Наукова періодика

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https://ela.kpi.ua/handle/123456789/2722
В фонді розміщується наукова періодика КПІ ім. Ігоря Сікорського.

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    10 років ГУУЗ-ДСТСЗІ СБ України: досягнення та перспективи
    (НТУУ "КПІ", 2002) Скриник, Олександр
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    18-ая Дрезденская конференция по упаковке
    (ВПІ НТУУ "КПІ", 2008) Козак, А.; Kozak, A.
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    1D CNN model for ECG diagnosis based on several classifiers
    (КПІ ім. Ігоря Сікорського, 2022) Mahmoud M. Bassiouni; Islam Hegazy; Nouhad Rizk; El-Sayed A. El-Dahshan; Abdelbadeeh M. Salem
    One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases.
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    3D computer algorithms for digital reconstruction of the polycrystalline structure of polygraphic polymers
    (ВПІ КПІ ім. Ігоря Сікорського, 2022) Ushenko, O. H.; Dubolazov, O. V.; Horskyi, M. P.; Soltys, I. V.
    Proposed and substantiated a method for 3D Mueller-matrixreproduction of the distributions of parameters of linear and circular birefringence and dichroism of partially depo-larizing polymer films in publishing and printing business.
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    3D frame models switching elements by berezovsky for software-configurable switching structures
    (КПІ ім. Ігоря Сікорського, 2018) Berezovsky, S. A.
    The frame 2D and 3D models of patented by Berezovsky switching elements are proposed in relation to the construction of topologies of switching structures admissible for reconfiguration. It has been revealed that the use of frame models by Berezovsky switching elements allows to visualize the information about the state of the structure of switching elements, to vary the number of independent inputs and outputs, and provides additional possibilities in the simulation of topologies of modern structures with separated by planes data and control. The method of formation of states of the switching structure topology elements has been proposed.
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    3D Modeling of Nitric Acid Production
    (КПІ ім. Ігоря Сікорського, 2019) Kontseva, M. V.; Krimets, G. V.; Kontsevoi, S. A.
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    3D polarization algorithms for processing digital microscopic images of polygraphic polymers
    (ВПІ КПІ ім. Ігоря Сікорського, 2022) Ushenko, O.; Horskyi, M.; Soltys, I.; Dubolazov, O.
    A method of azimuthally invariant 3D Mueller-matrix mapping of the distributions of the parameters of phase and amplitudeanisotropy of partially depolarizing layers of high-quality (group1 — high density) and low-quality (group 2 — low density) poly-ethylene polymer films has been proposed and substantiated.Layer-by-layer coordinate distributions of the magnitude of theset of Mueller-matrix invariants (MMI) of polymer films of bothtypes were obtained in the volume of polymer samples.
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    3D відеовимірювальна система для контролю виготовлення деталей підвищеної точності обробки
    (КПІ ім. Ігоря Сікорського, 2021) Прокопченко, С. В.; Воскресенський, В. Б.
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    3D-model reconstruction with use of monocular rgb camera
    (КПІ ім. Ігоря Сікорського, 2017) Vedmedenko, O. V.; Nikolaiev, S. S.; Tymoshenko, Y. A.
    Every year the edge between the real and digital worlds is becoming more and more blurred. Augmented and virtual reality rapid development creates new opportunities for more productive work and entertainment, revolution in 3D printing technologies begets boost in multiple DIY communities appearance and sharing economy growth. All these factors require new technologies that allow making 3D models from real world objects, but most of these solutions are either very expensive or require complex technical knowledge that most ordinary people do not have. This paper provides a review and comparison of modern methods for 3D models of physical objects real time reconstruction that can be used in present-day mobile solutions.
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    3D-models design concept of complex technical objects using knowledge-based technology
    (КПІ ім. Ігоря Сікорського, 2017) Konotop, D. I.; Zinchenko, V. P.
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    3D-моделювання систем блискавкозахисту об’єктів електричних мереж
    (НТУУ "КПІ", 2014) Казанський, С. В.; Мальцев, В. В.; Kazanskiy, S.; Maltsev, V.; Казанский, С. В.; Мальцев, В. В.
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    75 славних і знаменних років Навчально-Науковому Інституту Енергозбереження та Енергоменеджменту КПІ ім. Ігоря Сікорського!
    (КПІ ім. Ігоря Сікорського, 2021) Денисюк, С. П.; Ган, А. Л.; Данілін, О. В.; Сергієнко, М. І.
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    77 ГГц радар для автомобіля
    (НТУУ «КПІ», 2012) Ляшук, О. М.; Lyashuk, O. M.; Ляшук, А. Н.
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    A 2-Layer Perceptron Performance Improvement in Classifying 26 Turned Monochrome 60-by-80-Images via Training with Pixel-Distorted Turned Images
    (НТУУ «КПІ», 2014) Romanuke, Vadim V.; Романюк, Вадим Васильович; Романюк, В. В.
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    A collision-resistant hashing algorithm for maintaining consistency in distributed nosql databases
    (КПІ ім. Ігоря Сікорського, 2022) Nikitin, V.; E. Krуlov, E.
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    A comparison enterprise architecture frameworks for critical it infrastructure design
    (Institute of Special Communication and Information Protection of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 2017) Dorohyi, Yaroslav; Tsurkan, Vasyl; Telenyk, Serhii; Doroha-Ivaniuk, Olena; Дорогий, Ярослав Юрійович; Цуркан, Василь Васильович; Теленик, Сергій Федорович; Дорога-Іванюк, Олена Олександрівна
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    A comprehensive survey on load balancing techniques for virtual machines
    (КПІ ім. Ігоря Сікорського, 2023) Suman Sansanwal; Nitin Jain
    Cloud computing is an emerging technique with remarkable features such as scalability, high flexibility, and reliability. Since this field is growing exponentially, more users are attracted to fast and better service. Virtual Machine (VM) allocation plays a crucial role in cloud computing optimization; hence, resource distribution is not impacted by machine failure and is migrated with no downtime. Therefore, effective management of virtual machines is necessary for increasing profit, energy-saving, etc. However, it could utilize the virtual machine resources more efficiently because of the increased load, so load balancing is more concentrated. The predominant purpose of load balancing is to balance the available load equally among the nodes to avoid overloading or underloading problems. The present study conducted an extensive survey on virtual machine placement to describe the application of prediction algorithms and to provide more efficient, reliable, high response, and low overhead VM placement. Furthermore, the survey attempted to overview the challenges in load balancing in VM placement and various ideas of state-of-the-art techniques to resolve the issues.
  • Ескіз
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    A Computational Model of Electrophysiological Properties of Cardiomyocytes
    (КПІ ім. Ігоря Сікорського, 2018) Ivanushkina, N. G.; Ivan’ko, E. O.; Prokopenko, Yu. V.; Redaelli, A.; Tymofieiev, V. I.; Visone, R.; Іванушкіна, Н. Г.; Іванько, К. О.; Прокопенко, Ю. В.; Редаеллі, А.; Тимофєєв, В. І.; Вісоне, Р.; Иванушкина, Н. Г.; Иванько, Е. О.; Прокопенко, Ю. В.; Редаэлли, А.; Тимофеев, В. И.; Висонэ, Р.
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    A concatenation approach-based disease prediction model for sustainable health care system
    (КПІ ім. Ігоря Сікорського, 2023) Tharageswari, K.; Sundaram, N. Mohana; Santhosh, R.
    Abstract. In the present world, due to many factors like environmental changes, food styles, and living habits, human health is constantly affected by different diseases, which causes a huge amount of data to be managed in health care. Some diseases become life-threatening if they are not cured at the starting stage. Thus, it is a complex task for the healthcare system to design a well-trained disease prediction model for accurately identifying diseases. Deep learning models are the most widely used in disease prediction research, but their performance is inferior to conventional models. In order to overcome this issue, this work introduces the concatenation of Inception V3 and Xception deep learning convolutional neural network models. The proposed model extracts the main features and produces the prediction result more accurately than traditional predictive models. This work analyses the performance of the proposed model in terms of accuracy, precision, recall, and f1-score. It compares the proposed model to existing techniques such as Stacked Denoising Auto-Encoder (SDAE), Logistic Regression (LR), MLP, MLP with attention mechanism (MLP-A), Support Vector Machine (SVM), Multi Neural Network (MNN), and Hybrid Convolutional Neural Network (CNN)-Random Forest (RF).