Design of child’s safety management system based on ontology using context information of home network

This paper presents a system to alert of dangerous a child situation of a child by applying context information collected from a home network to ontology that is capable of inference. Radio frequency Identification (RFID) and sensors were used for the configuration of a home network, to obtain the raw data to convert into context information. To express the ontology, web ontology language (OWL) was used to provide the inference of context information. Then, simple object access protocol (SOAP) messages were used to notify of the dangerous situations that a child may be involved in via mobile devices. The proposed system consists of Context Manager, Service Manager, and Notification Manager. The child's safety management system can proactively detect the context data of a child on the basis of context awareness. In the experiment, the Jena 2.0 by ontology reasoner and the OSGi(Open Service Gateway initiative) Gateway developed using open source software Knopflerfish 1.3.3 were used to implement the service frame work.      

Development and Research on the Internet Resources Aggregation Platform

We proposed an Intemet resource aggregation platform based on semantic web. The platform includes an Web Ontology Language（OWL） ontology design toolkit（VO-Editor） and a selective inference algorithm engine so that it can visually editing ontology and using novel selective reasoning for information aggregation. We introduce the VO-Editor and the principle of selective inference algorithm. At last a case of budget travel system is used to interpret the approach of Internet resources aggregation by this platform.     

Short-term load forecasting based on fuzzy neural network

The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. the multiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learning algorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzy neural network model, the network is used to Predict short-term load by compaing the network output against the real load data from a local power system supplying electricity to a large steel manufacturer. The experimental results are satisfactory.     

Application of GIS-Based Spatial Filtering Method for Neural Tube Defects Disease Mapping

This study is to assess the prevalence rates spatial pattern of neural tube defects with geographic information system and spatial filtering technique. A total of 80 infants who diagnosed from neural tube defects in the area being studied between 1998 and 2001 were analyzed. Firstly, the geographic information system （GIS） software ArcGIS was used to map the crude prevalence rates. Secondly, the data were smoothed by the method of spatial filtering. We evaluated that the effect of changes in spatial filtering radius size was assessed by creating maps based on various filtering radius sizes. The 3 miles or larger filtering radius gives better section variability than the 2 and 2.5 miles or smaller ones. The maps produced by the spatial filtering technique indicate that prevalence rates in the villages in the southeastern region are to produce higher prevalence than that in the other regions. The smoothed maps based on Heshun County display a more adequate data representation than the raw prevalence rate map.     

Ontology-Based Context-Aware Middleware for Smart Spaces

Context-awareness enhances human-centric, intelligent behavior in a smart environment; however, context-awareness is not widely used due to the lack of effective infrastructure to support context-aware ap- plications. This paper presents an agent-based middleware for providing context-aware services for smart spaces to afford effective support for context acquisition, representation, interpretation, and utilization to ap- plications. The middleware uses a formal context model, which combines first order probabilistic logic (FOPL) and web ontology language (OWL) ontologies, to provide a common understanding of contextual in- formation to facilitate context modeling and reasoning about imperfect and ambiguous contextual informa- tion and to enable context knowledge sharing and reuse. A context inference mechanism based on an ex- tended Bayesian network approach is used to enable automated reactive and deductive reasoning. The middleware is used in a case study in a smart classroom, and performance evaluation result shows that the context reasoning algorithm is good for non-time-critical applications and that the complexity is highly sensi- tive to the size of the context dataset.     

Interoperability for Global Observation Data by Ontological Information

The Ontology registry system is developed to collect, manage, and compare ontological information for integrating global observation data. Data sharing and data service such as support of metadata deign, structuring of data contents, support of text mining are applied for better use of data as data interoperability. Semantic network dictionary and gazetteers are constructed as a trans-disciplinary dictionary. Ontological information is added to the system by digitalizing text based dictionaries, developing "knowledge writing tool" for experts, and extracting semantic relations from authoritative documents with natural language processing technique. The system is developed to collect lexicographic ontology and geographic ontology.     

A BP neural network based information fusion method for urban traffic speed estimation

Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an very important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings and tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, we developed a novel method employing a back propagation (BP) neural network to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, we can use the speed of its related road sections to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the speed of this road section estimated by our method is better.     

A Statistical Method for Analysis of Technical Data of a Badminton Match Based on 2-D Seriate Images

The use of computer vision technology to collect and analyze statistics during badminton matches or training sessions can be expected to provide valuable information to help coaches to determine which tactics should be used by a player in a given game or to improve the player＇s tactical training. A method based on 2-D seriate images by which statistical data of a badminton match can be obtained is presented. Image capture and analysis were performed synchronously using a multithreading technique. The regions of movement in the images were detected using a temporal difference method, and the trajectories of the movement regions were analyzed using sedate images. The shuttlecock trajectory was extracted from all detected trajectories using various characteristic parameters. The stroke type was determined by comparing the shuttlecock trajectory data with a set of stroke definition data. The algorithm was tested at a training center, and the results were compared with baseline data obtained by expert visual inspection using four video samples, which included approximately 10 000 frames. The shuttlecock trajectory and stroke type were detected correctly in almost 100% of the analyzed video sequences. The average speed of the automated analysis was approximately 40 frames/s, indicating that the method can be used for real-time analysis during a badminton match. The system is convenient for use by a sports coach.     

Application of Partially Connected Neural Network

This paper focuses mainly on application of Partially Connected Backpropagation Neural Network （PCBP） instead of typical Fully Connected Neural Network （FCBP）. The initial neural network is fully connected, after training with sample data using cross-entropy as error function, a clustering method is employed to cluster weights between inputs to hidden layer and from hidden to output layer, and connections that are relatively unnecessary are deleted, thus the initial network becomes a PCBP network. Then PCBP can be used in prediction or data mining by training PCBP with data that comes from database. At the end of this paper, several experiments are conducted to illustrate the effects of PCBP using Iris data set.     

A DNA sequence alignment algorithm using quality information and a fuzzy inference method

DNA sequence alignment algorithms in computational molecular biology have been improved by diverse methods. In this paper, we propose a DNA sequence alignment that uses quality information and a fuzzy inference method developed based on the characteristics of DNA fragments and a fuzzy logic system in order to improve conventional DNA sequence alignment methods that uses DNA sequence quality information. In conventional algorithms, DNA sequence alignment scores are calculated by the global sequence alignment algo- rithm proposed by Needleman-Wunsch, which is established by using quality information of each DNA fragment. However, there may be errors in the process of calculating DNA sequence alignment scores when the quality of DNA fragment tips is low, because only the overall DNA sequence quality information are used. In our proposed method, an exact DNA sequence alignment can be achieved in spite of the low quality of DNA fragment tips by improvement of conventional algorithms using quality information. Mapping score param- eters used to calculate DNA sequence alignment scores are dynamically adjusted by the fuzzy logic system utilizing lengths of DNA fragments and frequencies of low quality DNA bases in the fragments. From the experiments by applying real genome data of National Center for Biotechnology Information, we could see that the proposed method is more efficient than conventional algorithms.     

Design of child’s safety management system based on ontology using context information of home network

This paper presents a system to alert of dangerous a child situation of a child by applying context information collected from a home network to ontology that is capable of inference. Radio frequency Identification (RFID) and sensors were used for the configuration of a home network, to obtain the raw data to convert into context information. To express the ontology, web ontology language (OWL) was used to provide the inference of context information. Then, simple object access protocol (SOAP) messages were used to notify of the dangerous situations that a child may be involved in via mobile devices. The proposed system consists of Context Manager, Service Manager, and Notification Manager. The child's safety management system can proactively detect the context data of a child on the basis of context awareness. In the experiment, the Jena 2.0 by ontology reasoner and the OSGi(Open Service Gateway initiative) Gateway developed using open source software Knopflerfish 1.3.3 were used to implement the service frame work.     

AOP event manager in open service gateway

The Open Service Gateway Initiative (OSGi) has played an important role in ubiquitous environments that support interoperability among embedded devices, such as home appliances and network devices. However, the OSGi does not have a common event mechanism yet, and it is difficult to communicate among services asynchronously. In the present work, a common event manager, Aspect-Oriented Event Manager (AOEM), was designed on an OSGi framework. AOEM supports services to generate and provide notification of events. This paper presents the implementation of AOEM as an OSGi bundle with AspectJ. The experiment on transferring between device service and application service demonstrate that AOEM provides good abstraction of the services and convenience.     

AOP event manager in open service gateway 〖WT5"HZ〗Abstract:〖WT5"BZ〗 〖WT5”HZ〗Key words:〖WT5”BZ〗

The Open Service Gateway Initiative (OSGi) has played an important role in ubiquitous environments that support interoperability among embedded devices, such as home appliances and network devices. However, the OSGi does not have a common event mechanism yet, and it is difficult to communicate among services asynchronously. In the present work, a common event manager, AspectOriented Event Manager (AOEM), was designed on an OSGi framework. AOEM supports services to generate and provide notification of events. This paper presents the implementation of AOEM as an OSGi bundle with AspectJ. The experiment on transferring between device service and application service demonstrate that AOEM provides good abstraction of the services and convenience.     

基于本体和规则的物流知识表示与推理

为了解决物流领域相关概念的共享和信息集成问题,研究了物流配送过程中相关知识的表示以及基于知识的推理．应用本体论方法分析配货规则,使用本体中表示的概念和属性制定配货规则库．运用Jena推理子系统,对本体和配货规则进行推理,开发了一套基于本体和规则的自动配货系统．由于本体是共享概念模型明确的、形式化的规范描述,可以利用本体强大的语义表达能力、本体所描述的共享知识以及自动推理机制,实现物流企业之间以知识集成为基础的业务集成．     

本体的概念、建模与应用

为了阐述本体的内涵,讨论了本体的概念、分析了本体与其他知识表示方法的本质区别,总结了本体的分类。本体建模是应用的基础,论述了本体的建模原始建模方法,总结了常用的本体建模语言和建模工具。本体可以应用于知识管理、系统分析、异构信息集成等领域。作为本体应用的演示性实例,讨论了基于本体的网管信息语言层集成,证明基于本体可以解决网管信息的语义异构性。     

IPv6家庭局域网初探

探讨了家庭局域网的概念以及IPv6通讯网络的技术优势,研究了家庭网关的结构组成和家电的通讯方式,并从家电信息编码、家电协议、家电地址等几方面对家庭网关的设计进行了讨论,实现对家电的远程控制.     

基于本体的医学资源库系统的设计与实现

阐述了一个高性能医学资源库系统的构造过程,利用本体技术实现了医学影像领域相关词汇的统一,弥补了原始数据信息和用户查询信息间、医学文本信息和医学影像信息间、资源库系统与其他系统间的语义鸿沟;通过语义推理、查询导航,以及对查询结果相关性反馈信息的处理,更好地理解用户查询意图;通过前置调度机及业务处理机构成的并行集群计算机制,提高了查询效率.经实验验证,该系统实现了高处理效率、高查全率和高查准率的有机统一.     

Web信息抽取与语义检索框架

提出了一种Web信息抽取与语义检索框架,采用定制本体模型,针对Web网页特点设计了抽取与语义标引策略,并在检索过程中引入语义推理机制,从而改善了信息检索的效果.最后介绍了基于Jena实现的该框架的原型系统.