The concept of Cyber Physical System (CPS) is widely used in different industries across the globe. In fact, it is the holistic approach towards dealing with cyber space and physical environments that do have inter-de...
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The binary Pseudo Random Sequences (PRSs) with maximum period and good statistical and correlation properties have been widely used in cryptography and modern communication and information systems. To achieve uncondit...
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This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with c...
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The faculty of computersystems and Technologies of the Technical University of Sofia develops methodology and curricula for teaching of Cyber-security in higher education following the basic principles, set out in th...
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The fourth industrial revolution often referred as Industry 4.0 poses a great cybersecurity risk for Supervisory control and data acquisition (SCADA) systems. Lot of enterprises are changing the energy dependency that...
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With the rapid worldwide industrialization in the last 20 years environmental pollution and the need for management in it have increased greatly. Different types of information systems have been created to facilitate ...
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The present article is based upon the prerequisite of existence of real needs for reading, storing and reliable transfer of information from sensors. The goal is to present, analyze and evaluate widely accessible and ...
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Electric vehicle (EV) emissions should be predicted and mitigated, which requires lowering EV emissions in line with global sustainability goals. Such accurate forecasting supports policymakers and other industry stak...
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There have been numerous methods for learning and predicting time series ranging from the traditional time-series analyses to recent approaches using neural networks. A central issue common to all of them is the deter...
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There have been numerous methods for learning and predicting time series ranging from the traditional time-series analyses to recent approaches using neural networks. A central issue common to all of them is the determination of model structure. Both mean prediction error and An Information Criterion (AIC) are useful in model selection;the model with the smallest mean prediction error or AIC is selected from among a set of models as the best one. In this way they give a solution to the problem of model selection. Due to huge search space, however, the mean prediction error or AIC alone is not powerful enough to find the best model structure from among all the candidates. In the present paper the authors propose to use both a structural learning with forgetting and the mean prediction error or AIC to find a model with better generalization ability. Jordan networks and buffer networks, popular in the modeling of time series, are examined in this paper. The structural learning with forgetting and backpropagation (BP) learning are applied to compare the learning and prediction performance of these two types of models. Simulation results demonstrate that the structural learning with forgetting has better generalization ability than BP learning both in Jordan networks and buffer networks.
This paper proposes an algorithm for CPU scheduling, which gives priority to those processes that use fewest resources. Thus most efficient use of resources is achieved and system performance is improved.
ISBN:
(纸本)9789604742523
This paper proposes an algorithm for CPU scheduling, which gives priority to those processes that use fewest resources. Thus most efficient use of resources is achieved and system performance is improved.
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