Reference models provide generic blueprints of process models that are common in a certain industry. When designing a reference model, stakeholders have to cope with the so-called 'dilemma of reference modeling...
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ISBN:
(纸本)9783319584577;9783319584560
Reference models provide generic blueprints of process models that are common in a certain industry. When designing a reference model, stakeholders have to cope with the so-called 'dilemma of reference modeling', viz., balancing generality against market specificity. In principle, the more details a reference model contains, the fewer situations it applies to. To overcome this dilemma, the contribution at hand presents a novel approach to mining a reference model hierarchy from large instance-level data such as execution logs. It combines an execution-semantic technique for reference model development with a hierarchical-agglomerative cluster analysis and ideas from process Mining. The result is a reference model hierarchy, where the lower a model is located, the smaller its scope, and the higher its level of detail. The approach is implemented as proof-of-concept and applied in an extensive case study, using the data from the 2015 BPI Challenge.
With the improvement of customization demand and the increase of market fluctuation, manufacturing enterprises gradually turn to intelligent manufacturing mode. However, there are various abnormal events in the manufa...
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ISBN:
(纸本)9798350359268
With the improvement of customization demand and the increase of market fluctuation, manufacturing enterprises gradually turn to intelligent manufacturing mode. However, there are various abnormal events in the manufacturing process, which hinder the normal production of the workshop. The analysis of anomaly propagation is considered to be an effective technique to solve this problem. In this paper, we propose a knowledge driven analysis framework of anomaly propagation in workshop. Firstly, a temporal knowledge graph(TKG) is built for anomaly propagation analysis based on the open platform communications unified architecture (OPC UA) information model and the ontology web language (OWL) mapping model. The time-series data in the manufacturing process is contained in the TKG, such as workpiece processdata, machine maintenance data, etc. These data are the basis for the analysis of anomaly propagation. Then, a time-series analysis method for anomaly is proposed. This method gives the breadth and depth of abnormal events propagation in a period of time, so that decision makers can make decisions in advance to precisely control the production process. The analysis framework provides a route for manufacturing workshop to resolve abnormal events.
The profile for modeling and analysis of Real-Time and Embedded systems (MARTE) defines a framework for annotating non-functional properties of embedded systems. In particular, the SAM (Schedulability analysis Model) ...
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ISBN:
(纸本)9789897581892
The profile for modeling and analysis of Real-Time and Embedded systems (MARTE) defines a framework for annotating non-functional properties of embedded systems. In particular, the SAM (Schedulability analysis Model) sub-profile offers stereotypes for annotating UML models with the needed information which will be extracted to fulfil a scheduling phase. However, SAM does not allow designers to specify data to be used in the context of adaptive systems development. It is in this context that we propose an extension for the MARTE profile, and especially the sub-profile Schedulability analysismodeling, to include adaptation mechanisms in scheduling view. We illustrate the advantages and effectiveness of our proposal by modeling a FESTO case study as an Adaptive Real-Time and Embedded system.
Operating distillation columns under control requires inferring the compositions of the distillate and bottom streams (which are challenging to measure) from other more easily measured variables, such as temperatures ...
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ISBN:
(纸本)9781467358934
Operating distillation columns under control requires inferring the compositions of the distillate and bottom streams (which are challenging to measure) from other more easily measured variables, such as temperatures at different trays of the column. Models that can be used in this regard are called inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least square (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction accuracy of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction ability of these models. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and filtering. The idea behind the IMSLVR modeling algorithm is to filter the processdata at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using two examples, one using synthetic data and the other using simulated distillation column data. Both examples clearly demonstrate the effectiveness of the IMSLVR algorithm.
At present, the resource modeling of workflow net (WF-net) are too simple to describe complex workflow. Thus, its application is not satisfying in practical use. Based on the analysis of resource management problems, ...
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ISBN:
(纸本)9780387759012
At present, the resource modeling of workflow net (WF-net) are too simple to describe complex workflow. Thus, its application is not satisfying in practical use. Based on the analysis of resource management problems, unified resources management which gets the identical resource request and release process is proposed. The method of identical resource-modeling reduces complexity of WF-net model. Based on colored Petri net, this paper proposes colored WF-net, which is easy to control and computerize. Furthermore, the design of unified resources manage is carried on using the Agent technology, and a method that transforms process definition of WF-net into abstract structure of Agent is produced. By using the appliance character of Agent technology, the method also provides feasibility for unified resources management.
Hidden Markov models (HMM) have recently risen as a key generative machine learning approach for time series data study and analysis. While early works focused only on applying HMMs for speech recognition, HMMs are no...
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ISBN:
(纸本)9781728185262
Hidden Markov models (HMM) have recently risen as a key generative machine learning approach for time series data study and analysis. While early works focused only on applying HMMs for speech recognition, HMMs are now prominent in various fields such as stock market forecasting, video classification, and genomics. In this paper, we develop a Maximum A Posteriori (MAP) framework for learning the Dirichlet and Beta-Liouville HMMs that have been proposed recently as an efficient way for modeling sequential proportional data. In contrast to the conventional Baum Welch algorithm, commonly used for learning HMMs, the proposed algorithm places priors for the learning of the desired parameters;hence, regularizing the estimation process. We validate our proposed approach on two challenging real applications;namely, dynamic texture classification and infrared action recognition.
Electro Discharge Machine (EDM) is the commonest untraditional method of production for forming metals and the Non-Oxide ceramics. The increase of smoothness, the increase of the remove of filings, and also the decrea...
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ISBN:
(纸本)9780819490254
Electro Discharge Machine (EDM) is the commonest untraditional method of production for forming metals and the Non-Oxide ceramics. The increase of smoothness, the increase of the remove of filings, and also the decrease of proportional erosion tool has an important role in this machining. That is directly related to the choosing of input parameters. The complicated and non-linear nature of EDM has made the process impossible with usual and classic method. So far, some methods have been used based on intelligence to optimize this process. At the top of them we can mention artificial neural network that has modelled the process as a black box. The problem of this kind of machining is seen when a workpiece is composited of the collection of carbon-based materials such as silicon carbide. In this article, besides using the new method of mono-pulse technical of EDM, we design a fuzzy neural network and model it. Then the genetic algorithm is used to find the optimal inputs of machine. In our research, workpiece is a Non-Oxide metal called silicon carbide. That makes the controlprocess more difficult. At last, the results are compared with the previous methods.
Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling, which has been limited mainly to ...
Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling, which has been limited mainly to modeling the control and coordination of activities, i.e. the control flow perspective. However, given a workflow specification that is flawless from the control flow perspective, errors can still occur due to incorrect dataflow specification, which is referred to as dataflow anomalies. Currently, there are no sufficient formalisms for discovering and preventing dataflow anomalies in a workflow specification. Therefore, the goal of this dissertation is to develop formal methods for automatically detecting dataflow anomalies from a given workflow model and a rigorous approach for workflow design, which can help avoid dataflow anomalies during the design stage. In this dissertation, we first propose a formal approach for dataflow verification, which can detect dataflow anomalies such as missing data, redundant data, and potential data conflicts. In addition, we propose to use the dataflow matrix, a two-dimension table showing the operations each activity has on each data item, as a way to specify dataflow in workflows. We believe that our dataflow verification framework has added more analytical rigor to business process management by enabling systematic elimination of dataflow errors. We then propose a formal dependency-analysis-based approach for workflow design. A new concept called "activity relations" and a matrix-based analytical procedure are developed to enable the derivation of workflow models in a precise and rigorous manner. Moreover, we decouple the correctness issue from the efficiency issue as a way to reduce the complexity of workflow design and apply the concept of inline blocks to further simplify the procedure. These novel techniques make it easier to handle complex and unstructured workflow models, including overlapping patterns. In add
The article presents the results of clustering the regions of the Russian Federation by the level of population health and the environment. The clustering solution is performed by unsupervised machine learning methods...
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The article describes the thermogasdynamic model of small-scale gas turbine engine. This model takes the engine size into account during the efficiency assessment. The engine size is defined by the value of mass flow ...
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ISBN:
(纸本)9781509065301
The article describes the thermogasdynamic model of small-scale gas turbine engine. This model takes the engine size into account during the efficiency assessment. The engine size is defined by the value of mass flow rate corrected by parameters at the exit of the compressor. The important features of working process of small-scale gas turbine engines are the increased hydraulic losses and decreased turbomachinery efficiency due to the increasing of a boundary layer relative thickness and relative tip clearances. This article describes the mathematical models that provide the corrected values of turbomachinery and combustion chamber efficiency ratios. This approach provides more adequate results of working process parameters optimization and may be used for conceptual designing of micro gas turbine engines.
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