Accurate stock price prediction is a challenging yet crucial goal in finance, with significant implications for investment decisions and risk management. This paper presents a comprehensive review of machine learning ...
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The oil spill is considered as a crisis that caused a lot of impact on the marine environment. At the beginning of 2022, an oil spill crisis happened in Rayong Province, Thailand. This crisis caused more than 140 barr...
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AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first gener...
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AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.
Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer iss...
Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection,...
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Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE
This paper investigates two distributed accelerated primal-dual neurodynamic approaches over undirected connected graphs for resource allocation problems(RAP)where the objective functions are generally *** the help of...
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This paper investigates two distributed accelerated primal-dual neurodynamic approaches over undirected connected graphs for resource allocation problems(RAP)where the objective functions are generally *** the help of projection operators,a primal-dual framework,and Nesterov's accelerated method,we first design a distributed accelerated primal-dual projection neurodynamic approach(DAPDP),and its convergence rate of the primal-dual gap is O(1/(t^(2)))by selecting appropriate parameters and initial ***,when the local closed convex sets are convex inequalities which have no closed-form solutions of their projection operators,we further propose a distributed accelerated penalty primal-dual neurodynamic approach(DAPPD)on the strength of the penalty method,primal-dual framework,and Nesterov's accelerated *** on the above analysis,we prove that DAPPD also has a convergence rate O(1/(t^(2)))of the primal-dual *** with the distributed dynamical approaches based on the classical primal-dual framework,our proposed distributed accelerated neurodynamic approaches have faster convergence *** simulations demonstrate that our proposed neurodynamic approaches are feasible and effective.
Interest in supporting Federated Learning (FL) using blockchains has grown significantly in recent years. However, restricting access to the trained models only to actively participating nodes remains a challenge even...
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This paper proposes a novel data-driven finite-time adaptive control method for the spacecraft attitude tracking control problem with inertial uncertainty. Based on the dynamic regression extension technique, the dist...
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The digital twin in manufacturing, also called digital factory twin (DFT), is not only a recent technology but also a polymorphic concept which generates a profusion of state-of-the-art work from industry and research...
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During the COVID-19 Pandemic, Thailand utilized Thai Chana contact tracing mobile application and the paper document. The purpose of this study was to explore the perceived usability of Thai Chana contact tracing by u...
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