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检索条件"任意字段=Conference on Data Analysis and Modeling for Process Control"

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Security analysis of the Enhanced Cryptographic Algorithm Based on Discrete Logarithm Problem that Uses Multiple Handshakes for Improved Security 11

Security Analysis of the Enhanced Cryptographic Algorithm Ba...

引用

11th IEEE conference on Systems, process and control, ICSPC 2023

作者： Ordoñez, Aris J. Bicol University Computer Science & Information Technology Department Legazpi City Philippines

ISBN: (纸本)9798350340860

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 handshakes into the process as a security method. Tests conducted on the enhanced DLP-based Algorithm showed the accuracy of the process, which was recorded at 100% using 1,000 samples. This study used the randomness test to analyze the Algorithm's security. It also determined if there are established patterns in the process, both for Encryption and Decryption, through observations of the inputs and outputs of the method. The Confusion Test checks if there are conversion patterns between the security key and the Ciphertext used in the process, while the Diffusion Test checks for conversion patterns between the plaintext and the Ciphertext. The level of security of the Enhanced DLP-Based Algorithm, as tested by the confusion test, was HIGH, as exhibited by the confusion value, which is 54 %. The Diffusion test yielded a diffusion value of 54.925%, which also manifested a HIGH level of security. The Strict Avalanche Criterion (SAC) also recorded an Avalanche Effect of 54% and 54.8% for Confusion and Diffusion Test results, respectively. This study found the Enhanced DLP-Based Algorithm reliable based on its SAC value, above the minimum of 50% for all test samples. Experiments and simulations also proved that the Enhanced DLP-Based Algorithm allowed successful implementation in a digital data-sharing scheme that utilizes Multiple Handshakes to ensure security. © 2023 IEEE.

关键词： Cryptography

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Reducing NPT Using a Novel Approach to Real-Time Drilling data analysis

Reducing NPT Using a Novel Approach to Real-Time Drilling Da...

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2023 SPE Annual Technical conference and Exhibition, ATCE 2023

作者： Wang, Junzhe Sajeev, Shyam Kareepadath Ozbayoglu, Evren Baldino, Silvio Liu, Yaxin Jing, Haorong University of Tulsa TulsaOK United States BP HoustonTX United States

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 data analysis 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 data analysis tasks, with a specific focus o

关键词： Long short-term memory

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Implementation & Application of coherent control systems: Digital Twin (high frequency open loop) with R2R (low frequency closed loop)

Implementation & Application of coherent control systems: Di...

引用

IEEE/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Pratik C. Kotcher Kasturi Sarang Miller Allen Siva Dhandapani Automation Products Group Applied Global Services Va USA Metal Deposition Product Semiconductor Product Group Santa Clara Ca USA Business Development Semiconductor Product Group Santa Clara Ca USA

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 process control 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.

关键词： Frequency locked loops Automation Computational modeling process control Production control Semiconductor device manufacture data models Digital twins High frequency Testing

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Detecting Complex Anomalous Behaviors in Business processes: A Multi-perspective Conformance Checking Approach

Detecting Complex Anomalous Behaviors in Business Processes:...

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4th International conference on process Mining (ICPM)

作者： Mehr, Azadeh Sadat Mozafari de Carvalho, Renata M. van Dongen, Boudewijn Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands

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 data processing 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.

关键词： Outlier behavior detection Anomalous behavior data privacy Conformance checking Multi-perspective analysis

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A Collection of Exploratory data analysis Techniques for process Fault Detection in the Tennessee Eastman dataset

A Collection of Exploratory Data Analysis Techniques for Pro...

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Information and Communication Technology (ICICT), International conference on

作者： Sadman Nasif Anika Tasnim Nahid Sanzida Dept. of Chemical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

The Tennessee Eastman process (TEP) is a widely studied industrial benchmark for testing fault detection and diagnosis techniques, given its complexity and the need for reliable fault detection systems in chemical plants. This paper proposes a comprehensive application of exploratory data analysis (EDA) techniques to the TEP dataset to enhance process fault detection and improve understanding of the dataset's features. The proposed methodology involves plotting time series data to capture temporal dynamics and applying Shewhart control charts to monitor process variability. Additionally, deviation atlases are used to detect anomalies across all features, dendrograms are employed to identify hierarchical clusters, and correlation heatmaps visualize interdependencies between process variables. By applying these EDA techniques, patterns and relationships that were less apparent in the raw dataset became identifiable, contributing to a more thorough fault diagnosis. The findings highlight the value of EDA in uncovering hidden insights, which could aid in the development of more robust fault detection algorithms for real-world industrial applications.

关键词： Fault diagnosis data analysis Fault detection Time series analysis process control data visualization control charts Feature extraction Stability analysis Monitoring

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Wind-Solar-BESS Dynamic modeling Applications In Hybrid Plants: An analysis of Electromechanical Stability Support In The Casa Nova System Case 15

Wind-Solar-BESS Dynamic Modeling Applications In Hybrid Plan...

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15th IEEE International conference on Industry Applications, INDUSCON 2023

作者： Filho, Danilo Pinchemel Cardoso Medrado, Ricardo Cerqueira Miranda, Rodrigo Lemos Filho, Dalton França Guedes de Carvalho Filho, Márcio de Melo Filho, José Bione Salvador Brazil Recife Brazil

ISBN: (纸本)9798350314182

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 context, a multitude of strategies for alternative sources hybridization have been explored, concerned with, for example, overall intermittency attenuation or frequency and voltage support. This work evaluates the potential of support control modes for one wind, solar, and BESS hybrid plant, through the combined application of CIG devices' dynamic modeling techniques. Regarding the modeling, DQ0-based controls are derived from the Western Electricity Coordinating Council generic framework, whereas an average (RMS) current source model with implemented phase-locked loop is used for the integration with the AC network (ABC signals being recovered directly from Park-Clarke transformations). The Casa Nova wind complex, in northeastern Brazil, is taken as a reference, with an equivalent system implemented on PSCAD. Although all CIG are treated as grid-following, results indicated that the frequency and voltage support controls enabled the hybrid plan to conveniently improve transient characteristics simply through dynamic modulation of active/reactive power outputs. However, in nominal terms, the improvements were relatively small due to the comparably smaller capacity of the plant. ©2023 IEEE.

关键词： converter-interfaced generation dynamic modeling electromechanical stability hybrid plants

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Distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

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 interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. This paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 IEEE.

关键词： Smart cities Soft sensors Transportation data integration process control Distributed databases data models

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An Improved Bayesian Learner Based on Optimized Kernel Density Estimation and Wasserstein Soft Ensemble Strategy 12

An Improved Bayesian Learner Based on Optimized Kernel Densi...

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12th IEEE International conference on Intelligent data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Ye, Zhiwei Liu, Yuanhu Lv, Jiazhi Liu, Yingying Wu, Zhenwei Bai, Wanfang School of Computer Science Hubei University of Technology China Fiberhome Telecommunication Technologies Co Ltd. China Xining Big Data Service Administration China

ISBN: (纸本)9798350358056

Bayesian classification is a common data analysis and modeling method in data mining. In this paper, an improved ensemble method and the optimized Kernel density estimation used to Bayesian classifier. Unlike the traditional Bayesian classifier in that the conditional possibility density is calculated by an assumed statistical model, our method estimated the possibility value without model assumption, they are obtained by kernel density estimator with optimized window width and the Wasserstein distance is used to solve the problem of how to cause the classifier difference in the integration process. The optimized window parameters are solveded by minimizing the Unbias Cross-Validation (UCV) objective function using Chaos-Particle Swarm Optimization (CPSO). Wasserstein distance is innovational used to evaluate the similarity between possibility distributions and generates weights of base classifiers. The final ensemble outputs are calculated by a soft voting strategy. The experimental results illustrate the effectiveness and improvements of the proposed method in terms of density curve fitting, ensemble ability, and overall classification accuracy. © 2023 IEEE.

关键词： CPSO Ensemble Bayesian classifier Kernel density estimation Wasserstein distance

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Scalable Big data analysis and processing Technology for Intelligent Traffic control

Scalable Big Data Analysis and Processing Technology for Int...

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Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Yu Zheng School of Rail Transit Chongqing Vocational College of Transportation Chongqing China

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The improvement of people's living standards, every family has a private car, and large trucks used in industry are the reason for the continuous increase in the traffic flow, and traffic congestion has become an increasingly serious problem. When traffic congestion becomes an urgent problem that restricts the development of the city, intelligent traffic control becomes a technology that can improve traffic efficiency and traffic safety. When we look at the traffic system from the perspective of big data, we will find that the data involved in the intelligent transportation system is very large, and the traditional method of data analysis and processing is no longer applicable. This research conducts in-depth research on the various data sources involved in the intelligent transportation system, and analyzes the features and scale of these data sources. A distributed computing based scalable big data analysis framework is proposed in order to solve the problem of big data analysis and processing. The framework employs parallel computing and distributed storage technology to process large-scale data efficiently, and to obtain real-time analysis results. The scalability of the proposed scalable big data analysis framework and the data preprocessing methods was verified by experimental evaluation, and the performance was highlighted. The research results show that the big data analysis technology can process and analyze big data generated in ITS, and provide important decision support for intelligent transportation control based on the true results of the analysis.

关键词： data analysis Accuracy Soft sensors Scalability process control Transportation Big data Traffic control Real-time systems Traffic congestion

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Theoretical and simulation study on the vibration modes of shear walls in prefabricated modular construction 3

Theoretical and simulation study on the vibration modes of s...

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2023 3rd International conference on Computational modeling, Simulation and data analysis, CMSDA 2023

作者： Kong, Va Wang, Xiaoguang Wang, Qiong Li, Zhen Chen, Hongbing Wang, Jiang University of Science and Technology Beijing Beijing100083 China Zhong Jian Hai Long Technology Group Co. Ltd Shenzhen518110 China China State Construction International Holdings Group. Ltd Hong Kong

As prefabricated construction advances, the enhanced informatization and the industrialized modular construction method of MIC (Modular integrated construction) shear walls have been widely applied in real projects. MIC shear walls are generally made up of prefabricated wall modules with varying strength grades, using concrete of various ages, and are combined with cast-in-place concrete. Due to the unique construction requirements of these walls, defects such as interfacial delamination and internal voids might occur, arising from factors like concrete shrinkage and creep, complex interfacial structures, inadequate compaction during vibration, and construction process issues. Since these interfacial defects cannot be visually inspected from the exterior, developing non-destructive testing (NDT) methods specifically for detecting such defects in shear walls becomes an urgent necessity. This paper utilizes a combination of theoretical analysis and numerical simulation to explore the vibration modes of MIC shear walls with hidden defects. First, a theoretical model is proposed for the local vibration modes of MIC shear wall shells with consideration of interfacial delamination defects. Then, a numerical simulation model is established based on MIC shear wall construction and defect features to simulate the vibration behavior accurately. Finally, after examining the theoretical and simulated vibration characteristics of MIC shear walls, a significant theoretical foundation is established for identifying interfacial defects in MIC shear walls. © Published under licence by IOP Publishing Ltd.

关键词： Vibration analysis

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