This paper proposes a methodology of combining eigenstructure assignment, feedforward gain with sliding mode control technique to realize an enhanced robust pitch pointing flight control system. This control system ca...
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This paper proposes a methodology of combining eigenstructure assignment, feedforward gain with sliding mode control technique to realize an enhanced robust pitch pointing flight control system. This control system can track step input commands without error and it turns out to be consistently robust against parameter variations and external disturbances. The design methodology is illustrated by application to an AFTI/F-16 aircraft.
Both the fractal dimension (FD). and the multifractal dimensions (MFD) have been widely used to describe natural textures in image processing community. However, due to the essential difference between the fractal rea...
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Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast canc...
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ISBN:
(数字)9798331532147
ISBN:
(纸本)9798331532154
Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast cancer patients, focusing on delay factors and ML-based solutions. The goal is to develop precise ML models that predict treatment delays, thereby improving the quality and efficiency of medical services. We investigated socio-economic disparities affecting treatment access and developed models to predict these delays. The predictive models used include K-Nearest Neighbors, Decision Trees, Linear Regression, and Boosting Algorithms such as AdaBoost and Gradient Boosting Regressor. By accurately predicting the interval from diagnosis to the first treatment, our work aims to promote equity in healthcare, ensuring timely treatment for all patients. Our findings highlight the potential of ML in optimizing treatment timelines and resource allocation, contributing to improved patient outcomes and advancing the medical system.
Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on th...
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ISBN:
(数字)9798350374261
ISBN:
(纸本)9798350374278
Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics. The aim of this paper is to highlight the limitations of this approach; it is shown that re-tuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness.
This paper presents optimum pairing and ordering (P/O) conditions for simultaneous reduction in total capacitance, sensitivity and output noise of cascade SC filters. First, investigating relations among conditions pr...
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Resource management in nowadays agriculture imposes the use of new technological solutions for automation and energy efficiency and this paper concentrates on the control of internal temperature of a greenhouse using ...
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Resource management in nowadays agriculture imposes the use of new technological solutions for automation and energy efficiency and this paper concentrates on the control of internal temperature of a greenhouse using parabolic trough collectors (GHPTC) for internal heating. The actuator for the GHPTC control system is the conducting fluid pump that enables the liquid flow into the collector pipes. Both the nonlinear and linear models are presented in the paper in order to tune a classical controller. A comparison of controller performances (stationary and transient) was conducted by simulation, using Matlab/Simulink software. Also, the gradient descent method was applied for an optimal tuning of the temperature controller, imposing minimum overshoot.
In this paper, a hierarchical multirate control scheme for nonlinear discrete-time systems is proposed, composed of a robust model predictive controller (MPC) and a multirate integral sliding mode (MISM) controller. I...
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The aim of this work is to design a sound logger system for environmental monitoring using wireless sensors network WSN. The proposed solution is to place nodes in a fixed location in the city center of Annaba. These ...
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In this paper, a new tool for system identification and model predictive control (MPC) has been developed. The mathematical approximation of the model identification was derived using the neural network theory. The em...
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A simple algorithm for establishing the presence of points common to some singularity (hyper)surfaces associated to covariance matrices is developed. It allows, on the basis of the new algebraic and geometric properti...
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A simple algorithm for establishing the presence of points common to some singularity (hyper)surfaces associated to covariance matrices is developed. It allows, on the basis of the new algebraic and geometric properties recently given by Guidorzi (1994), the computation of the maximal number of linear relations that can be associated to a given set of data.
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