基于隐性故障模型和风险理论的关键线路辨识

在大规模电网中，由简单故障触发的电力系统连锁故障常常会引起大停电的灾难性后果。为了研究复杂电网连锁故障的传播机理，同时找出对连锁故障产生重要影响的关键线路，文中基于模拟连锁故障的隐性故障模型和风险理论，提出了复杂电力系统的线路故障风险评估方法，并辨识出了对系统大停电产生重要影响的一系列关键线路。模型考虑了线路过负荷保护、隐性故障、控制策略及系统运行等参数，对IEEE 118节点系统仿真结果表明，在重负荷状态运行下只对辨识出的少量关键线路加以改善，就可以使系统停电概率大幅减小，特别是大停电概率;在临界状态运行下改善少量关键线路，则可以使系统脱离临界状态，避免大停电发生。     

桥梁颤振气动导数识别的迭代法

本文提出一种利用自由振动响应通过年顿－拉夫逊迭代同时识别出桥梁全部8个颤振气动导的方法。此法具有对迭代初值要求不高，识别结果稳定的特点，并具有一定的抗噪声干扰能力。数字仿真与实物试验结果表明本文方法有效，可行。     

Approximate channel identification via δ‐signed correlation

A method of approximate channel identification is proposed that is based on a simplification of the correlation estimator. Despite the numerical simplification (no multiplications or additions are required, only comparisons and an accumulator), the performance of the proposed estimator is not significantly worse than that of the standard correlation estimator. A free (user selectable) parameter moves ‘smoothly’ from a situation with small sum‐squared channel estimation error but hard‐to‐identify channel peaks, to one with a larger sum‐squared estimation error but easy‐to‐identify channel peaks. The proposed estimator is shown to be biased and its behaviour is analysed in a number of situations. Applications of the proposed estimator to sparsity detection, symbol timing recovery and to the initialization of blind equalizers are suggested. Copyright © 2002 John Wiley & Sons, Ltd.     

构造性神经网络在测井岩性识别中的应用

测井岩性识别是石油勘探中十分重要的基础工作,准确的岩性识别结果可以为勘探和开发提供可靠的依据。人工神经网络方法可对复杂的高维数据进行非线性映射,在模式识别领域得到越来越广泛的应用。针对传统的BP神经网络算法存在收敛速度慢、隐层数以及隐层节点数难以确定等缺点,研究了一种构造性神经网络学习算法---级联算法（Cascade Correlation Algorithm,简称CC算法）及其在石油工程中的应用。采用该算法对甘肃镇原-泾川地区进行岩性识别研究,通过与BP神经网络的识别结果进行比较,体现出构造性神经网络的优越性。     

Automatic playlist generation based on tracking user’s listening habits

Algorithms for automatic playlist generation solve the problem of tedious and time consuming manual selection of musical playlists. These algorithms generate playlists according to the user’s music preferences of the moment. The user describes his preferences either by manually inputting a couple of example songs, or by defining constraints for the choice of music. The approaches to automatic playlist generation up to now were based on examining the metadata attached to the music pieces. Some of them took also the listening history into account. But anyway, a heavy accent has been put on the metadata, while the listening history, if it was used at all, had a minor role. Missings and errors in metadata frequently appear, especially when the music is acquired from the Internet. When the metadata is missing or wrong, the approaches proposed so far cannot work. Besides, entering constraints for the playlist generation can be a difficult activity. In our approach we ignored the metadata and focused on examining the listening habits. We developed two simple algorithms that track the listening habits and form a listener model—a profile of listening habits. The listener model is then used for automatic playlist generation. We developed a simple media player which tracks the listening habits and generates playlists according to the listener model. We tried the solution with a group of users. The experiment was not a successful one, but it threw some new light on the relationship between the listening habits and playlist generation.     

水利工程建设项目的风险分析

水利工程建设中潜藏着众多风险，对风险进行有效分析是工程顺利建设的重要保证。水利工程风险分析是由风险识别、估算、评价、管理、控制决策等组成的科学体系。明确了水利工程中风险的定义，识别了水利工程的风险因素，论述了风险估算和评价的方法，并提出对工程风险进行管理和控制，以保证工程建设顺利进行。     

Accumulation of amplified target DNAs using thiol/biotin labeling, S1 nuclease, and ferrocene-streptavidin-magnetic system and a direct detection of specific DNA signals with screen printed gold electrode

Combinations of PCR-based amplification platform using 5′ thiolated and biotinylated specific primers, S1 nuclease-PCR products treatment, ferrocene-streptavidin (Fc-Stv)-magnetic binding for DNA accumulation, and screen printed gold electrode for the DNA allocation, were applied to Hoechst 33258-induced DNA aggregation and signals induction system for direct signals detection and DNA quantification in food samples. Thiolated and biotinylated at each 5′ terminus enabled DNA purification through S1 nuclease treatment for primers and non-specific DNA elimination and enabled DNA trapping with a ferrocene-streptavidin-magnetic system. This facilitated the accumulation of target DNAs at higher concentration, resulting in enhanced signals. After allocation of DNA on the surface of gold electrode via thiol binding, intensity of DNA signals through these treatments could be measured directly after being induced by Hoechst 33258. Wider amplitude changes in anodic current peaks between negative and positive samples (increasing from 3.70 to 10.10 μA) compared with those applied with no treatment combinations (decreasing from 3.92 to 1.23 μA) were observed. This enhancement of the signals allowed a greater efficiency of DNA quantification. When this combination was used for GMOs content estimation in reference samples, results revealed an improved accuracy from 66% to 96%. The combined biosensor system, although more costly than the standard Hoechst 33258/carbon electrode system, provided an alternative choice for DNA quantification, offering labor-free immobilization of probe onto electrode surface, easy test administration, and efficient semi-quantitative test without expensive instruments.     

On-line emission and economic load dispatch using adaptive Hopfield neural network

This paper presents an adaptive Hopfield neural network (AHNN) based methodology, where the slope of the activation function is adjusted, for finding approximate Pareto solutions for the multi-objective optimization problem of emission and economic load dispatch (EELD). We have placed emphasis on finding solutions quickly, rather than the global Pareto solutions, so that our algorithm can be employed in large on-line power systems where variations in load are quite frequent. To enable faster convergence, adaptive learning rates have been developed by using energy function and applied to the slope adjustment method. The proposed algorithm has been tested on selected IEEE bus benchmark systems. The convergence of AHNN is found to be nearly 50% faster than the non-adaptive version.     

多变量系统辨识在电加热炉系统中的研究

介绍多变量系统辨识在电加热炉系统中的应用。在确定合适的采样周期和输入信号后，获得了可供辨识的输入输出数据，并利用Guidorzi法离线辨识出模型的结构。为了克服有色噪声的影响，在辨识过程中用Ⅳ（Instrumental Variable)法估计出模型的参数。通过对残差的白性检验，验证了所建立模型的可靠性。最后，对Ⅳ法和RLS（Recursive Least Square)不的辨识精度进行了比较。     

Proactive resource allocation for asynchronous real-time distributed systems in the presence of processor failures

We present two proactive resource allocation algorithms, RBA^*-FT and OBA-FT, for fault-tolerant asynchronous real-time distributed systems. The algorithms consider an application model where task timeliness is specified by Jensen's benefit functions and the anticipated application workload during future time intervals is described by adaptation functions. In addition, we assume that reliability functions of processors are available a priori. Given these models, our objective is to maximize aggregate task benefit and minimize aggregate missed deadline ratio in the presence of processor failures. Since determining the optimal solution is computationally intractable, the algorithms heuristically compute sub-optimal resource allocations, but in polynomial time. Experimental results reveal that RBA^*-FT and OBA-FT outperform their non-fault-tolerant counterparts in the presence of processor failures. Furthermore, RBA^*-FT performs better than OBA-FT, although OBA-FT incurs better worst-case and amortized computational costs. Finally, we observe that both algorithms robustly withstand errors in the estimation of anticipated failures.