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Demand side management aims to match power demand to supply through cutting the peak and filling the valley, is one of the most important factors in smart grid. The real-time pricing (RTP) mechanism is an ideal method to adjust power balance between supply and demand. Its implementation has a profound impact on users’ behavior, and on operation and management of the power grid. In this research, we propose an expectation social welfare maximization model, considering the classification of the smart home appliances (SHA) and the correlation of power consumption of multi-time slots. Users can arrange their appliances more profitable and more closely to reality with the advantage of multi-time slots RTP strategy. The constraint in the model reflects the fluctuation (uncertainty) of power consumption caused by operating continuity of the SHA. By introducing probabilistic constraints, the uncertainty optimization model is transformed into a convex optimization problem. The existence and uniqueness of the optimal solution are shown, and its properties are further analyzed. Considering the convex optimization problem is separable in dual domain, this study proposes a decentralized online RTP algorithm to determine each user’s demand and energy supplier’s supply simultaneously. By utilizing Armijo line search to instead of fixed step size of the dual subgradient method, the decentralized online RTP algorithm proposed in this research can overcome the defects of slow convergence and even no convergence from the original dual subgradient method. Finally, the simulation validates the rationality and feasibility of optimization model by the decentralized online RTP algorithm. [ABSTRACT FROM AUTHOR]

Motivation: Identification of ligand binding pockets on proteins is crucial for the characterization of protein functions. It provides valuable information for protein–ligand docking and rational engineering of small molecules that regulate protein functions. A major number of current prediction algorithms of ligand binding pockets are based on cubic grid representation of proteins and, thus, the results are often protein orientation dependent.Results: We present the MSPocket program for detecting pockets on the solvent excluded surface of proteins. The core algorithm of the MSPocket approach does not use any cubic grid system to represent proteins and is therefore independent of protein orientations. We demonstrate that MSPocket is able to achieve an accuracy of 75% in predicting ligand binding pockets on a test dataset used for evaluating several existing methods. The accuracy is 92% if the top three predictions are considered. Comparison to one of the recently published best performing methods shows that MSPocket reaches similar performance with the additional feature of being protein orientation independent. Interestingly, some of the predictions are different, meaning that the two methods can be considered complementary and combined to achieve better prediction accuracy. MSPocket also provides a graphical user interface for interactive investigation of the predicted ligand binding pockets. In addition, we show that overlap criterion is a better strategy for the evaluation of predicted ligand binding pockets than the single point distance criterion.Availability: The MSPocket source code can be downloaded from http://appserver.biotec.tu-dresden.de/MSPocket/. MSPocket is also available as a PyMOL plugin with a graphical user interface.Contact: hongboz@biotec.tu-dresden.de; mayte@biotec.tu-dresden.deSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Real-parameter single objective optimization has been studied for decades. In recent, a new setting is applied in this field based on the consideration that solving difficulty scales exponentially with the increase in dimensionality. Under the new setting, differential evolution (DE) still outstands in performance as before. Meanwhile, a new type of population-based metaheuristic—gaining–sharing knowledge-based algorithm, becomes a dark horse. Furthermore, ensemble of the above two types of algorithm is proposed in the literature. Although such ensemble shows good performance, provided that a more reasonable scheme is used for the communication between the constituent algorithms, better ensemble can be obtained. We believe that the new scheme should be with adaptiveness. In this paper, we propose an adaptive scheme for the communication. According to the scheme, individuals chosen based on fitness and lifetime are exchanged. In fact, in the field of DE, it is rare to consider lifetime of individual. However, lifetime is no less important than fitness in our scheme. In our experiment, our ensemble is compared with seven state-of-the-art algorithms. According to experimental results, our ensemble is comparable to one of the peers and better than the other ones. [ABSTRACT FROM AUTHOR]

A breakthrough in the technology for virtualizing satellite-borne networks and computing and storage resources can significantly increase the processing capacity and resource utilization efficiency of satellite-borne base stations in response to the development trend in multi-star and multi-system converged satellite internet iterative systems. The protocol processing function of traditional satellite communication systems is generally placed in the ground station system for processing, with poor flexibility and low efficiency. As a result, a reconfigurable digital satellite-borne base station architecture design is suggested, allowing for separation of the hardware and software of the satellite-borne base station and flexible programming and dynamic loading of the satellite-borne base station's functions by software. Meanwhile, a fast adaptive migration algorithm based on multi-dimensional environment awareness is proposed on top of the reconfigurable digital base station, and migration precomputation and real-time computation are added in order to realize rapid deployment of the digital base station system network. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing system stability and decreasing real-time calculation costs associated with system network migration under conditions of high dynamic changes for each network element in a star-loaded environment. In conclusion, a digital satellite-borne base station system that effectively addresses the issues of low flexibility and high dynamic changes of nodes in the resource-constrained satellite environment can be created by combining the adaptive migration algorithm and the reconfigurable digitized satellite-borne base station architecture. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement. [ABSTRACT FROM AUTHOR]

The mechanism of Coordinated Multi-Point (CoMP) can improve the spectral efficiency of the 4G and 5G cellular network, and also can reduce transmitting power of base stations (BSs). However, CoMP requires a large amount of energy due to extra signal processing and backhaul traffic. In this paper, an energy-efficient algorithm based on optimal dynamic clustering mechanism (ODCEM) for CoMP networks is proposed, which aims to minimize the overall network energy consumption. In the ODCEM, cells are pre-ordered in descending order according to their traffic load increments. The cell with lowest load increment is selected to switch off. Then, users in selected cell are handed over to other neighboring cells if quality of service (QoS) for users can be guaranteed. After that, the selected cell is switched off to conserve network energy. The remaining cells in each CoMP cluster will be evaluated after the previous order is updated. The simulation results show that the energy efficiency of ODCEM schemes is significantly higher than that of fixed CoMP algorithm in terms of SINR and traffic loads. Furthermore, the ODCEM algorithm can achieve better energy efficiency with lower computational complexity according to the performance evaluation of user association and BS switching off. [ABSTRACT FROM AUTHOR]

A real-time pricing scheme is formulated based on bilevel programming to tackle the uncertainties for smart microgrids equipped with renewable energy sources, dispatchable resources and storage devices. The main grid is taken as the leader, who sets electricity prices to maximize its revenue. At the lower level, the price-anticipating microgrids determine the amount of dispatchable resources generated, the load and the storage serving while maximizing their payoffs. When setting price, the main grid needs to consider the microgrids' response. By Karush–Kuhn–Tucker conditions and a smoothing method, the primal bilevel programming is transformed into an equivalent single-level optimization problem with only smooth equality constraints, and then a rolling penalty function algorithm is designed to obtain the optimal solution. The convergence of the smoothing method is demonstrated. Simulations show that the proposed approach has good performance in cutting peak, balancing system energy distribution and improving benefits for both supply and demand. [ABSTRACT FROM AUTHOR]

The fuzzy degree of lung nodule boundary is the most important cue to judge the lung cancer in CT images. Based on this feature, the paper proposes a novel lung cancer detection method for CT images based on the super-pixels and the level set segmentation methods. In the proposed methods, the super-pixels method is used to segment the lung region and the suspected lung cancer lesion region in the CT image. The super-pixels method and a level set method are used to segment the suspected lung cancer lesion region simultaneously. Finally, the cancer is determined by the difference between results of the two segmentation methods. Experimental results show that the proposed algorithm has a high accuracy for lung cancer detection in CT images. For gross glass nodule, pleural nodule, the vascular nodules and solitary nodules, the sensitivity of the detection algorithm are respectively 91.3%, 96.3%, 80.9% and 82.3%. [ABSTRACT FROM AUTHOR]