Aim To research the application of Petri net's theoretical method in the concurrent asynchronous communications control, find out the detect arithmetic of system's deadlock, and analyze the system's Iivene...
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In addressing the insufficiencies of feature insertion, inaccurate positioning, and incompatible feature sizes in data augmentation algorithms based on deep learning for detecting microscopic defects on printed circui...
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In addressing the insufficiencies of feature insertion, inaccurate positioning, and incompatible feature sizes in data augmentation algorithms based on deep learning for detecting microscopic defects on printed circuit boards (PCBs), this paper proposes a novel approach incorporating multiple strategies for small target alignment insertion. First, traditional linear feature extraction methods are transformed into a multiscale comprehensive analysisprocess. Subsequently, point-to-point matching calculations are converted into region-wise traversals to enhance accuracy and constrain the matching region. Next, geometric correspondences are determined through the computation of a transfer matrix, effectively eliminating perspective distortions. Finally, by constructing a top-down pyramid optical flow module, size limitations are overcome while enhancing features of small target defects. Experimental results demonstrate that this method significantly improves the recognition accuracy of the network model for small target defects on PCB surfaces.
作者：
Manley, JHDirector
Manufacturing Systems Engineering Program Professor of Industrial Engineering 1044 Benedum Hall University of Pittsburgh Pittsburgh Pennsylvania 15261 USA
A three-phase information system analysis and design methodology is being used to continuously improve enterprise information systems as part of a six-step annual business improvement process. Following senior managem...
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A three-phase information system analysis and design methodology is being used to continuously improve enterprise information systems as part of a six-step annual business improvement process. Following senior management's strategic decisions on next year's product and/or service portfolio content, interacting financial, management, engineering, and quality improvement processes are analyzed to determine their output product and/or service quality and timeliness. Concurrently, facilities, equipment, and personnel resources required for individual processes are inspected for possible immediate or future improvement. Throughout these analyses minimum essential information (MEI) requirements are derived using the Object Transformation process Model (OTPM). Individual OTPM models are linked to help identify all pertinent data sources, information destinations, and timing requirements. The linked OTPM models are mapped onto an Embedded Computer System (ECS) model that defines a physical architecture for improving telecommunication paths between all humans, machines and embedded computers that are component parts of the integrated processes. This approach yields comprehensive information system logical and physical architectural models that can recursively guide high-leverage enterprise-wide improvement projects over succeeding fiscal years.
processes are complex phenomena that emerge from the interplay of human actors, materials, data, and machines. process science develops effective methods and techniques for studying and improving processes. The BPM fi...
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ISBN:
(纸本)9783031161032;9783031161025
processes are complex phenomena that emerge from the interplay of human actors, materials, data, and machines. process science develops effective methods and techniques for studying and improving processes. The BPM field has developed mature methods and techniques for studying and improving process executions from the control-flow perspective, and the limitations of control-flow focused thinking are well-known. Current research explores concepts from related disciplines to study behavioral phenomena "beyond" control-flow. However, it remains challenging to relate models and concepts of other behavioral phenomena to the dominant control-flow oriented paradigm. This tutorial introduces several recently developed simple models that naturally describe behavior beyond control-flow, but are inherently compatible with control-flow oriented thinking. We discuss the Performance Spectrum to study performance patterns and their propagation over time, Event Knowledge Graphs to study networks of behavior over data objects and actors, and Proclets as a formal model for reasoning over control-flow, data object, queue and actor behavior. For each model, we discuss which phenomena can be studied, which insights can be gained, which tools are available, and to which other fields they relate.
Saccharomyces cerevisiae is a species of yeast with a long tradition in human history and a growing demand in industry and research. The yeast cells are produced in a series of fed batch reactors which are fed with ox...
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Saccharomyces cerevisiae is a species of yeast with a long tradition in human history and a growing demand in industry and research. The yeast cells are produced in a series of fed batch reactors which are fed with oxygen and glucose as the main carbon source. One problem during the production process is that the cell culture can switch to the undesired production of ethanol leading to a lost batch. For improving the production process a suitable modeling and control strategy is needed that should cover the switch to ethanol production and should be able to describe the growth of the cell culture so that the operating policies can be optimized. This work presents a novel method that uses dynamic flux balance analysis to derive a reduced metabolic model from a full biochemical stoichiometric network which is then used within a model predictive control. The reduced metabolic model covers the gene regulation by using the redox metabolites as key regulators. It is shown that this modeling approach is very flexible and can be used to control and to monitor the process. (C) 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
The key performance indicators in Chemical Mechanical Planarization (CMP) processes are usually assessed by measuring the material removal rate (MRR) and Within-Wafer-Nonuniformity (WIWNU), which are vitally dependent...
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ISBN:
(纸本)9780791850749
The key performance indicators in Chemical Mechanical Planarization (CMP) processes are usually assessed by measuring the material removal rate (MRR) and Within-Wafer-Nonuniformity (WIWNU), which are vitally dependent on the processing variables including down pressure, wafer rotation, polishing pad rotation, polishing table rotation, slurry flow, and the condition of the polishing pad etc. MRR is critical to the WIWNU also since MRR can infer the end-point in the polishing process. In this study, empirical approaches were conducted to model the MRR with the production CMP settings. With the collected data from real semiconductor manufacturing processes, correlation and principle component analysis (PCA) were conducted to select the features mostly related to the CMP processes, then neural network (NN) and adaptive neuro fuzzy inference system (ANFIS) based models were proposed to understand processing variables in CMP process and estimate the MRR. The NN and ANFIS models were compared on the performance metrics of 1) mean square error (MSE), and determination coefficient (R-2) based on bootstrap. The bootstrap based evaluation shows that NN achieved a MSE of 9.68e03 with the R-2 value of 0.81 in the training stage and MSE of 9.59e3 with the R-2 value of 0.81 in the validation stage;ANFIS achieved a MSE of 126.24 with the R-2 value of 0.9102 in the training stage and MSE of 6.17e4 with the R-2 value of 0.3133 in the validation stage. The empirical models are promising to be integrated with the data-driven based control of CMP processes.
data-driven feedforward learning enables high performance for industrial motion systems based on measured data from previous motion tasks. The key aspect herein is the chosen feedforward parametrization, which should ...
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ISBN:
(纸本)9781538654286
data-driven feedforward learning enables high performance for industrial motion systems based on measured data from previous motion tasks. The key aspect herein is the chosen feedforward parametrization, which should parsimoniously model the inverse system. At present, high performance comes at the cost of parametrizations that are nonlinear in the parameters and consequences thereof. A linear parametrization is proposed that enables parsimonious modeling of inverse systems for feedforward through the use of non-causal rational orthonormal basis functions. The benefits of the proposed parametrization are experimentally demonstrated on an industrial printer, including pre-actuation and cyclic pole repetition.
Telecom industry is a typical data-intensive industry where data mining applications will enable a good guidance on marketing strategies, and the cluster analysis can be used in customer segmentation. The paper makes ...
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This paper proposes a new process monitoring method using dynamic independent component analysis (ICA). ICA is a recently developed technique to extract the hidden factors that underlie sets of measurements, whereas p...
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This paper proposes a new process monitoring method using dynamic independent component analysis (ICA). ICA is a recently developed technique to extract the hidden factors that underlie sets of measurements, whereas principal component analysis (PCA) is a dimensionality reduction technique in terms of capturing the variance of the data. Its goal is to find a linear representation of non-Gaussian data so that the components are statistically independent. PCA aims at finding PCs that are uncorrelated and are linear combinations of the observed variables, while ICA is designed to separate the ICs that are independent and constitute the observed variables. The dynamic ICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables. The dynamic monitoring method was applied to detect and monitor disturbances in a full-scale biological wastewater treatment (WWTP), which is characterized by a variety of dynamic and non-Gaussian characteristics. The dynamic ICA method showed more powerful monitoring performance on a WWTP application than the dynamic PCA method since it can extract source signals which are independent of time and cross-correlation of variables.
The characterization of lightning occurrence by Non-Homogeneous Poisson process with multiple cyclic effects was first proposed on a ICLP 2012 paper. On that paper was presented an overview of the main reasons for pro...
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ISBN:
(纸本)9781479935444
The characterization of lightning occurrence by Non-Homogeneous Poisson process with multiple cyclic effects was first proposed on a ICLP 2012 paper. On that paper was presented an overview of the main reasons for proposing this approach, emphasizing the discrete nature of a lightning event seen as an epoch of a stochastic counting process. It was only presented a preliminary analysis of lightning data sets based on a multi-resolution analysis methodology that makes it feasible to show some potentials application on lighting protection analysis and atmospheric research. This paper is focused on the performance evaluation of a complete analysis tending to show its potential uses for descriptive and simulation purposes. The evaluation results show a good quality of representation supported on performance measures, and reduced deviations errors on repeated simulations. The fitted model is used to introduce the idea of Lightning Occurrence Cycle associated to each resolution of analysis and based on it are outlined some possible applications of this concept on risk analysis and atmospheric research.
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