Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.     

Modelling effects and their impact on the results of ATHLET solutions of Exercise 1 of the VVER-1000 Coolant Transient Benchmark

The VVER-1000 Coolant Transient Benchmark consists of two phases and refers to experimental data from the Kozloduy Unit 6 Nuclear Power Plant in Bulgaria. The paper describes the modelling features and their impact on the results of the Exercise 1, Phase 1 of the Benchmark obtained by two ATHLET user groups, namely GRS and NRI. The simulated transient is a main coolant pump (MCP) switching on in one loop at reduced power while three other MCPs are in operation. Particular attention is paid to the influence of the reactor vessel modelling and especially of the nodalization in the upper plenum. The comparison and discussion of the two simulation results confirm that the two solutions with the ATHLET system code achieve quite good system response of the plant transient.     

装配序列规划问题的Benchmark生成算法

装配序列规划本质上是一个NP组合优化问题,由于缺乏Benchmark,使得研究者在提出装配序列规划算法时难以对算法的优劣进行衡量.针对该问题,借鉴索玛立方体中将若干单位立方块组合成不同几何体的思想,在主要由连接矩阵和干涉矩阵构成的装配体模型表示法的基础上,设计了自动生成装配体和提取其模型数据的算法,并通过实验证明了算法的正确性.最后利用Matlab编程开发了该Benchmark自动生成系统,可以为装配序列规划算法提供有效的输入数据,进而对算法进行评价.     

QNX Neutrino实时操作系统性能分析

操作系统是实时嵌入式系统的核心,其性能是决定系统实时性的关键因素之一.介绍了微内核实时操作系统-QNXNeutrino,并以标准检查程序对内核进行性能测试和分析.     

The CARE platform for the analysis of behavior model inference techniques

ContextFinite State Machine (FSM) inference from execution traces has received a lot of attention over the past few years. Various approaches have been explored, each holding different properties for the resulting models, but the lack of standard benchmarks limits the ability of comparing the proposed techniques. Evaluation is usually performed on a few case studies, which is useful for assessing the feasibility of the algorithm on particular cases, but fails to demonstrate effectiveness in a broad context. Consequently, understanding the strengths and weaknesses of inference techniques remains a challenging task.ObjectiveThis paper proposes CARE, a general, approach-independent, platform for the intensive evaluation of FSM inference techniques.MethodGrounded in a program specification scheme that provides a good control on the expected program structures, it allows the production of large benchmarks with well identified properties.ResultsThe CARE platform demonstrates the following features: (1) providing a benchmarking mechanism for FSM inference techniques, (2) allowing analysis of existing techniques w.r.t. a class of programs and/or behaviors, and (3) helping users in choosing the best suited approach for their objective. Moreover, our extensive experiments on different FSM inference techniques highlight that they do not behave in the same manner on every class of program. Characterizing different classes of programs thus helps understanding the strengths and weaknesses of the studied techniques.ConclusionExperiments reported in this paper show examples of use cases that demonstrate the ability of the platform to generate large and diverse sets of programs, which allows to carry out meaningful inference techniques analysis. The analysis strategies the CARE platform offers open new opportunities for program behavior learning, particularly in conjunction with model checking techniques. The CARE platform is available at http://care.lip6.fr.     

应用佳点集的混合反向学习人工鱼群算法

为了改善人工鱼群算法求解精度较低、容易过早收敛的弱点,提出了一种应用佳点集和反向学习的人工鱼群算法.改进算法在迭代中对当前种群中部分优质个体执行一般动态反向学习,生成它们的反向种群,引导种群向包含全局最优的解空间逼近,以提高算法的平衡和探索能力.当种群的拥挤程度超过阈值λ时,利用佳点集机制对大部分个体重新初始化,以帮助算法脱离局部最优的约束.在六个Benchmark函数上的实验表明,该算法收敛速度快、求解精度高,适合求解函数优化问题.     

Quantitative analysis of packet-processing applications regarding architectural guidelines for network-processing-engine development

This paper presents a simulation-based profile-driven quantitative analysis of packet-processing applications. In this domain, demands for increasing the performance and the ongoing development of network protocols both call for flexible and performance-optimized engines. Based on the achieved profiling results, we introduce platform-independent analysis that locates the performance bottlenecks and architectural challenges of a packet-processing engine. Finally based on these results, we extract helpful architectural guidelines for design of a flexible and high-performance embedded processor that is optimized for packet-processing operations in high-performance and cost-sensitive network embedded applications.     

基于时间尺度的一类典型高阶系统的二阶线性自抗扰控制

高阶系统由于其本身的性质,使得控制它变得很困难。为了控制一类典型的可用传递函数描述的高阶对象,用二阶线性自抗扰(LADRC)控制器对高阶系统进行参数整定。将已整定好的高阶系统作为基准系统,通过时间尺度概念将基准系统参数转换为需要控制的新的高阶系统的参数,使得新系统具有基准系统的响应特性。该方法能方便、快捷地算出新系统的参数,通过仿真证明了该方法的可行性,具有广泛的参考价值。     

基于基准测试的高性能计算云研究

随着云计算技术的发展,高性能计算云(HPC in the Cloud)已得到学术界和产业界的关注。由于虚拟化技术带来的性能开销,高性能计算云面临着一些挑战。针对“高性能计算+云”的计算模式,分析了高性能计算云的优势,深入介绍了国内外基于基准测试的高性能计算云的性能评测、性能优化、能耗和成本效益等关键问题,得出了针对基准测试的高性能计算云研究的基本思路,并对当前面临的问题和今后的发展趋势进行了总结和展望。     

Self-similarity of parallel machines

Self-similarity is a property of physical systems that describes how to scale parameters such that dissimilar systems appear to be similar. Computer systems are self-similar if certain ratios of computational forces, also known as computational intensities, are equal. Two machines with different computational power, different network bandwidth and different inter-processor latency behave the same way if they have the same ratios of forces. For the parallel conjugate gradient algorithm studied in this paper, two machines are self-similar if and only if the ratio of one force describing latency effects to another force describing bandwidth effects is the same for both machines. For the two machines studied in this paper, this ratio, which we call the mixing coefficient, is invariant as problem size and processor count change. The two machines have the same mixing coefficient and belong to the same equivalence class.