The work revealed that the destruction process occurs in three stages. Each stage is characterized by its own kinetic model. The values of the pre-exponential factor and the activation energy of the reaction rate cons...
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ISBN:
(数字)9798331532178
ISBN:
(纸本)9798331532185
The work revealed that the destruction process occurs in three stages. Each stage is characterized by its own kinetic model. The values of the pre-exponential factor and the activation energy of the reaction rate constant were obtained. The calculation according to this method with the found values of the reaction rate constants showed a fairly good match with the experimental data.
The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self-attention has enjoyed major success, it notably treats all qu...
This research enhanced the Discrete Logarithm Problem-Based (DLP-based) Algorithm, enabling it to utilize two or more security keys in the encryption process. The enhancement enabled the Algorithm to inject multiple h...
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A Digital Twin (Applied Twin™) is a computational model that represents a physical asset such as a process chamber, evolving over time to reflect its structure, behavior, and context. This model treats the asset-twin ...
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ISBN:
(数字)9798331531850
ISBN:
(纸本)9798331531867
A Digital Twin (Applied Twin™) is a computational model that represents a physical asset such as a process chamber, evolving over time to reflect its structure, behavior, and context. This model treats the asset-twin system as a set of coupled dynamical systems that evolve over time, interacting through observed data and control inputs. [1] They are an evolution of modeling & simulation. The Digital Twin (DT) can be useful in integration, testing, monitoring, and maintenance. Eventually it can be utilized as an early and ad hoc fast learning tool for systems and processes. In semiconductor manufacturing, DTs have found their way in their early rudimentary form as control or predictive applications such as for production control applications like planning, scheduling, and dispatching and for equipment and processcontrol applications like, Run-to-Run (Applied SmartFactory™ Run-2-Run) (R2R), Virtual Metrology, and predictive maintenance. Through this paper we explore the idea of idea of engineering DT and R2R algorithms with intersecting system manipulated variables or inputs between the two systems.
The use of face recognition technology can greatly improve the effectiveness of the management of college students’ apartments in view of the current situation that there are many security risks in school apartments,...
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The propagation of converter-interfaced generation (CIG) is drawing attention, due to the issues elicited by their large-scale application, and also their potential to advance more complex operational modes. In such c...
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Early detection and characterization of anomalous events during drilling operations are critical to avoid costly downtime and prevent hazardous events, such as a stuck pipe or a well control event. A key aspect of rea...
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ISBN:
(纸本)9781613999929
Early detection and characterization of anomalous events during drilling operations are critical to avoid costly downtime and prevent hazardous events, such as a stuck pipe or a well control event. A key aspect of real-time drilling dataanalysis is the capability to make precise predictions of specific drilling parameters based on past time series information. The ideal models should be able to deal with multivariate time series and perform multi-step predictions. The recurrent neural network with a long short-term memory (LSTM) architecture is capable of the task, however, given that drilling is a long process with high data sampling frequency, LSTMs may face challenges with ultra-long-term memory. The transformer-based deep learning model has demonstrated its superior ability in natural language processing and time series analysis. The self-attention mechanism enables it to capture extremely long-term memory. In this paper, transformer-based deep learning models have been developed and applied to real-time drilling data prediction. It comprises an encoder and decoder module, along with a multi-head attention module. The model takes in multivariate real-time drilling data as input and predicts a univariate parameter in advance for multiple time steps. The proposed model is applied to the Volve field data to predict real-time drilling parameters such as mud pit volume, surface torque, and standpipe pressure. The predicted results are observed and evaluated. The predictions of the proposed models are in good agreement with the ground truth data. Four Transformer-based predictive models demonstrate their applicability to forecast real-time drilling data of different lengths. Transformer models utilizing non-stationary attention exhibit superior prediction accuracy in the context of drilling data prediction. This study provides guidance on how to implement and apply transformer-based deep learning models applied to drilling dataanalysis tasks, with a specific focus o
In order to explore the efficiency of Taiwan's Exchange Traded Funds, we apply a three-stage network data envelopment analysis model to assessing overall and stage-level performance. These empirical results reveal...
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In order to explore the efficiency of Taiwan's Exchange Traded Funds, we apply a three-stage network data envelopment analysis model to assessing overall and stage-level performance. These empirical results reveal that good overall performance, generally, may not suggest good allied process performance or good portfolio management performance. In the ETF performance appraisal literature, the focus is predominantly on the fund portfolio with main considerations being risks, returns, and costs. Our performance appraisal approach is beneficial to ETF management because findings are based on models that accommodate multiple measures of performance and in a comprehensive network representation of the overall fund management process. Out of the 87 ETFs considered in our empirical analysis of Taiwan ETFs, we find the top 10 best performers ETFs in terms of overall efficiency, each of these ETFs is either a bond ETF or an ETF that shortens (or bets against) the financials space. The portfolio managers can use the source of ETF's inefficiency revealed by three-stage network DEA to identify which ETFs that they could emulate in order to achieve efficiency in the future.
In recent years, organizations are putting an increasing emphasis on anomaly detection. Anomalies in business processes can be an indicator of system faults, inefficiencies, or even fraudulent activities. In this pape...
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ISBN:
(纸本)9783031278143;9783031278150
In recent years, organizations are putting an increasing emphasis on anomaly detection. Anomalies in business processes can be an indicator of system faults, inefficiencies, or even fraudulent activities. In this paper we introduce an approach for anomaly detection. Our approach considers different perspectives of a business process such as control flow, data and privacy aspects ***, it is able to detect complex anomalies in business processes like spurious dataprocessing and misusage of authorizations. The approach has been implemented in the open source ProM framework and its applicability was evaluated through a real-life dataset from a financial organization. The experiment implies that in addition to detecting anomalies of each aspect, our approach can detect more complex anomalies which relate to multiple perspectives of a business process.
Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high i...
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