Cybernetics and Physics最新文献

{"title":"Routing algorithms in internet of things complex network with the role of machine learning","authors":"F. Kamalov, Zahra Sayari, Mehdi Gheisari, Cheng-Chi Lee, S. Moussa","doi":"10.35470/2226-4116-2023-12-3-182-193","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-3-182-193","url":null,"abstract":"In recent years, the growth of internet-based technologies increased at a rapid pace. The development of technologies such as the Internet of Things (IoT) influenced the enormous increase in the use of data and internet services. IoT devices use different algorithms for facilitating connectivity between devices and control them. However, ensuring smooth connectivity using protocols across a shared medium of network resources is challenging. IoT ecosystems utilize several routing algorithms to deliver the best path/ route for network traffic to control cyber physical systems. These routing algorithms allow IoT networks to use network routes, thereby increasing network traffic mobility effectively. So, a comprehensive survey is needed, which paves the path for researchers. Specifically, this survey investigates and compares the routing solutions in the IoT environment from different perspectives than current surveys such as safety, flow control of data and other essential parameters in IoT physical and nonphysical systems.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139197582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical solution of the initial-boundary value problem describing separation processes in a distillation column","authors":"Alexander Arguchintsev, Daniil A. Kopylov","doi":"10.35470/2226-4116-2023-12-3-169-173","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-3-169-173","url":null,"abstract":"A modification of the numerical method of characteristics is developed to solve the initial-boundary value problem that arises when modeling the rectification process in the column. The process is described by a system of first-order hyperbolic equations. A specific peculiarity of the model is in the boundary conditions of a special type. At each of the boundaries, boundary conditions are determined from a system of ordinary differential equations, which also includes unknown values of functions on another boundary. A characteristic difference grid is constructed on the base of a linear transformation of a classical rectangular grid. Implicit second-order difference schemes are used, taking into account the features of the problem at the boundaries. The advantage of this approach is in consideration of the specifics of the propagation of perturbations in hyperbolic equations. Numerical implementation of the method was carried out. An illustrative example shows the effectiveness of the proposed modification of the characterization method. This method is a base for further solution of optimal control problems of flows in columns.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139200626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning muscle segmentation model for CT images in DICOM format","authors":"Ian Schmidt, Elena Kotina, Pavel Buev","doi":"10.35470/2226-4116-2023-12-3-201-206","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-3-201-206","url":null,"abstract":"This work solves the problem of automatic segmentation of medical images in DICOM format using machine learning methods. A new developed tool is used in the form of a separate module for labeling medical data in the DICOM format. The trained model, proposed in the paper, can be useful in the tasks of muscle segmentation. One can apply it in different ways, but some of the most common include assessment of diseases related to muscles, and sarcopenia is one of them. The further applications of the muscle segmentation model may include examining various medical cases with patients, that tend to have muscle-related diseases. For instance, detecting cachexia may be one of the extensions of the model’s application field.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"30 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139205875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for crop yield forecasting","authors":"Bolotbek Biibosunov, Baratbek Sabitov, Saltanat Biibosunova, Zhamin Sheishenov, Sharshenbek Zhusupkeldiev, Zhyldyz Mamadalieva","doi":"10.35470/2226-4116-2023-12-3-174-181","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-3-174-181","url":null,"abstract":"Amid the persistent rise in global population, there has been a heightened focus on food security by academia, governmental initiatives, and international endeavors. Food security serves as a critical pillar in the national security framework, contributing to a nation’s sovereignty and self-sufficiency in food supply. To fulfill global requirements for essential food items, there is an imperative need to enhance agricultural efficiency across countries. Concurrently, agricultural practices must align with contemporary quality standards and meet consumer needs, drawing upon an integrated approach to crop cultivation technologies and yield classifications. Methodologies and tools for yield augmentation, grounded in scientific advancements in predictive modeling, are of paramount importance. Investigating the plethora of variables that contribute to optimal crop development, which in turn influences yield, poses significant challenges. Comprehensive inquiries that incorporate cutting-edge scientific and technological methodologies are essential for creating precise yield forecasts. The evolving landscape of yield modeling and prediction has emerged as a technologically sophisticated domain. Advanced methods such as machine learning and deep learning offer robust platforms for addressing crop yield forecasting, particularly when coupled with extensive datasets on environmental variables. A growing body of literature suggests the promising role of computational technologies and machine learning paradigms, inclusive of various forms of remote sensing data, in fine-tuning yield models. Yield prediction models are often characterized by intricate nonlinear equations influenced by a range of factors: seed quality and diversity, soil attributes, climatic variables, fertilizer usage, and other agronomic practices. The impacts of these variables on crop yield are varied, with some exerting greater influence than others. Additionally, crop yield is susceptible to adverse environmental and climatic conditions. While there exists a rich corpus of research on yield forecasting, addressing this issue remains an exigent priority in the agricultural sector.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139198142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trainable unravelling for quantum state discrimination","authors":"V.A. Tomilin, L.V. Il’ichov","doi":"10.35470/2226-4116-2023-12-2-152-156","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-2-152-156","url":null,"abstract":"We propose a way for partial compensation of maladaption of decoder to non-ideal quantum communication channel by means of optimal choice of unravelling of the decoding operation. No physical modification of the decoder itself is required. We show that it is sufficient to add an interface that modifies the decoder environment interaction. Tuning of this interface can be done by methods of (quantum-inspired) machine learning. We suggest a search algorithm for an optimal unravelling, as an alternative for the classical gradient descent method.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136343816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stabilizing ℓ-semioptimal fractional controller for discrete non-minimum phase system under unknown-but boundeddisturbance","authors":"Dmitriy Ivanov, Oleg Granichin, Vikentii Pankov, Olga Granichina","doi":"10.35470/2226-4116-2023-12-2-121-128","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-2-121-128","url":null,"abstract":"We consider the problem of optimal stabilizing controller synthesis for a discrete non-minimum phase dynamic plant described by a linear difference equation with an additive unknown-but-bounded disturbance. When considering the ’worst’ case of disturbance, solving this optimization problem has combinatorial complexity. However, by choosing an appropriate sufficiently high sampling rate, it becomes possible to achieve an arbitrarily small level of suboptimality using a noncombinatorial algorithm. In this article, we propose using fractional delays to achieve a small level of suboptimality without significantly increasing the sampling rate. We approximate fractional delays by minimizing the ℓ1-norm of the objective function. The proposed approximation of the fractional delay allows obtaining zero additional error for many non-integer solutions. Furthermore, it is shown that with a non-zero approximation error, the resulting controller may have a smaller additional error than the controller obtained using integer optimization. The theoretical results are illustrated by simulation examples with non-minimum-phase plants of the second and third orders.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136343674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning model with hierarchical attention mechanism for sentiment classification of Vietnamese comments","authors":"Luu Van Huy, Ngo Le Huy Hien, Nguyen Thi Hoang Phuong, Nguyen Van Hieu","doi":"10.35470/2226-4116-2023-12-2-111-120","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-2-111-120","url":null,"abstract":"In the current digital era, text documents become valuable for businesses to reach potential customers and curtail advertising costs. However, extracting and classifying beneficial information from texts can prove challenging and time-consuming, particularly in complex languages like Vietnamese. This study aims to classify the sentiment of Vietnamese comments on e-commerce websites into negative and positive classes. To enhance the performance of sentiment classification, the study fine-tuned traditional models of Convolutional Neural Networks and Recurrent Neural Networks (RNN). Then, this research proposed a combination of RNN and attention mechanisms at the word and word-and-sentence levels of the input document. The results showed an impressive accuracy of 93.72% and an F1 score of 93.7% on the RNN model with a word-and-sentence-level attention mechanism. This research outcome contributes to the field of text classification and could be applied in opinion mining, customer feedback analysis, and natural language processing. Future work aims to enhance sentiment analysis accuracy and expand the models’ scope to encompass more languages.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136343817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guidance, navigation and attitude control of mini-satellites in a low earth orbit constellation for areal survey","authors":"Yevgeny Somov, Sergey Butyrin, Sergey Somov","doi":"10.35470/2226-4116-2023-12-2-145-151","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-2-145-151","url":null,"abstract":"The problems of guidance, navigation and attitude control in a constellation of mini-observation satellites are considered. The developed methods and algorithms for scanning areal survey performed by these constellations in the low sun-synchronous orbits are presented. The most important new results are methods for coordinated angular guidance of satellites in the constellation’s orbital planes and the comparison results for sequences of the areal space surveys.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136344887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On average values of nonlinear functions of oscillating quantities and their applications","authors":"Leonid Blekhman","doi":"10.35470/2226-4116-2023-12-2-103-110","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-2-103-110","url":null,"abstract":"Oscillations of arguments in non-linear dependencies change the average values of respective functions. As the simplest example, the harmonic oscillations of the ball radius increase the average volume and surface area, while the average radius remains unchanged. Despite their elementary nature, such considerations are often ignored, which may lead to inaccuracies and errors. This paper presents a study of such effects in algebraic, geometric and trigonometric relations, as well as in certain basic formulas of mathematical analysis. A number of applications in solving technical problems are considered; in particular, the influence of parameter oscillations on the efficiency of industrial operations. The results of the study may be of interest for the theory of vibrational processes and devices, and the theory of accuracy, as well as for the theory of control and optimal processes.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136343676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the state of complex network systems using machine learning methods","authors":"Nikita Knyazev, Anton Pershin, Anna Golovkina, Vladimir Kozynchenko","doi":"10.35470/2226-4116-2023-12-2-129-135","DOIUrl":"https://doi.org/10.35470/2226-4116-2023-12-2-129-135","url":null,"abstract":"","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136344886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}