基于粒子群优化和支持向量机的花粉浓度预测模型

为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（PSO）算法和支持向量机（SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量；其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数；然后利用最优参数优化花粉浓度预测模型；最后，使用优化后的模型对花粉未来24 h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。     

PSO优化多核RVM的模拟电路故障预测

针对模拟电路健康管理的特点，提出了一种基于PSO优化多核RVM的模拟电路故障预测方法。利用参数分析得到电路的输出频域响应作为特征，计算其与电路无故障标准响应的欧氏距离来表征电路元件健康值，将多个核函数线性组合，并用PSO优化多核RVM参数后的模型实现对各个时间点元件的健康值变化轨迹进行预测。仿真结果表明，该方法在小样本情况下，预测效果优于单一核函数的RVM模型，适用于健康管理中实时预测，具有较好的实用性。     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

一种宽带矢量调制器的设计及其应用

矢量调制器是一种可以同时控制微波信号幅度和相位的器件。本文介绍了一种基于新型微带定向耦合器的宽带矢量调制器。新的耦合器结构克服了传统微带耦合器耦合度低、方向性差的问题,也不需要Lange耦合器复杂的加工工艺,在平衡放大器、移相器和衰减器等场合具有广泛的应用。其次,研究了用串联电感对衰减器中PIN二极管的寄生参数进行补偿的一种简单方法,以改善衰减器衰减量变化时的相位性能。该方法原理简单,可在一定带宽内替代复杂的平衡结构,并给出相近的性能。最后给出了矢量调制器的测试结果和它在自适应天线阵等实际系统中的应用情况,并讨论了用于提高载波和边带抑制、满足高精度要求的校准方法。     

H.264变换编码与量化原理分析

详细分析了 H .2 64视频编码标准中的整数变换和量化过程。 H .2 64采用 4× 4整数变换 ,并将尺度调整融合在量化过程中 ,降低变换算法复杂度和减少乘法次数 ,相对于以前的编码标准更加有效     

中低速率语音波形编码

提出了一种波形编码的新方法，阐述了其编码原理。该编码算法简单，恢复的语音质量较好。由于利用了矢量量化技术，所以该编码速率较低。     

Sequential Decoding of Convolutional Codes for Rayleigh Fading Channels

The performance of sequential decoding of long constraint length convolutional codes is evaluated for Rayleigh fading channels. Sequential decoding is not practical below a certain theoretical signal-to-noise ratio, and these theoretical limits are calculated for a number of modulation methods and code rates. As an example, with BPSK modulation, soft decisions and code rate 1/2, the theoretical signal-to-noise ratio per information bit is 5.7 dB. Above this limit the bit error rate can be made arbitrarily small by increasing the constraint length at no significant complexity cost. Furthermore, it is shown that with carefully chosen quantization steps, 8 level uniform quantization gives a negligible loss also for sequential decoding on a Rayleigh fading channel. Simulation results using 8 level quantization correspond well with the theoretical performance bounds. Also, the performance on a correlated channel with finite interleaving has been obtained. With an interleaver depth of 50×50 and a normalized doppler frequency equal to 0.01 we are only 0.5 dB away from the performance with perfect interleaving. Finally, bit error rate results show this scheme to compete well with Turbo codes.     

Hardware system of the Earth Simulator

The Earth Simulator (ES), developed under the Japanese government’s initiative “Earth Simulator project”, is a highly parallel vector supercomputer system. In this paper, an overview of ES, its architectural features, hardware technology and the result of performance evaluation are described.

In May 2002, the ES was acknowledged to be the most powerful computer in the world: 35.86 teraflop/s for the LINPACK HPC benchmark and 26.58 teraflop/s for an atmospheric general circulation code (AFES). Such a remarkable performance may be attributed to the following three architectural features; vector processor, shared-memory and high-bandwidth non-blocking interconnection crossbar network.

The ES consists of 640 processor nodes (PN) and an interconnection network (IN), which are housed in 320 PN cabinets and 65 IN cabinets. The ES is installed in a specially designed building, 65 m long, 50 m wide and 17 m high. In order to accomplish this advanced system, many kinds of hardware technologies have been developed, such as a high-density and high-frequency LSI, a high-frequency signal transmission, a high-density packaging, and a high-efficiency cooling and power supply system with low noise so as to reduce whole volume of the ES and total power consumption.

For highly parallel processing, a special synchronization means connecting all nodes, Global Barrier Counter (GBC), has been introduced.     

支持向量机推广能力估计方法比较

支持向量机是一种新的机器学习算法，与其它学习算法相比，它的最大优点是基于结构风险最小化原则，因而能够保证推广能力。推广能力估计是机器学习中的一个重要问题，是实现自适应调整、参数选择、模型选择的等方法的基础。本文详细比较当前较有影响的几种推广能力估计方法，指出了这些方法适应范围和优缺点，并结合各种方法的原理讨论了推广能力估计可能的发展方向。     

A protocol for performance evaluation of line detection algorithms

Accurate and efficient vectorization of line drawings is essential for any higher level processing in document analysis and recognition systems. In spite of the prevalence of vectorization and line detection methods, no standard for their performance evaluation protocol exists. We propose a protocol for evaluating both straight and circular line extraction to help compare, select, improve, and even design line detection algorithms to be incorporated into line drawing recognition and understanding systems. The protocol involves both positive and negative sets of indices, at pixel and vector levels. Time efficiency is also included in the protocol. The protocol may be extended to handle lines of any shape as well as other classes of graphic objects.