Kalman Filter Employment in Image Processing

Fronckova, Katerina; Slaby, Antonin

doi:10.1007/978-3-030-58799-4_60

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…where x is a priori state estimation, F is the state transition matrix, u is the control variable, B is the control matrix, P is the state matrix variant, Q is the process matrix variant, y is the sensor measurement variable, H and R each is a measurement matrix, and K is the Kalman Gain. t|t represents the current period, t-1|t-1 is the previous period [45,46]. The next stage modifies the Kalman filter equation into two stages, namely predicting the state and predicting the error [47,48].…”

Section: 2 Implementation Of Kalman Filter Algorithm To Reduce Noisementioning

confidence: 99%

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Pratiwi,

Fadli,

Cahyanto

et al. 2024

EEJET

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The Kalman filter algorithm is very important as a recursive algorithm method to optimize sensor output from physical parameter measurement systems, especially physics practicum demonstration systems. One of the distance parameter measurement demonstration systems used in Hooke’s law demonstration system is applied in physics practicum, the system has problems related to fluctuating or unstable sensor output. This research implements the Kalman filter algorithm on the Arduino IDE sketch to reduce noise that appears at the ultrasonic sensor output. The methodology used in this study includes the application of the Kalman filter algorithm to the Arduino IDE sketch with the variable value of the Kalman filter algorithm equation modified with a value of R=10, H=1, and Q=1, and returns the filtered Kalman out value. The Arduino output results are exported to Ms. Excel for further analysis and generate a filtered ultrasonic sensor output signal graph compared without using the Kalman filter. The ultrasonic sensor output noise filtration effectively reduces noise by showing a decrease in the mean squared error (MSE) value and obtaining the best performance of up to 89.23 %. The accuracy of Kalman filter filtration results can be seen from the calculation that the spring constant of filtered metal materials is smaller than the conventional measurement spring constant. Accurate and effective results with the implementation of the Kalman filter algorithm can be developed for the variation values of distance parameters and Kalman filter algorithm variables (R, Q, and H) with other value variations, especially variables that produce filtering curves close to straight lines. It was concluded that the Kalman filter algorithm was able to improve the performance of Hooke’s law prop system

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Section: 2 Implementation Of Kalman Filter Algorithm To Reduce Noisementioning

confidence: 99%

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Pratiwi,

Fadli,

Cahyanto

et al. 2024

EEJET

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“…The process information captured, using the relevant sensors listed in Table 1, was incorporated using the Kalman filter in their study. The Kalman filter is essentially an algorithm estimating an unknown state utilising specific measured data [45]. Figure 5 shows the slopping alarms for heat with medium slopping [7].…”

Section: Plant-based Studies On Dynamic Control Of Slag Foam Using Ac...mentioning

confidence: 99%

Acoustic Analysis of Slag Foaming in the BOF

Heenatimulla

Brooks

Dunn

et al. 2022

Metals

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The control of slag foam that is produced during the Basic Oxygen Furnace (BOF) process has been the subject of significant research. The behaviour of slag foams is complex. Hence, the control of slag foam in the dynamic process of the BOF is challenging. Acoustic analysis of the BOF is one of the most promising methods for the indirect measurement of slag foam height. This paper reviews different studies on the fundamental behaviour of acoustics in liquid foams and various acoustic studies related to determining the slag foam height during the BOF process. Studies on the BOF have been carried out using both cold water models and plant trials, where acoustic measurements taken directly from the process were analysed. These studies showed that the attenuation of sound through liquid foam was influenced mainly by factors such as viscosity, bubble size, and foam height. Current systems are said to be 70 to 87 per cent accurate in detecting and/or predicting slopping events in the BOF, though there is a lack of systematic data in the literature to fully quantify this accuracy. There have been various attempts to combine sound with vibration and image signals to improve the prediction of slopping events in BOFs. The review substantiates the lack of accuracy of the current systems in determining the slag foam height using acoustic analysis and the need to address fundamental questions about the behaviour of sound in dynamic foam, its reliance on different factors, and the relatability of comparing cold model data to industrial data.

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