Determinants of accepting wireless mobile data services in China

Data services via wireless networks and mobile devices have experienced rapid growth worldwide. We investigated the factors influencing adoption of wireless mobile data services (WMDS) in China and tested our model for explaining adoption intentions there. We argued that individuals form their intention to adopt WMDS under the influence of wireless mobile technology, the social environment, personal innovativeness of IT, trust awareness, and the facilitating conditions. We examined the simultaneous effects of these five influences on beliefs in the context of wireless Internet data services via mobile phones. Survey data were collected from 1432 participants in several metro cities across China. The findings suggest that WMDS adoption intention in China is determined by consumers’ perceived usefulness and perceived ease of use of WMDS. Theoretical and practical implications are included in our paper.     

Stability of iterative processes with errors for a system of nonlinear -accretive variational inclusions in Banach spaces

In this paper, by using the concept of (A,η)-accretive mappings and the new resolvent operator technique associated with (A,η)-accretive mappings, we introduce and study a system of general mixed quasivariational inclusions involving (A,η)-accretive mappings in Banach spaces, and construct a new perturbed iterative algorithm with mixed errors for this system of nonlinear (A,η)-accretive variational inclusions in q-uniformly smooth Banach spaces. Our results improve and generalize the corresponding results of recent works.     

A power and data front-end IC for biomedical monitoring systems

To enable the evolution towards electronically assisted healthcare, future medical implants require sensors and processing circuitry to inform patient and doctor on the rehabilitation status. An important branch of systems are those where implant strain is monitored through strain gauges. Since batteries inside the human body are avoided as much as possible, a transcutaneous power link is used to wirelessly power the implant. The same RF link provides an elegant way of establishing bi-directional data communication between the external base station and the medical device. This paper describes a front-end IC that manages both power reception and bi-directional data communication. It has a clock generation circuit on board to drive additional digital processing circuits. A new architecture that uses a current driven data demodulation principle is introduced. It is able to detect an AM signal with modulation depth of a mere 4%, which is better than recent similar systems in the field. The IC is fabricated in a solid 0.35 μm HVCMOS technology and consumes only 0.56 mA.     

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of the SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem.     

Imputation techniques for multivariate missingness in software measurement data

The problem of missing values in software measurement data used in empirical analysis has led to the proposal of numerous potential solutions. Imputation procedures, for example, have been proposed to ‘fill-in’ the missing values with plausible alternatives. We present a comprehensive study of imputation techniques using real-world software measurement datasets. Two different datasets with dramatically different properties were utilized in this study, with the injection of missing values according to three different missingness mechanisms (MCAR, MAR, and NI). We consider the occurrence of missing values in multiple attributes, and compare three procedures, Bayesian multiple imputation, k Nearest Neighbor imputation, and Mean imputation. We also examine the relationship between noise in the dataset and the performance of the imputation techniques, which has not been addressed previously. Our comprehensive experiments demonstrate conclusively that Bayesian multiple imputation is an extremely effective imputation technique.

Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems

If the production process, production equipment, or material changes, it becomes necessary to execute pilot runs before mass production in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Although, artificial neural networks are widely utilized to extract management knowledge from acquired data, sufficient training data is the fundamental assumption. Unfortunately, this is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize bootstrap to generate virtual samples to fill the information gaps of sparse data. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.     

一种面向非平衡数据的邻居词特征选择方法

在非平衡数据情况下,由于传统特征选择方法,如信息增益(Information Gain,IG)和相关系数(Correlation Coefficient,CC),或者不考虑负特征对分类的作用,或者不能显式地均衡正负特征比例,导致特征选择的结果下降.本文提出一种新的特征选择方法(Positive-Negative feature selection,PN),用于邻居词的选择,实现了文本中术语的自动抽取.本文提出的PN特征选择方法和CC特征选择方法相比,考虑了负特征;和IG特征选择方法相比,从特征t出现在正(负)训练文本的文本数占所有出现特征t的训练文本数比例的角度,分别显式地均衡了正特征和负特征的比例.通过计算特征t后面所跟的不同(非)领域概念个数占总(非)领域概念个数比值分别考察正、负特征t的重要性,解决了IG特征选择方法正特征偏置问题.实验结果表明,本文提出的PN特征选择方法优越于IG特征选择方法和CC特征选择.     

基于序贯模式挖掘的宏观网络流量异常检测

基于序贯频繁模式挖掘,提出并实现了一种宏观网络流量异常检测的方法。定义了一个新的频繁模式和相对应的异常度概念。对863-917网络安全监测平台提供的全国流量数据进行了实验,得出对应于“橙色八月”的2006年8月上旬流量严重异常的结论。通过与相关的其他传统算法进行对比,如使用绝对流量的算法和简单使用不同小时流量排名的算法,进一步说明序贯频繁模式对网络流量分析的实用性。     

基于社会网络的聊天数据噪声过滤

聊天室中的聊天数据充斥着大量噪声,极大地降低了话题检测的监控效率。但聊天数据只有对话发出时间这一线索可供直接利用,因此噪声过滤是聊天室监控的一个难题。该文提出一种基于社会网络的聊天数据噪声过滤方法,通过分析聊天数据的时序关系,推断出聊天用户间的社会网络关系,根据社会网络蕴含的用户交流特点判断并过滤出噪声。实验证实了该方法能较准确地过滤出噪声,提高话题识别的准确率。     

面向网格简化的数据结构快速生成方法

针对网格简化算法需要的显式和完整拓扑关系,筛选出有效的数据结构,结合MC(MT)算法计算过程的特点,设计六体素法算法。该算法能在MC(MT)算法抽取等值面的同时完成简化算法所需的数据结构,减少了简化算法前数据结构预处理所耗费的时间和空间。