基于无信息变量消除法与岭极限学习机的新型变量选择方法：以CO气体浓度反演为例

变量选择是光谱分析领域一个重要的组成部分。为了克服传统区间选择法的缺点与不足,基于无信息变量消除法和岭极限学习机提出一种新型的变量选择与评价方法。首先,利用无信息变量消除法剔除整个光谱区间中无信息的波长点;其次,为了解决传统建模方法(偏最小二乘法、BP神经网络等)存在的共线性问题,采用岭极限学习机方法建立回归模型;最后,最佳的特征光谱波长点组合利用特征选择路径图和稀疏度-误差折中曲线进行确定。CO气体的浓度反演实验结果表明：(1)利用无信息变量消除法可以有效筛选出最能表征CO气体透过光谱的特征波长点;(2)岭极限学习机方法具有快速建模、避免共线性和高精度等优点(CO气体浓度反演模型的决定系数可达0.995);(3)特征选择路径图和稀疏度-误差折中曲线可以直观地帮助用户寻找出最佳的特征波长点组合。     

基于特征投影图的小麦近红外光谱变量选择方法研究

为了简化模型,提高模型预测精度,利用特征投影图(LPG)进行变量选择。对原始光谱进行连续小波变换(CWT),利用主成分分析(PCA)得到LPG,假定LPG中共线性光谱变量对建模作用相同,选出少数特征光谱变量建立预测模型,所得模型预测均方根误差(RMSEP)为0.345 4,优于其他建模方法,研究结果表明,LPG变量选择可有效简化近红外光谱模型,提高模型预测精度。     

Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification

The identification of disease-relevant genes represents a challenge in microarray-based disease diagnosis where the sample size is often limited. Among established methods, reversible jump Markov Chain Monte Carlo (RJMCMC) methods have proven to be quite promising for variable selection. However, the design and application of an RJMCMC algorithm requires, for example, special criteria for prior distributions. Also, the simulation from joint posterior distributions of models is computationally extensive, and may even be mathematically intractable. These disadvantages may limit the applications of RJMCMC algorithms. Therefore, the development of algorithms that possess the advantages of RJMCMC methods and are also efficient and easy to follow for selecting disease-associated genes is required. Here we report a RJMCMC-like method, called random frog that possesses the advantages of RJMCMC methods and is much easier to implement. Using the colon and the estrogen gene expression datasets, we show that random frog is effective in identifying discriminating genes. The top 2 ranked genes for colon and estrogen are Z50753, U00968, and Y10871_at, Z22536_at, respectively. (The source codes with GNU General Public License Version 2.0 are freely available to non-commercial users at: http://code.google.com/p/randomfrog/.)     

A maximum-efficiency-first multi-path route selection strategy for optical burst switching networks

In this paper, the impact of a path selection on other existing paths in optical burst switching (OBS) networks is studied by analyzing the contention among different traffic streams and the interaction between the route selection and traffic load balance. The results show that there exists a mutual reinforcement interaction among the traffic load of a path, the path burst loss ratio and the contention ability of the path when burst loss ratio based multi-path selection strategies are adopted, which may increase the unbalance of traffic and lead to severe congestion further. A maximum-efficiency-first multi-path selection strategy, which considers the performance of the burst flows and the impact of a path selection on existing OBS paths at the same time by a combined metric of route efficiency, is proposed to maximize the utility of the burst flows and minimize the increment of lost throughput on the path. The performance of the proposed multi-path selection strategy is evaluated through simulation. The results show that the presented strategy obviously outperforms the least burst loss ratio strategy and shortest path first strategy in terms of the burst loss ratio in the practical unbalanced background traffic, especially when the network is heavily loaded.     

Non parametric estimation of the diffusion coefficients of a diffusion with jumps

In this article, we consider a jump diffusion process ${\left(X_t\right)}_{t\ge 0}$, with drift function $b$, diffusion coefficient $\sigma$ and jump coefficient ${\xi }^2$. This process is observed at discrete times $t=0,\Delta ,\dots ,n\Delta$. The sampling interval $\Delta$ tends to 0 and the time interval $n\Delta$ tends to infinity. We assume that ${\left(X_t\right)}_{t\ge 0}$ is ergodic, strictly stationary and exponentially $\beta$-mixing. We use a penalized least-square approach to compute adaptive estimators of the functions ${\sigma }^2+{\xi }^2$ and ${\sigma }^2$. We provide bounds for the risks of the two estimators.     

3种现场快速水质检测仪的性价对比

选择了市场上口碑好、占有率高的3款便携式水质现场应急检测设备,通过准确度、精密度、检测时长及价格等指标,以权重积分法对3款仪器进行了比选,结果表明德国MN公司的PF-12型便携式多参数分光光度计的综合性能最优.     

A robust decentralized reputation management system for service selection

Reputation mechanism is a novel approach to automate QoS-aware service selection in service oriented computing. The reputation system collects ratings on QoS that consumers feedback and aggregates them to derive a reputation value, which can in turn assist other consumers in service selection in future. However, current approaches fail to combat the malicious ratings and hence the calculated reputation values can be biased severely or even manipulated. Moreover, the centralized management of rating data restricts its application to large open environment. In this paper, we present a robust decentralized reputation system which can resist various unfair ratings and manipulation behaviours. It can evolve and become more mature against malicious ratings with the system running continuously. At last, we experimentally verify the robustness of the proposed approach through a simulation study.     

三维荧光光谱的特征区域选择方法

将数学中的二元凸函数判定和数据挖掘中的聚类分析方法结合,提出了针对三维荧光的光谱区域选择方法,并利用此种方法从光谱图中提取出含有丰富光谱信息的凸集区域。对水体中总有机碳的检测和白酒中黄曲霉素的检测进行了实验研究,实验结果表明,采用本文提出的三维荧光光谱区域选择方法提高了模型的精度,与利用全光谱所建立的回归模型相比,模型精度分别提高了6.17%和4.97%。