Search Results - (( learning application force algorithm ) OR ( java implication based algorithm ))
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Active force control with iterative learning control algorithm for a vehicle suspension
Published 2013“…The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. …”
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A comparative study of vibrational response based impact force localization and quantification using different types of neural networks / Wang Yanru
Published 2018“…In the study, the effort to find better neural network algorithms in the application of impact force indetification continues. …”
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Algorithm animation for cryptanalysis of caesar and hill ciphers / Sapiee Haji Jamel and Giuseppina Sherry Sayan
Published 2009“…This paper presents an algorithm animation for cryptanalysis of Caesar Cipher using ciphertext only attack and Hill Cipher using Brute force technique. …”
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Machine learning model to predict the contact of angle using mineralogy, TOC and process parameters in shale
Published 2021“…The application aims to develop a machine learning program using the algorithm of Support Vector Machine or Gaussian Process Regression to successfully predict the contact angle. …”
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Improving robotic grasping system using deep learning approach
Published 2020“…The developed model achieved 74.8 milliseconds as prediction time, which makes it possible to use the model in real-time robotic applications. By observing the real-time feedback of a force sensing resistor sensor, the proposed slip detection algorithm indicated a quick response within 86 milliseconds. …”
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Practical implementation of skyhook and adaptive active force control to an automotive suspension system
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
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A wheelchair sitting posture detection system using pressure sensors
Published 2024“…Three classification algorithms based on the supervised learning of machine learning, such as support vector machine (SVM), random forest (RF),and decision tree (DT) were used to classify the postures which produced an accuracy of 95.44%, 98.72%,and 98.80%, respectively. …”
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