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Policy gradient algorithms suffer from the large variance of gradient estimation. the algorithm of policy gradient with parameter based exploration mitigates this problem to some extent. However, PGPE estimates its gradient based on the Monte Carlo, which requires a large number of samples to achieve the fairly stable policy update. And thus hinders its application in the real world problem. In order to further reduce the variance of policy gradient, the algorithm of function approximation for policy gradients with parameter-based exploration (PGPE-FA) implements the algorithm of PGPE in the Actor-Critic framework. More specifically, the proposed method utilized value function to estimate the policy gradient, instead of using trajectory samples to estimate the policy gradient as PGPE method does, thereby reducing the variance of gradient estimation. Finally, the experiment verifies that the proposed algorithm can reduce the variance of gradient estimation. [ABSTRACT FROM AUTHOR]

With the increasing maturity of wireless charging technology, especially the development of magnetic resonance wireless charging technology, the employment of mobile charging vehicles and wireless charging technology to replenish sensors' energy to ensure the continuous operation of wireless sensor networks has become a new research hotspot. This paper focused on how to schedule multiple charging vehicles to replenish energy to the sensors in a large-scale wireless sensor network. In order to balance the charging tasks of multiple charging vehicles and reduce the completion time of the whole charging task, the paper proposed the charging task completion time minimization problem, hoping to find K closed charging circles for K charging vehicles, so that the longest completion time among these K vehicles was the shortest. Since the charging task completion time minimization problem was an NP-hard problem and it was difficult to find an optimal solution in polynomial time, this paper proposed an approximation algorithm with a ratio of 5 for this problem. Finally, it demonstrated the performance of the algorithm by simulation experiments, and the experiments show that the actual approximation ratio of the proposed algorithm is less than 2. [ABSTRACT FROM AUTHOR]

For the nonlinear energy harvesting process in downlink multi-user simultaneous wireless information and power transfer network, this paper proposed a energy harvesting scheme using successive convex approximation. Considering the transmission security requirements of authorized users, the research aimed for maximizing the secrecy energy efficiency. It jointly optimized the security transmission rate of users and network energy consumption. To solve the non-convex optimization problem of multivariable coupling, it transformed non-convex function by Taylor series. In the theoretical framework combining successive convex approximation and Dinkelbach algorithm, it designed a resource allocation algorithm satisfying maximum secrecy energy efficiency requirement to obtain the minimum input power required by the authorized users. Simulation results show that the algorithm is effective in optimizing the secrecy energy efficiency of the system. The research provides a basis for the study of communication security and energy efficiency in more real SWIFT networks, such as multi-input multi-output (MIMO) or with incomplete channel state information. [ABSTRACT FROM AUTHOR]

In order to solve the problem of large deviation between hidden Markov model and actual equipment health diagnosis,this paper developed an improved degenerated hidden Markov model(DGHMM) with a core of the quasi power relation.Firstly,the model adopted the degradation factors,modeling the process of recession for the equipment’s continuous decrease in performance.Compared with the conventional exponential accelerated degradation,the quasi power relation accelerated degradation could better describe the process that the performance of the equipment decreases gradually with the increase of service age.Then,the improved genetic algorithm could replace the conventional EM algorithm for parameters’ estimation,which overcame the limitation that the EM algorithm was easy to fall into local optimization.At the same time,in terms of the limitation of life prediction problem as a result of the hidden Markov model must obeyed exponential distribution,an algorithm named greed& approximation based on approximation algorithm and Viterbi algorithm came out,and to seek maximum probability remaining observation,for the purpose of seeking maximum probability dynamically surplus state path,to predict the residual life of equipment.Finally,it validated and evaluated the proposed method with the data set of caterpillar hydraulic pumps.The results show that the method of equipment health diagnosis and life prediction based on the improved degraded hidden Markov model is more effective in describing equipment’s degeneration and the accuracy of equipment state diagnosis,and is also feasible in the prediction of residual life. [ABSTRACT FROM AUTHOR]

The virtual backbone (VB) in wireless networks is a subset of some wireless nodes, such that only VB nodes are responsible for routing related tasks. The smaller the weight of VB, the less the overhead. In a node-weighted wireless network, not only the number of nodes in VB should be considered, but also the total weight should be considered. Frequently, a weighted wireless network is modeled as a node-weighted unit disk graph ( UDG) . Correspondingly, the minimum-weighted VB problem in the weighted wireless network is abstracted as the minimum-weighted connected dominating set ( MWCDS) problem in the node-weighted UDG. Finding MWCDS is a NP-hard problem. To reduce the approximate ratio of the connected part of the MW CDS problem in node-weighted UDG, this paper proposed a new method-degree-based node-weighted Steiner tree algorithm in the connected part. Combining the best results at present, it will obtain a ( 3. 32 + s ) -approximation algorithm for MWCDS problem in UDG. Similarly, it will also obtain a ( 3. 3 2 + s) -approximation algorithm for MWCVC problem in UDG. It is proved that the method of reducing the approximate ratio of the MWCDS problem in node-weighted UDG is feasible by improving the approximate ratio of connected parts. [ABSTRACT FROM AUTHOR]

In today' s big data era, big data processing frameworks such as MapReduce often appear slow and inefficient when processing data, specially related to graphs. Therefore, it is necessary to explore an efficient algorithm to handle this type of clique counting problem. Since the predecessor literatures have thoroughly explored the 3-clique counting, the extended version of the problem( the 4-clique counting problem) improves its position gradually. Under the guidance of a heuristic idea, this paper proposed a probability sampling algorithm based on neighboring edge sampling to solve the extended problem. With the usage of Chernoff inequality, the algorithm only needed a certain number of samplers as the performance guarantee of relative error under the approximate condition. Later, the experimental evaluation and comparison shows that the probability sampling algorithm loses a small amount of precision compared with the traditional precision algorithm, but it has great advantages in algorithm running time and space occupation. Finally, it comes to the conclusion that it has great practical value in practical applications. [ABSTRACT FROM AUTHOR]

A virtual backbone of wireless sensor networks is a subset of nodes and only virtual backbone nodes are responsible for routing-related tasks. The smaller a virtual backbone is, the less the network overhead, the size of a virtual backbone is the key factor to measure the quality of the vi11ual backbone. Frequently, the unit disk graph is used to model a wireless sensor network. In a wireless sensor network, the problem of finding the minimum virtual backbone is abstracted to the problem of finding the minimum connected dominating set in the unit disk graph corresponding to the wireless network. However, the problem of finding the minimum connected dominating set in the unit disk graph is a NP-hard problem, many works contribute to obtain the approximation algorithm of the minimum connected dominating set rather than finding the minimum connected dominating set in the unit disk graph. Constructing a 3-connected r-hop dominating set in wireless sensor networks is an effective means to obtain a connected dominating set with a smaller size such that can reduce the total amount of information forwarded between any nodes, which is a better approximation for finding the minimum virtual backbone. This paper proposed an algorithm to construct a 3-connected r-hop dominating set in wireless sensor network. It shows that the size of a 3-connected r-hop dominating set produced by the algorithm is no more than 5 (2r + 2β + 1) (r + 1) β| U' I - 10(2 + β) (r + 1) - 5r - 12. At the end, it analyzed the performance of the proposed algorithm by experiment simulation, and the experimental results are consistent with what expected. [ABSTRACT FROM AUTHOR]

To solve the problem of controller load imbalance caused by static network structure can't adapt to the dynamic change of traffic in software defined networking, this paper proposed a stage dynamic controller load balancing strategy ( SDCLB). In phase one, it determined the candidate set of immigration controller with the aim of load equalization, and considered the delay and loaded comprehensively, designed index function to determine the switch to be migrated. In phase two, it considered the connectivity of network nodes, proposed an improved EMD model with minimum migration cost and used the linear approximation algorithm to solve it quickly. The method realized the fast parallel migration of switches. Results show that compared with the existing load balancing strategy, this strategy improves the process of selecting immigration controllers and switches, and optimizes network performance. The load balancing degree of this strategy increases by about 31. 4 % [ABSTRACT FROM AUTHOR]

The successive cancellation(SC) decoding method for polar codes under additive white Gaussian noise (AWGN) channels is performed in the log-likelihood ratio(LLR) domain. The calculation of the f function nodes use a sum-product algorithm based on hyperbolic tangent rules. This paper proposed a polyline approximation algorithm, which simplified the hyperbolic tangent function and the inverse hyperbolic tangent function into a 9-segment polyline function respectively. In order to obtain better FER performance under the polyline approximation algorithm, this algorithm added a 16 bit CRC to the information bits before encoding. Simulation experiments show that for the polar codes with code length N = 1 024 and information bit length K = 496, the proposed algorithm has better FER performance than the sum-product algorithm, and it reduces the decoding complexity and improves the decoding speed. [ABSTRACT FROM AUTHOR]

In solving the problem of multi-objective optimization operation of distributed power supply in micro-grid, aiming at the limitations of traditional NSGA-II algorithm in terms of approximation and computational efficiency, considering the difference of individual similarity in evolution process and the possibility of the algorithm falling into local optimum, this paper proposed an improved NSGA-II algorithm. The new algorithm introduced the information entropy mechanism to improve the operator, and approximated the crossover probability and mutation probability as the decreasing function model and the Cauchy distribution model respectively. The performance test proves that the new algorithm is effective. Taking distributed power supply access to IEEE 30-node power system as an example for simulation experiment, compared with the traditional NSGA-II multi-objective optimization algorithm, the result shows the superiority of the improved algorithm in improving the convergence speed and the optimization indices. [ABSTRACT FROM AUTHOR]

In the past more than 20 years, the ring loading problem has been studied widely. The ring loading problem with penalty cost is the generalized form of the ring loading problem. There are some research results on undirected rings and directed rings. This paper proposes a mixed ring loading problem with penalty cost. Given a mixed ring C and a set of requests, each request rj has a demand dj and a penalty pj. When the request rj is accepted, its traffic can be transmitted along the ring clockwise and counter clockwise. When the request rj is rejected, the penalty pj is generated to minimize the sum of the maximum load among all links on the ring and the total penalty cost of the rejected requests. When the demand can be split, a 2-approximation algorithm is given by using the LP-rounding technique. Further, a 1.58-approximation algorithm is obtained by using the random rounding technique. Similarly, when the demand cannot be split, a 3-approximation algorithm and a (1.58+ε)-approximation algorithm are given, where ε>0 is a constant. [ABSTRACT FROM AUTHOR]

To improve the efficiency of frequent itemset mining upon uncertain dataset, addressing the issue of heavy computation cost of existing algorithms on judging whether to build sub header table for a certain item in the header table, this paper proposed an approximation algorithm called AAT-Mine, at the cost of losing a small portion of frequent itemsets, improved the overall mining performance. It evaluated the AAT-Mine algorithm using three datasets against classical and state of art algorithms. Experimental results show that AAT-Mine not only outperforms AT-Mine, MBP, IMBP, UF-Growth and CUFP-Mine in terms of running time, but also remains efficient memory usage. [ABSTRACT FROM AUTHOR]

Based on the classical problem of project portfolio selection (PPS), the multi-phase rolling model of PPS was constructed under uncertainty, the objective was maximization of benefits of each phase. Related constraints were built relying both matching of strategy equilibrium between enterprise strategy requirement and synthesis of strategy contribution of projects which were selected, and the enhancement effect of resources. Upper-side and down-side nearness were put up based on fuzzy theory, and was used to measured the matching of strategy equilibrium. A crisp transformation was implemented with the fuzzy model based on possibility theory. Genetic algorithm was presented to solved the model, through development of path relinking (PR), the algorithm of successive substitute and successive retroposition was designed to applied to 0-1 encoding as local search, and get the better solution. Simulation test show that when project portfolio selection of multi phase rolling is doing, enterprise can get benefits steadily during five phases, and benefits is differentiate in 6-8 phases. This conclusion can help enterprise adjust its strategy periods. Statistics about numbers of project styles are selected in every phase show the periodic regularity. Research resultion can give a guidance to execution of project portfolio selection in fact. [ABSTRACT FROM AUTHOR]