Internet of Things (IoT) has seen a lot of traction over the last few years and is expected to play a major role in controlling devices and communication. IoT network involves sensors with constrained memory, limited ...
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Internet of Things (IoT) has seen a lot of traction over the last few years and is expected to play a major role in controlling devices and communication. IoT network involves sensors with constrained memory, limited processing and power capabilities. The process of network formation largely decides the Quality of service in these networks. IPv6 Routing Protocol for LLNs (RPL) is specially designed by the IETF ROLL working group to cater to routing requirements in such networks. In RPL, an objective function specifies a set of metrics or constraints which can be used as criteria for best parent and path selection, ensuring a faster route to the destination. In our work, we propose a novel objective function OF-FZ, which uses four metrics to make routing decisions. Fuzzy Logic is used to combine the metrics hop count (HC), ETX, delay and node residual energy (RE) to obtain a single decision metric termed as Quality Assurance (QA) score. During routing, a neighbor node having the highest QA score is chosen as the best parent. The proposed objective function leads to significant savings in energy consumption (EC) (9% lower), Packet Delivery Ratio (PDR) (3% higher), End-to-end Delay (8% lower) as compared to existing OFs. Controlling advertisement messages circulating in the network leads to a significant reduction in control overhead to the tune of 45%. Performance evaluation and deep dive of OF-FZ is done in three configurations: network scalability, increasing packet transmission rates and multi-sink scenarios using Contiki OS and COOJA network simulator.
A control strategy for a bearingless induction motor (BL-IM) based on the fuzzy dynamic objective function is proposed in this paper. Firstly, based on the discrete mathematical model of the BL-IM, the stator current ...
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A control strategy for a bearingless induction motor (BL-IM) based on the fuzzy dynamic objective function is proposed in this paper. Firstly, based on the discrete mathematical model of the BL-IM, the stator current and flux linkage are predicted according to the given stator current and flux linkage, the objective function of model predictive current control (MPCC) is designed. Secondly, the fuzzy control algorithm is introduced in the objective function of the MPCC to dynamically assign the weighting factors before the current component on thed-qaxis and the influence of the objective function on the performance of the BL-IM is analyzed under different weighting factors. By discretizing the rotational speed deviation Delta omega and rotational speed deviation rate, fuzzy reasoning is performed to obtain the optimal fuzzy dynamic function. Finally, the optimal fuzzy dynamic function is selected as the objective function of the MPCC to perform the simulations and experiments. The results show that the performance of the BL-IM under the MPCC strategy based on the fuzzy dynamic objective function is improved compared with the traditional MPCC and the vector control based on the fuzzy PID, due to its better dynamic and suspension performance. Meanwhile, the stability of rotor current component is enhanced.
As an important factor in the fast fixed-point algorithm of independent component analysis (ICA), robustness has a significant influence on the separate performance of ICA. However, the traditional objective functions...
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As an important factor in the fast fixed-point algorithm of independent component analysis (ICA), robustness has a significant influence on the separate performance of ICA. However, the traditional objective functions used in fast fixed-point algorithm of ICA will be invalid in separating the original signals when the outliers mix in signals. In this paper, we introduce a new robust objective function based on the Negentropy maximization. With second order approximation with Maclaurin expansion, the proposed function enables the estimation of individual independent components. In addition, it guarantees the separate performance of ICA that the original signals whether mix with outliers. Furthermore, combined with the proposed objective function, the fast fixed-point algorithm of ICA is reliable in the scenario of the signals mix with outliers. Simulation results show that the separate performance of proposed objection function is superior to the traditional objective functions as the outliers appear in the original signals.
An assisted history matching process always requires two distinct parts: a parameterization to select the uncertain attributes of the model and an automatic procedure that minimizes the distance between the observed p...
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An assisted history matching process always requires two distinct parts: a parameterization to select the uncertain attributes of the model and an automatic procedure that minimizes the distance between the observed production and the simulation model curves. The focus of this work is the second part, where an objective function is necessary to represent mathematically the quality of the model. However, due to the complexity of the models, this function is frequently a combination of several functions that represent the quality of the match in several wells and less attention is given to the influence of the objective function in the optimization process. This paper proposes a study to show the influence of a global objective function on the history matching process using a synthetic reservoir model with 20 uncertain attributes. Results of the quality matching index of eight different global objective functions are compared at the end of the process. The optimized simulation models, generated by the optimization phase with different global objective functions, are compared with the base model and production history. (C) 2011 Elsevier B.V. All rights reserved.
An algorithm to predict the composition of n -paraffin mixtures from calorimetry is proposed and applied for two thermodynamic approaches: one considering the formation of solid solutions and the other, known as multi...
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An algorithm to predict the composition of n -paraffin mixtures from calorimetry is proposed and applied for two thermodynamic approaches: one considering the formation of solid solutions and the other, known as multisolid phase model, assuming the presence of several independent pure solid phases. This methodology compares modeled calorimetric curves with DSC results through Particle Swarm Optimization. In order to better predict the mixtures compositions an appropriate proposal of the objective function was necessary. The present paper elucidates the process of defining an enhanced objective function aiming to improve the quality of predictions allowing the employment of calorimetric curves without information about sample's mass. The deviations in calculated compositions are related to a difference of up to 1 K in the location of calorimetric peaks for solid solution approach. (C) 2017 Elsevier B.V. All rights reserved.
To circumvent the existing shortcoming of optimisation algorithms in trunk biomechanical models, both agonist and antagonist trunk muscle stresses to different powers are introduced in a novel objective function to ev...
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To circumvent the existing shortcoming of optimisation algorithms in trunk biomechanical models, both agonist and antagonist trunk muscle stresses to different powers are introduced in a novel objective function to evaluate the role of abdominal muscles in trunk stability and spine compression. This coupled objective function is introduced in our kinematics-driven finite element model to estimate muscle forces and to identify the role of abdominal muscles in upright standing while lifting symmetrically a weight at different heights. Predictive equations for the compression and buckling forces are developed. Results are also compared with the conventional objective function that neglects abdominal muscle forces. An overall optimal solution involving smaller spinal compression but higher trunk stability is automatically attained when choosing muscle stress powers at and around 3. Results highlight the internal oblique muscle as the most efficient abdominal muscle during the tasks investigated. The estimated relative forces in abdominal muscles are found to be in good agreement with the respective ratios of recorded electromyography activities.
Water network synthesis has been used to conserve water resources and reduce economic costs. In this study, all contributors to environmental burdens and economic costs of water network systems were estimated to analy...
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Water network synthesis has been used to conserve water resources and reduce economic costs. In this study, all contributors to environmental burdens and economic costs of water network systems were estimated to analyze the effects of objective functions on their environmental and economic performances. A total freshwater flowrate-minimized water network system (FWNS) and a total freshwater cost-minimized water network system (CWNS) were independently synthesized. Life cycle assessment and life cycle costing were performed to evaluate and compare the environmental and economic performances of the two water network systems. The CWNS was more environmentally and economically friendly than the FWNS because the CWNS was synthesized by minimizing the consumption of deionized water, which has higher unit cost and unit environmental effect scores than industrial water. Also this study demonstrated that the consumption rates of freshwater and electricity, as well as their unit environmental effect scores, should be used as principal contributors and weighting factors for the formulation of an objective function to generate the most environmentally friendly water network system. while the costs of piping and freshwater. as well as their unit costs. should be included to generate the most economically friendly system. (c) 2008 Elsevier B.V. All rights reserved.
Improving fault tolerance of a neural network has been studied for more than two decades. Various training algorithms have been proposed in sequel. The on-line node fault injection-based algorithm is one of these algo...
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Improving fault tolerance of a neural network has been studied for more than two decades. Various training algorithms have been proposed in sequel. The on-line node fault injection-based algorithm is one of these algorithms, in which hidden nodes randomly output zeros during training. While the idea is simple, theoretical analyses on this algorithm are far from complete. This paper presents its objective function and the convergence proof. We consider three cases for multilayer perceptrons (MLPs). They are: 1) MLPs with single linear output node;2) MLPs with multiple linear output nodes;and 3) MLPs with single sigmoid output node. For the convergence proof, we show that the algorithm converges with probability one. For the objective function, we show that the corresponding objective functions of cases 1) and 2) are of the same form. They both consist of a mean square errors term, a regularizer term, and a weight decay term. For case 3), the objective function is slight different from that of cases 1) and 2). With the objective functions derived, we can compare the similarities and differences among various algorithms and various cases.
This paper proposes a novel edge detection method for both gray level images and color images. The 3 x 3 mask in the image is considered and two pixel sets S-0 and S-1 in the mask are used to define an objective funct...
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This paper proposes a novel edge detection method for both gray level images and color images. The 3 x 3 mask in the image is considered and two pixel sets S-0 and S-1 in the mask are used to define an objective function. The values of the objective function corresponding to four directions determine the edge intensity and edge direction of each pixel in the mask. After all pixels in the image have been processed, the edge map and direction map are generated. Then we apply the non-maxima suppression method to the edge map and the direction map to extract the edge points. The proposed method can detect the edge successfully, while double edges, thick edges, and speckles can be avoided. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
The problem of the layout of objects of different sizes is analyzed. The objective function is shown to depend on several variables, which are various combinatorial configurations. According to this principle, the pro...
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The problem of the layout of objects of different sizes is analyzed. The objective function is shown to depend on several variables, which are various combinatorial configurations. According to this principle, the problem is divided into several subproblems;therefore, a self-adjusting algorithm is developed to solve it. A location problem for objects of the same size is solved by the structural-alphabetic search, which is based on the known solvable case.
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