AUTOMATIC APPROACH TO PRODUCT CONCEPTUAL DESIGN BASED ON CONSTRAINT NETWORK

Product design is considered as the constraint satisfaction problem (CSP),and a new design approach basedon constraint network is proposed and applied to make designautomatic partially. By means of constraint extraction, constraint network construction andoperation, constraint chains are acquired, and then the conceptual design scheme can be got bydecoding the information stored in the design constraint chains, which provides a novelty method formechanical product design automation. Shearing mechanism of shearing machine has been designed bythis way.

Conflict detection in collaborative architectural pipe routing design based on constraint network

This paper analyzes conflict features in architecture pipe routing,and builds a pipe routing design conflict model by taking into account of discrete nominal internal diameter selection of pipes,material costs,and conflict solution sequence.Considering pipe routing as an assembling process,a conflict detection approach for pipe routing in collaborative architectural design is proposed based on an aforementioned model.Constraint network is used to describe the relationship among pipe routing design parameters and constraints;design conflicts are detected by matching designers' input and constraint network;and detected design conflicts are reordered according to the number of pipe parameters in conflicts.In order to support the collaborative requirement of pipe routing design,a prototype system using browser/server architecture is developed.An illustrative example of water pipe routing in a room is used to show the effectiveness and efficiency of the approach.

Network-based structure optimization method of the anti-aircraft system

The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities.Firstly, the thought of combat network model(CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength(CAST) logic and influence network(IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network(TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed.Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-Ⅱ(NSGA2) is used to solve the multiobjective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-Ⅲ(NSGA3)and strength Pareto evolutionary algorithm-Ⅱ(SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

The Distributed Network Monitoring Model with Bounded Delay Constraints

We address the problem of optimizing a distributed monitoring system and the goal of the optimization is to reduce the cost of deployment of the monitoring infrastructure by identifying a minimum aggregating set subject to delay constraint on the aggregating path. We show that this problem is NP-hard and propose approximation algorithm proving the approximation ratio with lnm+1, where is the number of monitoring nodes. At last we extend our modal with more constraint of bounded delay variation. Key words network - distributed monitoring - delay constraint - NP-hard CLC number TP 393 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: LIU Xiang-hui(1973-), male, Ph. D. candidate, research direction: algorithm complexity analysis, QoS in Internet.

Learning Bayesian network parameters under new monotonic constraints

When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.

Impact of Optical Hop Constraints on the IP/MPLS-over-WSON Networks CAPEX

In this work,the impact of hop constraints on the CAPEX of multi-layer IP/MPLS over WSON networks is evaluated under different scenarios. By routing lightpaths with different number of transparent hops,the cost of networks varies significantly. This effect is thoroughly analyzed with a heuristic algorithm for different network topologies belonging to some European network operators. The main conclusion extracted is that limiting the transparent reach in such multi-layer networks can give a good tradeoff between CAPEX of IP/MPLS networks and WSON networks,and even lead to signifi cant savings in CAPEX when compared to longer lightpaths routing strategies.

Fault detection for networked systems subject to access constraints and packet dropouts

This paper addresses the problem of fault detection（FD） for networked systems with access constraints and packet dropouts.Two independent Markov chains are used to describe the sequences of channels which are available for communication at an instant and the packet dropout process,respectively.Performance indexes H∞ and H_ are introduced to describe the robustness of residual against external disturbances and sensitivity of residual to faults,respectively.By using a mode-dependent fault detection filter（FDF） as residual generator,the addressed FD problem is converted into an auxiliary filter design problem with the above index constraints.A sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities（LMIs）.When these LMIs are feasible,the explicit expression of the desired FDF can also be characterized.A numerical example is exploited to show the usefulness of the proposed results.

Robust Fuzzy Tracking Control for Nonlinear Networked Control Systems with Integral Quadratic Constraints

This paper investigates the robust tracking control problcm for a class of nonlinear networked control systems （NCSs） using the Takagi-Sugeno （T-S） fuzzy model approach. Based on a time-varying delay system transformed from the NCSs, an augmented Lyapunov function containing more useful information is constructed. A less conservative sufficient condition is established such that the closed-loop systems stability and time-domain integral quadratic constraints （IQCs） are satisfied while both time-varying network- induced delays and packet losses are taken into account. The fuzzy tracking controllers design scheme is derived in terms of linear matrix inequalities （LMIs） and parallel distributed compensation （PDC）. Furthermore, robust stabilization criterion for nonlinear NCSs is given as an extension of the tracking control result. Finally, numerical simulations are provided to illustrate the effectiveness and merits of the proposed method.

考虑电力网络约束的工业园区虚拟电厂调控边界求解方法

构建以新能源为主体的新型电力系统,亟须挖掘负荷侧灵活调节资源参与电网调控。含电解铝、矿热炉等高耗能负荷的工业园区具备良好的调控潜力,但是考虑园区内部电力网络约束,其精确调控边界求解面临着变量维数高、约束非线性的难题,现有方法不能较好地兼顾计算效率和精度。对此,文中将上述问题抽象为高维非线性状态空间在P-Q耦合平面的投影问题:分别建立考虑工业园区安全运行线性化和非线性约束的调控边界投影求解模型,采用一种新颖的高维状态空间投影算法,通过顶点“搜索-映射”的两步式求解过程,得到工业园区型虚拟电厂调控边界的精确投影。算例结果表明,采用所提方法求解的调控边界可由线性不等式组完全表征,与现有调度系统完全兼容,结合与叠加柔性资源调控能力和传统采样方法的对比,验证了该方法的可行性以及高求解精度和效率。

通信受限条件下多无人机协同环境覆盖路径规划

多无人机协同覆盖旨在有效分配多个无人机任务,实现给定区域的快速、高效全覆盖。然而,在现实应用场景中常常因为无人机之间距离超出通信范围,信号传输受阻,导致无人机之间的协作和信息交互面临极大挑战。为此,提出一种基于Deep Q Networks(DQN)的多无人机路径规划方法。采用通信中断率和最大通信中断时间两个指标来评价路径质量,通过构建与指标相关的奖励函数,实现了无人机团队的自主路径决策。仿真实验表明,所提方法在最短路径上可以与传统优化算法效果保持一致,权衡路径下在增加20%路径长度的情况下可以降低80%通信中断率,在全通信路径下则可以实现100%的全过程连接通信,因此可以根据不同的通信环境生成高效覆盖所有环境节点的路径。

An Exact Virtual Network Embedding Algorithm Based on Integer Linear Programming for Virtual Network Request with Location Constraint

Network virtualization is known as a promising technology to tackle the ossification of current Internet and will play an important role in the future network area. Virtual network embedding(VNE) is a key issue in network virtualization. VNE is NP-hard and former VNE algorithms are mostly heuristic in the literature.VNE exact algorithms have been developed in recent years. However, the constraints of exact VNE are only node capacity and link bandwidth.Based on these, this paper presents an exact VNE algorithm, ILP-LC, which is based on Integer Linear Programming(ILP), for embedding virtual network request with location constraints. This novel algorithm is aiming at mapping virtual network request(VNR) successfully as many as possible and consuming less substrate resources.The topology of each VNR is randomly generated by Waxman model. Simulation results show that the proposed ILP-LC algorithm outperforms the typical heuristic algorithms in terms of the VNR acceptance ratio, at least 15%.

基于最优经济运行区域的主动配电网日前日内协同调度方法

传统的日前-日内协同调度通常以与日前时序计划曲线偏差最小作为日内目标函数,当日内新能源出力预测值与日前相差较大时,储能装置(energy storage systems,ESS)等由于其时间耦合约束日内调整范围有限,导致经济性和灵活性较差。对此,提出了基于最优经济运行区域(optimal economic operation region,OEOR)的主动配电网(active distribution networks,ADN)日前-日内协同调度方法。在日前阶段,构建线性化ADN调度模型,基于拉丁超立方采样法生成的大量随机场景下调控设备优化曲线,以全时间段内设备出力上下界内所包含的随机场景优化结果数量最大为目标,考虑储能装置荷电状态的相邻时段约束和微型燃气轮机的爬/滑坡率,构建OEOR生成模型。最后,在日内阶段,调控设备在OEOR内滚动优化调整,当该时段优化值贴近OEOR边界时,考虑相邻时段出力约束,将OEOR扩展为最优经济极限运行区域(E-OEOR)。算例结果表明,所提方法相比于传统方法能够更有效地提升配电网经济性。

融合物理约束的压裂水平井产能智能预测框架构建与应用

水平井分段压裂是实现非常规油气藏有效开发的关键技术,准确预测压裂水平井产能对于井位优选和压裂参数优化至关重要。随着历史开发数据的不断积累和人工智能技术的迅速发展,数据驱动的人工智能方法为压裂水平井产能预测提供了新的渠道。为此,从压裂水平井生产物理过程出发,分析了产能智能预测的物理约束,提出了与物理过程相匹配的产能智能预测框架,并结合四川盆地南部(以下简称川南)地区页岩气开发生产数据开展了实例验证。研究结果表明:(1)压裂水平井产能智能预测需要以压裂段为单位进行特征融合处理,单井产能是由初始压裂段到末尾压裂段依次累计作用的结果,各压裂段之间存在顺序关系,各单井的因素输入维度存在差异;(2)采用循环神经网络能够完全匹配压裂段之间的顺序关系和汇聚作用,而Mask屏蔽机制则能够解决各单井压裂段数量不统一的矛盾。结论认为:(1)该智能预测模型能够学习各单井输入序列与产能之间的复杂映射关系,训练集相对误差为0.098、测试集相对误差为0.117,较循环神经网络(RNN)模型误差的下降幅度分别为37.6%和37.0%,较多层感知机(MLP)模型误差的下降幅度分别为77.3%和77.4%,展现出优异的预测性能;(2)该研究成果能够为非常规油气藏压裂水平井产能预测的技术进步和快速发展提供新的思路与借鉴。

基于物理约束的分布式神经网络三维地应力预测模型

地应力是油气藏全生命周期中井壁稳定分析、水力压裂设计、地层出砂预防、套损预测防治和油气开采措施制定的重要基础参数.针对缺乏三维地震资料情况下的三维地应力场预测问题,以工区已钻井地应力测井解释结果为基础,建立了一种基于物理约束的分布式神经网络三维地应力预测模型.首先,采用测井解释方法获得工区已钻井地应力单井剖面,并通过Kriging插值构建三维地质模型;其次,将测井数据和三维地质模型的三维空间坐标喂入3个并行的全连接神经网络,引入地应力物理约束条件,提出了数据和物理双约束的条件,进而通过三维空间坐标实现三维地应力场的预测;再次,优选了数据和物理双约束的权重参数,与人工神经网络、支持向量回归、随机森林等三种机器学习模型以及克里金插值模型进行了对比;最后,评价了不同机器学习模型预测地应力的效果.研究结果表明:(1)研究区域的地应力满足潜在正断层应力状态,即垂向地应力>最大水平地应力>最小水平地应力;(2)数据和物理双约束权重参数取值对预测结果有显著影响,当权重参数λ=0.2时,三维地应力预测效果最优;(3)与人工神经网络、支持向量回归、随机森林、克里金插值等模型相比,本文模型预测得到的地层三维地应力和孔隙压力更准确,在工区内测试集预测得到的垂向地应力、最大水平地应力、最小水平地应力和地层孔隙压力的最大相对误差分别为0.63%、7.59%、7.16%、3.21%,而且,本文模型能够更加准确捕捉地应力梯度的三维变化特征,水平井段地应力和地层孔隙压力参数的正态分布特征明显.结论认为,物理约束的分布式神经网络模型能有效融合地形构造与地应力之间的联系,提高三维地应力预测的准确性和可解释性,为油田三维地应力场的预测提供了一种新的思路.

基于机器学习的通信网络入侵检测系统

文章提出一种基于机器学习的创新型方法,以提高通信网络入侵检测系统的检测效果。首先,深入研究了通信网络入侵检测的基本架构,以全面理解入侵行为的多样性和复杂性。其次,将正则化约束引入循环神经网络(Recurrent Neural Networks,RNN)模型,旨在提高检测准确性和模型的泛化能力。最后,利用UNSW-NB15数据集进行实验,证明所提方法的有效性。实验采用混淆矩阵进行结果分析,并通过精确度、召回率、F1分数等指标综合评估模型性能。结果表明,文章所提方法在通信网络入侵检测任务中表现出色,具有较高的准确性和泛化能力。

战术网络中基于资源负载均衡和服务优先级的业务调度策略

针对战术网络环境的多样化业务和网络异构性、高动态特性导致的网络负载易失衡的问题,提出了一种基于资源负载均衡和服务优先级的业务调度策略。通过所承载业务类型的优先级感知,基于通信网络当时可用资源等约束,将业务按需调度到战术通信网络的通信资源池,达到通信网络的自动负载均衡和资源可靠合理利用,提高业务服务质量,并保证高优先级业务可靠传输。通过仿真,验证了该策略算法可大大提高网络吞吐量和资源利用效率。

风险沟通悖论视域下风险治理“共同体”网络的构建

风险沟通悖论的形成机制十分复杂,由多种内外约束因素在心理机制的基础上耦合而成。风险事件的紧急性与不确定性并存,复杂性与多变性交织,政府的“封闭倾向”与专家的“欠缺模型”仍然占据主导地位,公众主体的异质性特点制约着公众参与的有序性。为此,有必要基于风险沟通的困境根源回归风险沟通悖论视域,重新勾勒和界定多元主体的权利与义务,在达成共识的基础上促进多元主体协同合作,培养以实现集体行动为目标的风险治理“共同体”网络。

具有介质访问约束的网络化控制系统H∞状态估计

研究了一类存在介质访问约束和信号量化的网络化控制系统的H∞状态估计问题。首先,考虑到介质访问约束和信号量化的影响,将信号量化误差转化为扇形有界的不确定性,对由于访问约束而未获得网络传输的系统输出采用加权补偿策略,根据网络介质的随机访问机制将网络化系统建模为具有不确定性的马尔可夫跳跃系统。然后,在此基础上借助李雅普诺夫稳定性理论和H∞性能约束条件,利用线性矩阵不等式(linear matrix inequality,LMI)方法给出状态估计器存在的充分条件,所设计的状态估计器使得估计误差系统渐近稳定且具有给定的H∞性能。最后,通过仿真实例验证了该设计方法的有效性。