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A MATLAB-based simulator for the study of process control of fed-batch yeast fermentations

Education for Chemical Engineers 2024年 49卷 67-77页

作者： Hrnčiřík, Pavel Kohout, Jan Department of Mathematics Informatics and Cybernetics University of Chemistry and Technology Prague Technická 5 Prague16628 Czech Republic

This paper presents a MATLAB-based simulator for the study of process control of fed-batch yeast fermentations that meets the educational needs of undergraduate and graduate students. Against the background of challenges in interdisciplinary education, the paper examines the evolving environment of simulation tools in the field of bioprocesses. It emphasizes the need for interdisciplinarity to prepare students for the complexities of the modern biotechnology industry. Built with accessibility and flexibility in mind, the simulator offers a modular structure with a graphical user interface (GUI) for novice users and direct access to MATLAB functions for advanced users. This design choice ensures ease of use for students with different programming backgrounds and allows for adaptability to alternative software environments. The introductory sections provide a detailed overview of the simulator development, including the mathematical models that govern biomass growth kinetics, mass transfer, and process quality indicators. Selected process control strategies such as rule-based and feedforward approaches are incorporated in the simulator to enhance the learning experience by allowing students to experiment with different scenarios. The paper concludes by emphasizing the simulator's adaptability, modularity, and user-friendly interface as a valuable asset in educating students about the complexities of bioprocess control. © 2024 Institution of Chemical Engineers

关键词： MATLAB

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Improving Voice Pathology Classification Using Artificial Data Generation 28th

Improving Voice Pathology Classification Using Artificial Da...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Jirsa, Tomáš Verde, Laura Marulli, Fiammetta Marrone, Stefano Vrba, Jan Department of Mathematics Informatics and Cybernetics University of Chemistry and Technology in Prague Prague Czech Republic Department of Mathematics and Physics University of Campania Luigi Vanvitelli Caserta Italy

Human Digital Twin is an emerging technology that could revolutionize the current healthcare system by enabling the delivery of Personalized Health Services through the use of tools such as artificial intelligence. However, the considerable complexity of the structure of the human body, brought about by continuous molecular and physiological changes, makes it extremely difficult to process medical data extracted by artificial intelligence techniques. The latter requires a large amount of data for reliable performance, which is often difficult to obtain due to limited quality and availability. In this paper, we propose a methodology to generate artificial medical data. In detail, we focus on generating artificial voice signals. The analysis of voice recordings is fundamental to diagnose specific pneumo-articulatory apparatus diseases, such as dysphonia. The generative neural network employed is based on the WaveNet model, due to its autoregressive sampling, which enables generating recordings of variable length. We propose a setup which enables to generate artificial samples of required sex and pathology to balance and augment the dataset using only one generative network. The quality of the generative network is assessed by balancing the training dataset by generated data and training a convolutional classifier, which is tested on a dataset which was not introduced to the generative network during training. We achieved reasonable improvements in classification accuracy, particularly for the under-represented sex in terms of accuracy, arguing that this approach is worthy of future research. © 2024 The Authors.

关键词： Generative adversarial networks

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Learning to Solve Hard Minimal Problems

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年 PP卷 1-16页

作者： Hruby, Petr Duff, Timothy Leykin, Anton Pajdla, Tomas ETH Zurich Switzerland Department of Mathematics University ofWashington Washington School of Mathematics Georgia Tech in Atlanta USA Czech Institute of Informatics Robotics and Cybernetics of the Czech Technical University in Prague Czechia

We present an approach to solving hard geometric optimization problems in the RANSAC framework. The hard minimal problems arise from relaxing the original geometric optimization problem into a minimal problem with many spurious solutions. Our approach avoids computing large numbers of spurious solutions. We design a learning strategy for selecting a starting problem-solution pair that can be numerically continued to the problem and the solution of interest. We demonstrate our approach by developing a RANSAC solver for the problem of computing the relative pose of three calibrated cameras, via a minimal relaxation using four points in each view. On average, we can solve a single problem in under 70 μs. We also benchmark and study our engineering choices on the very familiar problem of computing the relative pose of two calibrated cameras, via the minimal case of five points in two views. IEEE

关键词： Optimization

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Optimization of Marketing department Activities using Machine Learning Technologies 14

Optimization of Marketing Department Activities using Machin...

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14th International Conference on Advanced Computer Information Technologies, ACIT 2024

作者： Buiak, Lesia Shynkaryk, Mykola Semenenko, Yurii Pryshliak, Kateryna West Ukrainian National University Department of Economic Cybernetics and Informatics Ternopil Ukraine West Ukrainian National University Department of Applied Mathematics Ternopil Ukraine

ISBN: (纸本)9798350350036

This paper examines the impact of machine learning technologies on the effectiveness of modern companies' marketing strategies. It investigates the application of machine learning models in personalizing marketing efforts, including recommendation systems, customer data analysis, advertising campaign optimization, and consumption trend forecasting. The study involves the development of an algorithm for clustering customers based on their interests and past purchases using Python. Additionally, it presents and describes the conceptual design of a mobile application aimed at personalizing client offers through machine learning techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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Digital Transformation of the process of Monetary Evaluation of Agricultural Land 14

Digital Transformation of the process of Monetary Evaluation...

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14th International Conference on Advanced Computer Information Technologies, ACIT 2024

作者： Buiak, Lesia Pryshliak, Kateryna Bashutska, Oksana Buiak, Andriy Shynkaryk, Mykola Semenenko, Yurii West Ukrainian National University Department of Economic Cybernetics and Informatics Ternopil Ukraine West Ukrainian National University Department of Transport and Logistics Ternopil Ukraine West Ukrainian National University Department of Applied Mathematics Ternopil Ukraine

ISBN: (纸本)9798350350036

This research is dedicated to the analysis of the pressing issue of monetary evaluation of agricultural land and the implementation of information systems in this field. The integration of digital technologies and information systems is a key factor in improving management and optimizing the processes of land valuation. The article describes the development of a web-oriented information system for monetary evaluation of agricultural land, aimed at improving the assessment processes and management of land assets. © 2024 IEEE.

关键词： agricultural land digital transformation information technology land valuation web-based information system

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Improving Voice Pathology Classification Using Artificial Data Generation

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Procedia Computer Science 2024年 246卷 5175-5184页

作者： Tomáš Jirsa Laura Verde Fiammetta Marulli Stefano Marrone Jan Vrba Department of Mathematics Informatics and Cybernetics University of Chemistry and Technology in Prague Prague Czech Republic Department of Mathematics and Physics University of Campania ”Luigi Vanvitelli” Caserta Italy

Human Digital Twin is an emerging technology that could revolutionize the current healthcare system by enabling the delivery of Personalized Health Services through the use of tools such as Artificial intelligence. However, the considerable complexity of the structure of the human body, brought about by continuous molecular and physiological changes, makes it extremely difficult to process medical data extracted by Artificial intelligence techniques. The latter requires a large amount of data for reliable performance, which is often difficult to obtain due to limited quality and availability. In this paper, we propose a methodology to generate Artificial medical data. In detail, we focus on generating Artificial voice signals. The analysis of voice recordings is fundamental to diagnose specific pneumo-articulatory apparatus diseases, such as dysphonia. The generative neural network employed is based on the WaveNet model, due to its autoregressive sampling, which enables generating recordings of variable length. We propose a setup which enables to generate Artificial samples of required sex and pathology to balance and augment the dataset using only one generative network. The quality of the generative network is assessed by balancing the training dataset by generated data and training a convolutional classifier, which is tested on a dataset which was not introduced to the generative network during training. We achieved reasonable improvements in classification accuracy, particularly for the under-represented sex in terms of accuracy, arguing that this approach is worthy of future research.

关键词： Generative AI WaveNet Voice Pathology Detection Dataset Augmentation Digital Twin Dysphonia

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Modeling Russian-Ukrainian War Impact on Global Food Safety. Preliminary Evaluations 13

Modeling Russian-Ukrainian War Impact on Global Food Safety....

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13th International Conference on Advanced Computer Information Technologies, ACIT 2023

作者： Kovalchuk, Olha Berezka, Kateryna Danylyuk, Iryna Babala, Ludmila Chopyk, Pavlo Basistyi, Pavlo West Ukrainian National University Department of Applied Mathematics Ternopil Ukraine West Ukrainian National University Department of Economic Cybernetics and Informatics Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Physics and Mathematics Ternopil Ukraine

Climate change, the COVID-19 pandemic, the 2021 oil crisis, and the Russian-Ukrainian war have created serious risks for the development of polycrisis. The military invasion of the Russian Federation created mega threats to the security of the entire world. The issue of global food security is particularly acute. The paper attempts to obtain preliminary estimates of the impact of the Russian-Ukrainian war on the food crisis. A correspondence analysis model was built to assess the relationship between Ukrainian food export and the Global Hunger Index, which is a tool for assessing the level of hunger in the country. It has been established that Ukraine's food export significantly impacts the food supply of countries that are on the brink of starvation or starving. Food security of highly developed countries with low and serious levels of the Global Hunger Index does not significantly depend on Ukraine's food export. © 2023 IEEE.

关键词： Food supply

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Skeleton Detection Using MediaPipe as a Tool for Musculoskeletal Disorders Analysis 1

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Böhm, Josef Chen, Taotao Štícha, Karel Kohout, Jan Mareš, Jan Faculty of Electrical Engineering and Informatics University of Pardubice Studentská 95 Pardubice 2532 10 Czech Republic Department of Mathematics Informatics and Cybernetics University of Chemistry and Technology in Prague Technická 1905/5 Praha 6166 28 Czech Republic

ISBN: (数字)9783031535499

ISBN: (纸本)9783031535482

Skeleton detection, also known as human pose estimation (HPE), is becoming more and more popular as it can be applied in a range of applications such as game entertainment, human-machine interaction, VR-based projects, medical rehabilitation, etc. Thanks to the booming development of deep learning, HPE solutions can be implemented using deep learning methods which require standard 2D RGB images or video sequences as input. That is, technology nowadays is making HPE solutions more and more lightweight and fast which is possible to run on mobile devices for the daily use of skeleton detection. This article covers a brief survey of current deep learning-based human pose estimation approaches in the first place. Then, a lightweight deep learning model – MediaPipe – will be illustrated from all the perspectives of its structure, working flow, strengths & weaknesses and the more concerned compatibility in platforms and programming languages. As a result, a multi-platform application for collecting movement data from patients suffering from musculoskeletal diseases relying on MediaPipe is introduced. Finally, there is a summary of achievements and obstacles of application development, which is significant as it can be a signpost for teams who are doing or about to do an application based on the MediaPipe library. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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Mathematical Models of Network Energy Systems

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Moscow University Computational mathematics and cybernetics 2025年第4期48卷 278-290页

作者： Vasin, A. A. Davidson, M. R. Novikova, N. M. Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Federal Research Center of Informatics and Control Russian Academy of Sciences Moscow Russia

An overview is presented of mathematical models that describes the functionality of energy facilities and systems, including generators, energy storage units, and network infrastructure. Problems of optimizing the development of energy networks are considered. Much attention is given to electricity markets in Russia and elsewhere. Problems of calculating steady states and determining price indicators when choosing the composition of generating equipment when optimizing the operation of Russia’s unified electric power system by managing the loading modes of generating equipment based on price bids from generating companies are considered in detail. Problems of integrating storage units and renewable energy sources into the market are discussed.

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Forming an Agreement between Buyers of Frequencies for Open Access at a Spectrum Auction

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Moscow University Computational mathematics and cybernetics 2024年第1期48卷 31-44页

作者： Novikova, N. M. Pospelova, I. I. Federal Research Center for Informatics and Management Russian Academy of Sciences Moscow Russia Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia

The problem of an unadvertised agreement between buyers of non-exclusive rights at a spectrum auction for the agreed formation of price bids is considered from the viewpoint of game theory and operations research. Such auction participants are potential freeriders claiming free access to the frequencies being sold, which results in their non-standard behavior. Two ways of organizing an agreement for an auction with pricing according to the Vickrey rule are proposed for the full awareness of the participants about the amount of income of the partners from using the range of frequencies purchased by pooling. It is shown that the lack of information results in an equal distribution of payment between negotiating buyers, and this greatly reduces their competitive advantage in a spectrum auction. The Clarke–Groves mechanism that stimulates the identification of true preferences is analyzed and a modified version of it is developed. Unfortunately, analysis shows it does not make sense for any of them to be applied to this problem. An alternative way of choosing a joint solution based on the Germeyer–Vatel model is discussed.

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