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.
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.
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.
17872919
Extremal search of decision policies for scalable distributed applications
The ongoing trend of constructing open, scalable distributed systems such as peer-to-peer (P2P) systems demands for effective tools to manage the interactions between constituent entities (or nodes). One such tool is through imposing decision policies at the network level. However very few techniques are available to allow computers autonomously identify good policies with limited human intervention. In this paper, we propose an Extremal Programming (EP) algorithm to achieve automatic policy identification. The algorithm is inspired by recent advances in understanding far from equilibrium phenomena in terms of self-organized criticality (SOC). The effectiveness of EP is evaluated through a P2P application called location-aware video streaming (LAVS). The simulation studies in LAVS demonstrate that with EP, the fast and effective sharing of video streams is achieved.
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.
2501005
Coordinating Multiple Agents via Reinforcement Learning
In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, ‘‘coarse-grained’’ and ‘‘fine-grained’’, are proposed to address agents coordination behavior at two different levels. The ‘‘coarse-grained’’ algorithm operates at one level and tackle hard system constraints, and the ‘‘fine-grained’’ at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.
29357540
Multi-feature Fusion Method of Driver Face Location Based on Area Coincidence Degree and Prior Knowledge
Speedy and reliable face location is a key for monitoring driver fatigue driving condition using machine vision methods. According to the disadvantage of face location methods based on single feature in accuracy and reliability at present, first, an improved face location method based on Haar-like feature and Adaboost is used to detect the possibly existing face region in the whole image, then the detected region is extended properly and a face location method based on skin color feature in normalized rgb and HSV color spaces is used to locate the face again in the extended area, finally, fusion location of driver face region is achieved by the defined area coincidence degree and prior knowledge of human face. Experiments carried out in various road environments demonstrate the validity of the fusion method proposed.
7865354
Modifying the DPClus algorithm for identifying protein complexes based on new topological structures
BackgroundIdentification of protein complexes is crucial for understanding principles of cellular organization and functions. As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.ResultsBased on the study of known complexes in protein networks, this paper proposes a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density. Following the approach of that of the previously proposed clustering algorithm DPClus which expands clusters starting from seeded vertices, we present a clustering algorithm IPCA based on the new topological structure for identifying complexes in large protein interaction networks. The algorithm IPCA is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm IPCA recalls more known complexes than previously proposed clustering algorithms, including DPClus, CFinder, LCMA, MCODE, RNSC and STM.ConclusionThe proposed algorithm based on the new topological structure makes it possible to identify dense subgraphs in protein interaction networks, many of which correspond to known protein complexes. The algorithm is robust to the known high rate of false positives and false negatives in data from high-throughout interaction techniques. The program is available at http://netlab.csu.edu.cn/bioinformatics/limin/IPCA.
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.
31400188
Image Recovery from Broken Image Streams
This paper presents an approach for image recovery from broken image streams based on an image continuity model. The information to be recovered includes the header of an image (width, height, color space etc.) and the data stream which might have been partially damaged. We begin our discussion on common image formats and necessary information for the image recovery. We then define several measures on image contents to facilitate the image recovery. Using these measures, an effective recovery algorithm is proposed. Experimental results show that the algorithm can successfully recover images of different formats, even multi-frame images from broken image streams in most cases.
22766407
Watermarking Abstract Tree-Structured Data
Digital watermarking techniques have been successfully applied in multimedia data such as image, audio, video, etc. With the rapid development of Internet, the requirement of copyright and integrity protection over semi-structured data, such as XML documents and complex hypertext content, is becoming more and more urgent. However, it is more challenging to apply the effective watermarking schemes into semi-structured data, because watermark for semi-structured data must have the dual protection feature toward both structure and content. In general, most semi-structured data can be represented as tree. Therefore, in this paper, we propose a novel watermark scheme for tree-structured data based on the value lying both in the tree structure and in the node content, which gives a comprehensive protection for both node content and structure of tree. The experiments on the robustness of the solution against various forms of attacks are conducted. The results highlight correctness and efficiency of our solution.