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It is intended to state clearly the strategy of introduc- ing optical networks into access networks. A tool for evalu- ating the initial cost and the maintenance operation cost of the networks is investigated. The evaluation proce...
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It is intended to state clearly the strategy of introduc- ing optical networks into access networks. A tool for evalu- ating the initial cost and the maintenance operation cost of the networks is investigated. The evaluation procedure and the software structure for that purpose are reported. This paper is the first, as far that purpose are reported. This paper is the first, as far as the authors know, to report on a tool that can evaluate costs up to the maintenance cost.
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Several algorithms have been proposed to filter information on a complete graph of correlations across stocks to build a stock-correlation network. Among them the planar maximally filtered graph (PMFG) algorithm uses 3n - 6 edges ...
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Several algorithms have been proposed to filter information on a complete graph of correlations across stocks to build a stock-correlation network. Among them the planar maximally filtered graph (PMFG) algorithm uses 3n - 6 edges to build a graph whose features include high frequency of small cliques and good clustering of stocks. We propose a new algorithm which we call proportional degree (PD) to filter information on the complete graph of similarities between stocks. Our results show that the PD algorithm produces a network showing better homogeneity with respect to cliques, as compared to economic sectoral classification than its PMFG counterpart regardless of the similarity measure used-the Pearson correlation coefficient or normalised mutual information (NMI). We also show that the partition of the PD network obtained through normalised spectral clustering (NSC) agrees better with the NSC of the complete graph than the corresponding one obtained from PMFG. Finally, we show that the clusters in the PD network are more robust with respect to the removal of random sets of edges than those in the PMFG network. (C) 2020 Elsevier B.V. All rights reserved.
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In this paper neural network (NN) control techniques for non-model based PD controlled robot manipulators are proposed. The main difference between the proposed technique and the existing feedback error learning (FEL) technique is...
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In this paper neural network (NN) control techniques for non-model based PD controlled robot manipulators are proposed. The main difference between the proposed technique and the existing feedback error learning (FEL) technique is that compensation of robot dynamics uncertain- ties is done outside the control loop by modifying the desired input trajectory. By using different NN training signals, two NN control schemes are developed. One is comparable to that in the FEL technique and another has to deal with the Jacobian of the PD controlled robot dynamic system. Performances of both controllers for various trajectories with different PD controller gains are examined and compared with that of the FEL controller. It is shown that the new control technique performed better and robust to PD controller gain variations.
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Establishing suitable differential dynamical models to describe the real natural phenomenon in chemistry and physics has become a very hot topic in nowadays society. In this present research, we deal with a fractional-order chemic...
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Establishing suitable differential dynamical models to describe the real natural phenomenon in chemistry and physics has become a very hot topic in nowadays society. In this present research, we deal with a fractional-order chemical reaction system. Taking advantage of the fixed point theorem, we prove the existence and uniqueness of the fractional-order chemical reaction system. Using the inequality skill, we prove the non-negativeness of the fractional-order chemical reaction system. By applying a suitable function, we prove the uniform boundedness of the solution to the fractional-order chemical reaction system. With the aid of a hybrid controller including state feedback and parameter perturbation, we discuss the Hopf bifurcation anti-control issue of the fractional-order stable chemical reaction system. A novel delay-independent condition ensuring the stability and the onset of Hopf bifurcation of the involved fractionalorder stable chemical reaction system is set up. The study manifests that the delay in the hybrid controller plays a vital role in stabilizing the system and controlling the occurrence of Hopf bifurcation of the fractional-order stable chemical reaction system. In order to validate the derived key conclusions, MATLAB simulations are executed and bifurcation plots are given. The obtained results of this article have momentous theoretical guiding value in controlling the chemical compositions. The exploration idea can also be utilized to investigate the bifurcation control and bifurcation anti-control problems in lots of other fractional-order differential systems in numerous disciplines.
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This study aims to develop a PD control system based on the neural network algorithm to reduce the ship rolling motion in the desired track through rudder and fin actions. The mathematical model including sea-keeping and maneuveri...
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This study aims to develop a PD control system based on the neural network algorithm to reduce the ship rolling motion in the desired track through rudder and fin actions. The mathematical model including sea-keeping and maneuvering characteristics is developed in the present paper and the nonlinear time history of ship motions is solved by the fourth order Runge-Kutta method. In order to achieve the purpose of roll reduction and track keeping, the rudder and fin stabilizer are used as the control tools for the ship advancing in the seaway. In addition, the PD controller based on the self-tuning neural network algorithm is applied to achieve the goals of multi-input and multi-output in the control system. Four different types of control modes on a container ship model are studied and the performances are investigated in different sea states. The results indicate that the PD controller based on the self-tuning neural network algorithm applying to the stabilizer fin control for roll reduction and the rudder control for track keeping in the seaway would be suggested.
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The use of a proposed recurrent hybrid neural network to control of walking robot with four legs is investigated in this paper. A neural networks based control system is utilized to the control of four-legged walking robot. The co...
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The use of a proposed recurrent hybrid neural network to control of walking robot with four legs is investigated in this paper. A neural networks based control system is utilized to the control of four-legged walking robot. The control system consists of four proposed neural controllers, four standard PD controllers and four-legged planar walking robot. The proposed neural network (NN) is employed as an inverse controller of the robot. The NN has three layers, which are input, hybrid hidden and output layers. In addition to feedforward connections from the input layer to the hidden layer and from the hidden layer to the output layer, there is also feedback connection from the output layer to the hidden layer and from the hidden layer to itself. The reason to use hybrid layer is that robot's dynamics consists of linear and non-linear parts. The results show that the proposed neural control system has superior performance to control trajectory of walking robot with payload. (C) 2007 Elsevier B.V. All rights reserved.
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A framework consisting of the Protocol Derivation System (PDS) and the Protocol Composition Logic (PCL) has been recently proposed by Datta et al. for the design and analysis of a secure composition of cryptographic protocols. How...
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A framework consisting of the Protocol Derivation System (PDS) and the Protocol Composition Logic (PCL) has been recently proposed by Datta et al. for the design and analysis of a secure composition of cryptographic protocols. However, the PDS in this proposed framework can only be used for the protocols of the Station-to-Station family, which are signature-based authenticated Diffie-Hellman key exchange protocols. In this paper, the PDS is extended to support key exchange protocols using a trusted third party and an encryption-based authentication such as those in the Needham-Schroeder family. This is achieved by means of adding new components, refinements, and transformations to the PDS. In addition, the PCL is applied to prove the correctness of the derived protocols. Then, the derivation graph of the Needham-Schroeder family is developed by using the extended PDS. Finally, the derivations and proofs of the protocols in the Needham-Schroeder family are shown in this paper. Copyright (c) 2012 John Wiley & Sons, Ltd.
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Partial discharge patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely...
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Partial discharge patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely used representation is phase resolved PD (PRPD) patterns. This paper describes a method for the automated recognition of PRPD patterns using a novel composite neural network system for the actual classification task. This paper elucidates the possible methods of extracting relevant features from the PRPD data in a knowledge based way i.e. according to physical properties of PD gained from PD modeling. This allows the novel complex neural network (NN) system for classification. The efficacy of composite neural network developed using original probabilistic neural network is examined. This innovative methodology of giving inputs to the composite neural network compares favorably with the traditional network architecture used previously for PD pattern recognition.
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The present study aimed to explain a modern and attractive protocol for synthesizing hypercross-linked conjugated supramolecular polymer network contains palladium(II) porphyrin based on calix[4]resorcinarene as an efficient heter...
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The present study aimed to explain a modern and attractive protocol for synthesizing hypercross-linked conjugated supramolecular polymer network contains palladium(II) porphyrin based on calix[4]resorcinarene as an efficient heterogeneous catalyst. Thus, characterizations were conducted by using spectroscopic methods including powder X-ray diffraction, energy dispersive spectroscopy, scanning electron microscopy, FT-IR, and UV-Vis spectroscopy. In addition, catalytic activity of Pd-porphyrin@polymer was evaluated for C-C coupling reactions. The catalyst demonstrated an excellent activity, which is highly potential for forming new bond under mild conditions.
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A secondary thioamide-based SCS pincer Pd complex, [2,6-bis(anilinothiocarbonyl)-κ{sup}2S,S'-phenyl-κC{sup}1]chloropalladium(ll) (1), forms the intermolecular N-H…Cl hydrogen bonds in the crystals. The intermolecular hydrogen b...
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A secondary thioamide-based SCS pincer Pd complex, [2,6-bis(anilinothiocarbonyl)-κ{sup}2S,S'-phenyl-κC{sup}1]chloropalladium(ll) (1), forms the intermolecular N-H…Cl hydrogen bonds in the crystals. The intermolecular hydrogen bonds induce spontaneous formation of layered and cage-shaped network structures of 1 in the solid state. Formation of the different two types of networks is assisted by the kind of solvated molecules.
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