Design of real-time data analysis system for physical training based on data mining technology

Real-time multiprocessor systems have an increasing importance in current technology and are the source of innovations to build new applications that require high levels of computations (in for example, industrial processes, transportation, telecommunications, multimedia and medical equipment). In the context of real-time systems, it is not sufficient to prove the correctness of applications in term of results but also that those results are delivered in time. The following papers are extended, peer-reviewed, journal-versions of papers focusing on multiprocessor systems that have been selected by the conference program committee of the 19th Real-Time and Network Systems Conference (RTNS’2011) for their high quality. RTNS’2011 was held in Nantes, France in September 2011. The annual RTNS conference is mainly focused on the following topics: realtime system design and analysis; infrastructure and hardware for real-time systems; and real-time applications in the automotive, avionics, telecommunications, process control and the multimedia industries. Each paper was extended to add significant extra material and the resulting papers were re-reviewed and subject to the usual editorial process. The result is a special issue that we hope will be of interest to the real-time community. In recent years, multiprocessor architectures have become a firm favorite with the real-time community, and multicore processors are found in products ranging from small portable cell phones to large computer servers. New scheduling approaches

The purpose is to enhance the timeliness of news, provide real-time news for the audience, and attract more traffic for the news media, thereby helping news media grow financially and strengthening the network security communication efficiency and accuracy. First, an intelligent real-time image acquisition system is introduced, and its principle and advantages are analyzed. Second, an intelligent real-time image acquisition model based on DL (Deep Learning) algorithm is established by combining DM (Data Mining) technology. Finally, the application of an intelligent image acquisition system in network communication is adjusted. The research results show that the combination of DL intelligent real-time image acquisition system with DM technology can effectively deal with various network communication scenarios, enhancing the accuracy and timeliness of network communication. The AlexNet model performs excellently in all aspects, with or without data enhancement or equalization. The recognition rate reaches 99.6% after data equalization and enhancement, and the AlexNet model can accurately classify news and non-news sceneries. This shows that the application of an intelligent real-time image acquisition system based on DM technology can effectively enhance the real-time performance of network communication. The results set an example for the application of relevant intelligent image acquisition technology in the journalism industry.

Recently, significant progress has been made in the development of timed process algebras for the specification and analysis of real-time systems. This paper describes a timed process algebra called ACSR, which supports synchronous timed actions and asynchronous instantaneous events. Timed actions are used to represent the usage of resources and to model the passage of time. Events are used to capture synchronization between processes. To be able to specify real systems accurately, ACSR supports a notion of priority that can be used to arbitrate among timed actions competing for the use of resources and among events that are ready for synchronization. The paper also includes a brief overview of other timed process algebras and discusses similarities and differences between them and ACSR.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The purpose of the work is to study the current problems and prospects of the solution for processing big data received or stored in the Internet (web data), as well as the possibility of practical realization of Data Mining technology for big web data on practical example. Materials and methods. The study included a review of bibliographic sources on big data analysis problems.Data Mining technology was used to analyze large web data, as well as computer modeling of a practical problem using the C # programming language and creating a DDL database structure for accumulating web data.Results. In the course of the work, the specifics of big data were described, the main characteristics of big data were highlighted, and modern approaches to processing big data were analyzed. A brief description of the horizontal-scalable architecture and the BI-solution architecture for big data processing is given. The problems of processing large web data are formulated: limiting the speed of access to data, providing access via network protocols through general-purpose networks.An example showing the approach to processing large web data was also implemented. Based on the idea of big data, the described complexities of web data processing and the methods of Data Mining, techniques were proposed for effectively solving the practical problem of processing and searching patterns in a large data array.The following classes have been developed in the C # programming language:Class of receiving web data via the Internet; Data conversion class;Intelligent data processing class;Created DDL script that creates a structure for the accumulation of web data.A single UML class diagram has been developed.The constructed system of data and classes allows to solve the main part of the problems of processing large web data and perform intelligent processing using Data Mining technology in order to solve the problem posed of identifying certain records in a large array. The combination of object-oriented approach, neural networks and BI-analysis to filter data will speed up the process of data processing and obtaining the result of the studyConclusion. According to the results of the study, it can be argued that the current state of technology for analyzing large web data allows you to efficiently process data objects, identify patterns, get hidden data and get full-fledged statistical data.The obtained results can be used both for the purpose of the initial study of big data processing technologies, and as a basis for developing an already real application for analyzing web data. The use of neural networks and the created universal classes-handlers makes the created architecture flexible and self-learning, and the class declarations and the base DDL structure will greatly simplify the development of program code.

: This report summarizes a survey of published research in real time systems. Material is presented that provides an overview of the topic, focusing on communications protocols and scheduling techniques. It is noted that real-time systems deserve special attention separate from other areas because of the unique properties. This has not been the case in computer science and operations research until quite recently, resulting in the absence of formal tools for design and analysis of real-time systems. The early work on applications as well as notable theoretical advances are summarized. Descriptions of more recent work in the field from the mid-1980s to the present are also provided.

Using different statistical analysis methods to examine hydrologic time-series data is the basis of accurate hydrologic status analysis. With the wide application of the Internet of Things and sensor technologies, traditional statistical analysis methods are unable to meet the demand for real-time and accurate hydrologic data analysis. The existing mainstream big-data analysis platforms lack analysis methods oriented to hydrologic data. In this context, a real-time statistical analysis library based on the new generation of big data processing engine Flink, called HydroStreamingLib, was proposed and implemented. Furthermore, in order to prove the efficiency and handiness of the proposed library, a real-time statistical analysis system of hydrologic stream data was developed based on the concepts available in the proposed library. The results showed that HydroStreamingLib provides users with an efficient, real-time statistical verification method, thus extending the application capabilities of Flink Ecology in some specific fields.

Real-time systems must avoid hazardous situations. To achieve this, their functionality should be investigated under time constraints. In this paper, a modeling based on Hierarchical Communicating Real-time State Machine (H- CRSM) and analysis methodology is proposed to examine statically ANSI-C code to obtain the hazardous events in the input system. A hazardous event equation is taken as input to the proposed system. The output is a list of hazardous scenarios. A path in the code showing the cause of the undesirable event is associated with each hazardous scenario. The strength of the proposed methodology is that the process of hazardous situations detection does not require running the ANSI-C program multiple times with different input values. It also focuses on analyzing the software level of the life cycle. Most of the verification tools check the system level. The system level may be bug-free but the software level may not.

This study aims to develop a scientific medical service analysis and evaluation system to improve the limitations of traditional evaluation methods in medical service quality assessment. By integrating big data technology and intelligent algorithms, the system realizes real-time collection, processing, analysis, and application of various indicators in the medical service process. Methodologically, data mining technology is used to extract valuable information from massive data, and model construction and algorithm optimization are used to achieve comprehensive evaluation of medical service quality. In terms of results, the system has demonstrated high accuracy and reliability in practical applications, providing accurate data support for hospital management decision-making. In addition, the system also has the ability to monitor and early warn the hospital service process in real-time, which helps to identify and solve problems in a timely manner. In conclusion, the application of the medical service analysis and evaluation system not only improves hospital management efficiency but also provides strong support for improving medical service quality and optimizing resource allocation.

With the process of non-functional properties research on real-time systems, resource estimation and analysis of realtime systems become a hotspot. Process Algebra is a formal method fit for analyzing the functional properties of real-time systems, but it can not analyze the resource properties. Resource Communicating Sequential Process (RCSP) proposed in this paper can handle it efficiently by extending resource semantics on Communicating Sequential Process (CSP). In this paper, the resource consumption of real-time instructions is mapped into the resource of RCSP, then the resource consumption of realtime systems can be modeled and optimized by using RCSP; the resource optimal checking algorithms are proposed to check the resource satisfiability of instructions and calculate the minimum resource and accumulated resource of real-time systems. This formal method improves the accuracy and efficiency of resource calculation, the calculation results can be used to quantitatively analyze and optimize the resource consumption of real-time systems.

A FPGA-based two-channel low frequency signal generator and analysis system has been designed to analyze the occurrence and analysis of low frequency signal. It uses FPGA for low frequency signal producing control chip module and analysis module. Control signal is sent to the FPGA by keys, FPGA generates frequency range step adjustable low frequency signal. Through the adder, fast Fourier transform is used to extract frequency domain signal by analysis module FPGA, the oscilloscope measures frequency amplitude and other information, to achieve low frequency signal frequency domain analysis capabilities. The system whose encapsulation is compact has friendly interface and it is very easy to operate and demonstrate.

The occurrence of severe weather events has shown increasing frequency and intensity due to global climate change. The Korean Peninsula, characterized by complex inland orography and surrounded by seas on three sides, exhibits diverse meteorological phenomena significantly influenced by seasonal wind regimes. While forecasters traditionally analyze synoptic conditions using locally available observations, the irregular spatial and temporal distribution of these observations limits their ability to conduct comprehensive three-dimensional analyses. Furthermore, although global model analysis fields have been widely used for operational forecasting, their coarse spatial and temporal resolutions constrain the real-time analysis of localized severe weather events. To address these limitations and enhance nowcasting capabilities, the Korea Meteorological Administration (KMA) has implemented the Korea Analysis System (KAS) since May 2024, a real-time analysis system that utilizes a high-resolution regional model to provide rapid updates of current atmospheric conditions essential for monitoring and predicting mesoscale weather phenomena.This study evaluates KAS's effectiveness in reproducing real-time atmospheric phenomena and its practical utility for severe weather analysis through extensive synoptic case studies. KAS generates hourly three-dimensional nowcast analysis fields at 3 km resolution by integrating 15 categories of synoptic and non-synoptic observational data with the operational Korean Integrated Model-regional (KIM-regional) forecast fields, assimilating observations up to 15 minutes past each hour to provide near real-time atmospheric conditions. The system demonstrated remarkable capability in capturing critical meteorological features across various weather regimes. During summer, KAS effectively identified precursors of convective precipitation by analyzing real-time low-level convergence zones, dewpoint depression fields, high equivalent potential temperature areas, and vertical p-velocity distributions. The system's skew T-log P diagrams revealed significant Convective Available Potential Energy (CAPE) values, providing quantitative measures of atmospheric instability and potential for convective cloud development and subsequent precipitation in specific regions. In winter scenarios, KAS accurately depicted strong wind variations, including northwesterly cold air flows and easterly winds associated with orographic precipitation. Notably, the system's thermal advection analysis fields effectively identified regions of warm air advection and their interaction with cold air masses, providing crucial indicators for potential snowfall accumulation zones, particularly in areas where warm maritime air masses encounter pre-existing cold air.The results validate KAS's capability to provide forecasters with coherent three-dimensional nowcast analyses, overcoming the limitations of traditional forecasting methods based on irregularly distributed observations and coarse-resolution global model analyses. This advancement establishes a foundation for improved real-time severe weather detection and forecast accuracy across the Korean Peninsula and East Asia region.Acknowledgement: This work was supported by Development of Numerical Weather Prediction and Data Application Techniques (KMA2018-00721).

This study proposes a machine learning-enhanced framework to predict and analyze magnetohydrodynamic (MHD) natural convection within nanofluid-filled trapezoidal cavities featuring variable obstacle shapes, wall corrugations, and inclination angles. A comprehensive dataset was generated through Finite Element Method (FEM) simulations covering a wide parametric range, including Rayleigh numbers (103 - 106) and Hartmann numbers (0 - 50), for Cu-H2O nanofluids. Key thermal performance metrics Nusselt number (Nu), entropy generation (ST), and Ecological Coefficient of Performance (ECOP), were extracted and used to train supervised machine learning models: Support Vector Regression (SVR), Decision Tree (DT), and Random Forest (RF). Among them, the RF model achieved superior performance, yielding R2 scores of 0.991 (Nu), 0.982 (St), and 0.989 (ECOP), with mean prediction errors under 1.5%. The results show excellent agreement between FEM and ML outputs across diverse configurations, including different obstacle geometries (star, square, triangular), wall undulations (sinusoidal, square, triangular), and inclination angles (15˚, 30˚, 45˚). The integration of ML significantly reduced computational cost while preserving high accuracy, thus demonstrating its viability for rapid prediction and optimization. This hybrid FEM-ML methodology offers a powerful tool for real-time thermal system analysis and design in advanced MHD nanofluid applications.

Clinical evaluation of electroencephalographic (EEG) recordings is based on complex subjective processes of data reduction and feature extraction. The high dimensionality of the EEG signal, its variability, and the lack of standard population values have retarded development of automated systems. An interactive, real-time analysis system (ADIEEG) has been implemented to develop features to simplify visual interpretation and facilitate automated classification. It uses a 40 000 word PDP15-PDP11 dual processor computer. Resident code occupies approximately 11 000 locations, while a maximum of 12 000 locations are used for buffers. The system performs 1) continuous spectral analysis using the fast Fourier transform to produce estimates of power and coherence, 2) parallel time domain analysis to detect sharp transients significant to diagnosis, 3) several forms of graphics, 4) simple algorithms to reject noncortical and instrumental artifact, 5) interactive parameter alteration and on-line feedback to adjust decision thresholds when necessary, and 6) extraction of diagnostically helpful features using heuristics based on clinical EEG. The ADIEEG system resides in the University of California, San Francisco Medical Center, and Langley Porter Neuropsychiatric Institute.

Advanced data acquisition and real-time data analysis system for vehicle operational noise testing

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records