Data Processing System Research Articles

Design of Aquaponics System Based on Offshore Equipment

Offshore equipment is a large-scale engineering device set up in the open sea area of the deep and distant sea, which is an important way to expand the new area of aquaculture, oil and gas development and so on. In order to solve the problem of replenishment difficulty and wastewater discharge pollution existing in offshore equipment, this paper is based on the current situation of practical research and application of offshore equipment, through the literature and data calculation, systematic design of an aquaponics system based on offshore equipment, through the aquaponics system, through the automation of the data processing system, intelligent control, in the offshore equipment platform to achieve the soilless cultivation of vegetables, cultivation of fish fry in the tail water nitrification tank processing Recycling. The aquaponics system designed in this paper, in ecological, economic and social aspects, there is a positive role in promoting, especially for the future of offshore equipment independent operation to do the corresponding possibility of exploration, for the design and construction of supporting facilities for offshore equipment to provide advice and thinking.

Auditing of hadoop log file for dynamic detection of threats using H-ISSM-MIM and convolutional neural network

Hadoop is a big data processing system that enables the distributed processing of massive data sets across multiple computers using straightforward programming techniques. Hadoop has been extensively investigated in many attacks as a result of its growing significance in industry. A company may learn about the actions of invaders as well as the weaknesses of the Hadoop cluster by examining a significant quantity of data from the log file. In a Big Data setting, the goal of the paper is to generate an analytical classification for intrusion detection. In this study, Hadoop log files were examined based on assaults that were recorded in the log files. Prior to analysis, the log data is cleaned and improved using a Hadoop preprocessing tool. For feature extraction, the hybrid Improved Sparrow Search Algorithm with Mutual Information Maximization (H-ISSA-MIM). Then the CNN (Convolutional Neural Network) classifier will detect the intrusions. The implementation is performed using the MATLAB 2020a software. The performance metrics like accuracy, precision, F-score, recall, specificity, FPR, FNR are calculated for the proposed methodology and it is compared with the existing techniques like Decision Tree (DT), Principal Components Analysis (PCA)- K means, Long Short Time Memory (LSTM). The maximum value of accuracy finds out in the proposed method 98% .

Establishing accounting efficiency mechanism in SMEs on workplace anxiety disorders analysis

BackgroundWorkplace anxiety disorder has a negative impact on the mental health and work performance of employees in small and medium-sized enterprises. The research aims to explore the impact of establishing financial accounting management efficiency mechanisms on workplace anxiety disorder in small and medium-sized enterprises.Subjects and MethodsThe study focused on small and medium-sized enterprise employees and divided them into an experimental group and a control group. The employees in the experimental group have established a financial accounting management efficiency mechanism, including standardized financial systems, clear accounting processes, and effective internal controls. The control group maintained traditional financial accounting management methods. The study used a questionnaire survey method to obtain data on employees’ workplace anxiety disorder scores, and used software Statistical Analysis Systems (SAS) for data processing and analysis.ResultsThe study found that the workplace anxiety disorder scores of the experimental group employees were significantly lower than those of the control group. The average anxiety score of the experimental group employees was 3.25, while that of the control group was 4.62. The t-test and analysis of variance were used to verify the statistically significant differences between the experimental group and the control group.ConclusionsResults shown that small and medium-sized enterprises can significantly reduce workplace anxiety disorders by establishing financial accounting management efficiency mechanisms. Standardized financial systems, clear accounting processes, and effective internal controls can help alleviate employees’ anxiety.

SPATIAL ANALYSIS OF DENGUE HEMORRHAGIC FEVER IN EAST MANGGARAI, INDONESIA: A CASE STUDY

Background: East Manggarai is one of the District in East Nusa Tenggara Province with a high transmission area and a high number of Dengue Hemorrhagic Fever cases. Spatial analysis can provide spatial information related to the distribution pattern of DHF. Purpose: To spatially analyze the cases of DHF in East Manggarai District in 2021. Methods: This study used a descriptive survey method with a case study design sampling technique, Purposive sampling approach used a sample size of 103 cases. Data processing and spatial data analysis use Quantum Geographic Information Systems software with Nearest Neighbor Analysis and buffer analysis to describe the distribution of cases based on population density, House Index, Container Index, and Buffer Zone. Results: Spatial analysis using QGIS showed that the Nearest Neighbor Index was 0.323 < 1, and the CI value was 34.7, the HI value was 54 while the buffer zone showed the tendency for dengue transmission to occur in most cases within a radius of 100 meters. Conclusion: The pattern of DHF transmission is clustered, the sub-districts of Borong, Rana Mese, and Komba became the sources of DHF transmission where Container Index and House Index were classified as high categories, and population density was classified as high and low categories. Preventive action such as eliminating mosquito breeding areas were essential.

ADMINISTRATION OF MEASURES FOR THE RESTORATION OF SOILS TRANSFORMED AS A RESULT OF MILITARY OPERATIONS

The Earth is a wealth of society. Its role in addressing the food problem cannot be overstated. It is known that the population's demand for staple food products doubles on average every thirty years. Therefore, in order to provide enough food for all the people living in the years 2010-2020, the productivity of agriculture needs to be doubled. The solution to this incredibly complex task primarily depends on the efficiency of land utilization. Observations are conducted using ground-based methods, primarily field-based techniques, and remote sensing tools. The establishment of correlation relationships between ground-based and remote sensing methods is carried out on dedicated test sites. The purpose of the article is to provide proposals for implementing measures for the restoration of lands transformed as a result of military actions. The methodology should include modern mathematical support, including principles of database creation, automated data processing and retrieval systems, and methods for real-time and long-term forecasting. For an accurate assessment of such transformations and the implementation of targeted regulation of soil processes, there is a need to organize systematic monitoring services to observe them. The absence of such monitoring can lead to irreversible degradation of the soil cover, which would later require enormous costs and time for restoration. It is clear that from both economic and socio-environmental perspectives, it is more practical and advantageous to prevent adverse changes rather than trying to remedy them later. By processing such information, it is possible to identify optimal natural conditions for various activities, predict both positive and negative factors for agricultural operations, and take measures to reduce the impact of negative factors on human life and activities. It is known that for a long time, observations were only conducted on changes in the state of the natural environment caused by natural factors. Indeed, the state of the biosphere changes under the influence of both natural and anthropogenic factors. However, there is a significant difference in the outcomes of these influences: the state of the biosphere, which constantly changes due to natural causes, usually returns to its previous state. Large balanced ecological systems, geosystems, change extremely slowly under the influence of natural processes. These gradual evolutionary changes occur only over time measured by historical epochs. The deteriorating ecological situation in Ukraine, including the agroecological state of the soil cover, emphasizes the need for the State Agrochemical Service to transition from soil agrochemical surveys to comprehensive soil-agrochemical monitoring of agricultural lands. Such monitoring belongs to the ecological monitoring type and consists of three main components: soil surveys (including laboratory analysis), assessment of their agro-ecological condition, prediction of changes in relevant indicators, and soil fertility management, which refers to the specific properties of soils that distinguish them qualitatively from their parent geological formations.

Method of planning data processing tasks in distributed systems with limited information about available resources

In today's digital landscape, distributed data processing systems (DDPs) are becoming increasingly critical to efficiently process, analyze, and manage large volumes of data. These systems are often used in commercial, scientific and social domains to process complex data in real-time or batch mode. One of the key components of such systems is task scheduling, which is an extremely complex process, particularly when information about resource requirements is not complete or accurate. The subject of research are algorithms, methods and approaches used for scheduling tasks between nodes in distributed systems. The purpose of the study is to create an optimized method of task planning in the RSOD with limited availability of information about available resources. The task of the research: to analyze the limitations of modern methods for scheduling tasks in distributed data processing systems (DDS); optimize the method of scheduling tasks based on metadata between RSOD nodes, based on the methodology of searching for nearest neighbors using the method of localized hashing and the algebra of finite predicates; develop the architecture of the software solution and its implementation based on the optimized method; test the algorithm on the example of a video decoding task. The following methods were used: statistical algorithms and techniques such as classification and cluster analysis were used to predict resource requirements, and visualization techniques assisted in the analysis and interpretation of results. As a result of the work: the limitations of modern methods for the distribution of tasks in distributed data processing systems (DDPs) were analyzed; an optimized method of task planning based on metadata in RSOD was created, based on the methodology of searching for nearest neighbors using the method of localized hashing and the algebra of finite predicates; the processes in the modified nearest neighbor search algorithm are detailed; the architecture of the software solution was developed, which integrates an optimized method of task planning based on metadata and resource allocation; validation of the software solution was carried out with the help of a practical scenario – the use of the created algorithm in the planning task for decoding video information. The conclusions of this study confirmed that the proposed method, based on the methodology of localized hashing and the use of finite redicate algebra, is effective even with insufficient or limited information about resource needs. This highlights the possibility of using dynamic scheduling strategies to adapt to changing load conditions and resource availability.

IMPLEMENTATION OF COMPUTER PROCESSING OF RELAXATION PROCESSES INVESTIGATION DATA USING EXTENDED EXPONENTIAL FUNCTION

The object of research is the development of a specialized measuring information system for the study and control of relaxation processes in materials and technical systems. The purpose of the work is the use of computer technologies to eliminate routine operations associated with the processing of experimental data, increase the speed, accuracy and information content of the process of studying the control of gas sensors. A variant of using computer data processing to automate the processing and primary analysis of experimental data of scientific research and control of the physicochemical parameters of gas-sensitive materials is proposed. The developed computer data processing system provides a practical opportunity to use the measurements of the kinetic characteristics of the gas sensitivity of gas sensors for their experimental research and control and, thus, to achieve higher accuracy and information content. The testing of the developed information-measuring system confirmed its operability and compliance with the requirements for improving the accuracy and speed of the processing process.

A dataset of carbon and water fluxes observed in Xishuangbanna tropical seasonal rain forest from 2011 to 2015

The proposal for China's dual-carbon target is based on the foundation of addressing climate change. It is imperative to enhance carbon sinks and reduce carbon sources to implement climate governance. The terrestrial ecosystem is an important atmospheric carbon sink, while the tropical seasonal rain forest, as a component of the terrestrial ecosystem in China characterized by rich biomass, and strong productivity, plays an essential role in carbon sequestration. As one of the basic observation and research stations of China Flux Research Network (ChinaFLUX), the Xishuangbanna Tropical Seasonal Rainforest Ecological Station built in 2002 has accumulated 20 years of carbon and water flux observation data. Based on the ChinaFLUX data processing system, this dataset processes and collates the tropical seasonal rain forest flux and conventional meteorological data of Xishuangbanna from January 2011 to December 2015. The result is a collection of data products at half-hourly, daily, monthly, and yearly intervals. This dataset can be applied to research fields such as the analysis of the carbon cycle response mechanism of ecosystems and the assessment of carbon sequestration benefits under climate change, and it can also serve as a valuable reference for the optimization of ecosystem model parameters and the verification of results. Through multi-platform architecture, multi-scale integration, and multi-method coupling, the dataset is expected to further provide basic support for analyzing the mechanisms of regional and even global carbon-water-heat cycles in ecosystems, spatiotemporal dynamics and predicting future carbon sink potential, thus providing the scientific basis for achieving China's carbon neutrality goal.

Sistem Informasi Manajemen Pada Dinas Pangan Kabupaten Labuhanbatu

Labuhanbatu District Food Service is an agency that has the task of assisting the Regent, in carrying out the affairs of the field of food, food distribution and food consumption patterns. As for the data processing business at the Labuhanbatu District Food Service, it has been computerized using Microsoft Excel. But it still has shortcomings in its use, such as data can be lost at any time, and for shared use it is still not appropriate to use because users will find it difficult if they want to update data, because the data is not stored in the database. This information management system of data processing based website is built using the laravel framework. The system design method used is to use the waterfall method. This application can make data processing at the Labuhanbatu District Food Service better and support food progress in Labuhanbatu Regency.

The PreProcessor module for the ATLAS Tile calorimeter at the HL-LHC

The Large Hadron Collider (LHC) has played a crucial role in advancing our understanding of fundamental physics. With the discovery of the Higgs boson in 2012, the ATLAS and CMS experiments have made significant progess in studying its properties and searching for new physics beyond the Standard Model. To maintain and expand the LHC’s discovery potential, the High-Luminosity LHC (HL-LHC) project is planned as a major upgrade. As part of this upgrade, the ATLAS experiment has developed a comprehensive roadmap for upgrades, including the installation of new detector components and advancements in data acquisition and processing systems. A crucial component of these enhancements involves the complete replacement of the ATLAS central Tile hadronic calorimeter readout electronics. This upgrade aims to optimize the system’s ability to handle higher data rates and improve its resilience to radiation. Through these comprehensive improvements, the ATLAS experiment aims to meet the requirements and seize the opportunities presented by the HL-LHC era. This paper presents the design and architecture of the TilePPr (Tile PreProcessor) module, which serves as a crucial interface between the on-detector electronics and the central systems of the ATLAS experiment, including Data Acquisition, Detector Control, and Trigger systems. The TilePPr module is based on the Advanced Telecommunications Computing Architecture (ATCA) and incorporates high-speed optical links, communication interfaces, and data processing capabilities. Through a series of certification tests, the module has demonstrated its compliance with industry standards and functional requirements, confirming its suitability for seamless integration into the ATLAS experiment during the HL-LHC phase.

A multiline fitting method for measuring ethylene concentration based on WMS-2f/1f

Coal spontaneous combustion risk assessment is a global technical challenge for the sustainable development of deep mining technology, and C2H4 is a key indicator for early warning of coal spontaneous combustion. Tunable diode laser absorption spectroscopy (TDLAS) has the advantages of high selectivity, high sensitivity, high accuracy and real-time on-line measurement, and it can detect multiple gases simultaneously, so it has significant advantages in the accurate detection of coal spontaneous combustion indicator gases. To address the problem of cross-interference between the near-infrared absorption lines of CH4 and C2H4, which are the indicator gases of spontaneous combustion in coal, a multi-line fitting method was proposed in this study to calibrate the concentration of C2H4. The high-precision Environics2000 automatic standard gas dispenser from the United States, which has a built-in CPU computer control and data control and processing system, was used. Its gas concentration accuracy: ± 1.0%, gas flow accuracy: ± 1.0%, gas repeatability accuracy: ± 1.0%, flow linearity accuracy: ± 0.5%, and inlet operating pressure: minimum 10 psig (0.67 bar) ~ 75 psig (5.04 bar). The measured and simulated WMS-2f/1f signals were multilinearly fitted using a multilinear fitting algorithm and wavelength modulation spectroscopy (WMS), and the measurement of C2H4 concentration was achieved based on the extracted spectral line information. The results show that the maximum relative error of C2H4 concentration measurement is 2.40%, which is 54% lower than that of the conventional 2f peak measurement method, thus demonstrating the effectiveness of the multilinear fitting algorithm in the inversion of C2H4 concentration under the interference of absorption lines. In addition, this study has far-reaching implications for the application of TDLAS technology in the accurate detection of coal spontaneous combustion indicator gases.

TECHNICAL REQUIREMENTS FOR THE PHYSICAL AND APARTMENT INFRASTRUCTURE OF THE MOBILE DATA PROCESSING AND STORAGE SYSTEM FOR THE DATA PROCESSING CENTRE OF THE CONTAINER TYPE OF FINANCIAL INSTITUTIONS, ENTERPRISES, ORGANISATIONS

In modern conditions, the amount of information is constantly increasing, and at the same time, the speed of doing business is increasing. According to Gartner Group estimates, the global average amount of user data per company is 120 terabytes. In turn, according to IDC calculations, last year 35 billion messages were sent in one hour in the world. If these numbers are taken into account, it turns out that one message accounts for approximately 3.4 Mв of information. To be successful, companies not only have to operate with large volumes of data, but also process it quickly and efficiently. At the same time, according to the opinion of the world's leading experts, there are about 40 key macrotechnologies in the world today that determine the level of the economy. These processes were significantly influenced by the specifics of the formation, constant development and transformation of cyberspace. In these areas, breakthrough achievements are predicted, first of all, in artificial intelligence, cloud technologies, internet of things, productivity and nature of computing devices, possibilities of storage, processing and transmission of large arrays of data and information (Big Data), means and technologies for their implementation on radically new principles. The capabilities and vulnerabilities of almost all modern infocommunication and cybernetic systems increasingly depend, in addition, on the growth of the interrelationships of various information systems and control systems among themselves in multiparametric, multidimensional cyberspace and their informational and cybernetic interpenetration, interaction and interdependence, etc. Therefore, taking into account the above, it is necessary to focus primarily on the creation of permanently functioning data processing centers (data centers), and under the conditions of our state's opposition to the aggressor country, to develop appropriate data processing and storage systems on mobile platforms. Another, quite significant and meaningful component is taking into account the modern structures of information protection and cyber security systems, which involve the use of a fairly powerful line of tools, both at the technological and software levels, the use of extensive relevant architectures, protection methods aimed at prejudice, detection and response to potential cyber threats.

ДОСЛІДЖЕННЯ ЕФЕКТИВНОСТІ ВИКОРИСТАННЯ РОЗПОДІЛЕНОЇ МАТРИЧНОЇ ФАКТОРИЗАЦІЇ В ПРОМИСЛОВИХ СИСТЕМАХ

The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.

WIRELESS BLACK BOX FOR VEHICLE USING IOT

The project aims to develop a wireless black box system for vehicles using IoT technology. The system will collect data from various sensors installed in the vehicle, such as GPS, accelerometer, gyroscope, and camera, and transmit the data wirelessly to a cloud-based data storage and processing system. The data will be processed and analyzed using advanced data analysis and visualization techniques to provide insights into vehicle performance and driver behavior. The system will also provide driver feedback in real-time, helping to improve driving behavior and reduce the risk of accidents. The project will involve the design and development of hardware and software components, as well as testing and deployment of the system in vehicles. The wireless black box system has the potential to revolutionize the transportation industry by providing real-time data on vehicle performance and driver behavior, which can be used to improve safety, reduce fuel consumption, and optimize fleet management

ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems

The past decade has seen rapid growth of distributed stream data processing systems. Under these systems, a stream application is realized as a Directed Acyclic Graph (DAG) of operators, where the level of parallelism of each operator has a substantial impact on its overall performance. However, finding optimal levels of parallelism remains challenging. Most existing methods are heavily coupled with the topological graph of operators, unable to efficiently tune under-provisioned jobs. They either insufficiently use previous tuning experience by treating successively tuning independently, or explore the configuration space aggressively, violating the Service Level Agreements (SLA). To address the above problems, we propose ContTune, a continuous tuning system for stream applications. It is equipped with a novel Big-small algorithm, in which the Big phase decouples the tuning from the topological graph by decomposing the job tuning problem into sub-problems that can be solved concurrently. We propose a conservative Bayesian Optimization (CBO) technique in the Small phase to speed up the tuning process by utilizing the previous observations. It leverages the state-of-the-art (SOTA) tuning method as conservative exploration to avoid SLA violations. Experimental results show that ContTune reduces up to 60.75% number of reconfigurations under synthetic workloads and up to 57.5% number of reconfigurations under real workloads, compared to the SOTA method DS2.

WEBSITE-BASED LIBRARY DATA PROCESSING DESIGN

The main problem Indonesia faces, especially in education in the era of globalization, is the low quality of human resources. One of the efforts to improve the quality of human resources is to increase interest in reading and the habit of reading. From this fact, the library is expected to be the center of activities to develop interest in reading and reading habits. Libraries have a great responsibility to increase and generate interest in reading. Library data management is one of the essential activities in running a library. Librarians must be able to process and manage book data efficiently and effectively to avoid losing library property. This study uses PHP to create a website design to assist librarians in processing and storing data about existing books. This study designs a library data processing system that contains library book loans, such as recording books, transactions, and student data collection in the library. The results of this website design can facilitate library staff in organizing and tracking library management quickly and efficiently. Keywords: Digital Library, Library Website, Electronic Journal

Isolation and Optimization of Aflatoxin B1 Degradation by Uniform Design and Complete Genome Sequencing of Novel Deep-Sea Kocuria rosea Strain 13.

Aflatoxin B1 is a natural carcinogenic mycotoxin. The biological detoxification of aflatoxin could result in less environmental pollution, more moderate conditions, and less impact on food and feed, and be more convenient than physical and chemical methods. In this study, strain 13 with aflatoxin B1 degradation activity (67.47 ± 1.44%) was isolated and identified as Kocuria rosea. A uniform design was applied to optimize the degradation activity using a software Data Processing System, and a quadratic polynomial stepwise regression model was selected to investigate the relationships between the degradation rate and five independent variables. Furthermore, the optimal degradation conditions (culture temperature of 30 °C, culture time of 4.2 days, seawater ratio of 100%, pH of 7.11, and inoculation dosage of 0.09%) were verified with a degradation rate of 88 ± 0.03%, which was well matched with the predicted value (92.97%) of the model. Complete genome sequencing of Kocuria rosea, conducted with a combination of Illumina and single-molecule real-time sequencing, was used to analyze the genomic features and functions of the strain, which were predicted by the annotation based on seven databases, and may provide insights into the potential of Kocuria rosea, as well as providing a reference for degradation gene and protein mining. These results indicate that Kocuria rosea strain 13 has the ability to degrade aflatoxin B1 efficiently, and it also has the potential to provide aflatoxin-degrading enzymes.

Sistem Informasi Inventory Barang Menggunakan Metode Reorder Point Berbasis Java Pada LSP Ataknas Profesional Konstruksi

The purpose of the research is to design and implement a Java-based Inventory Information System at LSP Ataknas Professional Construction. The research methodology used in the data processing system is data collection techniques such as interviews, observations, documentation studies, and conducting literature research relevant to the problem of the previous goods inventory system. The system development method used is the Re-Order Point method. The results of this study are the existence of an application tool created with the Java Netbeans programming language and data storage in the MySQL database can provide smooth processing of the Goods Inventory Information System system.

Robust and provably monotonic networks

The Lipschitz constant of the map between the input and output space represented by a neural network is a natural metric for assessing the robustness of the model. We present a new method to constrain the Lipschitz constant of dense deep learning models that can also be generalized to other architectures. The method relies on a simple weight normalization scheme during training that ensures the Lipschitz constant of every layer is below an upper limit specified by the analyst. A simple monotonic residual connection can then be used to make the model monotonic in any subset of its inputs, which is useful in scenarios where domain knowledge dictates such dependence. Examples can be found in algorithmic fairness requirements or, as presented here, in the classification of the decays of subatomic particles produced at the CERN Large Hadron Collider. Our normalization is minimally constraining and allows the underlying architecture to maintain higher expressiveness compared to other techniques which aim to either control the Lipschitz constant of the model or ensure its monotonicity. We show how the algorithm was used to train a powerful, robust, and interpretable discriminator for heavy-flavor-quark decays, which has been adopted for use as the primary data-selection algorithm in the LHCb real-time data-processing system in the current LHC data-taking period known as Run 3. In addition, our algorithm has also achieved state-of-the-art performance on benchmarks in medicine, finance, and other applications.

Development and Implementation of an Energy-Efficient Test Stand for Railway Car Bogies

The bogies of railway cars are important components of the rolling stock system. The test stand solution that we have developed and proposed is used for testing car bogies under load to inspect and highlight the features that ensure operational safety and energy efficiency. This article presents the development and manufacturing of a prototype test stand for railcar bogies, incorporating specific elements such as hydraulic pressure cylinders, force transducers, displacement transducers, a hydraulic pumping system, a data computation and processing system, as well as a control and command unit. The technical topic of developing the test stand is aimed at testing bogies under a controlled, variable and uniformly distributed load, closely simulating the operational conditions of railcar bogies. The accurate and complete determination of the bogie's parameters assures the railroad operator that they are safely interchangeable—Moreover, decreasing the time spent on testing tasks and increasing the testing process efficiency through process automation is leading to a cutback in energy consumption and better predictability of the time intervals required for the repair of railway cars. The prototype consists of three hydraulic pressure cylinders: a centrally located hydraulic cylinder exerting a maximum pressing force of 20 tons-force and two lateral hydraulic cylinders, each applying a maximum pressing force of 10 tons-force. The hydraulic cylinders operate independently from each other, also supporting asymmetric load distributions that recreate the operating conditions as faithfully as possible. The data processing system monitors the magnitudes of the forces and the displacement of the hydraulic cylinders for reporting the specific characteristics of the bogie. To ensure rapid response in the control of the testing process, a control logic unit was developed, entirely dedicated to the control of this technological process. The graphical user interface of the embedded computer system was designed in the LabVIEW programming environment.