全流程污水生化处理过程的仿真设计

根据国际水协会IWA发布的国际评价基准Benchmark,基于2号基准仿真模型BSM2协议,构建了面向全流程污水生化处理过程的仿真平台。该仿真平台采用模块化结构,具有动态信息处理量大、直观性好以及操作简便等特点。利用该仿真平台,可完成稳态计算、动态特性模拟、三类性能指标的计算以及各种先进控制策略的评价等任务。通过对关键水质参数的仿真和相关性能指标的计算,验证了该仿真平台的有效性。     

Predictive control of an activated sludge process: An application to the Viikinmäki wastewater treatment plant

In this work, we discuss the application of multivariable predictive control for the activated sludge process in a full-scale municipal wastewater treatment plant. Emphasis is given to the selection of a control configuration that contributes to minimising the economic costs while improving the removal efficiency of the nitrogen compounds. For this task, a simple dynamic matrix control algorithm is favoured for controlling the nitrogen concentrations at the end of the biological process. The behaviour of the activated sludge process is reproduced in a commercial simulator that acts as a real-time testing platform and that is also used for identifying the multivariable input–output models for the predictive controller. For demonstrating the effectiveness of the proposed approach, different control configurations are considered and compared against the aeration control strategies currently used at the plant. Based on the simulation results, this work shows the potentiality of the dynamic matrix control which is able to decrease the energy consumption costs and, at the same time, reduce the ammonia peaks and nitrate concentration in the effluent.     

A nonlinear observer design for an activated sludge wastewater treatment process

This paper treats the problem of estimating simultaneously the state and the unknown inputs of a class of nonlinear discrete-time systems. An observer design method for nonlinear Lipschitz discrete-time systems is proposed. By assuming that the linear part of this class of systems is time-varying, the state estimation problem of nonlinear system is transformed into a state estimation problem for LPV system. The stability analysis is performed using a Lyapunov function that leads to the solvability of linear matrix inequalities (LMIs). Performances of the proposed observer are shown through the application to an activated sludge process model.     

Improved transmission of vector quantized data over noisy channels

The conventional channel-optimized vector quantization (COVQ) is very powerful in the protection of vector quantization (VQ) data over noisy channels. However, it suffers from the time consuming training process. A soft decoding self-organizing map (SOM) approach for VQ over noisy channels is presented. Compared with the COVQ approach, it does not require a long training time. For AWGN and fading channels, the distortion of the proposed approach is comparable to that of COVQ. Simulation confirmed that our proposed approach is a fast and practical method for VQ over noisy channels.     

Efficient music note recognition based on a self-organizing map tree and linear vector quantization

Using classical signal processing and filtering techniques for music note recognition faces various kinds of difficulties. This paper proposes a new scheme based on neural networks for music note recognition. The proposed scheme uses three types of neural networks: time delay neural networks, self-organizing maps, and linear vector quantization. Experimental results demonstrate that the proposed scheme achieves 100% recognition rate in moderate noise environments. The basic design of two potential applications of the proposed scheme is briefly demonstrated.     

多媒体报警及监视系统的设计

阐述了一些多媒体报警系统的概念,以及利用多媒体技术与电视监视技术的防火,防交界报警及监视系统的设计,系统中主要器件可编程序控制器的应用。     

Face recognition across illumination

Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

供电企业信息安全集中监测预警体系探索与实践

国家电网公司信息化建设发展跨八“十二五”时期以后,大量应用系统在省级电力公司层面完成集中部署,地市供电企业业务应用仅保留部分自建业务系统和省公司两级分布部署系统,保障信息网络可靠运行、机房动力环境稳定工作及信息安全指标可控成为当前地市供电企业信息安全运检工作的主要目标和重点内容,针对这一问题提出了基于指标体系化、流程标准化基础上的实时集中监测预警体系的建设思路。     

Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model

The aim of this study is to show how a Kohonen map can be used to increase the forecasting horizon of a financial failure model. Indeed, most prediction models fail to forecast accurately the occurrence of failure beyond 1 year, and their accuracy tends to fall as the prediction horizon recedes. So we propose a new way of using a Kohonen map to improve model reliability. Our results demonstrate that the generalization error achieved with a Kohonen map remains stable over the period studied, unlike that of other methods, such as discriminant analysis, logistic regression, neural networks and survival analysis, traditionally used for this kind of task.     

基于模糊自组织映射神经网络的故障诊断方法

在研究Kohonen自组织映射网络理论的基础上运用模糊理论方法建立了刹车系统模糊故障诊断模型。该模型只需选择足够的具有代表性的故障样本训练神经网络，将代表故障的信息输入给训练好的神经网络，根据神经网络的输出结果，就可以判断发生故障的类型。该模型除能识别已训练过的故障，还能识别未训练过的故障，并且聚类能力强、速度快，因此很符合复杂系统的故障诊断。     

Intravascular ultrasound-based tissue characterization using modular network self-organizing map

The intravascular ultrasound-based tissue characterization of coronary plaque is important for the early diagnosis of acute coronary syndromes. The conventional tissue characterization techniques however cannot obtain sufficient identification accuracy for various tissue properties, because the feature employed for characterization are static features, which lack dynamical information about backscattered radio-frequency (RF) signals.In this work, we propose a new intravascular ultrasound-based tissue characterization method that uses a modular network self-organizing map (mnSOM) in which each module is composed of an autoregressive model for representing the dynamics of the RF signals.The proposed method can create a map of various dynamical features from the RF signal. This map enables generalized tissue characterizations. The proposed method is verified by comparing its tissue characterization performance with that of the conventional method using real intravascular ultrasound signals.     

自组织映射在Web结构挖掘中的应用

该文讨论了用自组织映射进行Web结构挖掘的基本方法。用SOM可直观地表示数据的相似性和进行分类,还可方便地进行数据聚簇分析,并可在Web挖掘中找到权威页面等有用信息。     

A hierarchical neural model in short-term load forecasting

This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) nets—one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained on load data extracted from a Brazilian electric utility, and compared to a multilayer perceptron (MLP) load forecaster. It was required to predict once every hour the electric load during the next 24 h. The paper presents the results, the conclusions, and points out some directions for future work.     

自组织多模型逼近性能分析与改进

针对基于自组织映射神经网络的非线性函数逼近,研究其方法和原理,指出它与一般前向神经网络在逼近原理上的不同．在此基础上,进一步研究该方法的逼近性能,分析其两个不足之处,进而提出一种提高逼近性能的改进神经网络训练策略．最后通过仿真实例验证了所得结论,表明了改进方法的有效性．     

煤矿供电监控系统的设计

介绍了煤矿电网灾害监控预警系统的网络硬件框架、软件体系结构及所实现的功能,阐述了系统设计与实现过程中的关键问题及解决办法。实际运行表明,该系统实现了煤矿电网管理的智能化、数字化、矿井电力参数测控的现代化,解决了许多困扰煤矿生产现场多年的井下供电监控技术难点与安全运行中的重大隐患问题,具有一定的推广意义。     

蠕虫预警技术研究与进展

当前网络蠕虫对Internet构成重要威胁,如何防范蠕虫已经成为网络安全的重要课题。由于蠕虫传播速度快、规模大,因此必须在蠕虫传播初期就能发现并对其采取相应措施进行隔离。全面分析了蠕虫预警方面的最新研究进展,包括路由器级的蠕虫检测、基于行为的蠕虫检测、蠕虫特征的自动提取,并对蠕虫的特点进行了总结,最后对未来蠕虫检测的可能方向进行了展望。     

Weighted order-dependent clustering and visualization of web navigation patterns

This study introduces a simple but effective visualization system that allows decision makers to easily identify groups of visitors with different sequential navigation patterns. In particular, navigation sequences of visitors are encoded as an order-dependent format so that early visited pages have more weights in the clustering process. Experimental results on a real-world dataset show that Markov state-transition diagrams with transition probabilities based on the proposed scheme can be very useful for developing Web marketing programs tailored to visitors' preferences and interests.     

基于外观特征与神经网络的交通标志识别

不同种类的交通标志具有特定的颜色及形状等外观特征,本文利用此特点设计了一个自动交通标志识别系统。该系统首先应用HIS彩色模型及标志的形状特征确定彩色图像中的标志区域及标志所属的种类。系统再应用自组织神经网络（S0MNN）进一步识别标志模式。实验证明了该方法的有效性与鲁棒性。     

Extraction of rice-planted area using a self-organizing feature map

We introduce a neural network of self-organizing feature map (SOM) to classify remote-sensing data, including microwave and optical sensors, for the estimation of areas of planted rice. This method is an unsupervised neural network which has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data are observed before and after rice planting in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi–Hiroshima, Japan. The RADARSAT image has only one band of data and it is difficult to extract the rice-planted area. However, the SAR back-scattering intensity in a rice-planted area decreases from April to May and increases from May to June. Therefore, three RADARSAT images from April to June were used in this study. The SOM classification was applied the RADARSAT and SPOT data to evaluate the rice-planted area estimation. It is shown that the SOM is useful for the classification of satellite data.