The suspended dust produced in the production of process industry may cause damage to occupational health and even trigger explosion accidents. To accurately detect the dust concentration, and to reduce the noises and...
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The suspended dust produced in the production of process industry may cause damage to occupational health and even trigger explosion accidents. To accurately detect the dust concentration, and to reduce the noises and standard deviation volatility of the inductive signal of characterized dust concentration generated when applying the electrostatic induction method, the study designs the kalman filter based on a machine learning algorithm and realizes secondary processing of the standard deviation of inductive signal. Firstly, this study designs the dust concentration detection device using the electrostatic induction method, realizing an effective extraction and amplification of the dust inductive signal, confirming a positive correlation between the volatility of the inductive signal and dust concentration. A data processing procedure for the inductive signal is also designed. To eliminate the standard deviation volatility of inductive signal, the kalman filter aided by machine learning is selected to process mathematical models. By comparing the conventional sliding filter algorithm, median filter algorithm and kalmanfiltering aided by machine learning, it is confirmed that kalmanfiltering aided by ma-chine learning has a better effect on reducing the standard deviation of inductive signal. The standard deviation can be quickly converged to the target value through short-term iteration, effectively eliminating the fluctuation of the standard deviation value of the inductive signal, and improving the stability of the standard deviation value of the inductive signal.
Aiming at the problems of AC charging pile fault detection, such as large amount of iterative calculation and low ability of fault classification, it is difficult to adapt to the practical application environment. The...
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Aiming at the problems of AC charging pile fault detection, such as large amount of iterative calculation and low ability of fault classification, it is difficult to adapt to the practical application environment. The fault detection method of AC charging pile in coastal cities based on kalman filter algorithm is studied. Through data fusion, algorithm fusion and other methods, the information related to the working state of AC charging pile is obtained from many aspects, and then integrated. The advantages of different fault diagnosis algorithms can improve the accuracy of fault diagnosis results;the fault tree analysis algorithm is introduced into kalman filter algorithm to improve the modeling accuracy and efficiency of AC charging pile nonlinear model, and the relationship coefficient between the performance vector variation of the whole AC charging pile and its components is analyzed, so as to solve the problem of different times of the same model and different service life of the same AC charging pile. In order to improve the accuracy of component health parameter estimation results in hardware and software design, the fault detection of AC charging pile in coastal cities is realized. The simulation results show that the final output of the charging system is more scalable and the success rate of fault detection is higher. This method has good practical application effect in coastal cities and has strong applicability.
The purpose of this study was to investigate the shortcomings and deficiencies of video surveillance systems, thus presenting a new method for intelligent video surveillance. The specific hardware structure and softwa...
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The purpose of this study was to investigate the shortcomings and deficiencies of video surveillance systems, thus presenting a new method for intelligent video surveillance. The specific hardware structure and software algorithm design are adopted. And the performance of the algorithm is tested and verified through experiments. The results obtained in this study are that: combining the wireless sensor network with the traditional video surveillance system as a whole and interoperating with each other not only enables the wireless sensor network to monitor and judge live real time data, but also enables the camera to be shot and tracked when a sensitive event occurs and use the image processing algorithm to make two judgments on the warning event and issue the correct alarm. The findings of this study may serve as solving the problem that the traditional video surveillance system relies solely on image recognition technology to make an event alarm, which causes misdetection and missed detection under low illumination conditions. As a result, it improves the alarm accuracy of the system and also makes up for a single wireless sensor network. When an alarm occurs, the on-site real time screen cannot be confirmed.
Consumption of clean energy has been increasing in *** gas consumption is important to adjusting the energy consumption structure in the *** on historical data of gas consumption from 1980 to 2017,this paper presents ...
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Consumption of clean energy has been increasing in *** gas consumption is important to adjusting the energy consumption structure in the *** on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and kalman filtering algorithm is used to forecast gas *** results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^*** to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.
The locally optimal filter is designed for a class of discrete-time systems subject to stochastic nonlinearity functions, finite-step correlated noises, and missing measurements. The multiplicative noises are employed...
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The locally optimal filter is designed for a class of discrete-time systems subject to stochastic nonlinearity functions, finite-step correlated noises, and missing measurements. The multiplicative noises are employed to describe the random disturbances in the system model. The phenomena of missing measurements occur in a random way and the missing probability is characterized by Bernoulli distributed random variables with known conditional probabilities. Based on the projection theory, a class of kalman-type locally optimal filter is constructed and the filtering error covariance matrix is minimized in the sense of minimum mean square error principle. Also, by solving the recursive matrix equation, we can obtain the filter gain. Finally, two examples are provided: one is a numerical example to illustrate the feasibility and effectiveness of the proposed filtering scheme;the other is to solve the problem of target estimation for a tracking system considering networked phenomena.
Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical *** study aims at forecasting the c...
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Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical *** study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange（ECX） market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error（RMSE）. Based on the linear state space model and the kalman filtering algorithm, the root mean square error（RMSE） is 0.000016375, which is small enough, and it indicates that the algorithm performs well.
An original method is presented in this paper, which attempts to make use of the kalman filtering algorithm and maintain accuracy in position, attitude and velocity estimation for a fast moving projectile, the velocit...
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ISBN:
(纸本)9781510813465
An original method is presented in this paper, which attempts to make use of the kalman filtering algorithm and maintain accuracy in position, attitude and velocity estimation for a fast moving projectile, the velocity and the pose(position and attitude) could also be obtained, then the simulation are presented and compared with the conventional least square method. The simulation result showed that the kalman filter algorithm is more accurate and stability than the conventional least squares algorithm, meanwhile, the trajectory of the projectile obtained by the kalman filter algorithm is very close to the real trajectory.
Research on coke price forecasting is of theoretical and practical signiifcance. Here, the kalman ifltering algorithm was used to analyze the price of coke. As the only state variable, the historical coke price is sor...
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Research on coke price forecasting is of theoretical and practical signiifcance. Here, the kalman ifltering algorithm was used to analyze the price of coke. As the only state variable, the historical coke price is sorted out to build the state space model. The algorithm makes use of innovation composed of the difference between observed and predicted values, and alows us to obtain the optimal estimated value of the coke price via continuous updating and iteration of innovation. Our results show that this algorithm is effective in the ifeld of coke price tracking and forecasting.
The dynamic management of sensor nodes and advanced information fusion are necessary technologies to enhance the comprehensive performance of sensor networks. This paper presents a cascaded sensor dynamic activation a...
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The dynamic management of sensor nodes and advanced information fusion are necessary technologies to enhance the comprehensive performance of sensor networks. This paper presents a cascaded sensor dynamic activation and information fusion algorithm to simultaneously optimize the energy and sensing performance of wireless sensing networks. The proposed algorithm dynamically activates nodes that are most suitable for the current sensing task through a joint event-driven and state ranking activation algorithm that achieves a better sensing performance with lower energy costs. In addition, it further utilizes the sensing information of all the activated nodes with maximum efficiency, through an improved distributed kalman information fusion, which achieves an extra improvement in sensing accuracy as measured by the minimum variance. Finally, the superiority of the proposed cascaded algorithm is verified by a simulation comparison, achieving almost zero dead nodes in terms of energy, and a 62.1% decrease in average error in terms of sensing.
To reduce the false alarm rate and alarm rate of vehicle rear end warning, and improve the accuracy of warning, a vehicle rear end warning method based on MeanShift and kalman filter tracking is proposed. Determine th...
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To reduce the false alarm rate and alarm rate of vehicle rear end warning, and improve the accuracy of warning, a vehicle rear end warning method based on MeanShift and kalman filter tracking is proposed. Determine the rear end warning area, use a high-speed camera to capture the driving video image of the target vehicle, and perform image denoising and enhancement;detect the target vehicle through inter frame difference method, and combine the MeanShift algorithm with the kalman filter algorithm to complete the tracking and positioning of the target vehicle;Determine the position of the target vehicle and the relationship between the warning areas to achieve rear end warning. The experimental results show that the method proposed in this paper can effectively reduce the false alarm rate and alarm rate, with a false alarm rate always below 2% and an alarm rate of 98%. It has good application performance.
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