The paper proposes the modeling of a driver behavior in lateral vehicle control design purposes. Nowadays significant emphasis is placed on the development of autonomous vehicle systems which motivates to research in ...
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In this paper, the problem of privacy preservation in the continuous-time dynamic average consensus is addressed by using a state decomposition scheme. We first show that for a conventional dynamic average consensus a...
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Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption (MRSE) framework based on Paillier Cryptosystem with Threshold Decryption (PCTD). Compared to pre...
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With the prevalence of big-data-driven applications, such as face recognition on smartphones and tailored recommendations from Google Ads, we are on the road to a lifestyle with significantly more intelligence than ev...
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The analysis of safety-critical systems designed by architectural languages such as AADL (Architecture Analysis and Design Language) is a challenging research topic. In such a context, formal methods become an advocat...
The analysis of safety-critical systems designed by architectural languages such as AADL (Architecture Analysis and Design Language) is a challenging research topic. In such a context, formal methods become an advocated practice in software engineering for rigorous analysis. Moreover, they are applied on specific formalisms to be analyzed on dedicated tools. This paper studies the comprehensive formal specification for AADL language, in particular supporting major components of AADL and Behavior Annex. The presentation of this specification and modeling is the aim of this paper. This work is illustrated with a ARINC653 case study. As a study case, this work develops an AADL model from an ARINC653, specify a set of critical properties of the model and perform formal modeling in in Isabelle/HOL.
The human decision-making process is highly complex, involving individual emotion, personality, preference and even social background or environment. In order to mimic intergenerational interest conflicts within the c...
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The human decision-making process is highly complex, involving individual emotion, personality, preference and even social background or environment. In order to mimic intergenerational interest conflicts within the carbon generalized system of preferences, we propose a hybrid strategy update mechanism that integrates reinforcement learning (Q-learning) and Fermi-like imitation dynamics, aiming to explore the evolutionary patterns of cooperative behaviors in social dilemmas. By constructing a spatial evolutionary game model that simulates individual interactions in regular lattices, we analyze the impact of dynamic environmental state changes on strategic selection. The numerical results reveal that reinforcement learning significantly promotes the emergence of altruistic cooperators and maintains superior environmental conditions by balancing short-term gains with long-term environmental benefits. The hybrid update mechanism effectively mitigates the exploitation of cooperators by defectors, achieving the dynamic co-existence of three different strategies at the stationary state. Furthermore, the environmental payoff coefficient and the threshold range of the environmental health index emerge as critical parameters to resolve cooperation dilemmas, while the network scale does not show a significant impact on the extension and generalizability of the model. These findings provide some theoretical support for us to optimize carbon emission reduction policies, highlighting the crucial role of heterogeneous individual decision-making and long-term environmental feedback in low-carbon transitions.
The active anti-roll bar system has been proven to be one of the most effective solutions to improve roll stability of heavy vehicles. In a previous work, the authors proposed an Hcontroller for this system. The Genet...
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This paper proposes a multi-step ahead time series forecasting based on the improved process neural *** intelligent algorithm particle swarm optimization(PSO) is used to overcome the potential disadvantages of the neu...
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This paper proposes a multi-step ahead time series forecasting based on the improved process neural *** intelligent algorithm particle swarm optimization(PSO) is used to overcome the potential disadvantages of the neural network,such as slow convergence speed and derivative local ***,the theoretical analysis of the PSO is given to optimize the Multilayer perceptron(MLP) neural network ***,the theoretical analysis and processing flow about the MLP architecture optimization is ***,the performance criteria are applied to verify the performance of the proposed ***,the experimental evaluation based on a typically chaotic time series with rich spectrum information is utilized to demonstrate that the proposed approach has comparative results and superior on forecasting accuracy comparing to the traditional methods.
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