3870686
An improved dead reckoning method for mobile robot with redundant odometry information
Dead reckoning is an important method for mobile robot. If its accuracy can be improved, navigation tasks will be simplified. The mobile robot concerned in this paper is with five wheels, in which there are dual driving wheels and a castor. A measurement wheel with encoder is fixed beside each driving wheel. There are two encoders fixed on the castor. One measures the castor's movement, and another indicates the angle of yawing. A new kind of kinematics equations derived from the robot's turning radius and angle of movement trajectory is presented. We can get a group of turning radius and angle from the data of four encoders, in which there are four different values for radius and six different values for angle. The estimated values of radius and angle can be gotten by fusion using fuzzy algorithm. The improved dead reckoning method has the advantages of simplicity, less computation cost, cheapness and good performance in uneven road. The simulation shows its effectiveness.
14272859
Ant Colony Inspired Self-Healing for Resource Allocation in Service-Oriented Environment Considering Resource Breakdown
The ant colony optimization (ACO) algorithm is a metaheuristic inspired from the behavior of foraging ants. Instead of exploring its ability in finding optimal solutions, the current study investigates another unique property - self-healing mechanism for resource allocation in a service-oriented environment where unexpected resource breakdown can occur. A system architecture is first proposed to detect, diagnose and react to disturbances. Then the performance of the ACO self-healing mechanism is tested and compared based on a modified benchmark problem. The experimental results show that the self-healing mechanism can promptly recover an obsolete schedule with high quality solutions.
537363
Change propagation algorithm in a unified feature modeling scheme
Product lifecycle stages are inter-related and mutually constraining. Due to the sequential nature of the product development processes, some constraints or conflicts may emerge in a later stage and require modifications to the decisions made in earlier stages. The iterations between stages are hence unavoidable and must be managed carefully to maintain the consistency, integrity and validity of product information models. Due to the inter- or intra-stage relations, a chain of changes is very likely to occur as the consequence of an initial change. Modeling and maintaining these relations are important in collaborative engineering to evolve the state of the whole product model in a consistent manner. This paper introduces a new method of modeling associative engineering relations in a unified feature modeling scheme and elaborates a change propagation algorithm for the information consistency control among multiple applications of product lifecycle stages. The algorithm is established on a JTMS-based dependency network. Two case studies are used to illustrate the proposed dependency network and change propagation algorithm.
8577812
Dynamic Self-Healing for Service Flows with Semantic Web Services
With an increasing complexity of business processes, self-healing capability is becoming an important issue in order to support robust service flow execution. In this paper, a dynamic self-healing mechanism is proposed, which can dynamically identify suitable alternatives and replace faulty services such that a service flow can be performed successfully despite of unexpected exceptions. This mechanism explicitly utilizes semantic Web services for service matching and selection of a composite service in business service flow, and Semantic Web services are equipped with rich business rules in a domain-dependent manner. We explore the self-healing mechanism for supporting self-healable service flow execution which is modeled in BPEL4WS. A demo system of self-healing capable service flow execution is built to validate its effectiveness by a concrete scenario, PC manufacturing application.
2378122
Self-organized manufacturing resource management: An ant-colony inspired approach
In a typical manufacturing system, jobs are released from a production planning stage to a shop floor, where they are allocated to resources like machines. An optimal or near optimal schedule is generally found for those jobs. In reality, the execution of this schedule is often interrupted by dynamic events like unexpected incoming new jobs, machine breakdowns, etc. A rapid recovery or a self-organized feature of the interrupted schedule is desirable for manufacturing resource management. This paper proposes an ant-colony inspired approach to recover the interrupted schedule through a self-organization mechanism. The experiments show that this mechanism significantly improves the quality of the recovered schedules.
15177546
Semantic enhanced rule driven workflow execution in Collaborative Virtual Enterprise
Globalization leads to an efficient new business paradigm generally known as collaborative virtual enterprise (CVE) which demands flexible service orchestration and robust workflow execution. As two major service orchestration strategies, OWL-S and BPEL have their own strength and deficiencies. To meet the challenge in CVE, in this paper, we propose a workflow execution system (WES) that takes advantage of the complementary strengths of these two technologies. On the one hand, having semantic support, OWL-S is used in dynamic service discovery and composition at high level. On the other hand, at the concrete level, industry-based BPEL is exploited in service execution. The description of each semantic Web service is enhanced with rule-based modeling of the essential business logic behind the service interface. In order to realize interoperability in OWL-S and BPEL without loss of semantic information, we further proposed an OWLS2BPEL Mapper to facilitate the workflow robustness and support rule evaluation to increase responsiveness to customers. A concrete scenario in PC manufacturing collaborative virtual enterprise is analyzed to demonstrate the effectiveness of our workflow execution system.
16003770
A Caching System for XML Queries Using Frequent Query Patterns
In this paper, we present an optimization framework for querying XML data by caching frequent query patterns. In this framework, frequent query patterns are mined online from user queries, and these query results are cached for future use. The mining process of frequent query patterns is launched automatically when user queries meet predefined requirements. To deal with queries that are similar to but not exactly same as the cached ones, a novel technique named query rewriting is adopted. This technique is able to handle four kinds of similar queries namely exact matching, exact containment, semantic matching and semantic containment. A cache replacement scheme that utilizes both the query pattern support and query pattern accessing time is employed to perform a fine-grained region purging. Experiments are carried out on the XMARK datasets. The results show that the proposed methods are both effective and efficient in improving the performance of XML queries.
1107258
Improving image clustering: An unsupervised feature weight learning framework
We address the problem of feature weight learning for image clustering. In practice, before clustering data, we generally normalize all data features between 0 and 1, because we cannot determine which features are more important. In this paper, we provide a feature weight learning framework for clustering which can obtain the feature weights and cluster labels simultaneously. An alternative optimization algorithm is adopted to solve this problem. Empirical studies on the toy data and real image data demonstrate our algorithm's effectiveness in improving the clustering performance.
34310397
Predictive Queries Algorithm Based on Probability Model over Data Streams
Mining the evolving trends of an online data stream and forecasting the data values in the future can provide important support for the decision-making in many time-sensitive applications. This paper models an online data stream as a continuous state transitions process by mapping the possibly infinite stream data into finite states, and uses state transition disGraph (STG) to maintain the track of the state transactions. By studying the statistic information of the history state transitions, the future values can be predicted based on the theory of Markov chain. Extensive simulation experiments are conducted and show that the predictive performance of our method is preferable to that of the existing analogous algorithms.
14077082
Collaborative Agents Supported Automatic Physical Database Design Based on Description Logics Reasoning
Physical database design is a key phase in devising and deploying a database system to improve overall system performance, and automatic physical database design becomes an important value-added feature in a database system to decrease the TCO (total cost of ownership). A GAPDD (general automatic physical database design) framework, with two phases, the workload mining phase and the design advising phase, is provided to support automatic physical database design for relational database systems. Three collaborative agents, the workload mining agent (WMA), the feature generation agent (FGA), and the optimal search agent (OSA) work together in GAPDD. The feature generation procedure in FGA is further studied, and a reasoner based on description logics (DL) is offered to reason useful features. An index advisor for OSCAR relational engine is prototyped, and experiment results show its feasibility and efficiency
37239622
Classifying Motion Time Series Using Neural Networks
This paper proposes an effective time-series classification model based on the Neural Networks. Classification under this model consists of three phases, namely data preprocessing, training, and testing. The main contributions of the paper are described as following: We propose a feature extraction algorithm, which involves computation of finite difference of sequences, for preprocessing. We employ two different types of Neural Networks for training and testing. The results of the experiments on real univariate motion capture data and synthetic data show that our approach is effective in providing good performance in terms of accuracy. It is therefore a promising method for classifying time-series, in particular for univariate human motion capture data.