Location Based and Customized Voice Information Service for Mobile Community

Mobile commerce has been increasingly recognized as one of the most important prosperous areas for deploying information technology. This paper aims to advance the value of the information by providing a novel voice-information sharing mechanism that is a combination of a location-based information service (known as a Killer App) and a virtual community that consequently becomes a WCP (Wireless Content Provider). This community embodies context aware intelligence by analyzing the context-sensitive behavior of the community members and enabling proactive and precise context sensitive voice-information sharing; furthermore. This voice-information sharing mechanism is comprised of an IVR system, a location service, EPN (Euclidean distance with Positive and Negative Strength) Clustering, Naïve Bayesian Prediction, and a set of metrics for monitoring the progression of the community. The primitive results show that our mechanism satisfactorily reaches the goal of proactive precise sharing of voice information between community members.     

Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information

Indoor location determination has emerged as a significant research topic due to the wide-spread deployment of wireless local area networks (WLANs) and the demand for context-aware services inside buildings. However, prediction accuracy remains a primary issue surrounding the practicality of WLAN-based location determination systems. This study proposes a novel scheme that utilizes mobile user orientation information to improve prediction accuracy. Theoretically, if the precise orientation of a user can be identified, then the location determination system can predict that user’s location with a high degree of accuracy by using the training data of this specific-orientation. In reality, a mobile user’s orientation can be estimated only by comparing variations in received signal strength; and nevertheless the predicted orientation may be incorrect. Incorrect orientation information causes the accuracy of the entire system to decrease. Therefore, this study presents an accumulated orientation strength algorithm which can utilize uncertain estimated orientation information to improve prediction accuracy. Implementation of this system is based on the Bayesian model, and the experimental results indeed show the effectiveness of our proposed approach.     

Location awareness through trajectory prediction

Location-aware computing is a type of ubiquitous computing that uses user’s location information as an essential parameter for providing services and application-related optimization. Location management plays an important role in location-aware computing because the provision of services requires convenient access to dynamic location and location-dependent information. Many existing location management strategies are passive since they rely on system capability to periodically record current location information. In contrast, active strategies predict user movement through trajectories and locations. Trajectory prediction provides richer location and context information and facilitates the means for adapting to future locations. In this paper, we present two models for trajectory prediction, namely probability-based model and learning-based model. We analyze these two models and conduct experiments to test their performances in location-aware systems.     

基于WSN的精准养牛业智能决策支持系统设计

准确及时地获取牛群的身体变化情况是保证牛群生存环境舒适度的基本前提。在对养牛业监测系统和无线传感网络深入了解的情况下,开发了以无线传感网络为基础的精准养牛业系统。按照标准生产化的要求,系统的总体架构分为感知层、传输层和应用层。并根据总体架构的层次模型设计了相应的软件和硬件。系统有效地利用贝叶斯网络智能决策支持系统建立模型,结合无线传感网络收集牛酮体的数据,进行相应的环境设置,让牛群的生存环境更加舒适。     

微博用户的个性分类分析

社交网络给每个社会中的人提供了自由表达个人情感、观点、兴趣、建议等言论的平台。用户在这些平台上发表的言论、所做的行为以及用户在平台上建立的社交圈子也给数据挖掘带来了新的数据和机会。提出了一种利用用户在微博上的公开数据信息实现对该用户的MBTI个性维度进行分类分析的方法。在该方法中,基于对用户微博数据的分析,提出了能够表征用户心理和行为的文本和非文本特征,然后采用三种机器学习的分类方法—提升决策树、支持向量机和贝叶斯逻辑递归来对微博用户的个性进行分类分析。实验结果表明,通过对微博数据的挖掘可以在不同MBTI个性维度上达到75%~90%的准确率。     

一种网络环境中故障诊断与维修决策方法

本文将贝叶斯网络引入到大型网络故障管理系统中，从贝叶斯网络推理模型与故障诊断和维修决策问题的一致性出发，提出了基于故障假设、观测和维修操作节点结构的DBN模型，并详细阐述了模型的内涵和故障诊断知识构成要素     

基于DBN的特种车辆前向防撞推理模型

针对特种车辆在动态环境中的前向碰撞风险评估问题,对特种车辆前向碰撞风险的自然因素、驾驶员行为特征等进行研究,并对固定的车辆安全防撞距离阈值进行改进,提出了一种基于动态贝叶斯网络的前向防撞推理模型。该模型将自车与周围环境的位置关系、环境关系、驾驶员行为等因素进行融合,一旦周围环境发生变化,该模型可以及时评估前向风险,并与静态贝叶斯网络的前向推理模型进行对比分析。仿真实验验证了该前向防撞推理模型的可行性和有效性。     

基于贝叶斯网络的电力变压器局部放电故障检测

针对传统电力变压器故障检测方法对电力系统中潜藏的故障问题检测水平不足,准确率较低,无法及时准确的发现异常隐患等问题,提出基于贝叶斯网络的变压器局部放电故障检测方法;首先通过传感器获取电力变压器不同状态下运行过程中的参数数据,对局部放电故障发生的概率和范围进行合理性评估,提取评估概率数据综合为样本数据集,构建贝叶斯网络故障树;根据逻辑规则转化为贝叶斯网络,推演计算故障节点之间的算例关系,利用贝叶斯原理抽取故障特征指标与异常概率之间的关联关系,利用模糊描述方法构建故障特征关联函数,计算可得故障特征模糊函数动态变化关系,实现对变压器故障发生的概率与位置信息的判断与确定;实验结果表明,利用贝叶斯网络对电力变压器局部放电故障检测准确率达到85%以上,最高可达96%,说明该方法具有较高的检测准确率,能够有效提高电力变压器放电故障检测的有效性。     

动态贝叶斯网络在水文预报中的应用

贝叶斯网络是目前人工智能中不确定知识与推理中最有效的理论模型之一。提出一种基于动态贝叶斯网络模型理论的水文预报方法。在综合考虑降雨径流成因的基础上,利用领域专家知识构建网络模型,在已有降雨、流量数据的基础上通过计算变量间的条件概率来计算流量发生的可能性。最后,通过渭河流域咸阳至临潼段历时数据进行仿真实验,对仿真结果和该模型进行了分析。     

A type-2 fuzzy embedded agent to realise ambient intelligence in ubiquitous computing environments

In this paper, we present a novel approach for realising the vision of ambient intelligence in ubiquitous computing environments (UCEs). This approach is based on embedding intelligent agents in UCEs. These agents use type-2 fuzzy systems which are able to handle the different sources of uncertainty and imprecision in UCEs to give a good response. We have developed a novel system for learning and adapting the type-2 fuzzy agents so that they can realise the vision of ambient intelligence by providing a seamless, unobtrusive, adaptive and responsive intelligence in the environment that supports the activities of the user. The user’s behaviours and preferences for controlling the UCE are learnt online in a non-intrusive and life long learning mode so as to control the UCE on the user’s behalf. We have performed unique experiments in which the type-2 intelligent agent has learnt and adapted online to the user’s behaviour during a stay of five days in the intelligent Dormitory (iDorm) which is a real UCE test bed. We will show how our type-2 agents can deal with the uncertainty and imprecision present in UCEs to give a very good response that outperforms the type-1 fuzzy agents while using smaller rule bases.     

Network availability prediction with hidden context

Modern mobile devices are increasingly capable of simultaneously connecting to multiple access networks with different characteristics. Restricted coverage combined with user mobility will vary the availability of networks for a mobile device. Most proposed solutions for such an environment are reactive in nature, such as performing a vertical handover to the network that offers the highest bandwidth. But the cost of the handover may not be justified if that network is only available for a short time. Knowledge of future network availability and their capabilities are the basis for proactive schemes which will improve network selection and utilization. We have previously proposed a prediction model that can use any available context such as GSM Location Area, WLAN presence or even whether the power cable is plugged in, to predict network availability.As it may not be possible to sense all of the context variables that influence future network availability, in this paper we introduce a generic, new model incorporating a hidden variable to account for this. Specifically, we propose a Dynamic Bayesian Network based context prediction model to predict network availability. Predictions performed for WLAN availability with the real user data collected in our experiments show 20% or more improvement compared to both of our earlier proposals of order 1 and 2 semi-Markov models.     

Proactive privacy practices in transition: Toward ubiquitous services

As the Internet paved the way for electronic businesses, ubiquitous services (u-services) will be the next wave launched by electronic services based on current customer information potential. However, privacy is a strategic issue and has been identified as a key hindrance to u-services. As a proactive approach and drawing upon integrative social contracts theory, this study presents a proactive privacy practices framework to examine how the interplays within electronic service, providers’ proactive approaches influence customer disclosure willingness for future u-services, adoption. The results and implications of this study are discussed and expected to shed light on privacy practices.     

Predicting intrusion goal using dynamic Bayesian network with transfer probability estimation

Predicting the intentions of an observed agent and taking corresponding countermeasures is the essential part for the future proactive intrusion detection systems (IDS) as well as intrusion prevention systems (IPS). In this paper, an approach of dynamic Bayesian network with transfer probability estimation was developed to predict whether the goal of system call sequences is normal or not, with early-warnings being launched, so as to ensure that some appropriate countermeasures could be taken in advance. Since complete set of system call state transfer can hardly be built in real environments, the empirical results show that the newly emerging system call transfer would have great impact on the prediction performance if we straightly use dynamic Bayesian network without transfer probability estimation. Therefore, we estimate the probability of new state transfer to predict the goals of system call sequences together with those in conditional probability table (CPT). It surmounts the difficulties of manually selecting compensating parameters with dynamic Bayesian network approach [Feng L, Guan X, Guo S, Gao Y, Liu P. Predicting the intrusion intentions by observing system call sequences. Computers & Security 2004; 23/3: 241–252] and obviously makes our prediction model more applicable. The University of New Mexico (UNM) and KLINNS data sets were analyzed and the experimental results show that it performs very well for predicting the goals of system call sequences with high accuracy and furthermore dispenses with much more manual work for selecting compensating parameters.     

基于贝叶斯网络构建RoboSim模型的自动驾驶行为决策

为汽车自动驾驶提供安全高效的自动驾驶行为决策,是汽车自动驾驶领域面临的挑战性问题之一.目前,随着自动驾驶行业的蓬勃发展,工业界与学术界提出了诸多自动驾驶行为决策方法,但由于汽车自动驾驶行为决策受环境不确定因素的影响,决策本身也要求实效性及高安全性,现有的行为决策方法难以完全支撑这些要素.针对以上问题,提出了一种基于贝叶斯网络构建RoboSim模型的自动驾驶行为决策方法.首先,基于领域本体分析自动驾驶场景元素之间的语义关系,并结合LSTM模型预测场景中动态实体的意图,进而为构建贝叶斯网络提供驾驶场景理解信息;然后,通过贝叶斯网络推理特定场景的自动驾驶行为决策,并使用RoboSim模型的状态迁移承载行为决策的动态执行过程,以减少贝叶斯网络推理的冗余操作,提高了决策生成的效率. RoboSim模型具有平台无关、能模拟仿真执行周期的特点,并支持多种形式化的验证技术.为确保行为决策的安全性,使用模型检测工具UPPAAL对RoboSim模型进行验证分析.最后,结合变道超车场景案例,进一步证实所提方法的可行性,为设计安全、高效的自动驾驶行为决策提供了一种可行的途径.     

RTS游戏中用户行为的神经网络预测模型

即时战略游戏(简称RTS游戏)中,用户的行为由于游戏自身庞大的决策空间而难以预测.针对这个问题,提出了通过对RTS游戏的对战记录进行分析,建立5种结构的神经网络模型来预测用户行为的方法.模型考虑了不同时间片的状态对于决策行为的影响,设计了单时间片输入和双时间片输入的神经网络,并与基于动态贝叶斯网络的模型进行了比较.实验结果表明,基于单时间片输入的神经网络模型能够更加快速地完成训练过程并达到满意的预测准确度.     

A Bayesian regularized artificial neural network for stock market forecasting

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. The accurate prediction of stock price movements could play an important role in helping investors improve stock returns. The complexity in predicting these trends lies in the inherent noise and volatility in daily stock price movement. The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The proposed technique reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network. Experiments were performed with Microsoft Corp. and Goldman Sachs Group Inc. stock to determine the effectiveness of the model. The results indicate that the proposed model performs as well as the more advanced models without the need for preprocessing of data, seasonality testing, or cycle analysis.     

Better Living Through Geometry

Mark Weiser described ubiquitous computing as “invisible, everywhere computing that does not live on a personal device of any sort, but is in the woodwork everywhere”[1]. The EasyLiving project is concerned with development of an architecture and technologies for ubiquitous computing environments that allow the dynamic aggregation of diverse I/O devices into a single coherent user experience. Though the need for research in distributed computing, perception and interfaces is widely recognised, the importance of an explicit geometric world model for enhancing the user’s experience of a ubiquitous computing system has not been well-articulated. This paper describes three scenarios that benefit from geometric context and introduces the EasyLiving Geometric Model.     

开放式动态网络中可渗透路径预测方法仿真

针对机器学习、生物免疫以及条件概率算法下的三种可渗透路径预测方法存在的空间复杂度高、预测覆盖面小问题,提出基于贝叶斯算法的开放式动态网络可渗透路径预测方法。方法对贝叶斯算法进行描述,并基于贝叶斯算法设计可渗透路径预测方法,分析开放式动态网络可渗透过程,然后对可渗透数据进行采集并处理,提取可渗透特征,建立基于贝叶斯算法的预测模型,实现可渗透路径预测。结果表明,与机器学习、生物免疫以及条件概率算法下的三种可渗透路径预测方法相比,所提方法空间复杂度最低,预测覆盖面最大,最高可达98%。     

基于贝叶斯网络的多Agent服务推荐机制研究

普适计算环境中的服务推荐需要满足系统异构性和移动性的要求。提出了一种基于贝叶斯网络的多Agent服务推荐机制并进行实现,将贝叶斯网络和聚类方法应用于服务推荐中,并设计了推荐模型自学习机制,充分考虑了上下文对服务推荐的影响及改进。实现系统由完成历史上下文汇集、知识训练、决策推荐和自学习功能的多个Agent构成,通过Agent之间的通信内容设计,在Agent之间建立流程控制和数据共享通道。