Demand response (DR) is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar) and plug-in hybrid electric vehicles, and strengthen the r...
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Demand response (DR) is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar) and plug-in hybrid electric vehicles, and strengthen the reliability of power system. In smart grid, implementing DR through home energy management system (HEMS) in residential sector has a great significance. However, an algorithm that only optimally controls parts of HEMS rather than the overall system cannot obtain the best results. In addition, single objective optimization algorithm that minimizes electricity cost cannot quantify user's comfort level and cannot take a tradeoff between electricity cost and comfort level conveniently. To tackle these problems, this paper proposes a framework of HEMS that consists of grid, load, renewable resource (i.e., solar resource), and battery. In this framework, a user has the ability to sell electricity to utility grid for revenue. Different comfort level indicators are proposed for different home appliances according to their characteristics and user preferences. Based on these comfort level indicators, this paper proposes a multiobjective optimization algorithm for HEMS that minimizes electricity cost and maximizes user's comfort level simultaneously. Simulation results indicate that the algorithm can reduce user's electricity cost significantly, ensure user's comfort level, and take a tradeoff between the cost and comfort level conveniently.
Graph partitioning is required for solving tasks on graphs that need to be distributed over disks or computers. This problem is well studied, but the majority of the results on this subject are not suitable for proces...
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Graph partitioning is required for solving tasks on graphs that need to be distributed over disks or computers. This problem is well studied, but the majority of the results on this subject are not suitable for processing graphs with billions of nodes on commodity clusters, since they require shared memory or lowlatency messaging. One of the approaches suitable for cluster computing is the balanced label propagation, which is based on the label propagation algorithm. In this work, we show how multi-level optimization can be used to improve quality of the partitioning obtained by means of the balanced label propagation algorithm.
[...]modern sensor network applications pose increasingly complex and stringent performance requirements on localization in terms of scalability, robustness, and accuracy. [...]many new location-related issues such a...
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[...]modern sensor network applications pose increasingly complex and stringent performance requirements on localization in terms of scalability, robustness, and accuracy. [...]many new location-related issues such as security and privacy in WSNs are rapidly emerging and must be carefully considered and addressed. [...]we hope that the papers published in this special issue would be helpful to other researchers with a common interest in these location-related topics.
The phase error caused by the speed mismatch issue is researched in the line-scan images capturing 3D profile measurement. The experimental system is constructed by a line-scan CCD camera, an object moving device, a d...
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The phase error caused by the speed mismatch issue is researched in the line-scan images capturing 3D profile measurement. The experimental system is constructed by a line-scan CCD camera, an object moving device, a digital fringe pattern projector, and a personal computer. In the experiment procedure, the detected object is moving relative to the image capturing system by using a motorized translation stage in a stable velocity. The digital fringe pattern is projected onto the detected object, and then the deformed patterns are captured and recorded in the computer. The object surface profile can be calculated by the Fourier transform profilometry. However, the moving speed mismatch error will still exist in most of the engineering application occasion even after an image system calibration. When the moving speed of the detected object is faster than the expected value, the captured image will be compressed in the moving direction of the detected object. In order to overcome this kind of measurement error, an image recovering algorithm is proposed to reconstruct the original compressed image. Thus, the phase values can be extracted much more accurately by the reconstructed images. And then, the phase error distribution caused by the speed mismatch is analyzed by the simulation and experimental methods.
Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications invo...
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Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost. RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost. RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner's performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of "weak" learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.
The MOSFET is an important power electronic transistor widely used in electrical systems. Its reliability has an effect on the performance of systems. In this paper, the failure models and mechanisms of MOSFETs are br...
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The MOSFET is an important power electronic transistor widely used in electrical systems. Its reliability has an effect on the performance of systems. In this paper, the failure models and mechanisms of MOSFETs are briefly analyzed. The on-resistance (R-on) is the key failure precursor parameter representing the degree of degradation. Based on the experimental data, a nonlinear dual-exponential degradation model for MOSFETs is obtained. Then, we present an approach for MOSFET degradation state prediction using a strong tract filter based on the obtained degradation model. Lastly, the proposed algorithm is shown to perform effectively on experimental data. Thus, it can provide early warning and enhance the reliability of electrical systems.
Bio-optical algorithms have been classify with different terms such as empirical, semi-empirical, semi-analytical, quasi-analytical or analytical algorithms. However, one algorithm has been classified differently in r...
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Bio-optical algorithms have been classify with different terms such as empirical, semi-empirical, semi-analytical, quasi-analytical or analytical algorithms. However, one algorithm has been classified differently in remote sensing literature and a lack of a consistent terminology was found. In this article, description of types of bio-optical algorithm is present as well as a procedure to define the most suitable terminology. This procedure is based on the goal, processes and products of the bio-optical algorithm. The adoption of the proposed classification and terminology for this relatively new area for remote sensing applications is an important step for the development of this growing field.
An algorithm of traversal (retrieval of full information on the structure) of an a priori unknown directed graph by an unbounded set of finite automata that interact through the exchange of messages and can move along...
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An algorithm of traversal (retrieval of full information on the structure) of an a priori unknown directed graph by an unbounded set of finite automata that interact through the exchange of messages and can move along the arcs of the graph according to their direction is described. Under the assumption that the execution time of basic operations and the transmission time of individual messages are bounded, the total operating time of the algorithm is bounded, at worst, by O(m + nd), where n is the number of vertices of the graph, m is the number of its arcs, and d is the diameter of the graph;moreover, this estimate is unimprovable. The full proofs of the propositions formulated in this paper have been published in [6].
An introduction is presented in which the authors discuss topics within the issue including science of quantitative imaging biomarkers terminology, review of statistical methods for computer algorithm and meta-analysi...
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An introduction is presented in which the authors discuss topics within the issue including science of quantitative imaging biomarkers terminology, review of statistical methods for computer algorithm and meta-analysis of the technical performance of an imaging procedure.
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