Modern Methods Research Articles

Ultra Fast Deep Lane Detection With Hybrid Anchor Driven Ordinal Classification.

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problems of efficiency and challenging scenarios like severe occlusions and extreme lighting conditions. Inspired by human perception, the recognition of lanes under severe occlusions and extreme lighting conditions is mainly based on contextual and global information. Motivated by this observation, we propose a novel, simple, yet effective formulation aiming at ultra fast speed and the problem of challenging scenarios. Specifically, we treat the process of lane detection as an anchor-driven ordinal classification problem using global features. First, we represent lanes with sparse coordinates on a series of hybrid (row and column) anchors. With the help of the anchor-driven representation, we then reformulate the lane detection task as an ordinal classification problem to get the coordinates of lanes. Our method could significantly reduce the computational cost with the anchor-driven representation. Using the large receptive field property of the ordinal classification formulation, we could also handle challenging scenarios. Extensive experiments on four lane detection datasets show that our method could achieve state-of-the-art performance in terms of both speed and accuracy. A lightweight version could even achieve 300+ frames per second(FPS). Our code is at https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2.

Local Means Binary Networks for Image Super-Resolution.

The success of modern single image super-resolution (SISR) algorithms is inspired by the development of deep convolutional neural networks (CNNs). However, these CNN-based methods require considerable computation and complexity, making it impossible for these methods to perform real-time calculations in edge devices. Thus, lightweight model design has become a development trend in the super-resolution field, including pruning, quantization, and other methods. The 1-bit quantization is an extreme lightweight method which can reduce the calculation amount of the model in an extreme manner and is friendly to hardware such as edge devices. Most existing binary quantization approaches lead to a large information loss during forward propagation, especially in detailed color information (e.g., edge, texture, and contrast). The loss of color information makes modern binary methods unsuitable for SISR tasks. We think the loss occurs because these methods typically utilize a uniform threshold to quantize the weights and activations. Thus, in this article, we thoroughly analyze the difference between normal classification tasks and SISR tasks, and present a binarization scheme based on local means. The proposed method can maintain more detailed information in feature maps using dynamic thresholds during quantization. Specifically, each value in the full precision activations has a corresponding threshold during the quantization process, and those thresholds are determined by the full precision values of the surroundings. In addition, a gradient approximator is introduced to adaptively optimize the gradient for updating binary weights. We then verify the effectiveness of our method for training binary networks on several SISR benchmarks including VDSR and SRResNet. Experimental results show that the proposed method can outperform the state-of-the-art algorithms to obtain binary networks for image super-resolution with better peak signal-to-noise ratio (PSNR) values and visual quality.

A Comprehensive Review of Modern Methods for Load Prediction in the Smart Grid

: Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and Accuracy. Machine learning-based techniques showed a reduction of 9.17% in MAPE, 0.0429% in RMSE, and 5.23% in MSE, and achieved 90% accuracy. Deep learning-based techniques resulted in a 9.61% decrease in MAPE and achieved 91% accuracy. Bioinspired techniques provided a reduction of 9.66% in MAPE, 0.026% in RMSE, and 5.24% in MSE, and achieved 95% accuracy. These findings concluded that optimization techniques are more encouraging in predicting load demand and, as a result, can represent a reliable decision-making tool.

ДОСЛІДЖЕННЯ ТЕХНОЛОГІЧНИХ ПОКАЗНИКІВ РОБОТИ ВІБРАЦІЙНОГО ГРОХОТА З ЕЛАСТИЧНИМИ СИТАМИ

In the metallurgical branch of industry, the problems of separation according to the given size - screening of the original metallurgical raw materials, in particular coke, are relevant. This ensures high-quality operation of the technological equipment of blast furnaces and supply of blast furnace coke of regulated size. In this regard, research is constantly being conducted and the development of new methods of screening and modernization of devices to improve the performance of vibrating screens continues. The purpose of the scientific work: an experimental study of the technological parameters of the vibration screen with elastic sieves. The paper gives a description of the laboratory vibrating sieve with an elastic sieve, which is used in the research. An experimental study of the technological parameters of the screening process of the vibrating screen was carried out and the dependence of the technological indicators on the parameters of the vibrating screen was constructed. It has been shown that the highest screening efficiency is observed at oscillation frequencies in the range of 22-27 Hz. At these frequencies, the efficiency indicators of the screening process vary between 78-90%. A change in the value of the specific load from 10 t/(h m^2) to 16 t/(h m^2) causes a gradual decrease in the screening efficiency from 90% to 65%.

PERIODIC CONTROL OF CONCRETE PROPERTIES IN THE SYSTEM OF TECHNICAL CONDITION ASSESSMENT FOR HIGH-STORY BUILDING

The assessment of the structures’ technical condition is conducted in accordance with current regulations. During such assessment, various methods are used to determine the physical and mechanical characteristics of the structure’s material. Usually, a single measurement is performed, but in some cases, it is necessary to establish observations to determine the behaviour of the observed defects. Particular attention should be paid to buildings that have not been in use for some time and no measures have been taken to preserve the structures. Since the conditions of concrete formation and hardening in the samples during the establishment of taring dependencies and in the structure in operation are different, the existing non-destructive testing methods do not adequately reflect the properties of the structure’s concrete. Experience in the use of ultrasonic devices and our own research to improve the ultrasonic method allowed us to recommend limiting the scope of this method to periodic inspection, since it is quite easy and accurate to track relative changes in the material of the structure. The purpose of this work – verification of the effectiveness of the improved method of conducting periodic control of concrete properties in the conditions of a real object. Results. The technical condition of the structures of the multi-story building was assessed. Within the scope of the survey, the capabilities of the modernized method of periodic control of the concrete structures’ properties during their operation were checked. Ultrasonic devices are involved in the work, provided that the measurement sites are specially prepared. An increase in the accuracy of determining material properties has been demonstrated. Conclusion. The developed method of periodic control of the concrete structures’ properties demonstrates the low complexity of the work and the sufficient reliability of the obtained results, which allows us to propose it for use in determining the technical condition of the structures in operation.

Decision support method for optimal modernization of residential buildings

Decision-making for the refurbishment of multi-family residential buildings is a complex and computationally difficult task. Therefore, the authors have developed a model that supports modernization planning in a long-term and comprehensive manner, i.e. from assessing the building to indicating the optimal scope of modernization. The comprehensive scope of the model includes the acquisition and provision of relevant knowledge to the model. The original methods proposed for its acquisition are derived from common expert knowledge based on linguistic terms. The methods adopted are not mandatory and may be replaced by others that provide more reliable knowledge. The fundamental aim of the proposed approach, however, is to select the optimal modernization option and allocate it over the planned modernization time horizon. An innovative optimisation approach based on decision matrices is used, allowing the selection of possible scenarios of repair options at each stage. These matrices are a set of constraints written in binary variables allowing the optimisation calculus to maintain a fixed sequence of repairs. In addition, the solutions used in the optimisation modules make it possible to take into account assumptions regarding the assumed assessment of the building’s condition and financial constraints. The developed model provides a practical and versatile tool that can be used by managers at the maintenance stage of residential buildings.

COMPREHENSIVE CONDITION ANALYSIS AND PERSPECTIVES OF ENERGY DEVELOPMENT IN UKRAINE ACCORDING TO THE SMART GRID CONCEPT

The article examines development trends of the energy complex of Ukraine, possibility and prospects of creating a modern energy complex through the use of an innovative technical base for energy management in accordance with the Smart Grid concept. The general properties of Smart Grid technology and its main advantages are given by authors. It is shown that within the framework of the Smart Grid concept, the intelligent electric power system is considered as a single network of information and control systems. The process of electric power industry modernization in the direction of the creation of "smart" power supply networks in a number of European countries, which aims to ensure stable development, economic growth, growth of living standards and protection of the natural environment, is considered. The paper describes modernization methods of the electric power complex of Ukraine based on foreign experience. It is shown that from the point of view of energy security and sustainable development, Smart Grid is able to ensure the operation of the power grid even in case of damage or destruction of one segment, which is a key advantage during post-war reconstruction. They will also make it possible to effectively integrate renewable generation and energy storage systems into the grid, as well as provide auxiliary services for forecasting the power system operation. The paper analyzes the problematic issues of implementing the Smart Grid concept in Ukraine. They include the lack of technological solutions and methods that provide an effective algorithm for determining the state of power networks cyber security; large length of power distribution networks and insufficiently developed infrastructure. A comparative description of the existing energy systems functional properties and energy systems based on the Smart Grid concept is presented in general. An analysis of possible ways of electric power industry development was carried out, which showed the presence of serious limitations of electric power industry development within the framework of the former concept, based mainly on the improvement of certain types of equipment and technologies. It is shown that Ukraine is at the initial familiarization and formation stage of the first organizational initiatives for Smart Grid, namely the implementation of the government-approved Concept of the "smart grids" introduction in Ukraine for the period up to 2035. The purpose and expected results of this Concept implementation are considered.

Методы борьбы с отмыванием средств в транзакциях с цифровым рублем

The article presents a comprehensive analysis of modern methods of combating money laundering in transactions with digital ruble. Given the growing adoption of digital technologies and the increasing role of electronic payments, the need to adapt financial control mechanisms becomes obvious. The article highlights key problematic aspects in the field of security of digital currency transactions and demonstrates actual methods of their solution.

Overview of clinical and laboratory evaluation results of the effectiveness of digital methods for knowledge-based fabrication of complete removable acrylic dentures

This article provides an overview of the conceptual studies of modern digital technologies for the fabrication of complete removable acrylic dentures. Analysis of data of subtractive and additive methods of digital manufacturing shows the advantages of digital CAD/CAM methods over traditional methods. The use of known prepolymerized acrylic blocks in the subtractive milling method has enabled achieving high physical and mechanical properties, optimal spatial accuracy, and minimal thickness of the fabricated dentures. Moreover, residual monomer volume reduction in solid polymer blocks has allowed the high biological inertness and safety of the fabricated structures for prosthetic bed tissues and the patient’s body. Layer-by-layer printing of removable dentures by fused polymer deposition modeling technology results in high spatial accuracy of constructing structures of any degree of complexity and reduced total production time of removable dentures.
 Despite the intensity of implementation of digital CAD/CAM technologies in the practice of complete removable denture manufacturing, the search for alternative methods of modernization of the known traditional means and methods continues. However, we believe that automation and digitalization of the clinical and laboratory manufacturing of complete removable acrylic dentures has clear prospects for the total replacement of compression pressing and hot polymerization techniques of acrylates. In summary, the potential prospect in the future is a global reassessment of traditional concepts for the fabrication of complete removable dentures, considering the formation of an innovative digital CAD/CAM philosophy and the introduction of computer intelligent systems.

Controller-Guided Partial Label Consistency Regularization with Unlabeled Data

Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid. In recent years, benefiting from the strong capability of dealing with ambiguous supervision and the impetus of modern data augmentation methods, consistency regularization-based PLL methods have achieved a series of successes and become mainstream. However, as the partial annotation becomes insufficient, their performances drop significantly. In this paper, we leverage easily accessible unlabeled examples to facilitate the partial label consistency regularization. In addition to a partial supervised loss, our method performs a controller-guided consistency regularization at both the label-level and representation-level with the help of unlabeled data. To minimize the disadvantages of insufficient capabilities of the initial supervised model, we use the controller to estimate the confidence of each current prediction to guide the subsequent consistency regularization. Furthermore, we dynamically adjust the confidence thresholds so that the number of samples of each class participating in consistency regularization remains roughly equal to alleviate the problem of class-imbalance. Experiments show that our method achieves satisfactory performances in more practical situations, and its modules can be applied to existing PLL methods to enhance their capabilities.

Оrganization of the initial training process for athletes in bodybuilding

The article examines generalized data on approaches to the construction of sports training during the year. The systematization of the data of special scientific and methodical literature on the construction of the training process of athletes in bodybuilding is presented. The article discusses the key components of methodical methods of building the educational and training process of athletes. The training of highly qualified athletes is characterized by a clearly expressed cycle. Unsolved issues of building the training process of highly qualified bodybuilders in the annual macrocycle have been identified. The purpose of the study is to systematize the data of special scientific and methodological literature on the issues of building an education. To achieve this goal, modern methods were developed: theoretical analysis and data processing, descriptive method, classification, systematization and referenced scientific and methodological literature, sociological research methods. The expansion of the number of competitions, periodic changes in the programs and rules of competitions significantly affect the training system, including its periodization. However, the analysis of numerous innovations in the field of building sports training during the year only shows that they do not in any way contradict the main provisions of the periodization system, but only complement and develop its individual parts, taking into account the features of the modern stage of bodybuilding development.
 Summing up, it should be noted that in the scientific and methodological literature, the specifics of the training process of athletes engaged in bodybuilding have been studied in depth, but the issue of developing training programs taking into account the body structure of athletes remains unresolved. Therefore, research in this direction is relevant for the theory and practice of bodybuilding.

Does the composition of meteoritic metal change with forging? An experimental study

AbstractThe use of meteoritic iron in the manufacture of human artifacts since the Bronze Age has been well documented, including the iron blade of Tutankhamun's dagger. Whereas the preservation of textures and mineral inclusions suggest relatively low temperature (<950°C) working of meteoritic metal used in artifacts, higher temperature working—that is, forging—could have occurred, based on studies of Bronze Age slag. The extent to which the forging of meteoritic iron might change the bulk composition, especially the trace elements used for classification of iron meteorites, is largely unknown. Using electron microbeam methods (SEM and EPMA), and trace element analysis (ICP‐MS), we analyze metal obtained at different stages during the modern forging of a set of knife blades from fragments of the Gebel Kamil meteorite, and assess the degree to which bulk element composition, mineral inclusions, and textures are modified. We find that while forging does destroy the original texture and removes mineral inclusions, it does not significantly modify the trace elements typically used in iron meteorite classification, at least for the relatively Ni‐rich composition represented by Gebel Kamil. While we acknowledge that the modern method by which the knife blades were forged from Gebel Kamil would not have occurred in the Bronze Age, our results represent an upper temperature limit relative to the inferred conditions used in ancient forging. The identification of the meteorite (if still in existence) that was used for artifacts is feasible, based on our results and current literature on ancient meteoritic artifacts.

Consumer Price Index in the Czech Republic – New Sources and Data Processing

Consumer price index has been in the centre of interest for many years, since being published in 1990s in the Czech Republic but recent price growth raised more questions on methodology and data sources used in price statistics. Users are interested not only in the figures itself but also in statistical issue influencing interpretation and the quality of consumer price index that is often used as an approximation of inflation rate. The paper introduces price statistics compiled by the Czech Statistical Office and it specifically focuses on data sources and in particular scanner data. The paper explains how advanced statistical methods such as machine learning are implemented in official statistical production. We think that the official statistics is being on the historical junction where modern methods are going to be implemented. Our paper shows the usage of machine learning procedures applied on scanner data within consumer price index. Used method is based on logistic regression and powerful Python solution and that provides fast and high quality results.

MODERN APPROACHES TO TEACHING HOW TO READ FICTION IN THE ORIGINAL LANGUAGE

Learning how to read fiction in the original language is an integral part of foreign language teachers training and requires the use of modern methods of blended learning. The purpose of this work is to develop a method of reading the novel “Headlong” by Michael Frayn using the “flipped classroom” technique. As a result, we published a student’s book, the content of which is structured in accordance with the theory of text understanding types. The article focuses on the results of the study.

Cyanobacterial Proteomics: Diversity and Dynamics.

Cyanobacteria (oxygenic photoautrophs) comprise a diverse group holding significance both environmentally and for biotechnological applications. The utilization of proteomic techniques has significantly influenced investigations concerning cyanobacteria. Application of proteomics allows for large-scale analysis of protein expression and function within cyanobacterial systems. The cyanobacterial proteome exhibits tremendous functional, spatial, and temporal diversity regulated by multiple factors that continuously modify protein abundance, post-translational modifications, interactions, localization, and activity to meet the dynamic needs of these tiny blue greens. Modern mass spectrometry-based proteomics techniques enable system-wide examination of proteome complexity through global identification and high-throughput quantification of proteins. These powerful approaches have revolutionized our understanding of proteome dynamics and promise to provide novel insights into integrated cellular behavior at an unprecedented scale. In this Review, we present modern methods and cutting-edge technologies employed for unraveling the spatiotemporal diversity and dynamics of cyanobacterial proteomics with a specific focus on the methods used to analyze post-translational modifications (PTMs) and examples of dynamic changes in the cyanobacterial proteome investigated by proteomic approaches.

Modern Methods for Monitoring the Electrical Safety Parameters of Electrical Installations

Modern Methods for Monitoring the Electrical Safety Parameters of Electrical Installations

18-49 yaş arası evli kadınlarda sağlık okuryazarlığı ve gebeliği önleyici yöntem kullanımının değerlendirilmesi

Objective: To determine the level of health literacy and assess contraceptive use in married women. Methods: From August 2020 to October 2020, a cross-sectional survey was conducted among married women aged 18 years and older residing in various regions across Turkey. The survey targeted this demographic via an online questionnaire, carefully crafted by existing literature and aligned with the study's aims. The study cohort consisted of 1024 married women who voluntarily participated in the research. Results: The study group included women between the ages of 18 and 49, with a mean age of 32.74 years and a standard deviation of 6.85. Scores on the European Health Literacy Survey Questionnaire spanned from 1.11 to 50.0, with a central score at the median of 34.38. Health literacy was found to be lower in those aged 35 years and above, those who graduated from primary school, those who have been married for 20 years or more, those who had given birth 3 times or more, those with 2 and more living children and those with a history of stillbirth. While no significant relationship was found between the use of contraceptive methods and health literacy, health literacy was determined to be higher among women who stated that they would prefer a modern method if they had to use a contraceptive method again. Conclusion: It can be suggested that married women had moderate health literacy. Preference for modern contraceptive methods can be encouraged through informative activities to improve the health literacy of women.

Classification of optical coherence tomography images using deep machine learning methods

Backgraund. Optical coherence tomography (OCT) is a modern high-tech and informative method for detecting pathology of the retina and preretinal layers of the vitreous body. However, the description and interpretation of the research results require high qualifications and special training of an ophthalmologist, and significant time expenditure for the doctor and the patient. At the same time, the use of mathematical models based on artificial neural networks (ANN- models) currently makes it possible to automate many processes associated with image processing. Therefore, solving problems associated with automating the process of classifying OCT images based on ANN models is actual.
 Aims. To develop architectures of mathematical (computer) models based on deep learning of convolutional neural networks (CNN) for classification of OCT images of the retina. To compare the results of computational experiments conducted using Python tools in the Google Colaboratory with single-model and multi-model approaches and evaluate classification accuracy. To make conclusions about the optimal architecture of ANN models and the values of the hyperparameters used.
 Materials and methods. The original dataset, which was anonymized OCT images of real patients, included more than 2000 images obtained directly from the device in a resolution of 1920 × 969 × 24 BPP. The number of image classes is 12. To create the training and validation data sets, a subject area of 1100 × 550 × 24 BPP was “cut out.” Various approaches were studied: the possibility of using pretrained CNNs with transfer learning, techniques for resizing and augmenting images, as well as various combinations of hyperparameters of ANN-models. When compiling the model, the following parameters were used: Adam optimizer, categorical_crossentropy loss function, accuracy metric. All technological processes with images and ANN-models were carried out using Python language tools in Google Colaboratory.
 Results. Single-model and multi-model principles for classifying OCT images of the retina are proposed. Computational experiments on automated classification of such images obtained from a DRI OCT Triton 3D tomograph using various ANN model architectures showed an accuracy of 98-100% during training and validation and 85% during an additional test, which is a satisfactory result. The optimal architecture of the ANN model - a six-layer convolutional network - was selected and the values of its hyperparameters were determined.
 Conclusions. The results of deep training of convolutional neural network models with various architectures, their validation and testing showed satisfactory classification accuracy of retinal OCT images. These developments can be used in decision support systems in the field of ophthalmology.

A View of Civil Law and Islamic Law on the Practice of Surrogate Mother

IVF is one of the modern methods that can help legal couples obtain children. Both Indonesian Civil Law and Islamic Law allow this practice. Still, it must be carried out based on the provisions, namely by involving couples who are legal in the eyes of the law. IVF carried out through surrogate motherhood is prohibited by both positive law and Islamic law because its existence can disrupt lineage. Children born from this practice are not considered legitimate due to confusion and ambiguity during the process. This research is a normative juridical literature study that is processed based on primary and secondary legal sources consisting of primary, secondary, tertiary legal materials, and non-legal materials with the aim of: 1) Understand how Indonesian civil law and Islamic law view the issue of surrogate mothers; and 2) Knowing how the guardianship status of IVF children born through surrogacy practices. It was found that both Positive Law and Islamic Law prohibit the practice of IVF with surrogate mothers because it results in legal confusion for the child. This research is expected to contribute to further research, especially research that raises the theme of IVF.

Reconsidering Sampling and Costs for Face-to-Face Surveys in the 21st Century

Abstract Some fundamental factors important for designing multistage face-to-face household surveys in the United States have not been reconsidered since they were established in the 1940s despite vast changes in all aspects of conducting these surveys. This research examines how some changes, particularly data sources and data collection costs, might affect sample designs. Modern data collection methods and tools, address-based household lists, multimode designs, and training and management methods are critical components in the cost structure of today’s surveys. We explore a sample design that uses Census tracts at the first stage rather than counties as traditionally used. The tract design then samples households directly without the additional stages of subsampling of segments in the traditional design. We present statistical and operational issues associated with the designs and discuss the cost implications. Our examination finds that, for household surveys in the United States that include face-to-face data collection, tract designs that have twice as many sampled first-stage units (but the same overall sample size) have roughly the same cost as the county design, with effective sample sizes in the range of about 25 percent higher to as much as four times higher than the county design.