Automated Processing Methods Research Articles

An improved convolutional architecture for quantitative characterization of pore networks in fine-grained rocks using FIB-SEM

Focused ion beam scanning electron microscope (FIB-SEM) is one of the most advanced imaging techniques for analyzing and understanding complex pore networks in shale and other fine-grained formations. However, FIB-SEM imaging tends to be time-consuming and labor-intensive and can result in biased interpretations associated with pore analysis. Recently, U-Net or its variants for image segmentation have been applied to capture microscopic pores at higher resolutions. The ‘traditional’ encoder-decoder-based approaches tend to detect very fine-scale microscopic pores poorly. This study presents an improved convolutional architecture for automatically analyzing pore structures in shale reservoirs using FIB-SEM. It does so by applying an overcomplete convolutional architecture, KiU-Net, to capture very fine-scale microscopic pores by accurately defining their edges in the input FIB-SEM images. The KiU-Net learns low and high-level features by making the model more sensitive to fine-scale microscopic pores in the input images. The purpose of this study is to demonstrate KiU-Net's capabilities by analyzing different shale formations with varying characteristics. The results indicate that KiU-Net is more accurate and efficient than other methods in predicting nanopores in the Longmaxi, Niutitang, Qingshankou, Qianjiang, and Yanchang Formations (China), Bakken shale (Canada), and coal reservoirs (China). Furthermore, KiU-Net demonstrated the advantage of requiring fewer parameters and achieving super convergence compared to the Attention U-Net technique. KiU-Net addresses the challenges of the Edge-Threshold Automatic Processing (ETAP) methods by capturing very fine-scale microscopic pores with accurate edges. This study further enhances the accuracy and efficiency of pore analysis in shales, thereby offering an improved method for understanding shale reservoir quality with the potential to improve petroleum recovery from such formations.

Development and implementation of effective Web Scraping methods for automated data collection and processing using Python

With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.

Comparative analysis of Russian language textbooks for primary school using quantitative methods

Despite the intense scientific and practical interest in assessing the textual content quality of a school textbook, there is still no transparent algorithm and objective criteria for such evaluation. This article aims to outline objective linguistic criteria for textbook text assessment using quantitative methods on the example of one of the main components of a Russian language textbook, i. e. exercise texts. The analysis was performed on the material of 8 lines of modern Russian language textbooks for primary school and 1 line of the digital educational service Yandex Textbook. The research employed the methods of automatic natural language processing, statistics, and corpus analysis of texts. As a result, we propose an algorithm for analysing the text library of a Russian language textbook according to the criteria of structural and lexical complexity, genre, stylistic and thematic diversity, and the degree of correspondence between the text library and the current state of the Russian language. The obtained results form an idea of the textual content of a modern Russian language textbook for primary school. Moreover, they highlight the specific features of text selection for modern traditional and digital Russian language textbooks and resources. Finally, the research outcomes raise questions concerning the methodological optimality of certain authorial decisions when compiling the text library of a textbook. The results of the study will be useful to authors of school textbooks on the Russian language and specialists in educational and methodological work.

Translation as a linguistic act in the context of artificial intelligence: the impact of technological changes on traditional approaches

The purpose of this article is to study translation as a human speech act in the context of artificial intelligence. Using the method of analysing the related literature, the article focuses on the impact of technological changes on traditional approaches and explores the links between these concepts and their emergence in linguistics and automatic language processing methods. The results show that the main methods include stochastic, rule-based, and methods based on finite automata or expressions. Studies have shown that stochastic methods are used for text labelling and resolving ambiguities in the definition of word categories, while contextual rules are used as auxiliary methods. It is also necessary to consider the various factors affecting automatic language processing and combine statistical and linguistic methods to achieve better translation results. Conclusions - In order to improve the performance and efficiency of translation systems, it is important to use a comprehensive approach that combines various techniques and machine learning methods. The research confirms the importance of automated language processing in the fields of AI and linguistics, where statistical methods play a significant role in achieving better results. Keywords: technological changes, linguistics, innovations, language technologies, automatic translation

Automated Processing Method for Chinese NOTAMs Based on Knowledge Graph

Notice to airmen (NOTAMs) constitutes a vital element in civil aviation operational intelligence. Historically, the processing of these notices has been manual. However, with the significant increase in the number of NOTAMs, issues including low efficiency, time-consuming processes, and high error rates associated with manual processing have become apparent. To address these challenges, we propose an enhanced approach utilizing the Bi-GRU-CRF-Attention model, based on a dataset of 105,797 NOTAMs collected from the Intelligence Center between September 2020 and April 2023. In this methodology, we employ preprocessing techniques to train the model using processed NOTAMs. Subsequently, the trained model is utilized for named entity recognition, identifying entities within the notices, such as status, facilities, and reasons and segmenting sentences into words. Following this, an advanced BERT-DPCNN method is employed to classify the identified entities, yielding triplets comprising NOTAM entities, their categories, and corresponding processing methods. By integrating rule-based approaches, we configure a NOTAM knowledge graph using neo4j. This process establishes an automated NOTAM processing system. This system can autonomously determine the category of a NOTAM upon reception and utilize the Cypher language to query for the appropriate processing method.

Метод виділення контурів для вирішення завдань моніторингу транспортної інфраструктури

Introduction. Technologies for monitoring transport infrastructure have been rapidly evolving in recent years, absorbing innovations and the latest developments. The main direction of development and use for this technology has been the implementation of continuous monitoring and control of different aspects of transport infrastructure to ensure its safety and allow efficient and timely management. Computer vision has been playing the main role in the evolution of these technologies and has made unmanned aerial vehicles (UAVs) the most cost-efficient remote monitoring tool. The purpose of the paper. Among the main tasks in the field are monitoring traffic and the conditions of road surfaces and markings. Fast and accurate monitoring systems enable quick responses and minimize negative consequences for citizens. Despite the active development of computer vision algorithms, there is no universal algorithm that suits all scenarios. Algorithms depend on the task, conditions, and even UAV trajectory; even a slight change in the visual scene can cause suboptimal results. Lately, significant progress has been made in the development of edge detection algorithms. However, they do not consider the specifics of the task of monitoring road markings. The algorithm should consider the characteristics of the objects of interest – their geometric and color features, and the presence of many other objects in the images. The goal of this paper is to present method crafted specifically for the task of monitoring transport infrastructure. Methods. Computer vision, threshold filtering, Sobel operator, noise removal, probabilistic Hough transform, histograms. Results. The main features of the task of monitoring transport infrastructure using visual data obtained from surveillance cameras or unmanned aerial vehicles have been analyzed. Developed an algorithm for boundary extraction of point clusters using histograms. A method for edge detection in images has been developed, which addresses the shortcomings of known methods and is specifically enhanced for transport infrastructure monitoring. The method leverages the narrow specialization of the task to improve the obtained results. The foundation of the method is based on the features of the HSL color model, filtering in the saturation and lightness channels using gradients obtained from the Sobel operator, segment detection based on the probabilistic Hough transform, and a developed algorithm for boundary extraction of point clusters using histograms. Conclusion. The proposed method can be used in automated and semi-automated decision-making systems, UAV design bureaus, UAV manufacturing enterprises, and information-analytical centers to develop unmanned aviation systems and aerial monitoring technologies to enhance human safety and the economic development of the state. The use of automatic remote monitoring data processing methods allows for faster acquisition of necessary results and improves the efficiency of using geospatial data. Keywords: Computer vision, object detection, edge detection, image filtering, transportation infrastructure, information technology, monitoring.

Моделирование КВ-радиотрасс на основе волноводного подхода

We present a scheme for modeling HF radio signal characteristics along paths of different lengths, which is based on the waveguide approach — the normal mode method. We use a representation of the recorded signal field in the form of Green function products of the angular operator, excitation coefficients, and reception coefficients of individual normal modes. Algorithms have been developed for calculating distance-frequency, frequency-angular, and amplitude characteristics of signals in large spatial regions through analysis and numerical summation of normal mode series. We have implemented a complex algorithm for simulating propagation conditions of HF radio signals, which includes a medium model, algorithms for calculating signal characteristics, and operational diagnostics of radio channel. We have compared the results of the HF signal propagation characteristic modeling and the experimental oblique sounding data obtained along paths of different lengths and orientation. To analyze experimental ionograms, determine the maximum usable frequencies for propagation modes along radio paths, we employ the method of automatic processing and interpretation of oblique sounding ionograms.

HF radio path modeling by waveguide approach

We present a scheme for modeling HF radio signal characteristics along paths of different lengths, which is based on the waveguide approach — the normal mode method. We use a representation of the recorded signal field in the form of Green function products of the angular operator, excitation coefficients, and reception coefficients of individual normal modes. Algorithms have been developed for calculating distance-frequency, frequency-angular, and amplitude characteristics of signals in large spatial regions through analysis and numerical summation of normal mode series. We have implemented a complex algorithm for simulating propagation conditions of HF radio signals, which includes a medium model, algorithms for calculating signal characteristics, and operational diagnostics of radio channel. We have compared the results of the HF signal propagation characteristic modeling and the experimental oblique sounding data obtained along paths of different lengths and orientation. To analyze experimental ionograms, determine the maximum usable frequencies for propagation modes along radio paths, we employ the method of automatic processing and interpretation of oblique sounding ionograms.

A module for automatic analysis of burst spectra for consonant place detection

Widespread methods for automatic speech processing have become increasingly powerful, but they do not aim to model aspects of human speech processing. Previous research has shown that individual acoustic cues are important in human speech perception. Developing a model that is capable of identifying individual acoustic cues in speech enhances our ability to extract meaningful information from a speech signal, irrespective of speaker variations or phonemic differences, in a way that provides a transparent and testable model of human speech processing. This research investigates a module for automatic analysis of the spectral burst cue in fricative and plosive speech sounds. The method determines the place of articulation of the spectral burst by utilizing spectral moment measurements near locations where a spectral burst is likely to occur as features in a Gaussian mixture model. This research lays the groundwork for a dynamic model that is developed to be consistent with the Bayesian belief updating framework, in alignment with prior work in human speech perception.

Vibration diagnostics of the main power plant of vessels: improvement and integration of methods

The study is devoted to the development of a comprehensive approach to vibration diagnostics of the main power plant of water transport vehicles. The method of automatic processing of narrowband spectra is being improved. The method of automatic adaptation of threshold values for timely detection of changes in technical condition against the background of natural monotonous changes in vibration parameters characteristic of ship conditions is being improved. In the course of the study, the peculiarities of the spectra of vibration signals of rotary equipment were formulated and their analysis was carried out from the point of view of evaluating the parameters of diagnostically significant components. Automatic systems of centralized control and management are traditionally used to assess the current performance of SEU on ships. Their main purpose is to assess the quality of the SEU performance of the specified operating modes. But these systems do not allow solving the task of early prediction of the loss of performance of the most loaded and failure-prone SEU elements - rotary machines, because the controlled parameters of technological processes are either inertial and do not change synchronously with the development of faults, or do not react to the development of a number of dangerous defects until the onset irreversible consequences. The method of automatic diagnostics was further developed based on the results of monitoring vibration parameters of various nature and the assessment of the diagnostic significance of their changes against the background of natural monotonous fluctuations

Ontology engineering of automatic text processing methods

<span lang="EN-US">Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.</span>

N-version programming for nanosatellite telemetry processing

Software is a key element that ensures the functioning of any modern complex technical system. One such system is the constellation of spacecraft and associated ground control complexes that provide reception, transmission, and processing of collected telemetry. The process of data acquisition and its subsequent processing is critical to the flight control of the spacecraft and its onboard scientific equipment. Furthermore, telemetry data processed by ground control systems involves large volumes of raw data, the processing of which is a complicated and time-consuming task. In order to solve this problem, various methods of automatic data processing are used. Improving them is a key factor in ensuring the fault tolerance of onboard software and hardware, improving its reliability. Of all the existing widely-used methods of data processing, we shall focus on N-version programming (NVP) approach. N-version programming has firmly established itself as an effective method for increasing software reliability and designing fault-tolerant systems. Since its inception in the 1970s, this approach has been deeply connected with the development of aerospace software systems, including, among others, satellite ground control stations. In light of the aforementioned, this paper discusses the application of NVP for processing telemetry data gathered from nanosatellites (CubeSats). Due to the fact that there exists a skeptical view on the NVP approach in terms of its efficiency, the author covers this issue in existing literature in terms of the approach’s applicability for processing satellite telemetry.

METHODS OF TRANSFERRING FULL WORD-FORMATION AFFIXES IN THE NATIONAL CORPUS OF THE KAZAKH LANGUAGE

This scientific article studies methods of transferring full word-formation affixes in the National Corpus of the Kazakh language. Affixes play an essential role in the forming of word formation and semantics of a language, and the study of their transmission is crucial for linguistic theory and practice. The purpose of the study is to analyze and systematize approaches to word-forming affixes in the corpus of the Kazakh language.The main areas of research include the analysis of the morphological features of affix transfer, as well as the identification of contexts in which various transmission options for transfer are possible.A short description of the scientific and practical significance of the work: the study allows us to better understand the proc esses of word formation in the Kazakh language and develop effective methods for automatic processing of texts in this language. It is also of practical importance for the creation of linguistic resources and the development of language applications. Brief description of the research methodology: the research methodology includes the analysis of textual material from the national corpus of the Kazakh language, as well as the use of linguistic tools and data analysis to determine the features of the transmission of affixes. The main results and analysis of the research work, conclusions: the study revealed various ways of transmitting wordforming affixes, including morphological constructions, in the corpus of the Kazakh language. The analysis made it possible to identify the features and frequency of these methods. Value of the conducted research: this study contributes to the linguistic theory, and enriches our knowledge about the mechanisms of word formation and ways of transmitting linguistic elements in the language corpus. In addition, the results obtained can be useful for the development of language technologies and resources for the Kazakh language. Practical significance of the results: the study can be used in the creation of automatic systems of text processing systems in the Kazakh language, and the development of language applications, such as in linguistic training and translation. The obtained results and conclusions can become the basis for further research in the field of linguistics and computational linguistics.

Automatic pore structure analysis in organic-rich shale using FIB-SEM and attention U-Net

The pore structure is an important factor in determining the productivity of shale, which can help us make better operational decisions. Shale formations have an extremely complex pore structure with a wide range of pore sizes, shapes, orientations, and connectivity. Advanced imaging techniques, such as focused ion beam scanning electron microscopes (FIB-SEM), contribute significantly to the analysis and understanding of complex pore networks in shale, but they are time-consuming. Developing new methods and techniques to capture the spatial distribution and connectivity of pores at various scales is essential to understanding pore structure and its impact on fluid flow. This study aims to analyze the pore characteristics of shale reservoirs by using artificial intelligence (AI) and FIB-SEM. We applied a novel attention gate (AG) model to FIB-SEM images that automatically learns to focus on target structures of varying pores’ shape and size. AG can be fully incorporated into the U-Net model with minimal computational impact. The AG improved prediction accuracy by making the U-Net model more sensitive to essential features in the input images and achieved a precision of 97%, recall of 71%, and F1 score of 81% at a confidence threshold of 1. Experimental results show that Attention U-Net outperformed in predicting the pore structures in shale samples from Longmaxi Formation, Qingshankou Formation, Cambrian Niutitang Formation, Yanchang shale oil, and Bakken shale while obtaining optimal performance without requiring multiple convolutional neural network (CNN) models. Additionally, Attention U-Net addresses some of the limitations of the Edge-Threshold Automatic Processing (ETAP) method, including the limited ability to obtain a complete description of the pore space as well as the low segmentation accuracy and sensitivity. The proposed workflow offers a promising solution for optimizing the automatic processing of microscopic images for pores and microfractures identification. Researchers and industry professionals can improve their understanding of shale properties and applications in various fields by maximizing the benefits of our workflow.

Automatic Detection of Plateau Phases in Light Curves of Variable Stars

Modern astronomical surveys produce millions of light curves of variable sources. These massive data sets challenge the community to create automatic light-curve processing methods for detection, classification, and characterization of variable stars. In this paper, we present a novel method for extracting the variable components of a light curve based on Otsu’s thresholding method. To validate the effectiveness of this method, we apply it to the light curves of detached eclipsing binaries and dwarf novae, sourced from Optical Gravitational Lensing Experiment catalogs.

IoT Based Machine Learning Weather Monitoring and Prediction Using WSN

A novel approach to analysis and prediction is provided by the internet of things-based time monitoring and prediction system using wireless sensor networks (WSN) and machine learning techniques (ML). To give accurate meteorological data in real time, the integrated system uses IoT, WSN, and ML. Making informed decisions requires these insights. Includes strategically positioned infrared points that are used to gather meteorological information, such as temperature, humidity, pressure, and wind speed, among other things.The machine's automatic data processing methods are then used in a central processing unit to collect and analyse the data. By seeing patterns and drawing diagrams utilising previously collected data, ML models are able to comprehend intricate temporal dynamics. An important development in this system is its predictive capabilities. Artificial intelligence has the processing power to precisely forecast short-term weather patterns, enabling the rapid transmission of warnings for extreme localised events and the reduction of potential dangers.The combination of historical data, real-time sensor inputs, and automated analysis produces the predictive potential. The "Internet of Things" architecture used to develop this system makes it simpler to gather meteorological data. A number of industries, including as agriculture, transportation, emergency management, and event planning, are encouraged to make data-based decisions since users can quickly obtain current meteorological conditions and forecasts through user-friendly web interfaces or mobile applications.

Retracted: Automatic Capture Processing Method of Basketball Shooting Trajectory Based on Background Elimination Technology

Retracted: Automatic Capture Processing Method of Basketball Shooting Trajectory Based on Background Elimination Technology

An automated method for topology consistent processing of parallel geological cross-sections based on topology reasoning

The 3D modeling method based on parallel geological cross-sections is suitable for modeling large-scale bedrock geological bodies. However, there are usually more branching, pinch-out strata, deformation structures (such as faults and folds), and the correspondence problem of geological boundaries between adjacent cross-sections. These problems have become the most crucial obstacle in the current application of 3D geological modeling based on parallel geological cross-sections. To this end, this paper proposes a topological reasoning automatic processing method for the consistency of parallel geological cross-sections to obtain topologically consistent adjacent cross-sections efficiently. The method involves (1) Stratigraphic inference based on the distance and topological relationships, transforming strata with m:n relationships into three relatively simple cases of 1:1, 1:n, and 1:0; (2) Splitting branching strata based on the Voronoi diagram, transforming strata with 1:n relationships into 1:1 relationships; (3) Dealing pinch-out strata based on the method of filling virtual strata, transforming strata with 1:0 relationships into 1:1 relationships (4) Topologically consistent processing of stratigraphic boundaries based on automatic recognition of no-corresponding arcs and addition of virtual stratum. Based on experiments in the Longtoushan Hill area in Jiangsu Province, China, and the southeast side of Chandler Mountain, Alabama, USA, this method can effectively replace the many manual processes such as determining the location of pinch-out adding auxiliary cross-sections and performing branching strata correspondence in the current application of 3D geological modeling based on arc segments. The geological cross-sections processed based on this method have an exemplary data structure and consistent topological relationships. The 3D geological modeling algorithm based on parallel cross-sections has the advantages of low algorithm complexity, high automation, and stable modeling quality.

ADVANCES ON THE USE OF SATELLITE DERIVED PRODUCTS TO DETECT COASTAL CHANGES: DEMONSTRATION CASE ON THE COAST OF SPAIN

Regular and efficient monitoring of coastal changes is necessary to inform coastal management decisions. In this regard, recent developments based on Google Earth Engine (Gorelick et al., 2017) enable the automatic detection of coastal changes using satellite data (Turner et al., 2021) However, such tools are usually calibrated with limited information and, consequently, they may lead to significant errors when applied to beaches with different conditions than those considered in the calibration data sets. In this work, we evaluate the capability to monitor changes in coastal morphology at various temporal and spatial scales using satellite-derived data obtained from an automatic processing method that includes site-specific information. These method was developed within the framework of the Coastal Erosion from Space Project (https://coastalerosion.argans.co.uk/) that brought together experts from several countries with different backgrounds, such as Earth Observation, Geology, Engineering and Management, to develop satellite products to monitor coastal change based on end-user requirements.

The interactive Leipzig Corpus Miner: An extensible and adaptable text analysis tool for content analysis

We present the interactive Leipzig Corpus Miner (iLCM), which is the result of the development of an integrated research environment for the analysis of text data. The key features of iLCM compared to existing software tools for computer-assisted text analysis are its flexibility and scalability. The tool includes functions to offer commonly needed methods for automatic processing of text, such as preprocessing, standard text analysis, and visualization, which would be very time consuming without a ready-to-use software tool. To also provide more methodological flexibility, the iLCM is not tied to one specific class of research question, but can easily be extended to other applications. In this article, we will focus on the capabilities and the aspects of adaptability, extensibility, and data exchange with other tools from the field of empirical content analysis. We will present the features of the iLCM and showcase individual examples and a case study that demonstrates the practical use of the tool.