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Statistical process Routing Optimization in the Product Line for Industrial Internet of Things 14

Statistical Process Routing Optimization in the Product Line...

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14th IEEE International conference on Communications, control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Ahakonye, Love Allen Chijioke Nwakanma, Cosmas Ifeanyi Lee, Jae Min Kim, Dong-Seong Jun, Taesoo Kumoh National Institute of Technology It Convergence Engineering Gumi Korea Republic of Kumoh National Institute of Technology Ict Convergence Research Center Gumi Korea Republic of

ISBN: (纸本)9781665455541

Internet-of-Things (IoT) is an emerging paradigm poised to drive success in the manufacturing execution system (MES). IoT focuses on improving the ability to connect numerous devices and processes, enhancing production capabilities, and providing process optimization and local data processing capabilities. This study focuses on applying statistical process routing optimization to enhance the production lines of the MES. The aim is to minimize wastage and improve the efficiency of the manufacturing process by optimizing the routing of processes of various production units. The study employs statistical analysis and mathematical modeling to identify the critical bottlenecks in the production line and propose optimization solutions. The study demonstrates the proposed approach's effectiveness in identifying the capabilities of various production units and task completion time, demonstrating its trade-off between production units. The findings provide valuable insights for decision-making to improve the choice and performance of production lines for effective resource management. © 2023 IEEE.

关键词： Internet of things

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Early Semiconductor Anomaly Detection Based on Multivariate Time-Series Classification using multilayer Perceptron 10th

Early Semiconductor Anomaly Detection Based on Multivariate ...

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10th IFAC Triennial conference on Manufacturing Modelling, Management and control (MIM)

作者： Mellah, Samia Trardi, Youssef Graton, Guillaume Ananou, Bouchra El Adel, El Mostafa Ouladsine, Mustapha Aix Marseille Univ Univ Toulon CNRS LIS UMR 7020 Ave Escadrille Normandie Niemen F-13397 Marseille 20 France Ecole Cent Marseille Technopole Chaleau Gombert F-13451 Marseille 13 France

This work is focused on the issue of semiconductor anomaly detection during the manufacturing process. It proposes an efficient multivariate time-series fault detection approach aiming to detect wafer anomalies at an early fabrication stage to reduce the yield loss. The raw data consist on eleven (11) multivariate time-series (MTS) measured for 150 seconds and collected during different levels of the fabrication process to describe the wafers status. First of all, the most useful information is extracted from each collected time-series (TS) data to handle the computational complexity of large-scale data processing. For that, three dimensionality reduction techniques, namely: (i) Independent Component analysis (ICA), (ii) Principal Component analysis (PCA), and (iii) Factor analysis (FA) are used for comparison and optimization sake. The aim is to define the better technique allowing to keep only the meaningful information from each time-series. Thereafter, the extracted data is combined to build a new dataset which is used to fit and optimize a multilayer perceptron (MLP) to perform the anomaly detection. The very interesting obtained results show that the proposed approach is promising and could provide a precious decision-making support for abnormal wafer detection in the semiconductor manufacturing process. Copyright (C) 2022 The Authors.

关键词： Anomaly detection data-driven methods Multivariate time-series analysis Multilayer perceptron Semiconductor manufacturing

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Design and Development of Power Failure and Restoration Scene State analysis System Based on AR 2

Design and Development of Power Failure and Restoration Scen...

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2nd International conference on Smart City Challenges and Outcomes for Urban Transformation, SCOUT 2023

作者： Dai, Hao-Qi Wang, Jin-He Fan, Zhong-Kai Liang, Zi-Jian China Southern Power Grid Digital Grid Research Institute Co. Ltd. Guangzhou51000 China

ISBN: (纸本)9798350301373

In the process of state analysis of power failure and restoration scenario, by integrating interdisciplinary research contents such as sensor technology, information and network technology, on the premise of ensuring the safe and stable operation of power grid, the purpose of saving energy and improving resource utilization efficiency is achieved. Focusing on the concepts of business data association fusion and data-driven modeling, this paper designs and develops an AR (Augmented Reality)-based power failure and restoration scenario state analysis system. The system combines traditional relational database and in-memory database, and utilizes the column database HBase and distributed file system HDFS in the big data Hadoop ecosystem to efficiently store unstructured data of supervision business. After receiving the target features sent by the augmented reality engine, the virtual object control module parses the identified information and presents the virtual object on the user screen according to the parsed data request. The test results of the system show that the proposed method can realize the panoramic visual analysis of the power failure and restoration scene state, and master the load state of each distribution node and branch during the operation of the power grid, which provides an effective reference for the optimal operation of the power grid. © 2023 IEEE.

关键词： Augmented reality

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A Comparative analysis of Mechanical Designs for Unmanned Aerial and Underwater Vehicles 11

A Comparative Analysis of Mechanical Designs for Unmanned Ae...

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11th International conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Hassan, Osama Khalid, Mohammad Nadeem Ali, Shaaban Ayadi, Walid Arif, Mohammad EMET. Abu Dhabi Polytechnic Institute of Applied Technology Abu Dhabi United Arab Emirates Sanskriti University Department of Mechanical Engineering Mathura India

ISBN: (纸本)9798350350357

This paper focuses on the comprehensive mechanical design of dual-functionality drones capable of both aerial and underwater operations, seamlessly transitioning between environments. The design process employs SolidWorks, a powerful 3D modeling and simulation software, to achieve precision and efficiency in the development of the drone's mechanical components. The aerial aspect incorporates aerodynamic considerations for optimal flight performance, stability, and maneuverability. The underwater design emphasizes hydrodynamic efficiency, buoyancy control, and water-resistant materials to ensure smooth submersion and navigation beneath the surface. Key features of the mechanical design include modular components for ease of maintenance, robust structural integrity to withstand diverse environmental conditions, and the integration of sensors for data collection both above and below the waterline. SolidWorks simulations were employed to validate and optimize the designs, ensuring reliability and performance in real-world scenarios. This interdisciplinary approach not only highlights the versatility of SolidWorks in addressing complex design challenges but also showcases the potential of drones with dual functionalities for applications in surveillance, environmental monitoring, and exploration. The project contributes to the advancement of autonomous systems capable of operating seamlessly across multiple domains, opening new possibilities for various industries. © 2024 IEEE.

关键词： Antennas

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modeling Intelligent Traffic control System for Urban Agglomeration

Modeling Intelligent Traffic Control System for Urban Agglom...

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International conference on control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Anatoly Pogodaev Vladimir Klyavin Anton Sysoev Galina Borovkova Svetlana Zhikhoreva Department of Applied Mathematics Lipetsk State Technical University Lipetsk Russia Research Institute Lipetsk State Technical University Lipetsk Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

This paper presents the modeling of an intelligent system for traffic control in urban agglomerations. The modeling process is complex and consists of several stages. The first step consists of modeling the capacity of urban transportation network using neural networks. The second step is to evaluate the level of traffic controllability using hierarchical sensitivity analysis. The third step involves classification of the transportation network using neuro- fuzzy classifier. All these stages precede managerial decisions on creation and regulation of traffic flows within the urban network.

关键词： Analytical models Sensitivity analysis Neural networks Transportation Traffic control Mathematical models data models Regulation Safety Intelligent systems

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A process modeling AND SIMULATION STUDY FOR URBAN FOREST WASTE TORREFACTION 31st

A PROCESS MODELING AND SIMULATION STUDY FOR URBAN FOREST WAS...

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31st European Biomass conference and Exhibition, EUBCE 2023

作者： Gonzales, Thiago da Silva de Oliveira Rodrigues, Pedro Paulo Lamas, Giulia Cruz Moreira, Mayara Gabi Barcelo, Rafaela Silva, Simone Monteiro E. Silveira, Edgar A. University of Brasília Mechanical Sciences Graduate Program Laboratory of Energy and Environment Brasília DF 70910-900 Brazil

Torrefaction is a promising thermochemical pretreatment of biomass, aiming to improve its energy properties as a solid biofuel. The torrefied product possesses coal-like properties and optimized characteristics to replace coal in sustainable energy and heat generation. In this context, this work built a biomass torrefaction model, which can provide essential information on pursuing waste-to-energy solutions, i.e., future exergetic analysis. Experimental data from a blend of urban forest waste (UFW) from the forest ecosystem of Brasilia was chosen as input for simulations. The model established within Aspen Plus® software was validated in different scenarios for a torrefaction temperature range of 225–275 °C, demonstrating good agreement with reported experimental data. The torrefaction module was successfully modeled by one RYield reactor, which was used with the two-step kinetic reaction model for the decomposition of biomass. The model was capable of i) presenting in detail the distribution of the products and by-products of the biomass torrefaction process and ii) predicting important fuel properties such as proximate and elemental analyses. Furthermore, energy performance properties and overall energy consumption can be estimated from the model. Depending on process conditions, drying accounted for 73–89% of the total heat demand. © 2023 ETA-Florence Renewable Energies.

关键词： Biomass

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Quality Prediction Method by modeling the Sustained Effects of Irregular process Disturbances

Quality Prediction Method by Modeling the Sustained Effects ...

引用

IEEE International conference on Industrial Engineering and Engineering Management (IEEM)

作者： Xiu, Q. Tanaka, M. Sakata, M. Hitachi Ltd Res & Dev Grp Tokyo Japan

ISBN: (纸本)9781665437714

In manufacturing domain, there are increasing needs for quality control by utilizing various data collected from on-site. Machine failure, equipment component replacement, and other process disturbances are now collected by various sensors. The analysis of these data can help on-site managers to detect product quality drifts and to cope with them quickly and properly. However, the irregular and sparse nature of process disturbances causes prediction accuracy issue and modeling time issue. In this research, we propose a product quality prediction method using a stochastic process to model the irregular disturbances, and make prediction based on dense, regular matrix of sustained effects sampled from the stochastic process for modeling time reduction. As the result of applying the proposed quality prediction method to actual manufacturing data, the MSE (mean squared error) is reduced by 84.6% and the modeling time can be shortened to within 3 hours for daily update. Therefore, it can be estimated that our quality prediction method can help on-site managers to detect quality drifts at early stage and have a better control of product quality.

关键词： data modeling Defect analysis Internet of things Manufacturing data Manufacturing process

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An Improved Hierarchical Fused Fuzzy Deep Neural Network Method for controlled Object Model of Furnace Temperature for MSWI process

An Improved Hierarchical Fused Fuzzy Deep Neural Network Met...

引用

Chinese control and Decision conference, CCDC

作者： Hao Tian Jian Tang Heng Xia Tianzheng Yang Aijun Yan Zhiwei Wu Faculty of Information Technology Beijing University of Technology Beijing China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Addressing the challenge of precisely controlling furnace temperature, this study introduces a modeling strategy based on an enhanced hierarchical fused fuzzy deep neural network (HF-FDNN) model. Initially, an analysis of the control characteristics of furnace temperature identifies key manipulated variables (MVs). Subsequently, we refine the HF-FDNN algorithm within the task-driven layer to develop a model for furnace temperature control. Finally, we validate the effectiveness of proposed method through experimental results derived from real municipal solid waste incineration (MSWI) process data.

关键词： Waste management Solid modeling Waste materials Furnaces Atmospheric modeling process control Artificial neural networks

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Application of Bayesian Spatial Method in Analyzing Tetanus Cases in Central Java Using Binomial-Poisson Model with R 11

Application of Bayesian Spatial Method in Analyzing Tetanus ...

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11th International conference on Computer, control, Informatics and its Applications, IC3INA 2024

作者： I Putu Aditya Brama, P.C.N. Rachmawati, Rofah Nur Khotimah, Purnomo Husnul Parlina, Anne Riswantini, Dianadewi Izzaturrahim, Muhammad Hafizh Natari, Rifani Bhakti Departemnet of Mathematics Indonesia Defence University Bogor Indonesia Research Centre for Data and Information Science National Research and Innovation Agency Bandung Indonesia Regional Research and Development Agency of Jambi Regional Research and Development Agency of Jambi Jambi Indonesia

ISBN: (纸本)9798331542313

This study investigates the spatial distribution of tetanus cases in Central Java, Indonesia, using a Bayesian spatial method with a Binomial-Poisson model. The analysis addresses the challenge of excess zeros in the data, common in rare diseases like tetanus, by incorporating a zero-inflated Poisson distribution. The Bayesian framework utilizes the Besag-York-Mollié (BYM2) model to account for spatial autocorrelation and employs complexity-penalized priors for the latent level process. The study leverages the Integrated Nested Laplace Approximation (INLA) for efficient and accurate inference. Results provide insights into the spatial patterns of tetanus cases in Central Java, potentially informing targeted interventions and public health strategies. The suggested Bayesian mixed models for Tetanus cases in 2022 across 36 districts/cities in Central Java, Indonesia, incorporating explanatory variables from various dimensions, including health, demographics, industry, and education. Significant dimensions with Tetanus cases are predominantly found in demographic and industrial aspects. Through the Deviance Information Criterion (DIC), the Binomial-Poisson mixture distribution is identified as the most suitable for the data, with an excess of zero values at approximately 62.8% and significant extreme observations in specific regions. © 2024 IEEE.

关键词： Poisson distribution

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Drought Prediction based Standardized Precipitation Index Using Multilayer Perceptron Model

Drought Prediction based Standardized Precipitation Index Us...

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2024 International conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Prabowo, Muhammad Agung Soekirno, Santoso Ananda, Naufal Asri, Devina Putri Yulizar, David Adi, Suko Prayitno Martarizal Suhandi, Nazori University of Indonesia Faculty of Mathematics and Natural Science Departement of Physics Depok Indonesia Bandung Institute of Technology Departement of Instrumentation and Control Bandung Indonesia Banten Climatology Station Indonesia Meteorology Climatology and Geophysics Agency South Tangerang Indonesia State College of Meteorology Climatology and Geophysics Tangerang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia

ISBN: (纸本)9798350364101

Drought events have an impact on various sectors such as the economic, agricultural, environmental and social sectors. The impact of drought needs to be minimized by designing a drought prediction system, this is an effort to disseminate early warning information based on climate and hydrological aspects. Drought prediction uses Multi-Layer Perceptron (MLP) algorithm model to predict drought based on Standardized Precipitation Index (SPI) for 1 and 3 months. SPI is one of the drought indices calculated through rainfall analysis. Predictions were made using rainfall data obtained from the FY-4A QPE satellite, then processed into monthly rainfall accumulations for the 2019-2020 period. FY-4A QPE data is corrected based on rainfall observation data at the observation rain gauge. The corrected FY-4A QPE data is used to predict drought using the SPI1 (1 Month) and SPI3 (3 Months) indices. SPI1 shows fluctuations and a very small Nash-Sutcliffe Efficiency (NSE) value of -0.11, while SPI3 shows a model that is able to follow the peak and valley fluctuations of the actual value as well as an increase in the NSE value of 0.65 and a decrease in the Root Mean Square Error (RMSE) value of 1.43 in SPI1 to 0.77 in SPI3. Drought modeling using MLP was successfully implemented and it showed that as the time span increases, the SPI prediction performance gets better. © 2024 IEEE.

关键词： Drought

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