A bottleneck Steiner tree based multi-objective location model and intelligent optimization of emergency logistics systems

Logistics networks could be very fragile in a global environment due to unexpected emergencies such as earthquakes, tsunamis and terrorists attacks. Therefore, the research on emergency logistics systems is extremely significant. The dynamic changes, quick responses and unpredictable events are main features of the location problems in emergency logistics systems, which make them quite different from the traditional logistics networks. The previous single-objective location models and solution algorithms do not capture the new characteristics that arise from the emergency logistics systems. This paper first proposes a new node-weighted bottleneck Steiner tree based multi-objective location optimization model for the emergency logistics systems. Then, a cellular stochastic diffusion search based intelligent algorithm is introduced to solve the proposed model. Under different emergent scenarios, several examples are used to illustrate the application of the proposed model. Numerical experiments show that the proposed approach is effective and efficient for solving the location problem of emergency logistics systems.     

智能电网信息安全及其对电力系统生存性的影响

目前的电网必将发展成为由电力网和信息网组成的相互依存的复合网络. 分析了智能电网网络构成的特点, 阐述了信息网络安全在智能电网安全中的重要性. 通过对智能电网信息化引入新元素的主要功能和特点的分析, 从信息的采集、传输、处理和互动等角度描述了智能电网的信息安全问题, 预想了各类攻击条件下的安全性事故场景. 从网络功能的耦合性和网络间故障的传播特性探讨了信息网络安全对电力系统生存性的影响. 最后, 从关键技术、标准体系、政策法规、培训管理等方面提出了加强智能电网信息安全的具体建议, 有助于提高智能电网安全性.     

基于互联网平台的配电网智能化运维管理模式

为了有效运维管理配电网, 获取配电网运行状态的全部数据以及配电网中电力设备可能出现异常及故障的情况, 提高配电网经济效益, 提出基于互联网平台的配电网智能化运维管理模式. 通过互联网平台融合其他专业系统数据, 构成配电网智能化运维管理平台, 采用归一化谱聚类算法, 分析多维状态量的历史正常数据和异常数据, 获取历史数据曲线的形状系数和轮廓系数, 提取多维状态量故障特征, 利用知识发现子模块与决策器设计子模块, 分析配电网中电力设备健康度等级, 根据关联规则挖掘, 获取不同电力设备的重要度指数, 评估运维决策风险, 实现配电网智能化运维决策管理. 实验结果表明: 所研究模式能够有效获取配电网运行的实时数据, 及时发现可能出现异常及故障的电力设备, 提高配电网经济效益.     

大量需求点下基于深度Q学习的受损路网抢修队调度

受损路网抢修是重特大自然灾害发生后开展应急处置和救援的一个基本前提,主要研究如何对道路抢修队进行合理的调度以快速恢复路网畅通、保障救援队伍和应急物资从出救点及时输送到各需求点.鉴于已有研究在面向大量需求点时往往很难给出有效的调度策略,首先基于路网模型和马尔科夫决策过程分析抢修队修复受损路网的关键因素,并设计一种双反馈回报函数;然后基于深度Q学习求解抢修队的最优调度策略;最后通过对比实验结果表明,在大量需求点环境下,所提出方法具有较好的稳定性和可靠性,兼顾受损路网的修复效率和运输效率,能够以更少的修复代价令所有需求点可达,为灾后复杂应急场景下的受损路网抢修提供有益的尝试.     

Using SVM based method for equipment fault detection in a thermal power plant

Due to the growing demand on electricity, how to improve the efficiency of equipment in a thermal power plant has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependant upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Data mining techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work proposes a support vector machines (SVM) based model which integrates a dimension reduction scheme to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness of the proposed SVM based model. Experimental results show that SVM outperforms linear discriminant analysis (LDA) and back-propagation neural networks (BPN) in classification performance.     

Predicting nuclear power-plant operator performance using discrete event simulation

A study was conducted to evaluate the use of discrete event simulation (DES) to predict human performance in a nuclear power plant control room environment. Computer simulation models of two disturbance scenarios were built using a simulation software program, Micro Saint. In parallel, data were also collected at a full-scope training simulator at the Halden man-machine laboratory (HAMMLAB) in Halden, Norway, using crews of commercial nuclear power plant operators from the Loviisa nuclear power plant in Loviisa, Finland. Comparisons were made between predicted operator performance data generated by the simulation models and crew performance in the HAMMLAB experiment to determine the degree of agreement between the simulated data and the data from operators. The models were then used to extrapolate advanced control room conditions and alarm systems that were not tested in the HAMMLAB experiment. This report summarizes these findings and provides recommendations for improvements to the DES approach for use by a regulatory agency.     

一种面向严重受损路网的抢修队调度算法

受损路网抢修是灾害应急响应中的一个非常重要的基础环节,主要研究如何对道路抢修队进行有效调度,以快速恢复受灾路网的交通能力,为后续顺利展开应急救援工作提供有效的保证.已有方法在路网受损严重的情形下往往难以给出有效的调度策略.为此,在已有工作的基础上,简化路网模型和决策模型,并基于动作集裁减和Q学习设计一种面向严重受损路网的抢修队调度算法.在该算法中,抢修队只能从当前可达的未修复受损路段集合中选择下一个动作,以确保Q学习的连续性.仿真实验结果表明,在节点数和受损率都较大的严重受损路网环境中,所提算法可以保证所有需求节点均可达,具有更高的稳定性和可靠性,且能够在更小的时间和修复代价内给出更优的调度方案.     

一种提高系统柔性的工作流建模方法研究

针对目前工作流管理系统在描述能力和柔性上存在的严重不足,提出了一种基于协调理论和反馈机制的工作流建模方法.该方法扩展了传统活动网络模型,提出了新的建模机制,增加了请求、服务、协调、多实例化等新的建模元素.详细给出了扩展建模元素的实现机制和应用场景,同时还给出了两个模型实例.研究结果表明,与传统的活动网络模型相比,该建模方法在提高模型描述能力、降低模型复杂性、提高系统柔性及适应性方面有明显优点,对于设计开发高可靠性、柔性和适应性的工作流管理系统具有重要的应用价值.     

Through-Walls Communication for Medical Emergency Services

The authors present a model for bringing the coordination power of workflow management systems to outdoor wearable augmented reality (AR) systems. They portray how mobile equipment may be integrated with adaptive, context-aware work environments. A scenario of a medical emergency task is described to illustrate the functionality of this form of collaboration system. Appropriate information stickers are introduced to support data collection in medical emergency scenarios in a sophisticated form through a hands-free user interface for medical personnel. They propose the use of new user interface technology, including multimedia, AR information stickers, and the allocation of patient medical records to identified locations of the human body. A key feature is the access to relevant information for users in the mobile environment as well as for those in the advanced control room. An additional advantage is the automatic recording of on-site data, which helps to build the medical record of a patient without interfering with the work of the emergency team.     

Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions

Artificial neural networks and genetic algorithms are two intelligent approaches initially targeted to model human information processing and natural evolutionary process, with the aim of using the models in problem solving. During the last decade these two intelligent approaches have been widely applied to a variety of social, economic and engineering systems. In this paper, they have been shown as modelling tools to support human supervisory control to reduce fossil fuel power plant emissions, particularly NO_x emissions. Human supervisory control of fossil fuel power generation plants has been studied, and the need of an advisory system for operator support is emphasized. Plant modelling is an important block in such an advisory system and is the key issue of this study. In particular, three artificial neural network models and a genetic algorithm-based grey-box model have been built to model and predict the NO_x emissions in a coal-fired power plant. In non-linear dynamic system modelling, training data is always limited and cannot cover all system dynamics; therefore the generalization performance of the resultant model over unseen data is the focus of this study. These models will then be used in the advisory system to support human operators on aspects such as task analysis, condition monitoring and operation optimization, with the aim of improving thermal efficiency, reducing pollutant emissions and ensuring that the power system runs safely.     

An intelligent water drops-based workflow scheduling for IaaS cloud

Cloud computing is an emerging technology in a distributed environment with a collection of large-scale heterogeneous systems. One of the challenging issues in the cloud data center is to select the minimum number of virtual machine (VM) instances to execute the tasks of a workflow within a time limit. The objectives of such a strategy are to minimize the total execution time of a workflow and improve resource utilization. However, the existing algorithms do not guarantee to achieve high resource utilization although they have abilities to achieve high execution efficiency. The higher resource utilization depends on the reusability of VM instances. In this work, we propose a new intelligent water drops based workflow scheduling algorithm for Infrastructure-as-a-Service (IaaS) cloud. The objectives of the proposed algorithm are to achieve higher resource utilization and minimize the makespan within the given deadline and budget constraints. The first contribution of the algorithm is to find multiple partial critical paths (PCPs) of a workflow which helps in finding suitable VM instances. The second contribution is a scheduling strategy for PCP-VM assignment for assigning the VM instances. The proposed algorithm is evaluated through various simulation runs using synthetic datasets and various performance metrics. Through comparison, we show the superior performance of the proposed algorithm over the existing ones.     

军事工程毁伤评估的组合智能模型

鉴于军事工程毁伤的复杂性,提出了基于组合智能评估模式的军事工程毁伤评估模型。以军事工程毁伤评估体系结构及其特征与功能约束为基础,设计军事工程毁伤效果组合智能评估的构件化模式,建立军事工程毁伤效果组合智能评估机制;以智能优化算法提高军事工程毁伤效果评估效率为目标,设计了一种易于仿真推理的军事工程毁伤效果组合智能评估流程。以人工神经网络为主体,集成小波分析和遗传算法,建立军事工程毁伤效果评估的非线性智能组合评估模型,应用于军事工程抗精确打击毁伤分析。案例表明,非线性组合智能评估模型充分发掘不同算法的差异优势,仿真实验验证了组合设计的可行性。     

A new specification-based qualitative metric for simulation model validity

Informal validation techniques such as simulation are extensively used in the development of embedded systems. Formal approaches such as model-checking and testing are important means to carry out Verification and Validation (V&V) activities. Model-checking consists in exploring all possible behaviors of a model in order to perform a qualitative and quantitative analysis. However, this method remains of limited use as it runs into the problem of combinatorial explosion. Testing and model-checking do not take into account the context of use objectives of the model. Simulation overcomes these problems but it is not exhaustive. Submitted to simulation scenarios which are an operational formulation of the V&V activity considered, simulation consists in exploring a subset of the state space of the model. This paper proposes a formal approach to assess simulation scenarios. The formal specification of a model and the simulation scenarios applied to that model serve to compute the effective evolutions taken by the simulation. It is then possible to check whether a simulation fulfills its intended purpose. To illustrate this approach, the application study of an intelligent cruise controller is presented. The main contribution of this paper is that combining simulation objectives and formal methods leads to define a qualitative metric for a simulation evaluation without running a simulation.     

基于构件的动态工作流建模方法研究

针对目前工作流管理系统在描述能力和动态性上存在的不足，提出了一种基于构件的动态工作流建模方法。该方法扩展了传统的活动网络模型，引入了新的建模元素，提出了新的建模机制。详细描述了模型的实现机制和应用场景，同时给出了一个模型实例。与传统的活动网络模型相比，该模型方法在提高模型描述能力、降低模型复杂性、提高系统动态性及适应性方面有明显的优点，对于设计开发高可靠性、动态性和适应性的工作流管理系统具有重要的应用价值。     

VIPS: A simulator for platoon system evaluation

Development of intelligent behaviors for vehicles has to cope with rigorous specifications. Many tests are performed for verification, validation and a detailed study of resulting behaviors. In this workflow, testing algorithms with real vehicles is a cornerstone step in developing new intelligent features for future transportation systems. This step requires a lot of experiments before efficiently set up embedded autonomous systems. However, the development of such tests is not linear and it highly depends on external variable factors (weather, hardware or software issues, etc.). To overcome these issues, it is possible to carry out a first series of validation under a simulation framework. In this paper, we describe a simulation platform called VIPS (Vehicles In Platoon Simulator), which provides a virtual environment designed to test and validate platoon control approaches, as close as possible to reality, before bringing them in the real vehicle system. Among the features provided by the simulator, one main contribution is the direct matching between perception and control of both real and virtual vehicles. Moreover, VIPS is also able to deal with perturbations, which are dynamically added to running scenarios so as to reproduce real world conditions. Simulation results in the context of platoon control systems show the benefits of the proposed simulation framework.     

基于区块链技术的P2P电能交易平台与配电网协同仿真

为了推动能源交易公平以及区块链技术在电力交易领域的应用,实现P2P电能交易平台与配电网高效配合,提出一种基于区块链技术的P2P电能交易平台与配电网的协同仿真框架.根据区块链去中心化、安全性强、可追溯的特点,提出分布式双边拍卖机制,结合智能合约,建立了能源交易模型并进行了仿真分析.最后,根据建立的协同仿真模型,通过两个场景下配电网参数的比较分析,得出了P2P电能交易机制对配电网影响的相关结论,为实现能源高效利用,电能交易安全稳定以及区块链技术在电能交易领域的落地推广提供了理论基础和技术支撑.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

基于扁平化管理模式的配电通信网管理及运维模式

为了实现配电通信网管理优化,提高配电通信网的运维管理能力,提出基于扁平化管理模式的配电通信网管理及运维模式。综合分析不同应用场景和组网方式下配电通信网在资源规划和优化、业务开通、业务保障、客户服务等方面的管理需求,构建配电通信网管理及运维模式的大数据信息分析模型,采用统计特征分析方法,进行配电通信网管理及运维模式分析。在适应扁平化管理模式下,实现配电通信网融合多数据源融合和通信组网设计,实现扁平化管理模式下的配电通信网管理及运维模式优化。仿真结果表明,采用该方法进行配电通信网管理及运维模式构造,提高了配电通信网的智能监视、故障分析与可视化定位的融合管理能力,提升了管理效率。     

基于Q学习的受灾路网抢修队调度问题建模与求解

受损路网的修复是灾害应急响应中的一个重要环节, 主要研究如何规划道路抢修队的修复活动, 为灾后救援快速打通生命通道.本文首先构建了抢修队修复和路线规划的数学模型, 然后引入马尔科夫决策过程来模拟抢修队的修复活动, 并基于Q学习算法求解抢修队的最优调度策略.对比实验结果表明, 本文方法能够让抢修队从全局和长远角度实施受损路段的修复活动, 在一定程度上提高了运输效率和修复效率, 可以为政府实施应急救援和快速安全疏散灾民提供有益的参考.