ZTE Corporation Selected for Telkom Indonesia's CDMA Network Expansion

August 30,2007,Shenzhen,China-ZTE Corporation ("ZTE"),a leading global provider of telecommunications equipment and network solutions,announced that it has recently signed an agreement with Telkom Indonesia ("Telkom"),the largest InfoCom company and full-service network provider in Indonesia,to help expand the CDMA2000 network capacity for national coverage. Under the contract,the network expansion will cover the four main cities of the eastern part of Sulawesi,Indonesia.

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.

A Scenario-Based Robust Transmission Network Expansion Planning Method for Consideration of Wind Power Uncertainties

This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.

Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Increasing the Maximum Capacity Path in a Network and Its Application for Improving the Connection Between Two Routers

Abstract:This paper addresses the problem of improving the optimal value of the Maximum Capacity Path(MCP)through expansion in a flexible network,and minimizing the involved costs.The only condition applied to the cost functions is to be non-decreasing monotone.This is a non-restrictive condition,reflecting the reality in practice,and is considered for the first time in the literature.Moreover,the total cost of expansion is a combination of max-type cost(e.g.,for supervision)and sum-type cost(e.g.for building infrastructures,price of materials,price of labor,etc.).For this purpose,two types of strategies are combined:(l)increasing the capacity of the existing arcs,and(l)adding potential new arcs.Two different problems are introduced and solved.Both the problems have immediate applications in Internet routing infrastructure.The first one is to extend the network,so that the capacity of an McP in the modified network becomes equal to a prescribed value,therefore the cost of modifications is minimized.A strongly polynomial-time algorithm is deduced to solve this problem.The second problem is a network expansion under a budget constraint,so that the capacity of an McP is maximized.A weakly polynomial-time algorithm is presented to deal with it.In the special case when all the costs are linear,a Meggido's parametric search technique is used to develop an algorithm for solving the problem in strongly polynomial time.This new approach has a time complexity of O(n^(4)),which is better than the time complexity of O(n4 log(n)of the previously known method from literature.

用于旋转机械故障诊断的一种张量增强型前向神经网络模型

在多层前向神经网络模型的研究基础上，提出了基于张量的增强型前向神经网络诊断模型，以实现在已知输入模式不变的情况下，增强原始模式的表达，从而提高了诊断的精度。试验结果表明，本模型对工程应用具有较高的实用价值。

路网扩张对董志塬景观生态的影响

Road network expansion can result in the fragmentation of ecological landscapes due to the transformation of landscape processes and patterns.However,knowledge about these processes and patterns is scarce.In this study,the road network and landscape patterns in the Dongzhi tableland of the Chinese Loess Plateau(CLP) between 2005 and 2020 were characterized,and their spatial relationships were analyzed.The results showed that(1) the kernel density estimation(KDE) method is useful in characterizing road network density.When the bandwidth value is four,the boundary of the road network kernel can be distinguished clearly.(2) The road network in the tableland expanded greatly over the past 15 years,and the total area of road kernels in the Dongzhi tableland increased from 55.73 km~2 in 2005 to 223.55 km~2 in 2020.(3) High-density road networks were generally distributed on cultivated and constructed lands where the slopes were generally 0°–5°,while low-and medium-density road networks were mostly distributed in grassland areas where the slopes were greater than 5°.(4) Road network density is closely related to the coverage of cultivated and constructed lands.The results of this study are helpful in understanding the potential impact of road network evolution on the landscape at a regional scale.

Capacity Market Mechanism Design,Considering Network Optimization and Energy Transition

The coordination of enrgy transition,fixed cost recovery,and sufficient generation supply leads to a new challenge for a traditional capacity market mechanism.Moreover,in order to better match network expansion at the same time,it is crucial to redesign the capacity market mechanism considering system topology.In this paper,a novel capacity market mechanism is proposed considering spot market operations,network expansion,and energy transition,which can minimize the total cost of capacity investment,network expansion,and generation operations,while satisfying the energy transition constraints and topology circumstances.Specifically,the capacity market mechanism co-ordinated with spot market operations is illustrated,in which the energy transition and network constraints are embedded.Then,a bi-level optimization model is established where the trade organizers minimize the total cost of both investment and operations,subject to the spot power market simultaneously minimizing the local dispatching costs.The numerical results of a test system show that more economical capacity portfolios can be obtained by constructing reasonable transmission lines,thereby obtaining a more optimal market cost.A detailed multi-scenario simulation is further analyzed to verify the effectiveness of the proposed market mechanism.

Nonlinear Reduced DNN Models for State Estimation

We propose in this paper a data driven state estimation scheme for generating nonlinear reduced models for parametric families of PDEs, directly providingdata-to-state maps, represented in terms of Deep Neural Networks. A major constituentis a sensor-induced decomposition of a model-compliant Hilbert space warranting approximation in problem relevant metrics. It plays a similar role as in a ParametricBackground Data Weak framework for state estimators based on Reduced Basis concepts. Extensive numerical tests shed light on several optimization strategies that areto improve robustness and performance of such estimators.