As a special nonperiodic transient system, the electromagnetic launch system realizes the conversion of ultrahigh power of energy in a few seconds, which is harmful when the system fails. It is urgent to study the onl...
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As a special nonperiodic transient system, the electromagnetic launch system realizes the conversion of ultrahigh power of energy in a few seconds, which is harmful when the system fails. It is urgent to study the online fault diagnosis method of the system to stop the launch in time. Fault diagnosis based on online detection of abnormal waveform of time series in launch period is an important direction to solve the problems. Compared with traditional waveforms anomaly detection, the time series data points of electromagnetic launch system are very large, the time distortion is serious, and the abnormal waveform characteristics are not obvious. Therefore, the traditional methods can not realize online anomaly detection and location. This article analyzes the characteristics of electromagnetic launch time series and proposes a novel named FWSSP-TSAD anomaly detection method. To verify the performance of the proposed method, multiple discharge tests were conducted based on an electromagnetic launch system, and the obtained PFN voltage time series dataset was used as an algorithm input. The results show that the proposed algorithm accurately identifies all abnormal waveforms and extracts all abnormal sub waveforms, achieving fault diagnosis and localization. The average calculation time is less than the window time, which meets the requirements of online fault diagnosis.
The hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) ...
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The hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) is challenging, it is often limited to only addressing model parametric uncertainty, neglecting other uncertainty sources. This paper focuses on exploiting the potential of state-of-the-art data-driven models (or DDMs) in quantifying the prediction uncertainty of process-based hydrological models. This is achieved by integrating the robust predictive ability of the DDMs with the process understanding ability of the hydrological models. The Bayesian-based data assimilation (DA) technique is used to quantify uncertainty in process-based hydrological models. This is accomplished by choosing two DDMs, random forest algorithm (RF) and support vector machine (SVM), which are distinctly integrated with two process-based hydrological models: HBV and HyMOD. particle filter algorithm (PF) is chosen for uncertainty quantification. All these combinations led to four different process-aware DDMs: HBV-PF-RF, HBV-PF-SVM, HyMOD-PF-RF and HyMOD-PF-SVM. The assessment of these models on the Baitarani, Beas and Sunkoshi river basins exemplified an improvement in the accuracy of the daily streamflow simulations with a reduction in the prediction uncertainty across all the models for all the basins. For example, the nash-sutcliffe efficiency improved by 54.69% and 10.61% in calibration and validation of the Baitarani river basin, respectively. Equivalently, average bandwidth improved by 79.37% and 71.59%, respectively. This signified the (a) potential of the DDMs in quantifying and reducing the prediction uncertainty of the hydrological model simulations, (b) transferability of the model with an appreciable performance irrespective of the choice of basins having varying topography and climatology and (c) ability to perform significantly irrespective of different process-bas
With the widespread adoption of electric vehicles (EVs) and energy storage in renewable energy systems, the use of lithium-ion batteries has increased significantly, making the battery safety performance a primary con...
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With the widespread adoption of electric vehicles (EVs) and energy storage in renewable energy systems, the use of lithium-ion batteries has increased significantly, making the battery safety performance a primary concern. The accurate state of charge (SOC) estimation can help mitigate the safety risks for the utilisation of EVs and renewable energy systems. Due to the dynamic and non-linear properties of batteries, an adaptive online SOC estimation is proposed in this paper by combining the online parameters estimation using equivalent circuit model (ECM) and the improved particlefilter (PF) algorithm. It firstly deduces ECM parameters equations using bilinear transformation with the elimination of the variation caused by the ambient temperature. Then, the seeker optimization algorithm (SOA)-based fixed-length weighted least square (LS) algorithm is introduced to online estimate the battery parameters accurately. With the established ECM, the battery SOC can be estimated by the improved genetic algorithm (IGA) resampling-based PF algorithm, which effectively alleviates the particle degeneracy problem during the estimation, consequently, offering a better performance in SOC estimation. Both simulations and experiments have been conducted to validate the effectiveness of the proposed method. Compared with other existing algorithms, it shows that the proposed algorithm can accurately model the battery with the root mean squared error (RMSE) <0.1 % and achieve the real-time SOC estimation with less computation burden and high accuracy.
The battery/ultracapacitor hybrid power supply system can solve the problems of high cost and short life of a single power system, and the energy management of hybrid power system has become a vital issue in the field...
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The battery/ultracapacitor hybrid power supply system can solve the problems of high cost and short life of a single power system, and the energy management of hybrid power system has become a vital issue in the field of electric vehicles. In this paper, a fuzzy energy management strategy on the state-of-charge (SOC) estimation of power battery is proposed. particlefilter (PF) algorithm is used to estimate SOC of power battery, then estimated result is regarded as the input variable of fuzzy energy management controller, and the energy distribution result is obtained after fuzzy logic operation. The simulation results show that the SOC estimation result of the PF algorithm is closer to the actual value of power battery SOC. When the SOC estimation result of PF is embedded into the fuzzy controller for joint simulation, it is found that the charge and discharge current, and SOC consumption of the power battery are reduced, which shows the algorithm's effectiveness. It also provides a specific reference value for the further study of the power supply control strategy of hybrid electric vehicles.
In modern engineering application, for the metal engine parts such as the crankshafts and some other related objects, sufficient high-cycle fatigue strength is necessary to guarantee the system reliability during the ...
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In modern engineering application, for the metal engine parts such as the crankshafts and some other related objects, sufficient high-cycle fatigue strength is necessary to guarantee the system reliability during the working period. The traditional resonant crankshaft fatigue test bench can determine the load-life relationship of the given crankshaft directly, but the experiment process usually lasts tens of days, which may result in the large consumption of time. In this paper, the fatigue test speed was accelerated based on the prediction of the residual fatigue life during the experiment process. Then the system state-space equation was modified based on the theory of fracture mechanics to improve the accuracy of the predictions. Finally, the statistical analysis based on the predicted data was adopted to determine the fatigue limit load of the crankshaft. The main conclusion of this paper is that the combination of the particle filter algorithm and the dynamic response signal can predict the residual fatigue life of the crankshaft conveniently based on the modified sampling range, and thus is able to shorten the experiment process and has very wide popularization and application prospects in actual engineering.
Due to limitations in computing power, the passive radar based on GNSS signal may not be able to use all the GNSS signals, requiring to make signal selection. Based on CRLB, a new radiation sources adaptive selection ...
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Due to limitations in computing power, the passive radar based on GNSS signal may not be able to use all the GNSS signals, requiring to make signal selection. Based on CRLB, a new radiation sources adaptive selection method for passive radar is proposed. With the goal of minimizing the CRLB of GNSS signals and the number of GNSS signals as constraints, an adaptive selection model is established. Simulation experiments were conducted using the selection model and particle filter algorithm. Experimental results show that the calculation accuracy of this algorithm is slightly lower than the tracking results obtained using all six satellites, but the calculation time is greatly reduced, reducing the requirement for radar computing power.
In order to solve the fusion estimation problem of multi-sensor with unknown cross-covariance, an improved suboptimal fusion algorithm weighted by matrices is proposed for nonlinear systems. Firstly, for significance ...
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ISBN:
(纸本)9798350366907;9789887581581
In order to solve the fusion estimation problem of multi-sensor with unknown cross-covariance, an improved suboptimal fusion algorithm weighted by matrices is proposed for nonlinear systems. Firstly, for significance of linear minimum variance, the simplest constraints based on fusion weighted by matrices are derived by Shure complement theorem. These constraints can ensure the positive definiteness of the fusion estimate error covariance, and the consistency of the proposed suboptimal fusion estimation. Further, a suboptimal fusion estimation weighted by matrices is proposed based on linear matrix inequality (LMI). Considering the time-consuming problem in the optimization process of LMI algorithm and the complexity of the nonlinear system, the optimal value is obtained by the nonlinear auto-regressive neural network with exogenous input (NARX). Finally, a nonlinear suboptimal fusion algorithm weighted by matrices based on LMI and NARX is proposed in combination with the particle filter algorithm (PF).
Aiming at the problem of indoor space detection mobile robots using particlefilter SLAM algorithm, which may experience poor system stability and particle degeneracy after multiple iterations and updates, this paper ...
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ISBN:
(纸本)9798400708305
Aiming at the problem of indoor space detection mobile robots using particlefilter SLAM algorithm, which may experience poor system stability and particle degeneracy after multiple iterations and updates, this paper proposes a Strong Tracking Cubature particlefilter SLAM algorithm (STF-CPF-SLAM). Firstly, the Cubature Kalman (CKF) algorithm are used as the importance sampling functions of the particle filter algorithm (PF) to generate the mean and covariance distributions, simultaneously utilizing the fading factor of the Strong Tracking algorithm (STF) to compensate the system and enhance its robustness;Then, Strong Tracking Cubature particle filter algorithm is used to filter and fuse the observation data with the system model to obtain the optimized pose data of the mobile robot, thereby constructing a more accurate indoor space map;Finally, the effectiveness of the algorithm was verified through a mobile robot simulation platform. The simulation results show that the proposed algorithm reduces the error of simultaneous localization and mapping by 55.7% compared to traditional particle filter algorithms, verifying the feasibility and effectiveness of the algorithm, and improving the accuracy of indoor space exploration mobile robots in map construction. This algorithm provides a new reference for simultaneous localization and mapping of mobile robots.
With lithium-ion batteries are more and more widely used in transportation, the estimation of battery state-of-health (SOH) is of great significance in the safe and reliable operation of battery management system and ...
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ISBN:
(纸本)9789881563804
With lithium-ion batteries are more and more widely used in transportation, the estimation of battery state-of-health (SOH) is of great significance in the safe and reliable operation of battery management system and the reduction of maintenance cost. Based on the analysis of the traditional particle filter algorithm, the genetic factor of genetic algorithm (GA) is introduced into the particlefilter and improved by adaptive mutation. In order to predict the SOH of lithium-ion battery, the health index (HI) is extracted from the measurable parameters of lithium-ion battery. The mapping model between HI index and SOH is established and applied to the observation of state space model. In this paper, a battery SOH estimation method based on improved particle filter algorithm is proposed. The experimental results show that the proposed method is superior to the traditional particlefilter (PF) algorithm and has good accuracy in estimating the degradation process of lithium-ion batteries.
All along, the identification of night-driving safety car features is a major research direction in the field of intelligent traffic management, with a wide range of applications and development space, and these ident...
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All along, the identification of night-driving safety car features is a major research direction in the field of intelligent traffic management, with a wide range of applications and development space, and these identification technologies include theoretical knowledge and important theoretical research in many fields. Due to the interference of lights and other light sources, the gray value of the background area also changes frequently. A common method during the day is to detect these background areas as moving vehicles, which greatly reduces the detection accuracy. The most reliable information at night is the headlights. If the light can be accurately detected and other sources are excluded, accurate detection can be performed and tracking accuracy can be guaranteed. Vehicle safety assisted driving technology is one of the main research directions of intelligent transportation systems. It mainly uses computer technology, sensor technology and communication technology to collect and analyze the state information of roads, other vehicles and drivers. Provide advice and warnings to the driver before reaching the danger, determine current traffic conditions and avoid traffic accidents in advance. This paper studies some problems of night vehicle target recognition and detection, mainly the division of target and background, and the classification and recognition of target extraction. To solve these problems, a particle filter algorithm is introduced to introduce nonlinear statistics. The fuzzy theory is introduced to classify the video processed by the particle filter algorithm. The target recognition is realized by the method, and the purpose of identifying the night vehicle target is achieved. Comparative experimental analysis shows that this method is more accurate and powerful than the common target recognition algorithm and can be applied to real scenes.
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