Intelligent application of Beidou satellite positioning and artificial intelligence algorithm in information verification of new energy automation equipment

Background As per the Global Burden of Disease Study (GBD) 2019, chronic obstructive pulmonary disease (COPD) and asthma had a significant global burden. COPD is the fourth leading cause of death in the world and the second leading cause of death and disability-adjusted life years (DALYs) in India. Pulmonary function tests (PFTs) are commonly used diagnostic tools. They include spirometry, body plethysmography, and diffusion capacity. In regions with limited resources, pulmonologists often only have access to spirometry. Additionally, PFT pattern interpretation is usually unreliable and subjective. Recent rapid advances in artificial intelligence (AI) algorithms can bridge the gaps. Objectives This study aims to compare the accuracy of the predictions made by AI algorithms with pulmonologists using limited clinical data and spirometry. It also examines the consistency and accuracy of pulmonologists' predictions based on the same information. Methodology Different AI algorithms were trained, and their accuracy was evaluated. Spirometry and limited clinical data from 440 patients were interpreted by an AI algorithm and eight senior pulmonologists. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the different patterns. Results Approximately 60% of the cases involved male patients, and about 70% were between the ages of 21 and 60. The Fleiss's kappa was 0.46. While the accuracy of pulmonologists against the gold standard was 65.82%, the accuracy of the AI was 86.59%. Conclusions PFTs, when interpreted by pulmonologists with limited clinical and spirometry data, have lower accuracy and higher variability. AI algorithms can consistently produce high accuracy. Adopting such technology among clinicians, especially in resource-constrained regions, could be pivotal for offering quality healthcare. In addition, it will also help in getting rid of inter-observer variability.

In recent years, the operation and maintenance of radio transmission equipment have faced challenges such as equipment aging, high maintenance costs, long response time, and technological updates. It is necessary to adapt to new technologies and improve maintenance efficiency. The operation and maintenance strategy of radio transmitting equipment based on big data (BD) aims to optimize the reliability and operating efficiency of equipment through data analysis and intelligent algorithms, and reduce failure risk and maintenance costs. By analyzing the operation data and maintenance records of a large number of radio transmission equipment, it revealed the patterns and trends of equipment failures, in order to timely identify and predict possible faults, and take corresponding maintenance measures. This article adopted methods such as data collection and preprocessing, fault prediction and diagnosis, and maintenance optimization to achieve accurate monitoring of equipment operation status, fault prediction and diagnosis, optimized maintenance, and decision support. This can improve the operational efficiency and reliability of radio transmission equipment, which is used for fault diagnosis of radio transmission equipment through SVM (Support Vector Machine) algorithms. By training SVM classifiers, this article associated different types of faults with their feature data, thereby quickly and accurately determining the type of equipment fault based on real‐time collected device data, providing a basis for maintenance decision‐making. The experimental results show that the failure frequency of the radio transmitting equipment in the control group was between 7% and 11%, while the failure frequency of the radio transmitting equipment in the experimental group was between 3% and 7%. Experiments show that BD technology can effectively reduce the failure frequency of radio transmitting equipment. The empirical results indicate that this strategy can improve the reliability of equipment and reduce operation and maintenance costs, which has important guiding significance for the operation and maintenance management of radio equipment. This would bring about more efficient and reliable operation and maintenance management of radio equipment, making important contributions to the smooth progress of broadcasting business and the improvement of user experience.

Service risk of energy industry international trade supply chain based on artificial intelligence algorithm

Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: A scoping review

Purpose. The aim of the article is the theoretical and practical substantiation of the prospects for interaction between the state and society in order to overcome the energy crisis associated with the consequences of the russian-Ukrainian war through the development of wind and solar power generation and, as a consequence, cryptocurrency heat generation. Methodology of research. The methodological basis of this economic scientific research is the dialectical method of scientific cognition. In the process of economic scientific research, general scientific and special scientific methods were applied, among which the main ones are: abstract and logical methods – for formulating general conclusions of economic scientific research; systemic approach – in determining the causes and factors of the influence of the interaction of the state and society in the development of wind and solar power generation and, as a consequence, the development of cryptocurrency heat generation, on the energy security of households. Findings. The role of green energy was studied, in particular, the trends in the installation of small solar and wind power plants by households and firms to ensure the economic, demographic and energy security of the country in the conditions of a full-scale russian-Ukrainian war. Based on the analysis, it was determined that state support for households' investment in the installation of their own solar and wind power plants through a reduction in the tax burden on the import of their components, as well as financial incentives for their purchase, is a key factor in the development of green energy. It is estimated that investments by economic entities in the development of solar and wind power plants in 2024 and subsequent years will allow citizens not only to reduce electricity costs, avoid blackouts and ensure stable operation of critical electrical appliances, but will also contribute to energy independence in the long term. In addition, such investments can become a source of profit throughout the entire period of operation of energy equipment. The possibilities of using excess electricity for cryptocurrency mining are analysed, which creates significant additional economic benefits for households. It is summarized that the combination of cryptocurrency mining and electricity generation by home solar and wind power plants creates a significant synergistic effect that has a mutual economic and security impact on households. Based on the research conducted, it is substantiated that both during the war and in the post-war period, it is economically feasible for households to invest simultaneously in two areas – electricity production from renewable sources and cryptocurrency mining. This will maximize the benefits from the use of generated electricity, increase financial stability, and promote energy autonomy. Originality. The substantiation of the feasibility of introducing state investment support for households by economic entities in the purchase of solar power plants and their payback with the possibility of ensuring a synergistic positive economic effect at the micro- and macro-economic level has gained further development. Practical value. The obtained results of the study can serve as the basis for households and firms to make decisions on the installation of wind and solar power plants for electricity generation in order to meet personal needs and, as a result, additional heat generation, and the mining of crypto coins using cryptocurrency farms is advisable for the state and its citizens due to the achievement of a significant positive synergistic economic effect. Key words: investments, wind power plants, solar power plants, green energy, cryptocurrency, mining.

Wind power generation is clean renewable power without environmental pollution, so wind power plant has been paid more and more attention, and has been popularized in recent years. As an unstable energy, wind power has high requirements for the operation, management and protection of facilities to ensure the safe operation of the system. In order to improve the power efficiency, it is necessary to conduct in-depth research and Discussion on the operation management and guarantee countermeasures of the current wind power plant equipment. Based on this, this paper deeply discusses the key factors restricting the operation safety of the power transmission and transformation equipment of the wind power plant, and puts forward the safety technical means to enhance the operation safety of the power transmission and transformation equipment. It is expected to have a certain reference significance for the development of the power transmission and transformation equipment of the wind power plant, Thus, it has a positive reference for the management and guarantee measures of the current operation of power transmission and transformation equipment in wind power plants.

Современная нефтегазовая отрасль сталкивается с рядом проблем, связанными с управлением и мониторингом оборудования в условиях высокой сложности и удаленности объектов. Традиционные методы обслуживания и контроля требуют значительных ресурсов, что подчеркивает необходимость внедрения новых технологий. Цель исследования – разработать комплексную систему мониторинга и управления, основанную на применении цифровых двойников и облачных вычислений. В работе использован подход, включающий создание цифровых двойников для моделирования и контроля работы оборудования в реальном времени. Основой системы является интеграция облачных технологий для обработки больших объемов данных, полученных с датчиков, установленных на оборудовании. Применены современные алгоритмы анализа данных и предиктивной аналитики. Развертывание прототипа осуществлено на базе собственной облачной платформы, обеспечивающей высокую производительность и устойчивость. Разработанная система продемонстрировала эффективность в реальных промышленных условиях. Цифровые двойники обеспечили визуализацию работы оборудования, прогнозирование возможных отказов и снижение времени обслуживания на 20%. Благодаря облачным вычислениям удалось сократить затраты на локальную инфраструктуру на 25%. Интеграция системы позволила реализовать предиктивное обслуживание и управление работой оборудования в удаленном режиме, что значительно повысило производительность процессов. Исследование подтвердило перспективность применения цифровых двойников и облачных вычислений в управлении оборудованием нефтегазовой отрасли. Предложенная система не только оптимизирует эксплуатацию и обслуживание оборудования, но и снижает операционные издержки. Внедрение данных подходов имеет потенциал для масштабирования и адаптации к другим отраслям. В дальнейшем планируется расширение функционала системы и разработка решений для интеграции с существующими промышленными платформами. The modern oil and gas industry faces a number of challenges related to the management and monitoring of equipment in conditions of high complexity and remoteness of facilities. Traditional methods of maintenance and control require significant resources, which highlights the need to introduce new technologies. The purpose of the study is to develop a comprehensive monitoring and management system based on the use of digital twins and cloud computing. The work uses an approach that includes the creation of digital doubles for modeling and monitoring the operation of equipment in real time. The basis of the system is the integration of cloud technologies for processing large amounts of data received from sensors installed on the equipment. Modern algorithms of data analysis and predictive analytics are applied. The prototype was deployed on the basis of its own cloud platform, which provides high performance and stability. The developed system has demonstrated its effectiveness in real industrial conditions. Digital twins have provided visualization of equipment operation, prediction of possible failures and a 20% reduction in maintenance time. Thanks to cloud computing, it was possible to reduce the cost of local infrastructure by 25%. The integration of the system made it possible to implement predictive maintenance and remote control of equipment, which significantly increased the productivity of processes. The study confirmed the prospects of using digital twins and cloud computing in the management of equipment in the oil and gas industry. The proposed system not only optimizes the operation and maintenance of equipment, but also reduces operating costs. The implementation of these approaches has the potential to scale and adapt to other industries. In the future, it is planned to expand the functionality of the system and develop solutions for integration with existing industrial platforms.

Traditional monitoring systems rely on different types of sensors in the field of new energy grid connection monitoring. The collected data have inconsistent problems; the monitoring method is not real-time enough; the fault detection and anomaly recognition accuracy is low. This paper combines intelligent sensing technology and GNNs (Graph Neural Networks) to design a more efficient, smart, and precise new energy grid connection monitoring information verification method. High-precision intelligent sensors are used to collect multidimensional data of the new energy grid connection system in real-time, and data fusion technology is used to solve the data inconsistency problem between different sensors to ensure efficient and accurate data collection. The graph neural network algorithm framework is used to build the relationship diagram of nodes and edges, and the GNN model is used for information verification and fault detection. The advantages of graph structure are used to accurately obtain the information transmission of each node and improve the accuracy and real-time performance of fault detection. The collected data of the intelligent sensor and the graph neural network model are synergistically optimized to form a closed loop of data processing, model training, and fault prediction. The experimental results show that among the 10 different folds, the GNN model has a lower loss value, with an average loss value of 0.312, which can reduce the error in the information transmission process when processing the monitoring information of the new energy grid connection system; the fault recognition rates of the GNN model in abnormal voltage, current, frequency, and temperature scenarios are 0.92, 0.87, 0.9, and 0.85, respectively, which is suitable for complex fault detection tasks.

Research and Evaluation of the Diagnostic Precision of an Artificial Intelligence Algorithm Using Ultrasound Images for Early Diagnosis of Arthropathy in People with Hemophilia

A survey of modeling for prognosis and health management of industrial equipment

Rotating equipment is widely used in large industrial and mining enterprises, so its safe and stable operation is of great significance. With rapid development of artificial intelligence algorithms in recent years, many researchers apply them to the fault diagnosis of rotating machinery and equipment. This paper takes a company’s rotating equipment as an object, discusses artificial intelligence algorithms suitable for remote fault diagnosis. Then, taking circulating water pump as an example, it summarizes the characteristic vectors, monitoring methods and fault types of circulating water pump. Based on operating status and characteristic parameters of the equipment management system accessed to circulating water pump, it designs four types of typical artificial intelligence algorithm models, and assesses its accuracy and effects through simulation software. In this way, it supports rapid diagnosis and analysis of the existing faults, points out the cause of the fault in time, effectively reduces the number of unit shutdowns for maintenance, extends the cycle of unit operation, and provides reliable guarantees for the safe operation of energy equipment.

Wind turbines are complicated non-linear systems with certain random disruptions. The pitch control system is a commonly employed method for regulating the electricity generated by a wind turbine. Many researchers have observed developments in the pitch control field during the last few decades. Traditional PID controllers have the drawback of being slow or imprecise when wind and pitch angles suddenly change. These drawbacks can be solved with artificial intelligent algorithms. However, the algorithms' design and implementation are highly complex. A new pitch-regulated variable-speed control strategy for wind turbines to address their nonlinear properties is presented. To manage the pitch system's control mechanisms with disturbances, this research evolved a mathematical model that illustrates HAWT's pitch angle control system and applied a proposed Simple Optimal Intelligent PID Controller (SOI-PID). Under various operating conditions, the proposed SOI-PID controller was tested with the Traditional PID, Fuzzy Logic Controller (FLC), and Fuzzy-Adaptive-PID controller. For system simulation, the MATLAB/Simulink software was used. According to simulation results, compared to PID, FLC, and Fuzzy-Adaptive-PID controllers, the proposed SOI-PID controller responds faster and has a better rise and settling time. Other benefits of the SOI-PID controller are its simplicity of implementation and design, distinguishing it from other intelligent algorithms.

Decision support in construction equipment management using a nonparametric outlier mining algorithm

As climate change and long-term energy security drive the global energy sector towards renewable resources, powerful tools are required to optimise integration and management. A novel framework is proposed for effectively utilising Artificial Intelligence (AI) in the renewable energy landscape. AI algorithms can analyse weather patterns, historical generation data, and environmental factors to predict renewable energy output. Energy dispatch is optimised, grid integration is improved, and energy storage requirements are reduced. A system powered by artificial intelligence also significantly reduces downtime, optimises maintenance schedules, and minimises operational costs in wind turbines, solar panels, and other renewable infrastructure. AI can also optimise energy flows, reduce grid instability, and ensure efficient resource utilisation within the smart grid by dynamically managing renewable sources, energy storage systems, and demand profiles. Furthermore, AI-driven spatial analysis and resource mapping can identify optimal locations for renewable installations, considering factors like wind speed, solar irradiance, and environmental constraints. This paper presents two AI frameworks, one for solar energy and one for wind energy, to demonstrate possible applications. They both utilise comprehensive data acquisition, including real-time sensor data and external factors like weather forecasts and historical generation patterns. AI algorithms use these combined data to perform critical tasks such as predictive maintenance, minimising downtime, and maximising efficiency. Power output forecasting enables real-time adjustments based on weather, and optimal site selection maximises energy production. AI is used for proactive issue identification, accurate power output forecasting based on wind conditions, grid demand, storage capacity, dynamic load optimisation for maximum energy efficiency, and wind farm site selection. Integrating these tailored AI frameworks with solar and wind energy can achieve significant benefits such as increased efficiency, reduced operational costs, and seamless grid integration. In addition to analysing the challenges and opportunities associated with this AI integration, the paper explores infrastructure development, ethical considerations, and data acquisition. A second benefit of the research methodology is that it highlights how these tailored AI frameworks can optimise the integration of solar and wind renewable energy sources, providing valuable insights for researchers, practitioners, and policymakers who wish to use AI to create a more sustainable and efficient energy system. Keyword: Artificial Intelligence, renewable energy, climate change.

Predictive maintenance (PdM) leverages Artificial Intelligence (AI) integrated with the Industrial Internet of Things (IIoT) to proactively monitor and predict equipment failures, significantly optimizing operational efficiency while reducing downtime and maintenance costs. PdM shifts the maintenance paradigm from reactive or preventive strategies to a data-driven, predictive approach that ensures timely intervention based on the actual condition of equipment rather than predetermined schedules. Embedded systems, serving as the backbone of PdM, are equipped with AI algorithms that enable real-time data collection, processing, and decision-making at the edge of the network. These systems are designed to interface seamlessly with IIoT devices, gathering data from various industrial sensors and analyzing it to detect anomalies, estimate the remaining useful life (RUL) of equipment, and predict potential failures. The integration of AI capabilities such as machine learning (ML) and deep learning (DL) within embedded systems allows them to handle complex data streams, identify patterns, and make intelligent predictions in real time. This paper explores the multi-faceted aspects of AI-driven embedded systems for predictive maintenance in IIoT environments. First, it delves into the architecture of these systems, highlighting the interplay between hardware components such as microcontrollers, sensors, and communication modules, and software frameworks that incorporate AI algorithms for data processing and analysis. The role of edge computing in reducing latency and enabling on-site decision-making is also emphasized. Second, the paper examines the AI algorithms commonly employed in PdM, such as neural networks, support vector machines, and ensemble methods, discussing their suitability for various industrial applications. Specific attention is given to the use of advanced techniques like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for handling time-series sensor data and identifying early warning signs of equipment degradation. Furthermore, the practical applications of these systems across industries are reviewed, showcasing use cases in sectors such as manufacturing, energy, transportation, and healthcare. For instance, AI-driven embedded systems have been used to monitor conveyor belts, wind turbines, railways, and medical equipment, providing tangible benefits like extended equipment lifespan, improved safety, and reduced operational costs. The paper also presents case studies and performance metrics to evaluate the effectiveness of AI-driven PdM systems. Metrics such as prediction accuracy, false positive rate, and computational efficiency are analyzed to demonstrate the strengths and limitations of this approach. Challenges such as the high initial cost of implementation, data privacy concerns, and the need for robust cybersecurity measures are discussed to provide a balanced perspective.