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Stochastic interpretation for a single server retrial queue with Bernoulli feedback and negative customers

Applied Mathematics(A Journal of Chinese Universities) 2025年第1期40卷 1-19页

作者： Mohamed Boualem Amina Angelika Bouchentouf Aicha Bareche Mouloud Cherfaoui Research Unit LaMOS(Modeling and Optimization of Systems) Faculty of TechnologyUniversity of Bejaia06000 BejaiaAlgeria Laboratory of Mathematics Djillali Liabes University of Sidi Bel AbbesB.P.89Sidi Bel Abbes 22000Algeria Research Unit LaMOS(Modeling and Optimization of Systems) University of Bejaia06000 BejaiaAlgeria

In this paper,we introduce a qualitative analysis in order to study the monotonicity and comparability properties of a single-server retrial queueing model with Bernoulli feedback and negative customers,relative to stochastic *** measures of such a system are available explicitly,while their forms are cumbersome(these formulas include integrals of Laplace transform,solutions of functional equations,etc.).Therefore,they are not exploitable from the application point of *** overcome these difficulties,we present stochastic comparison methods in order to get qualitative estimates of these *** particular,we prove the monotonicity of the transition operator of the embedded Markov *** addition,we establish conditions for which transition operators as well as stationary probabilities,associated with two embedded Markov chains,having the same structure but with different parameters,are comparable relative to the given stochastic ***,numerical examples are carried out to illustrate the theoretical results.

关键词： retrial queueing models negative arrivals stochastic orderings monotonicity simulation

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A framework of force of information influence and application for C4KISR system

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Journal of systems Engineering and Electronics 2024年第2期35卷 430-443页

作者： MAO Shaojie DIAO Lianwang SUN Yu WANG Heng YI Kan XU Xin MAO Xiaobin ZHANG Kecheng SHENG Long Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of China Electronics Technology Group CorporationNanjing 210007China Unit 94860 of the PLA Nanjing 210008China

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

关键词： information warfare command control communications computer kill intelligence surveillance reconnaissance(C4KISR)system information circulation force of information influence information entropy

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Automation of Predicting Malfunctions of Medical Equipment

Automation of Predicting Malfunctions of Medical Equipment

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Grigorenko, Ivan A. Nesterova, Elena V. Igrunova, Svetlana V. Institute of Engineering Technology and Natural Sciences Belgorod State National Research University Dept of Information and Robotic Systems Modeling Belgorod Russia Institute of Engineering Technology and Natural Sciences Belgorod State National Research University Dept of Information and Robotic Systems Belgorod Russia

ISBN: (纸本)9798350395044

During the analysis, it was revealed that modern medical treatment and preventive care institution AO 'Medicina', in particular, faces a number of challenges related to the management of technical and user operation of medical equipment. To solve these problems, changes are required in the system of maintenance and repair of medical equipment, as well as the constant development of personnel training in the operation of equipment. Predicting the service life of medical equipment components is an important component of a decision support system aimed at reducing the downtime of faulty medical equipment associated with the purchase and supply of necessary spare and consumable parts. To study the quality of the assessment, data on the service life of spare parts and identified faults obtained from the medical equipment in use in the medical treatment and preventive care institution AO 'Medicina' were used. The lack of an analytical unit and the ability to provide statistics makes it difficult to plan procurement activities for spare parts. As a result of the research, a software implementation of the information system and a forecasting module was carried out and tested in the conditions of medical treatment and preventive care institution AO 'Medicina'. Thanks to the forecast of the service life of the PHoX Plus analyzer parts, it was possible to adjust the purchase of consumable parts downwards. © 2024 IEEE.

关键词： Decision support systems

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Implementation of Anatomy Constrained Contrastive Learning for Heart Chamber Segmentation 15

Implementation of Anatomy Constrained Contrastive Learning f...

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15th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Ukibassov, B.M. Rakhmetulayeva, S.B. Zhanabekov, Zh.O. Bolshibayeva, A.K. Yasar, Ansar-Ul-Haque Information Systems Department International Information Technology University Almaty050040 Kazakhstan Mathematical Computer Modeling International Information Technology University Almaty050040 Kazakhstan Transportation Research Institute Hasselt University Hasselt3500 Belgium

The article covers the implementation of anatomy constrained contrastive (ACC) learning networks on a dataset of echocardiographic images with the purpose of segmenting heart chambers for further study. The dataset was partially synthetic, generated using slicing of 3D models of the heart. The images were generated using cycle-consistent generative adversarial networks (CycleGAN) and contrastive unpaired translation (CUT) approaches. By utilizing both CycleGAN and CUT approaches, the generated images captured a wide range of variations in heart chamber segmentation, allowing for a more comprehensive study of the different anatomical features. Proposed approach augments the implementation of ACC learning networks and partially solves the data scarcity problem when it comes to segmentation implementation. © 2024 Elsevier B.V.. All rights reserved.

关键词： Heart

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Optimizing Deep LSTM Model through Hyperparameter Tuning for Sensor-Based Human Activity Recognition in Smart Home

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第10期47卷 109-122页

作者： El Ghazi, Mariam Aknin, Noura Information Technology and Modeling Systems Research Unit Abdelmalek Essaadi University Tetouan Morocco

Human Activity Recognition (HAR) holds significant potential in healthcare, smart homes, sports, and security, mainly benefiting the well-being of elderly individuals and dependents. This research introduces an innovative deep learning-based approach to HAR, using wearable sensors in smart home environments. In this paper, we conduct a comprehensive review of the state of the art, offering insights into existing methods, classification techniques, their performances, hyperparameter tuning strategies, findings, limitations, and future directions. We propose an LSTM-based deep model enriched with batch normalization and perform a hyperparameter tuning using Bayesian Optimization;then, we evaluate the model on the PAMAP2 public dataset. The model outperforms previous studies, achieving remarkable performance metrics, including accuracy at 97.71%, F1 score, precision, and recall, approaching 96.66%, 96.85%, and 96.55%, respectively. We plan to assess the model's generalization capabilities for future work by training it on diverse datasets such as Opportunity and WISDM. Furthermore, we aim to enhance the model by exploring hybrid deep model architectures and alternative hyperparameter tuning approaches. These efforts maximize the model's efficiency and adaptability in real-world scenarios. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Long short-term memory

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Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

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Clean Energy 2024年第3期8卷 95-108页

作者： Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek LMTESE Laboratory of Materials Technology of Energy Systems and EnvironmentUniversitéde Ghardaia47000Algeria Renewable Energy Applied Research Unit(URAER) Ghardaia47000Algeria Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer ScienceFaculty of Sciences and TechnologyAhmed Draia University of AdrarAdrar 01000Algeria

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power *** study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour *** proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting *** evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all *** proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction ***,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior *** study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy

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Enhancing 3D Reconstruction Accuracy of FIB Tomography Data Using Multi‑voltage Images and Multimodal Machine Learning

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Nanomanufacturing and Metrology 2024年第1期7卷 48-60页

作者： Trushal Sardhara Alexander Shkurmanov Yong Li Lukas Riedel Shan Shi Christian J.Cyron Roland C.Aydin Martin Ritter Institute for Continuum and Material Mechanics Hamburg University of TechnologyHamburgGermany Electron Microscopy Unit Hamburg University of TechnologyHamburgGermany Institute of Materials Physics and Technology Hamburg University of TechnologyHamburgGermany Institute of Materials Mechanics Helmholtz-Zentrum HereonGeesthachtGermany Research Group of Integrated Metallic Nanomaterials Systems Hamburg University of TechnologyHamburgGermany Institute of Material Systems Modeling Helmholtz-Zentrum HereonGeesthachtGermany

FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of *** approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image ***,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current *** artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm *** study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of *** treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.

关键词： Multimodal machine learning Multi-voltage images FIB-SEM Overdeterministic systems 3D reconstruction FIB tomography

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The Impact of the Threshold Values on the CNN Partition Approach in the VVC Standard

The Impact of the Threshold Values on the CNN Partition Appr...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Abdallah, Bouthaina Belghith, Fatma Ayed, Mohamed Ali Ben Masmoudi, Nouri Enis Electronics and Information Technology Laboratory Sfax Tunisia ENET'COM New Technologies and Telecommunication Systems Research Unit Sfax Tunisia

ISBN: (纸本)9798350325539

In 2020, the joint video expert team (JVET) developed a new video compression standard named Versatile Video Coding (VVC). In addition to High Efficiency Video Coding (HEVC) tools, the VVC scheme has added new coding techniques such as the Quadtree with Nested Multi-Type Tree (QTMT) partition. The QTMT has improved the coding performance while increasing computational complexity by adding Binary Tree (BT) and Ternary Tree (TT) partitioning structures. Therefore, we proposed a fast QTMT decision tree for VVC based on a deep neural network. Our algorithm implemented a Convolution Neural Network (CNN) to predict the BT splitting, where threshold values were adopted in order to achieve a trade-off between the complexity reduction and the coding performance. In this paper, based on our published approach, we realize a study of how the threshold values were chosen at different levels of CU partition and how they affect the RD performance and the coding complexity. This study aims to propose an algorithm that can select the best threshold values. © 2023 IEEE.

关键词： Computational complexity

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Implementation of Anatomy Constrained Contrastive Learning for Heart Chamber Segmentation

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Procedia Computer Science 2024年 238卷 536-543页

作者： B.M. Ukibassov S.B. Rakhmetulayeva Zh.O. Zhanabekov A.K. Bolshibayeva Ansar-Ul-Haque Yasar Information Systems Department International Information Technology University Almaty 050040 Kazakhstan Mathematical Computer Modeling International Information Technology University Almaty 050040 Kazakhstan Transportation Research Institute Hasselt University Hasselt 3500 Belgium

关键词： self-supervised clustering network CycleGAN CUT synthetic datasets echocardiogram image processing

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Automating Prediction of Roller Mills Performance in a Concentrator Using a Predictive Model by an Artificial Neural Network

Automating Prediction of Roller Mills Performance in a Conce...

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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Ivashchuk, Orest D. Igrunova, Svetlana V. Nesterova, Elena V. Belgorod State National Research University Institute of Engineering Technology and Natural Sciences Dept of Applied Mathematics and Computer Modeling Belgorod Russia Belgorod State National Research University Institute of Engineering Technology and Natural Sciences Dept of Information and Robotic Systems Belgorod Russia

ISBN: (纸本)9798350345551

In this article, the authors propose to consider a multicomponent approach to evaluating the efficiency of roller mills in an enrichment plant, based on the use of a set of pre-built various mathematical models. The multicomponent approach makes it possible to predict the volume of finished concentrate output reliably during the period of the factory's transition to the use of ore with changed characteristics. Automation of forecasting will make it possible to analyze the effect of the physical and chemical characteristics of the feedstock adequately on the productivity of the main mills of the processing plant and plan the volumes of the concentrate. During the study, a database was created, the array size of which was 432x28: 432 is the number of daily data, and 28 is the total number of factors. Clustering methods were implemented by the software of the MATLAB R2021a application package, which made it possible to divide the generated database into clusters. As a result, the homogeneity of the data array connected by the same type of links was proved and, accordingly, the confirmation of the validity of combining the arrays of sections No. 1, 2 and 3 in order to form a single database for further processing and building a predictive model. Using the parallel use of the Statistica and Matlab packages, an optimal regression model was obtained. Artificial neural networks were formed with a different number of hidden layers. © 2023 IEEE.

关键词： Database systems

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