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

Statistical Process Routing Optimization in the Product Line...

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IEEE International conference on Smart Grid Communications (SmartGridComm)

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

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.

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Mitigating Climate Change: Utilizing Global Warming Through ARIMA Modelling

Mitigating Climate Change: Utilizing Global Warming Through ...

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data, Computation and Communication (ICDCC), International conference on

作者： Sayantika Laskar Computer Science and Engineering VIT Bhopal University Bhopal India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Climate change and global warming are some surging subjects characterized by unprecedented temperature increases in temperature, raining patterns and rising sea levels, contributed largely by human activities such as deforestation and burning fossils, etc. This paper concentrates on comprehensive dataset including parameters such as global temperatures on land by city, state, country, etc. collected from the internet and processed to predict and augment the future forecasting of the climate in the upcoming years. The use of time-forecasting algorithms has been used for the analysis and prediction process. The dataset used is a time series dataset and thus the use of ARIMA (Autoregressive Integrated Moving Average) modelling technique for an accurate forecast. The paper has undergone substantial research and studies to bring the finest results and assist humans towards a better future. Additionally, it provides insights about possible mediation and preventive measures, contributing to climate control awareness. Artificial Intelligence has made enormous changes in the field of nature conservation and is tending to contribute more in the upcoming future. This paper deals an aspect where we can use AI and its various tools in order to analyze the various aspects of global warming and find solutions for the upcoming problems.

关键词： Renewable energy sources Biological system modeling Prevention and mitigation Time series analysis Global warming Land surface temperature Forecasting Ocean temperature Water resources Meteorology

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Dynamic control of data-Intensive Services Over Edge Computing Networks

Dynamic Control of Data-Intensive Services Over Edge Computi...

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GLOBECOM 2022 - 2022 IEEE Global Communications conference

作者： Yang Cai Jaime Llorca Antonia M. Tulino Andreas F. Molisch University of Southern California CA USA New York University NY USA Universityà degli Studi di Napoli Federico II Naples Italy

ISBN: (纸本)9781665435413

Next-generation distributed computing networks (e.g., edge and fog computing) enable the efficient delivery of delay-sensitive, compute-intensive applications by facilitating access to computation resources in close proximity to end users. Many of these applications (e.g., augmented/virtual reality) are also data-intensive: in addition to user-specific (live) data streams, they require access to shared (static) digital objects (e.g., image database) to complete the required processing tasks. When required objects are not available at the servers hosting the associated service functions, they must be fetched from other edge locations, incurring additional communication cost and latency. In such settings, overall service delivery performance shall benefit from jointly optimized decisions around (i) routing paths and processing locations for live data streams, together with (ii) cache selection and distribution paths for associated digital objects. In this paper, we address the problem of dynamic control of data-intensive services over edge cloud networks. We characterize the network stability region and design the first throughput-optimal control policy that coordinates processing and routing decisions for both live and static data-streams. Numerical results demonstrate the superior performance (e.g., throughput, delay, and resource consumption) obtained via the novel multi-pipeline flow control mechanism of the proposed policy, compared with state-of-the-art algorithms that lack integrated stream processing and data distribution control.

关键词： Image edge detection Computational modeling process control Routing Throughput Stability analysis Task analysis

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Whole-slide Classification of H&E-stained Cervix Uteri Tissue using Deep Neural Networks 14

Whole-slide Classification of H&E-stained Cervix Uteri Tissu...

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14th International conference on Knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th International Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2022

作者： Idlahcen, Ferdaous Mboukou, Pierjos Francis Colere Zerouaoui, Hasnae Idri, Ali Modeling Simulation & Data Analysis - MSDA Mohammed VI Polytechnic University - UM6P Ben Guerir43150 Morocco Software Project Management Research Team ENSIAS Mohammed V University - UM5 Rabat10000 Morocco

ISBN: (纸本)9789897586149

Cervical cancer (CxCa) is heavily swerved toward low- and middle-income countries (LMICs). Without prompt actions, the burden is anticipated to worsen by 50% from 2020 to 2040 - nearly 90% of deaths to occur in sub-Saharan Africa (SSA). Yet, uterine cervix neoplasms are readily avoidable due to a protracted latent cancer period. As it stands, deep learning (DL) is a potent solution for enhancing the early detection of cervical cancer. This work assesses and compares the performance of seven end-to-end learning architectures to automatically recognize cervical lesions and carcinoma histotypes upon hematoxylin and eosin (H&E)stained pathology images. Pre-trained VGG16, VGG19, InceptionV3, ResNet50, MobileNetV2, InceptionResNetV2, and DenseNet201 were the implemented deep convolutional neural networks (dCNNs) throughout the present empirical analysis. Experiments are conducted on two datasets: (i) Mendeley liquid-based cytology (LBC) and (ii) The Cancer Genome Atlas (TCGA) Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma diagnostic slides. All tests were validated under a 5-fold cross-validation, with four key metrics, Scott-Knott (SK), and Borda count schemes. Both pathology data appear to promote InceptionV3 and DenseNet201. Yet, while VGG16 is a weak-performing approach for liquid-based cytology, it evinces promise in histopathology yielding 99.33% accuracy, 98.85% precision, 99.83% recall, and 99.34% F-measure. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Pathology

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Fluorescein videoangiography data analysis protocol for mapping retinal vascular permeability in humans 33

Fluorescein videoangiography data analysis protocol for mapp...

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conference on Ophthalmic Technologies XXXiiI

作者： Vavrek, Sarah Kayaalp-Nalbant, Elif Konopek, Nicholas Bou-Ghanem, Ghazi Fawzi, Amani A. Mieler, William F. Kang-Mieler, Jennifer J. Tichauer, Kenneth M. Illinois Inst Technol Biomed Engn Chicago IL 60616 USA Northwestern Univ Feinberg Sch Med Dept Ophthalmol Chicago IL 60611 USA Univ Illinois Illinois Eye & Ear Infirm Ophthalmol & Visual Sci Chicago IL 60607 USA Stevens Inst Technol Dept Biomed Engn Hoboken NJ 07030 USA

ISBN: (纸本)9781510658257;9781510658264

Fluorescein video angiographies (FVAs) are a diagnostic tool for eye diseases, such as diabetic retinopathy (DR). Currently, kinetic tracer model methods based on indicator-dilutions theory use FVAs to extract biomarkers (e.g., volumetric blood flow and retinal vascular permeability) via pixel mapping using two-step non-linear least square fitting. Prior to biomarker extraction, the FVAs must attain optimal quality. The objective of this research is to create a program to remove all frames experiencing signal drops (causes include blinking, squinting, and head movement). 15 FVAs (6 healthy control subjects, 6 diabetes mellitus no DR (DMnoDR) subjects, and 3 mild non-proliferative DR (NPDR) subjects) were analyzed for low quality frames. The average signal of each frame was analyzed as top, middle, and bottom thirds. The frame with maximum average signal up to the final frame of a created "Gold Standard" was compared with the raw AVI's frame with maximum average signal and subsequent frames. All frames before maximum average signal and any remaining frames were compared with the previous good-quality raw frame to determine if the frame of interest was of good quality. All remaining frames were subsequently re-evaluated and flagged if they had a local minimum prominence of 10% of the maximum average signal. The flagged frames', as well as former and subsequent frames', quality were subjectively determined. The AVI quality was subsequently tested via pre-DTKM processing and biomarker extraction via DTKM methods. Results displayed that the semi-automated frame removal process provides sufficient quality AVIs.

关键词： retinal vascular permeability kinetic tracers kinetic tracer modeling fluorescein video angiography diabetic retinopathy retina

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Generative manifold learning thermography for non-destructive evaluation of defects in composite materials 10

Generative manifold learning thermography for non-destructiv...

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10th IEEE data Driven control and Learning Systems conference, DDCLS 2021

作者： Liu, Kaixin Fan, Yu Zhang, Kuan Yang, Jianguo Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Suzhou Big Vision Medical Imaging Technology Company Suzhou215000 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan Institute of Innovation Research Zhejiang University of Technology Shengzhou Shengzhou312400 China

ISBN: (纸本)9781665424233

In the non-destructive evaluation of infrared thermography, the thermographic data modeling and analysis steps play an important role in improving the inspection results. However, thermal image analysis still faces challenges such as a small number of informative images and difficulty in extracting defect features. In this work, a novel generative manifold learning thermography (GMLT) method for defect detection of composite materials is proposed. In detail, the spectral normalization generative adversarial network is used as a data augmentation strategy to generate more informative thermal images. Sequentially, the MLT-based thermographic data analysis method is adopted to extract and visualize defects in the thermal images. Experiments on carbon fiber reinforced polymers verify the effectiveness and advantages of the proposed method. Key Words: non-destructive evaluation, generative adversarial network, manifold learning, thermographic data analysis, carbon fiber reinforced polymer © 2021 IEEE.

关键词： Defects

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Battery State of Health Estimation for Real-world Vehicles Based on Ensemble Learning

Battery State of Health Estimation for Real-world Vehicles B...

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IEEE PES Asia-Pacific Power and Energy Engineering conference (APPEEC)

作者： Haoyu Wang Hongwen He Pei Wang Yiteng Geng Xuncheng Guo National Engineering Laboratory for Electric Vehicles National Key Laboratory of Advanced Vehicle Integration and Control Beijing Institute of Technology Beijing China

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

Lithium-ion batteries (LIBs) are important energy storage components for new energy vehicles. Accurately estimating the State of Health (SOH) of LIBs ensures the safe and reliable operation of the power battery. Addressing the challenges of poor data quality, intricate sampling information, and incomplete battery cycles in real-world vehicle data, this paper proposes a pioneering SOH estimation method based on Light Gradient Boost Machine (LGBM) ensemble learning during charging process. A data preprocessing and operating condition reconstruction method is designed to streamline battery operating conditions and enhance overall data quality. The charging segments are extracted, and an aging features extraction method suitable for real-world vehicle data is proposed as well as high-value health indicators are selected using correlation analysis. The Bayesian optimization algorithm is employed to efficiently search for the optimal hyperparameters of the model, and comparisons and validations with other modeling algorithms and optimization methods and the estimation results on different vehicles are also carried out. The results show that LGBM method with Bayesian optimization algorithm achieves an overall superior estimation performance with the mean absolute percentage error limited within 2.71 % and exceptional computational efficiency.

关键词： Lithium-ion batteries Correlation data integrity data preprocessing Estimation Predictive models Feature extraction Bayes methods Ensemble learning data mining

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Design and Management of Web Game Interactive Application Platform Based on RETAIN Model

Design and Management of Web Game Interactive Application Pl...

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Big data, Information and Computer Network (BDICN), International conference on

作者： Kaihua Guo Qingdao Weiming School Qingdao China

In order to improve the interactivity capability of web game platform, so as to realize the management and real-time maintenance of game application environment, this paper researches on the design and management method of web game interactive application platform based on RETAIN model. According to the RETAIN model design scheme, this paper studies the interactive characteristics of web games, determines the presentation form of interactive elements, and completes the analysis of the interactive characteristics of web games. According to the modular application host layout form, judge the processing ability of the interactive server equipment to the web link protocol, and complete the design of the web game interactive application platform. Using the joint periodic management mode, complete the timely maintenance of the server and interactive application equipment, and then develop the management and later operation and maintenance plan of the web game interactive application platform by verifying the user objects.

关键词： Protocols Computational modeling Web pages process control Games Maintenance engineering data models

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Searching for Deviations in Trading Systems: Combining control-Flow and data Perspectives 6th

Searching for Deviations in Trading Systems: Combining Con...

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6th International conference on Software Testing, Machine Learning and Complex process analysis, TMPA 2021

作者： Carrasquel, Julio C. Lomazova, Irina A. HSE University Myasnitskaya ul. 20 Moscow101000 Russia

ISBN: (纸本)9783031504228

Trading systems are software platforms that support the exchange of securities (e.g., company shares) between participants. In this paper, we present a method to search for deviations in trading systems by checking conformance between colored Petri nets and event logs. Colored Petri nets (CPNs) are an extension of Petri nets, a formalism for modeling of distributed systems. CPNs allow us to describe an expected causal ordering between system activities and how data attributes of domain-related objects (e.g., orders to trade) must be transformed. Event logs consist of traces corresponding to runs of a real system. By comparing CPNs and event logs, different types of deviations can be detected. Using this method, we report the validation of a real-life trading system. © 2024, Springer Nature Switzerland AG.

关键词： Petri nets

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Softer is Better: Tweaking Quantum Dropout to Enhance Quantum Neural Network Trainability

Softer is Better: Tweaking Quantum Dropout to Enhance Quantu...

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Quantum Communications, Networking, and Computing (QCNC), International conference on

作者： Daniele Lizzio Bosco Riccardo Romanello Giuseppe Serra Carla Piazza Department of Mathematics Computer Science and Physics University of Udine Udine Italy Department of Biology University of Naples Federico II Naples Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Quantum Machine Learning (QML) has been acknowledged for its transformative potential in enhancing computational capabilities beyond classical approaches, with Quantum Neural Networks (QNNs) emerging as one of the most promising models. Despite the advancements, the adaptation of effective optimization techniques such as batch normalization or regularization, which are well-established in classical Neural Networks, remains an ongoing challenge for *** adaptation is crucial, especially when considering the implementation of techniques like quantum dropout, which, although inspired by its classical counterpart to reduce the risk of overfitting by "deleting" certain model components during the forward step, exhibits distinct behaviours in a quantum context. This study introduces a novel approach to modulate the impact of dropout in QNNs through a technique we term Soft Dropout. Our method introduces a parameterized softening mechanism for gate eliminations, enabling a more nuanced control over the dropout process, thereby mitigating its adverse effects on the network’s learning capacity, and reducing at the same time the risk of *** experimental analysis demonstrates that softening current quantum dropout consistently enhances model performance across a spectrum of configurations. This improvement is attributed to the intrinsic properties of quantum gates, which allow for a gradual adjustment of their impact on the quantum circuit up to the identity operation. The results highlight the importance of adapting classical machine learning techniques to the quantum context, considering the unique computational model of quantum processing, potentially accelerating the adoption of QML in real-world problems.

关键词： Hands Adaptation models Neural networks Logic gates Hardware Softening Quantum circuit Integrated circuit modeling Optimization Context modeling

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