Neural network training loss optimization utilizing the sliding innovation filter

Alsadi, Naseem; Hilal, Waleed; Surucu, Onur; Giuliano, Alessandro; Gadsden, Andrew; Yawney, John; Al-Shabi, Mohammad

doi:10.1117/12.2619029

Cited by 12 publications

(9 citation statements)

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“…Artificial intelligence (AI) [1][2][3][4][5][6][7][8] and robots [9][10][11][12][13][14][15][16][17][18][19][20] have given birth to a new wave of technological break throughs whereby the fusion of control systems with cognitive capabilities is not merely a dream but a reality that is not only being improved but also exploited with passion. The convergence is underlined by the Unscented Kalman Filter (UKF) [21][22][23][24][25][26][27][28][29][30][31][32][33], a sophisticated algorithm that has done a lot in advancing the abilities of AI-based robotics [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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This paper gives a bibliometric summary of Unscented Kalman Filter (UKF) in AI-infused robotics, highlighting its role in unifying control and cognition. Using a systematic approach that includes literature collection from IEEE Xplore, Web of Science and Google Scholar, rigorous screening and selection, and VOSviewer for a comprehensive bibliometric analysis. This analysis reports major trends, primary contributors and central themes, highlighting UKF's pivotal role in improving robotics cognitive and control capacities. The study emphasizes the universally used UKF in many fields of robotics, i.e. in navigation and mapping, sensor fusion, and state estimation, as one of its principal developers, which illustrates its vital role in promoting robotic autonomy and intelligence. The integration of findings from the bibliometric analysis thus not only presents the current state of research but also identifies possible future research directions, highlighting the increasing unification of control theories and cognitive processes in robotics. This research adds to the body of knowledge by delivering a comprehensive map of the UKF application. In this light, the UKF will be able to penetrate AI-infused robotics, the future of robotic developments will rely on the deep fusion of control and cognition facilitated by UKF and alike.

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Section: Introductionmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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“…The research in smart microgrids [24][25][26] and sustainable energy generation [27][28][29][30] emphasizes the significance of such filters in bettering the system reliability and efficiency. Their use also spills over to condition monitoring in hybrid electric vehicles [31][32][33][34], robotic system control [35][36][37][38][39][40][41][42][43][44], and renewable energy optimization [45][46][47][48][49][50][51][52][53][54][55][56], thus, an indication of their versatility and real-time control and decision-making capability.…”

Section: Introductionmentioning

confidence: 99%

Advanced crop monitoring: incorporating the Kalman filter into modern agriculture

Obaideen,

Faroukh,

Bonny

et al. 2024

Sensing for Agriculture and Food Quality and Safety XVI

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This paper investigates the integration of Kalman Filter technologies into modern agricultural practices, with a focus on advancing crop monitoring techniques. Through a comprehensive bibliometric and text mining analysis, we explore the evolution of research in this area from 1983 to 2023, highlighting the steady growth in scholarly publications and the interdisciplinary nature of the field. Our findings reveal a significant interest in leveraging Kalman Filter algorithms to enhance the precision and efficiency of agricultural operations, addressing the challenges of sustainability and food security. The study underscores the importance of data assimilation, real-time monitoring, and predictive analysis in agriculture, facilitated by the adoption of Kalman Filter and its variants. We also examine the integration of emerging technologies, such as unmanned aerial vehicles (UAVs) and remote sensing, with Kalman Filter techniques to develop sophisticated agricultural monitoring systems. The paper concludes with insights into future research directions, emphasizing the need for overcoming barriers to technology adoption and fostering interdisciplinary collaborations. This work contributes to the understanding of how computational intelligence can transform agricultural practices, offering solutions for more sustainable and efficient farming.

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“…The human age, weight, body mass index (BMI), height, and the frequency level, all beside the gender are used to study the BioR of the subjects while exited by a vibration platform. Beside the many usages [44,[50][51][52][53][54][55][56][57][58][59], previously, the transmissibility in all three directions were modeled using Artificial Neural Network (ANN). However, up to the knowledge of the authors, the effect on the apparent mass was not studied so far.…”

Section: Introductionmentioning

confidence: 99%

Studying the gender effect on apparent mass during whole-body vibration using ANN

Al-Shabi

Nawayseh

2023

Smart Biomedical and Physiological Sensor Technology XX

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Artificial Neural Network (ANN) is a powerful tool to model a system using only the inputs and outputs of that system. In this paper, ANN is used to model the relation between the subject's gender to its performance while been excited in a whole-body vibration machine (WBV). For training the ANN, 20 male and 20 female subjects were observed during an experimental setup using a WBV at different vibration frequencies in the range of 20 to 45 Hz. The apparent mass was measured for the subjects at different frequencies. The input to the ANN includes body mass index, mass, and gender of the subjects along with and the excitation frequency. The ANN shows a good performance and extract the relationship with a performance that has a root mean squared error of the relative percentage error less than 9%.

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Neural network training loss optimization utilizing the sliding innovation filter

Cited by 12 publications

References 0 publications

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Advanced crop monitoring: incorporating the Kalman filter into modern agriculture

Studying the gender effect on apparent mass during whole-body vibration using ANN

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