R&D project selection using the analytic network process

The analytic network process (ANP) is presented as a potentially valuable method to support the selection of projects in a research and development (R&D) environment. This paper first discusses the requirements of the R&D project selection problem, which requires the allocation of resources to a set of competing and often disparate project proposals. Among the factors complicating this task is the need to make the decision within the framework of an enterprise's strategic objectives and organizational structure while considering and integrating financial and strategic benefits of each project. The paper discusses the use of the ANP, a general form of Saaty's analytic hierarchy process, as a model to evaluate the value of competing R&D project proposals. A generic ANP model developed by the authors, which includes in its decision levels the actors involved in the decision, the stages of research, categories of metrics, and individual metrics, is presented. The paper concludes with a case study describing the implementation of this model at a small high-tech company, including data based on the actual use of the decision making model     

Reliability evaluation of wireless sensor networks using an enhanced OBDD algorithm

An enhanced ordered binary decision diagram (EOBDD) algorithm is proposed to evaluate the reliability of wireless sensor networks (WSNs), based on the considerations of the common cause failure (CCF) and a large number of nodes in WSNs. The EOBDD algorithm analyzes the common cause event (CCE) and the network structure when CCE takes place according to the stochastic graph and the CCF model of WSNs. After constructing the ordered binary decision diagram (OBDD) of the original network with node expansion, it uses a set of OBDD variables (SOV) to guide reliability computations along this OBDD. The two steps about OBDD can decrease the cost of OBDD constructions and storage. Furthermore, the efficient OBDD structure and Hash tables can greatly decrease redundant computations of isomorphs. The experiment results show that the EOBDD can be used to evaluate the reliability of WSN efficiently.     

Decision strategies that maximize the area under the LROC curve

For the 2-class detection problem (signal absent/present), the likelihood ratio is an ideal observer in that it minimizes Bayes risk for arbitrary costs and it maximizes the area under the receiver operating characteristic (ROC) curve [AUC]. The AUC-optimizing property makes it a valuable tool in imaging system optimization. If one considered a different task, namely, joint detection and localization of the signal, then it would be similarly valuable to have a decision strategy that optimized a relevant scalar figure of merit. We are interested in quantifying performance on decision tasks involving location uncertainty using the localization ROC (LROC) methodology. Therefore, we derive decision strategies that maximize the area under the LROC curve, A(LROC). We show that these decision strategies minimize Bayes risk under certain reasonable cost constraints. The detection-localization task is modeled as a decision problem in three increasingly realistic ways. In the first two models, we treat location as a discrete parameter having finitely many values resulting in an (L + 1) class classification problem. In our first simple model, we do not include search tolerance effects and in the second, more general, model, we do. In the third and most general model, we treat location as a continuous parameter and also include search tolerance effects. In all cases, the essential proof that the observer maximizes A(LROC) is obtained with a modified version of the Neyman-Pearson lemma. A separate form of proof is used to show that in all three cases, the decision strategy minimizes the Bayes risk under certain reasonable cost constraints.     

Traffic grooming in mesh WDM optical networks - performance analysis

Traffic grooming is an important task in interworking between the wavelength-division multiplexing (WDM) optical network that supplies "pipes" at the wavelength granularity, and the attached client networks that usually require connections of subwavelength granularity. The focus of this paper is to conduct performance analysis of grooming dynamic client traffic in WDM optical networks with a mesh topology. This paper first briefly introduces the traffic grooming problem in WDM optical networks and the issues related to performance analysis. It then develops two link blocking models, an exact model based on the stochastic knapsack problem and an approximation model based on an approximate continuous time Markov chain (CTMC). The end-to-end performance analysis is conducted using the reduced load approximation. The result obtained from analysis is shown to be accurate compared with the numerical result obtained from simulation.     

协同边缘网络中智能计算卸载与资源优化算法

针对具有依赖关系的计算密集型应用任务面临的卸载决策难题，提出了一种基于优先级的深度优先搜索调度策略。考虑到用户能量受限和移动性，构建了一种联合用户下行能量捕获和上行计算任务卸载的网络模型，并在此基础上建立了端到端优化目标函数。结合任务优先级及时延约束，利用深度强化学习自学习的优势，将任务卸载决策问题建模为马尔科夫模型，并设计了基于任务相关性的Dueling Double DQN(D3QN)算法对问题进行求解。仿真数据表明，所提算法较其他算法能够满足更多用户的时延要求，并能减少9%～10%的任务执行时延。     

Interference‐aware spectrum handover for cognitive radio networks

Cognitive radio (CR) is a promising technique for future wireless networks, which significantly improves spectrum utilization. In CR networks, when the primary users (PUs) appear, the secondary users (SUs) have to switch to other available channels to avoid the interference to PUs. However, in the multi‐SU scenario, it is still a challenging problem to make an optimal decision on spectrum handover because of the the accumulated interference constraint of PUs and SUs. In this paper, we propose an interference‐aware spectrum handover scheme that aims to maximize the CR network capacity and minimize the spectrum handover overhead by coordinating SUs’ handover decision optimally in the PU–SU coexisted CR networks. On the basis of the interference temperature model, the spectrum handover problem is formulated as a constrained optimization problem, which is in general a non‐deterministic polynomial‐time hard problem. To address the problem in a feasible way, we design a heuristic algorithm by using the technique of Branch and Bound. Finally, we combine our spectrum handover scheme with power control and give a convenient solution in a single‐SU scenario. Experimental results show that our algorithm can improve the network performance efficiently.Copyright © 2012 John Wiley & Sons, Ltd.     

基于DDPG的综合化航电系统多分区任务分配优化方法

综合化航电系统（Integrated Modular Avionics,IMA）通过时空分区机制实现共享资源平台下的多航电功能集成,分区间的任务分配方法的优劣决定着航电系统的整体效能。针对航电任务集合在多分区内的分配调度问题,提出了一种基于深度强化学习的优化方法。构建了航电系统模型与任务模型,以系统资源限制与任务实时性需求为约束,以提高系统资源利用率为优化目标,将任务分配过程描述为序贯决策问题。引入马尔科夫决策模型,建立基于深度确定性策略梯度（Deep Deterministic Policy Gradient,DDPG）法的IMA任务分配模型并提出通用分配架构;引入状态归一化、行为噪声等策略训练技巧,提高DDPG算法的学习性能和训练能力。仿真结果表明,提出的优化算法迭代次数达到500次时开始收敛,分析800次之后多分区内驻留任务方案在能满足约束要求的同时,最低处理效率提升20.55%。相较于传统分配方案和AC(Actor-Critic)算法,提出的DDPG算法在收敛能力、优化性能以及稳定性上均有显著优势。     

Stackelberg game-based task offloading in mobile edge computing-enabled hierarchical multi-coalition unmanned aerial vehicle networks

The unmanned aerial vehicle (UAV) coalition networks have been widely used in emergency mission scenarios. The introduction of the mobile edge computing (MEC) paradigm into multi-coalition UAV networks further improves the mission processing performance of UAV coalitions. In this paper, we investigate the problem of minimizing total task processing delay of UAV members in MEC-enabled coalition-based UAV networks. First, we propose a hierarchical offloading model in which multiple UAV heads decide its position selection strategy and multiple UAV members decide its offloading strategy when offloading tasks to UAV heads. Considering data arrival from multiple UAV member nodes at each UAV head, the first come first served (FCFS) queuing model is introduced when the UAV head processes tasks from members. Second, the hierarchical offloading delay minimization problem is formulated as a multi-leader multi-follower Stackelberg game. The existence of a Stackelberg equilibrium (SE) is proved by showing that multi-leader subgame and multi-follower subgame are exact potential games (EPGs) with Nash equilibrium (NE). We design a best response-based hierarchical iterative offloading algorithm to solve SE. Finally, the simulation results show that the performance of the proposed scheme is better than that of other benchmark methods and the proposed scheme can effectively reduce the total delay for all UAV members.     

Consolidated actor?critic model for partially-observable markov decision processes

A method for consolidating the traditionally separate actor and critic neural networks in temporal difference learning for addressing partially- observable Markov decision processes (POMDPs) is presented. Simulation results for solving a five-state POMDP problem support the claim that the consolidated model achieves higher performance while reducing computational and storage requirements to approximately half those of the traditional approach.     

Ant colony optimisation for tree-searching-based synthesis of monopulse array antenna

The design of simple feed networks is of great interest in synthesising monopulse radar array antennas in order to reduce the complexity of the antenna architecture, the costs as well as the occupied physical space (e.g. on aircraft). Sub-arraying techniques have been proposed to properly address such a task. Starting from the formulation of the sub-arraying problem in terms of a combinatorial one, the final compromise solution is obtained by looking for the minimum cost path inside a binary tree graph through an ant colony optimiser.     

Topology control in ad hoc wireless networks using cooperative communication

In this paper, we address the Topology control with Cooperative Communication (TCC) problem in ad hoc wireless networks. Cooperative communication is a novel model introduced recently that allows combining partial messages to decode a complete message. The objective of the TCC problem is to obtain a strongly-connected topology with minimum total energy consumption. We show that the TCC problem is NIP-complete and design two distributed and localized algorithms to be used by the nodes to set up their communication ranges. Both algorithms can be applied on top of any symmetric, strongly-connected topology to reduce total power consumption. The first algorithm uses a distributed decision process at each node that makes use of only 2-hop neighborhood information. The second algorithm sets up the transmission ranges of nodes iteratively, over a maximum of six steps, using only 1-hop neighborhood information. We analyze the performance of our approaches through extensive simulation.     

基于二次规划的任务卸载决策和资源分配方法

主要研究移动用户均有多个独立任务的多用户移动云计算系统,这些移动用户将任务卸载到云端时共享通信资源。如何对所有用户的任务卸载决策和通信资源分配进行联合优化,以便使所有用户的能耗、计算量和延时降到最低是目前研究的难点。将该问题建模为NP难度的非凸的具有二次约束的二次规划(QCQP)问题,提出一种高效的近似算法进行求解,通过单独的半正定松驰(SDR)处理后,确定二元卸载决策和通信资源最优分配。采用代表最小系统成本的性能下界作为性能基准进行仿真实验,结果表明,本文算法在多种参数配置下的性能均接近最优性能。     

On single-sample robust detection of known signals with additive unknown-mean amplitude-bounded random interference

The single-sample robust decision problem for known signals in additive unknown-mean amplitude-bounded random interference is investigated. The approach is based on the generalized Lagrange multiplier theory for infinite-dimensional linear spaces. For the probability-of-error performance measure (Bayes risk) and equally likely binary signaling both minimax and maximin subproblems are formulated and solved explicitly. There is no nonrandomized saddlepoint solution. Moreover, the respective solutions are not unique and the classes of maximin and minimax decision rules are not disjoint. A uniform density and its optimal decision rule are shown to yield performance slightly less than the maximin value.     

WSN低功耗低时延路径式协同计算方法

针对云计算应用于无线传感器网络(Wireless Sensor Network,WSN)时延敏感型业务时存在的高传输时延问题,提出了一种WSN低功耗低时延路径式协同计算方法.该方法基于一种云雾网络架构开展研究,该架构利用汇聚节点组成雾计算层;在数据传输过程中基于雾计算层的计算能力分步骤完成任务计算,降低任务处理时延;由...     

Coverage planning for optimizing HSDPA performance and?controlling R99 soft handover

Coverage planning is an important engineering task in deploying UMTS networks implementing both high speed downlink packet access (HSDPA) and Release 99 (R99) services. Coverage planning amounts to determining the cell coverage pattern by means of setting the common pilot channel (CPICH) power of the cells. A conventional strategy is to uniformly allocate a proportion of the total power to CPICH. In this paper, we develop mathematical modeling and optimization approaches to bring the benefit of power saving enabled by optimizing non-uniform CPICH to enhance HSDPA performance, while preserving a desired degree of soft handover (SHO) for R99. The study focuses on HSDPA performance at cell edges, where data throughput is typically low. An integer linear programming model is developed for the resulting optimization problem. The model admits optimal or near-optimal planning solutions for relatively small networks. Solution algorithms based on local search and repeated local search are developed. These algorithms are able to perform the optimization for large-scale networks time-efficiently. Experimental results for both synthesized networks as well as instances originating from real planning scenarios demonstrate the benefit of our optimization approach.     

A Minimum Mean-Square Error Equalizer for Nonlinear Satellite Channels

The problem of designing and evaluating the performance of a minimum mean-square error equalizer (MMSEE) for binary PSK transmission over band-limited nonlinear satellite channels is considered in this correspondence. The effect of intersymbol interference followed by AM/AM and AM/PM conversions are taken into account while optimizing the performance in the presence of the downlink white Gaussian noise. In analyzing the problem, the decision is made on a typical signal in a received sequence taking into account past and future interfering signals, i.e., ISI. As an illustrative example of the receiver, a typical channel model is considered in details. Based on the analysis, an alternative receiver structure which is more suitable for implementation is introduced. The taps gain coefficients for minimum mean-square error, between the received sample and the actual transmitted bit, are obtained using numerical methods. The performance of the equalizer is evaluated using computer simulation techniques and it is shown that significant performance improvement over the single-sample sign detector can be obtained.     

多传感器分布式检测系统的跨层优化设计

目标检测就是判断被监测对象是否出现,是无线传感器网络应用的首要前提.由于传感器节点协作执行检测任务,因此,应用于通信网中的解耦和最大吞吐量的网络设计不能使系统检测性能最优.提出了集成物理层、MAC层和应用层的跨层设计,设计了感知信息质量、信道状态和有效能量协同的传输控制策略,该传输策略可以有效地调节节点和融合中心的通信使系统的检测性能最优.最后,通过解决非线性约束优化问题分别为ALOHA和TDMA传感器网络得到最优的设计变量,并与典型的解耦和最大吞吐量设计比较检测性能增益.     

基于支持向量机的说话人辨认研究

支持向量机是统计学理论的一个重要的学习方法，也是解决模式识别问题的一个有力的工具，本文提出了用支持向量机来解决说话人辨认问题。结合语音信号的特点，解决了大数据量情况下支持向量机的训练问题。支持向量机对两类的分类问题有着突出的优势，本文用两种判决规则将两类问题应用到多类的识别问题。用支持向量机实现了一个与文本无关的说话人辨认系统，实验表明，本方法有良好的效果。     

Joint optimization of data network design and facility selection

The authors describe a data network design model based on a mixed integer/linear programming (MILP) formulation that does not, as do most other approaches, separate link capacity and facility selection from routing and topological design; it fully integrates these processes to capture the important couplings that exist between them. The performance constraints are incorporated into the model in such a way that they are linear, but lead to the same grade of service for a balanced network as nonlinear average network delay constraints. It is shown that this formulation leads to a natural decomposition of the optimal design problem into two subproblems solvable sequentially. In the absence of capacity allocation constraints, the capacity and flow assignment problem is solved optimally and efficiently as part of the overall design process. Moreover, the model leads directly to the solution of multifacility design problems. A fast link reduction algorithm that efficiently designs single or multifacility networks and yields robust local extrema is presented. This algorithm is based on a special-purpose monotonic greedy drop heuristic procedure. An important application of this model is the design of multifacility networks     

A hierarchical fuzzy approach to supervisory control of robot manipulators with oscillatory bases

The research focuses on the issue of controlling a manipulator attached to a deployment, which typically exhibits compliance due to its mechanical nature. The problem is generalized as a motion control of a robotic manipulator attached to oscillatory bases. Due to the complexity of this problem, we decompose the control task into two subtasks and distribute them over two different levels. In the design of hierarchical fuzzy control structure, the lower level controllers take into account each subsystem ignoring the interactions among them, while the upper level controller handles subsystem interactions. The upper level coordinator to deal with the model reduction error and makes the supervisory decision to the lower level. Moreover, the supervisory fuzzy rule set is used to adjust the correction factors of the hierarchical fuzzy controller to achieve better performance. Consequently, it is shown that the proposed control model offers several implementation advantages such as less on-line computation time, reduced effect of overshoot and chattering, and a fast convergent rate in simulation. The results of this study can be feasible to various mechanical systems, such as mobile robot, gantry cranes, underwater robot, and other dynamic systems mounted on oscillatory bases.