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Sustainable Manufacturing Model with Considering Greenhouse Gas Emission and Screening Process of Imperfect Items Under Stochastic Environment

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International Journal of Applied and Computational Mathematics 2022年第3期8卷 1-21页

作者： Dolai, Madhusudan Manna, Amalesh Kumar Mondal, Shyamal Kumar Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore 721102 India Department of Mathematics C. V. Raman Global University Bhubaneswar 752054 India

In this work, a production model with screening of imperfect product and green house gas emission has been developed. Here, it has been considered that the manufacturing process transfer from in-control situation to out-of-control situation after an uncertain time, which is a random variable. In the out-of-control situation, a part of produced items are imperfect. For this reason, the manufacturer decides to check each item through screening process before sale the perfect items to the market. The screening and production rates are not equal and new type of screening process has been considered here. The imperfect rate depends on the length of out-of-control situation and its random nature. Also, the manufacturer sales the imperfect items by reducing the selling price. The greenhouse gas (GHG) emission tax has been considered here and its rate depends on the production rate. The objective of this study is to find the optimal business and production period, optimal production rate which maximizes the expected average profit of the model. Two numerical examples have been considered to illustrate the feasibility of the model. Finally, the sensitivity analysis have been carried out to get the effect of some important parameters on the optimization problem related to the proposed model. © 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Greenhouse gas emission Imperfect production Price discount Screening

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A Multithreaded Approach to Masking Images used for Animation

A Multithreaded Approach to Masking Images used for Animatio...

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Technology Innovation Management and Engineering Science International Conference (TIMES-iCON)

作者： Veska Gancheva Kalin Belov Ivaylo Georgiev Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria Stephan Angeloff Institute of Microbiology Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (数字)9798350362602

ISBN: (纸本)9798350362619

The current research is focused on designing a multithreaded parallel solution for processing a pair of raster image files that are used to create an animation. In the computational process, a multithreaded model was used when processing arrays of data. The proposed approach is verified by a multithreaded parallel program implementation. The effectiveness of the proposed solution has been verified experimentally by processing an image with dimensions 4320x5400 and a white color mask. The obtained results in terms of execution time show an acceleration of the computational process when increasing the number of threads.

关键词： Image color analysis Instruction sets Computational modeling Parallel processing Innovation management Animation Data models

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Research and Development of Kubernetes Operator for Machine Learning Pipelines

Research and Development of Kubernetes Operator for Machine ...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Trapezin, Andrey Filianin, Ivan ITMO University Faculty of Programming and Infocommunications St. Petersburg Russia ITMO University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia

ISBN: (纸本)9798331511241

The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed to automate the delivery of machine learning models to production by integrating MLflow for model tracking and Seldon Core for model serving. The developed operator allows data scientists to deploy models while maintaining the familiar MLflow environment. The operator's automatic deployment triggers upon tagging models in MLflow, greatly simplifying engineers' tasks and minimizing the need for manual infrastructure configuration. By automating configuration tasks and optimizing deployment workflows, the solution achieves a 40-50% reduction in model time to deployment (TTD) metric compared to manual processes and decreases error rates from 15% to around 3%. The practical relevance of the work is that it simplifies collaboration between data and infrastructure teams by providing a unified deployment framework, resulting in faster, more reliable, and automated integration of machine learning models into an organisation's business processes. © 2025 IEEE.

关键词： Engineering education

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Investment Planning in the Region

Investment Planning in the Region

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Alexander Solomatin Department of Mathematical Methods of Regional Programming Federal Research Center "Computer Science and Control" of RAS Moscow Russia

The principle of multiplicity is introduced for the components of the investment planning process. Some algorithms are given that ensure the process of investment planning at the regional level: the formation of integrated assessments of investment projects, automated generation of projects, multicriteria evaluation and selection of projects, taking into account the mutual influence of projects, optimizing the composition of projects, and taking into account financing restrictions. The features of the program system intended for the formation of plans for the territorial and sectoral development of the region are considered.

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Research and Development of Kubernetes Operator for Machine Learning Pipelines

Research and Development of Kubernetes Operator for Machine ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Andrey Trapezin Ivan Filianin Faculty of Programming and Infocommunications ITMO University St. Petersburg Russia Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

关键词： Production systems Error analysis Machine learning Manuals Tagging Data models Reliability Research and development Load modeling Resilience

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Features of the distribution of computing resources in cloud systems 3

Features of the distribution of computing resources in cloud...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Petrovska, Inna Kuchuk, Heorhii Mozhaiev, Mykhailo National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine Computer Technology and Telecommunications Researches Sector National Scientific Center «hon. Prof Bokarius Forensic Science Institute» Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

The report considers the peculiarities of the distribution of computing resources in cloud systems. The most used classification of existing technologies of cloud services is considered. The advantages and disadvantages of such technologies are analyzed. The characteristic features of cloud computing that will need to be taken into account when allocating resources have been identified. IAAS technology has been shown to be most vulnerable to suboptimal resource allocation or reallocation. An expanded set of indicators for IAAS technology has been identified, including time-varying ones that need to be considered in resource allocation. Solving this problem will increase the efficiency of the use of cloud computing resources in general, increase application productivity and reduce the cost of infrastructure and its maintenance. © 2022 IEEE.

关键词： Resource allocation

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Influence of the Sequence of Drilling Fields on the Maximum Total Gas Production

Influence of the Sequence of Drilling Fields on the Maximum ...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Alexander Skiba Department of Mathematical Methods of Regional Programming Federal Research Center "Computer Science and Control" of RAS Moscow Russia

We analyze an aggregated dynamic model for the development of gas fields. The same company consistently drills all fields. We pose and solve the problem of maximizing accumulated gas production over a predetermined pe...

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Design of a Stereo Camera-based Perception System: A Comparative Analysis

Design of a Stereo Camera-based Perception System: A Compara...

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International Workshop on Research and Education in Mechatronics (REM)

作者： Kaushiknarayanan Chandrasekaran Samer Telawi Josef Börscök Masters student in Functional Safety Engineering University of Kassel Kassel Germany Institute for Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

The Formula Student Driverless discipline is a race car competition for university students competing with self-constructed autonomous vehicle to race in the tracks without the help of human race car drivers. This paper aims to investigate the development of the perception system using computer vision technique in different frameworks for the Herkules Racing Team (HRT) which is the Formula Student team of the University of Kassel.

关键词： Surveys computer vision Mechatronics Software packages Education Full stack Automobiles MATLAB Python Vehicles

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Machine Learning in Defectoscopy: DBSCAN for Localization, Agglomerative Clustering for Typification

Machine Learning in Defectoscopy: DBSCAN for Localization, A...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Ruslan Akhmadeev Yurii Litvinov Anastasiya Grigorieva Department of Computer Science Saint Petersburg State University Saint Petersburg Russia Department of System Programming Saint Petersburg State University Saint Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Acoustic emission (AE) is one of the most relevant and cost-effective non-destructive testing methods for identifying defects in industrial objects. This paper addresses two primary tasks in defectoscopy: locating hazardous AE zones and classifying signals according to the presumed defect type. Through our research, we have determined and experimentally confirmed that the most suitable machine learning method for localization is a modified DBSCAN, while Agglomerative Clustering is optimal for typification. This paper details the specific tuning and modifications applied to these methods for AE. The resulting models were successfully tested on real-world data. The accuracy of the localization results was assessed using statistical analysis and consultations with AE experts, while typification was validated probabilistically and tested on sample data.

关键词： Location awareness Accuracy Recurrent neural networks Statistical analysis Acoustic emission Probabilistic logic Real-time systems Reliability Tuning Testing

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Integration of weighted majority voting in machine learning algorithms to enhance pine tree crown mapping on UAV imagery

Integration of weighted majority voting in machine learning ...

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2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Hosingholizade, Ali Erfanifard, Yousef Alavipanah, Seyed Kazem Pirasteh, Saied Garcia Millan, Virginia Dept. of Remote Sensing and GIS Faculty of Geography University of Tehran Tehran Iran Institute of Artificial Intelligence School of Mechanical and Electrical Engineering Shaoxing University China Department of Geotechnics and Geomatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Dept. of Computer Science and Programming Languages School of Computer Science and Enginering University of Málaga Málaga Spain

The shape and area of the crown of each tree are among the most influential parameters for identifying and controlling the processes of photosynthesis, respiration, transpiration and its management. In such a way that various physiographic functions, such as carbon dioxide absorption, light energy absorption, oxygen release and transpiration, which are vital for the growth and development of the tree, are done in the crown. In this research, the RGB image of the UAV with a spatial resolution of 2 cm was resampled to three pixel sizes of 10, 30 and 50 cm. Then, each image was classified separately by SVM, ANN and MLC algorithms, which are all part of Ensemble. In the next step, each of the obtained crowns was compared with the digitization of the same crown, and based on the area of the crown obtained from each classification and normalization method, the weight was obtained specifically for the same crown. Finally, by using the weighted majority voting method, classifications were fusioned at the decision level. The results showed that the ANN method gives better results in all pixel sizes compared to MLC and SVM. Also, the combination of different classification methods with the weighted majority voting method based on the weight assigned to the same crown based on each classification method has significantly increased the classification accuracy of the tree crown in all the sizes of the analyzed pixels. © Author(s) 2024.

关键词： Pixels

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