaining low frequency IMFs are reconstructed as noise-free IMFs. Thirdly, all high frequency IMFs are reconstructed, and reconstructed high frequency IMFs are secondary decomposed by IWTVFEMD, and IMFs obtained by secondary decomposition are called SIMFs. K-means clustering and time-shift multi-scale amplitudeaware permutation entropy (TSMAAPE) are used to adaptively divide SIMFs into low complexity category and high complexity category. Then, weighted fusion filtering based on Gaussian mixture model clustering (GWFF) is proposed, which is used to filter low complexity category. High complexity category is discarded as noise. Finally, noise-free IMFs, boundary low frequency IMF after GWMAF and low complexity category after GWFF are reconstructed to obtain the denoised signal. Typical chaotic signal such as Chua and Duffing signal and actual measured five UAS are tested. The outcomes reveal that the proposed denoising method has achieved superior denoising results, which can improve signal-to-noise ratio of Chua and Duffing signal by 14 dB and 17 dB respectively, and make three-dimensional attractor phase diagram of actual measured UAS clearer and smoother.
This study investigates the stability of an autonomous ground vehicle dynamics model in the presence of novel denial of service (DoS) attacks. An algorithm that utilizes an asymmetric Lyapunov-Krasovskii function to r...
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This study investigates the stability of an autonomous ground vehicle dynamics model in the presence of novel denial of service (DoS) attacks. An algorithm that utilizes an asymmetric Lyapunov-Krasovskii function to reduce decision variables effectively is proposed. By partitioning the LKF for asymmetric LKF trimming, we ensure a tighter upper bound on the remaining part of the function using appropriate integral inequalities. Additionally, an improved alternating direction algorithm (ADA) is employed to optimize the threshold selection problem while considering initial constraints. Subsequently, we design an intelligent integral-based event-triggering controller (IIETC) that incorporates random trigger signal loss to guarantee AGV stability. We conduct a series of experiments utilizing the CarSim-Simulink AGV joint platform. The experimental results serve to validate the effectiveness and superiority of our proposed method.
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