The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd ...
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Smart grid is considered as a promising approach to solve the problems of carbon emission and energy crisis. In smart grid, the power consumption data are collected to optimize the energy ***, security issues in commu...
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Smart grid is considered as a promising approach to solve the problems of carbon emission and energy crisis. In smart grid, the power consumption data are collected to optimize the energy ***, security issues in communications still present practical concerns. To cope with these challenges, we propose EFFECT, an efficient flexible privacy-preserving aggregation scheme with authentication in smart grid. Specifically, in the proposed scheme, we achieve both data source authentication and data aggregation in high efficiency. Besides, in order to adapt to the dynamic smart grid system, the threshold for aggregation is adjusted according to the energy consumption information of each particular residential area and the time period, which can support fault-tolerance while ensuring individual data privacy during *** security analysis shows that our scheme can satisfy the desired security requirements of smart *** addition, we compare our scheme with existing schemes to demonstrate the effectiveness of our proposed scheme in terms of low computational complexity and communication overhead.
Timed abstract state machine(TASM) is a formal specification language used to specify and simulate the behavior of real-time systems. Formal verification of TASM model can be fulfilled through model checking activitie...
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Timed abstract state machine(TASM) is a formal specification language used to specify and simulate the behavior of real-time systems. Formal verification of TASM model can be fulfilled through model checking activities by translating into UPPAAL. Firstly, the translational semantics from TASM to UPPAAL is presented through atlas transformation language(ATL). Secondly, the implementation of the proposed model transformation tool TASM2UPPAAL is provided. Finally, a case study is given to illustrate the automatic transformation from TASM model to UPPAAL model.
Architecture analysis and design language （AADL） is an architecture description language standard for embedded real-time systems and it is widely used in safety-critical applications. For facilitating verifcafion an...
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Architecture analysis and design language （AADL） is an architecture description language standard for embedded real-time systems and it is widely used in safety-critical applications. For facilitating verifcafion and analysis, model transformation is one of the methods. A synchronous subset of AADL and a general methodology for translating the AADL subset into timed abstract state machine （TASM） were studied. Based on the arias transformation language （ ATL ） framework, the associated translating tool AADL2TASM was implemented by defining the meta-model of both AADL and TASM, and the ATL transformation rules. A case study with property verification of the AADL model was also presented for validating the tool.
This paper presents an LMI based robust H-infinity control scheme for constrained systems with norm- bounded uncertainties. The uncertainties are incorporated in the evaluation of the H-infinity norm and the time-doma...
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This paper presents an LMI based robust H-infinity control scheme for constrained systems with norm- bounded uncertainties. The uncertainties are incorporated in the evaluation of the H-infinity norm and the time-domain constraints. The robust closed-loop properties inclusive of stability, H-infinity performance and the satisfaction of the time- domain constraints are discussed. Analysis and simulation results for a 2 DOF quarter-car model show possible improve- ments on ride comfort, while robustly respecting safety related constraints such as good road holding, limited suspension strokes and actuator saturation.
Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource *** traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenge...
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Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource *** traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and *** address these issues,this study proposes a novel two-part invalid detection task allocation *** the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous *** to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public *** the second step of the framework,task allocation modeling is performed using a twopart graph matching *** phase introduces a P-queue KM algorithm that implements a more efficient optimization *** allocation efficiency is improved by approximately 23.83%compared to the baseline *** results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.
This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rat...
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This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rate. Specifically, a robust stroke width transform(RSWT) feature is proposed to better recover the stroke width by additionally considering the cross of two strokes and the continuousness of the letter border. In the second phase, a classification scheme based on visual appearance features is used to reject the false alarms while keeping the recall rate. To learn a better classifier from multiple visual appearance features, a novel classification method called double soft multiple kernel learning(DS-MKL) is proposed. DS-MKL is motivated by a novel kernel margin perspective for multiple kernel learning and can effectively suppress the influence of noisy base kernels. Comprehensive experiments on the benchmark ICDAR2005 competition dataset demonstrate the effectiveness of the proposed two-phase text detection approach over the state-of-the-art approaches by a performance gain up to 4.4% in terms of F-measure.
Due to the black-box characteristics of deep learning based semantic encoders and decoders, finding a tractable method for the performance analysis of semantic communications is a challenging problem. In this paper, w...
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The mining industry annually consumes trillions of British thermal units of energy,a large part of which is *** fuel is a significant source of energy in surface mining operations and haul trucks are the major users o...
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The mining industry annually consumes trillions of British thermal units of energy,a large part of which is *** fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy *** vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel *** this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total *** network was trained and tested using real data collected from a surface mining *** results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.
Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-ti...
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Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.
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