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.     

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 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 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.     

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.     

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.     

Fault Tolerant Data Aggregation Scheduling with Local Information in Wireless Sensor Networks

We study the problem of efficient data aggregation in unreliable wireless sensor networks by designing a fault tolerant data aggregation protocol.A fault tolerant data aggregation protocol consists of two parts:basic aggregation scheduling and amendment strategies.On default,data is aggregated according to the basic aggregation scheduling strategy.The amendment strategy will start automatically when a middle sensor node is out of service.We focus our attention on the amendment strategies and assume that the network adopts a connected dominating set (CDS) based aggregation scheduling as its basic aggregation scheduling strategy.The amendment scheme includes localized aggregation tree repairing algorithms and distributed rescheduling algorithms.The former are used to find a new aggregation tree for every child of the corrupted node,whereas the latter are used to achieve interference free data aggregation scheduling after the amendment.These amendment strategies impact only a very limited number of nodes near the corrupted node and the amendment process is transparent to all the other nodes.Theoretical analyses and simulations show that the scheme greatly improves the efficiency of the data aggregation operation by reducing both message and time costs compared to rebuilding the aggregation tree and rescheduling the entire network.     

Using Domain Ontology in a Semantic Blogging System for Construction Professionals

Smooth communication is essential for the success of construction projects. As an easy-to-use, context-rich, and high-capacity communication tool, blogging is gaining popularity in construction industry. In this paper, the features of blogging technology and how it could benefit construction organizations are presented. To further improve the effectiveness of blogging technology in information and knowledge sharing, an ontology-based semantic blogging system is proposed. Semantic blogging is an extension of conventional blogging and ontology is the key enabling technology for it. Domain-ontology-based semantic blogging site is composed of a network of concepts, which are clearly defined and interlinked according to their context and bound to certain behaviors. This paper reports how the e-Cognos ontology was implemented into a blogging system and how the system functions to process its contents. The paper concludes that using on-tology-based semantic blogging site can greatly enhance information sharing between construction professionals and it is a very promising tool for construction communities to publish and share their experience.     

Clustering approach based on hierarchical expansion for community detection of scientific collaboration network①

This study presents a clustering algorithm based on hierarchical expansion to solve the problem of community detection in scientific collaboration network .The characteristics of achievements infor-mation related to scientific and technological domains are analyzed , and then an ontology that repre-sents their latent collaborative relations is built to detect clusters from the collaboration network .A case study is conducted to collect a data set of research achievements in the electric vehicle field and better clustering results are obtained .A hierarchical recommendation framework that enriches the domain ontologies and retrieves more relevant information resources is proposed in the last part of this paper .This work also lays out a novel insight into the exploitation of scientific collaboration net-work to better classify achievements information .     

Development of an invasive brain machine interface with a monkey model

Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey’s arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.     

Application of neural network in the study of combustion rate of natural gas／diesel dual fuel engine

In order to predict and improve the performance of matural gas/diesel dual fuel engine(DFE),a combustion rate model based on forward meural network was built to study the combustion process of the DFE.The effect of the operating parameters on combustion rate was also studied by means of this model.The study showed that the predicted results were good agreement with the experimental data.It was proved that the de-veloped combustion rate model could be used to successfully predict and optimize the combustion process of dual fuel engine.     

Experimental investigation and thermodynamic assessment of the yttriumhydrogen binary system

A coupled experimental investigation and thermodynamic study of the yttrium-hydrogen(Y-H) binary system were carried out to provide more comprehensive and quantitative insights into the key thermodynamic properties of this system. Y-H system in the full range of H/Y = 0–3.0 was investigated by accurate pressure composition isotherm(PCI) measurement to provide credible phase equilibria information and thermodynamic data.The phase boundaries obtained were in agreement with previous experimental data but with improved accuracy.With the guide of the crystal structures, all the solid phases were modelled using the three sublattice model. The Y-H phase diagram and thermodynamic parameters were calculated and assessed with the CALPHAD technique.The obtained results are in very good agreement with our experimental data and the published data reported in literature. The obtained thermodynamic database of Y-H system can be used to predict the hydrogenation behavior and decomposition temperatures of hydrides.     

Design Optimization of Charger Based on PSPICE Simulation

The design of a high-frequency switching mode charger (HFSMC) was optimized using a PSPICE simulation of the charging system including a valve regulated lead-acid (VRLA) battery pack and the HFSMC.A high-frequency battery charging circuit model was developed to describe the battery dynamics when charging with DC current having high-frequency ripples. An IGBT circuit model, a high-frequency pulsed transformer coupling model and a silicon fast recovery diode model were also developed. Simulations were compared to laboratory measurements to verify the battery and system models. Simulation of the working states of an Fe-based nanocrystalline magnetic core used in the transformer shows that the transformer design can be optimized by adjusting the core gap and by employing an RC network. Further simulations show that the components in the output unit of the charger main circuit can also be optimized. Simulations also show that the battery dynamics Including the inductance should be considered for design optimization of the HFSMC.     

Cooperative Sensor Anomaly Detection Using Global Information

Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous (i.e., dirty) data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis (PCA) model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic (ROC) curves.     

BIM-Based Indoor-Emergency-Navigation-System for Complex Buildings

The imminence of terrorist activities and the necessity of the maximum possible disaster preparedness in the sense of indoor-navigation support have been brought to evidence by several catastrophes, e.g., the fire at Istanbul Airport in May 2006 or the terror attacks on the London Underground on July 7, 2005. Since 2001 ten terror attacks have been thwarted only in Great Britain. For that reason the aim of the presented research project is to develop a solution for response and recovery to support rescuers in finding the shortest way within a public building and provide them with important information in their particular spa-tial context. Existing building information models (BIM) are used for displaying plans on mobile devices and for routing purposes. The indoor navigation system is based on wireless LAN (WLAN), ultra-wide-band (UWB), and radio frequency identification (RFID). These technologies are described in detail and an overview on data formats which are used to retrieve building data out of the BIM for generating routing networks is given.     

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.     

Globalization and long-run co-movements in the stock market for the G7: An application of VECM under structural breaks

This paper analyzes the process of long-run co-movements and stock market globalization on the basis of cointegration tests and vector error correction (VEC) models. The cointegration tests used here allow for structural breaks to be explicitly modeled and breakpoints to be computed on a relative-time basis. The data used in our empirical analysis were drawn from Datastream and comprise the natural logarithms of relative stock market indexes since 1973 for the G7 countries. The main results point to the conclusion that significant causal cointegration effects occur in this context and that there is a long-run relationship that governs the worldwide process of market integration. Globalization, however, is a complex adjustment process and in many cases there is only evidence of weak market integration which means that non-proportional price transmission occurs in the market along with proportional changes. The worldwide markets, as expected, appear to be driven in general by the US stock market.