基于收网算法的变电站周界噪声分布重现技术研究

针对变电站噪声的研究主要集中在建模仿真领域,具体方法是根据现有的设备及设施布置方式、材料运用等因素进行站内与站外的声场计算。此种方法受实际环境的温湿度、材料特性变化、外界干扰等因素的影响,仿真结果并不能准确地反映实际噪声分布情况。本文将探索基于收网算法的噪声传播模型的参数拟合研究,利用有限的噪声测试点推演邻近空间的噪声分布情况,摆脱复杂环境的建模困扰。案例验证表明,该方法可以有效地还原实际变电站周界噪声分布情况,值得推广使用。

收网算法及其在低压配电网潮流优化中的应用

针对低压配电网存在三相不对称现象及有较多分布式电源的现状,建立了综合考虑分布式电源、有载调压变压器、无功补偿器和插电式混合动力汽车的配电网优化模型。提出了一种新颖的由边界初始种群逐步向内压缩式搜索的启发式算法:收网算法。给出了收网算法的更新迭代公式和计算步骤,并从数学上严格推导证明了该算法的收敛性。为有效解决低压配电系统三相不对称或非全相运行问题,提出一种改进的分相前推回代算法。将收网算法应用于某测试算例和IEEE 123节点系统,优化结果表明所提算法有效可行。

Lazy loading algorithm for traffic assignment of road networks under fixed charge condition

The measures of path charge are important considerations in traffic assignment of road networks. Factors, such as travel time, fixed charge and traffic congestion which affect road users＇ choices of trip paths, are analyzed. Travelers usually decide their trip paths based on their personal habits, preferences and the information at hand. By considering both deterministic and stochastic factors which affect the value of time （VOT） during the process of path choosing, a variational inequality model is proposed to describe the problem of traffic assignment. A lazy loading algorithm for traffic assignment is designed to solve the proposed model, and the calculation steps are given. Numerical experiment results show that compared with the all-or-nothing assignment, the proposed model and the algorithm can provide more optimal traffic assignments for road networks. The results of this study can be used to optimize traffic planning and management.

Global optimization by small-world optimization algorithm based on social relationship network

A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.

A Robust Energy Efficiency Power AllocationAlgorithm in Cognitive Radio Networks

In order to solve the problem that traditional energy efficiency power allocation algorithms usually require the assumption of constant or perfect channel state information in cognitive radio networks(CRNs),which may lead to performance degradation in real systems with disturbances or uncertainties,we propose a robust energy efficiency power allocation algorithm for underlay cognitive radio(CR)systems with channel uncertainty in consideration of interference power threshold constraint and minimum target SINR requirement constraint.The ellipsoid sets are used to describe the channel uncertainty,and a constrained fractional programming for the allocation is transformed to a convex optimization problem by worst-case optimization approach.A simplified version of robust energy efficiency scheme by a substitutional constraint having lower complexity is presented.Simulation results show that our proposed scheme can provide higher energy efficiency compared with capacity maximization algorithm and guarantee the signal to interference plus noise ratio(SINR)requirement of each cognitive user under channel uncertainty.

Guaranteed Rendezvous Algorithms for Cognitive Radio Networks

Rendezvous is a blind process establishing a communication link on a common channel between a pair of nodes in cognitive radio networks. We propose two guaranteed rendezvous algorithms for cognitive radio networks under both single-radio and multi-radio scenarios with an asynchronous setting. For single-radio scenario, each cycle length is a prime number associated with a channel hopping sequence.The rendezvous can be guaranteed as long as the IDs of the two nodes are different. For multi-radio scenario, we propose a cycle length and rotation based rendezvous algorithm. Each node generates a channel hopping sequence with only one cycle length. Then these radios of each nodes rotate on the generated sequence with different rotation numbers at each hopping cycle. The rendezvous between two nodes is guaranteed as long as they have different cycle lengths or the same cycle length with different number of rotations. We conduct simulations on three metrics and the results show that the proposed algorithms outperform the existing ones.

Maximum Data Collection Rate in Rechargeable Wireless Sensor Networks with Multiple Sinks

In rechargeable wireless sensor networks, a sensor cannot be always benefi cial to conserve energy when a network can harvest excessive energy from the environment due to its energy replenished continually and limited energy storage capacity. Therefore, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving data collection rate. In this work, we propose an algorithm to compute an upper data generation rate that maximizes it as an optimization problem for a network with multiple sinks, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms are guaranteed to converge to an optimal data generation rate, which are illustrated by an example in which an optimum data generation rate is computed for a network of randomly distributed nodes. Through extensive simulation and experiments, we demonstrate our algorithm is efficient to maximize data collection rate in rechargeable wireless sensor networks.

Finite Convergence of On-line BP Neural Networks with Linearly Separable Training Patterns

In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.

Deriving Network-Constrained Trajectories from Sporadic Tracking Points Collected in Location-Based Services

The paper proposes an economical and fast algorithm for deriving trajectories from sporadic tracking points collected in location-based services (LBS). Although many traffic studies or applications can benefit from the derived trajectories, the sporadic tracking points are always implicitly overlooked by most of existing map-matching algorithms. The algorithm proposed in this paper finds network paths or trajectories traveled by vehicles through augmenting GPS data with odometer data. An odometer can provide data of traveled distance which are compared with the lengths of candidate network paths in order to find the most approximate network path approaching the trajectory of a vehicle. Tracking points are classified into anchor points and non-anchor points. The former are used to divide trajectories, and the latter screen candidate network paths. An elliptic selection zone and a reduction process are applied to the selection of possible road segments composing candidate network paths. A brute-force searching algorithm is developed to find candidate network paths and calculate their lengths. A two-step screening process is designed to select the final result from candidate network paths. Finally, a series of experiments are conducted to validate the proposed algorithm.