Machine Technology Research Articles

Study of the Vibration Isolation Properties of a Pneumatic Suspension System for the Seat of a Working Machine with Adjustable Stiffness

This paper presents a study of the vibration isolation properties of pneumatic suspension systems for work machinery seats, with a particular focus on adjustable stiffness. It highlights the contribution that semi-active seat suspension systems make to vibration reduction, ultimately leading to improved passenger comfort levels and increased safety for vehicle users. The primary objectives of the research were twofold: firstly, to identify the key parameters of the apneumatic vibration isolation system; and secondly, to evaluate its performance in improving vibration damping. This entailed the development of a mathematical model that would foreground the movement through simulations based on different initial pressures, thus enabling the accurate prediction of real-life scenarios concerning the vibration-damping characteristics of the seating system, taking into account the different design options available for working machine technology applied at the test bed level, of which the pneumatic isolator is an integral component. In the cognitive process, the verification and validation of the formulated theoretical model play an important role. This approach enables the behaviour of the actual system to be inferred from the results of simulation studies, thus allowing the design of an appropriate vibration control system. By simulating different air bellow pressures, the characteristics of the seat suspension system can be assessed. This study provides valuable insights into optimising the vibration-damping capability of the air suspension system.

Ergonomics in the Polish Business Environment

Theoretical background: Economic processes, the COVID-19 pandemic, evolving technology and aspects of demographic change including, above all, an “ageing population” remaining on the labor market have brought about dynamic changes in many canons, beliefs and areas of activity for businesses and economic organizations. In line with the idea of humanocentrism and nurturing human capital (its knowledge and experience), companies are looking for new organizational and technological solutions, whose main objective is to ensure safety and quality of work. One of the techniques/sciences that integrate the achievements of many sciences humanizing the working and living environment so that it is friendly to the psychophysical and social needs of the user is ergonomics. Ergonomics is the science that deals with the adaptation of technology (machines and tools) and working conditions to the specific needs of the user, and through its multidisciplinary and multifaceted nature has many meanings, roles and characteristics. Purpose of the article: The aim of this article is to answer questions related to the ergonomic activities of Polish enterprises and its application in practice. What does ergonomics give them? What is the source of inspiration in carrying out ergonomic activities? What is the purpose of conducting ergonomic activities? What is the essence and role of ergonomics in their business operations? Research methods: In this article, based on literature research conducted by the European Statistical Office (Eurostat), Social Insurance Institution (ZUS), National Labour Inspectorate (PIP) and Statistics Poland (GUS), two authoritative questionnaires were created (under expert supervision) to investigate ergonomic activity in the Polish business arena. The first questionnaire was sent to 15,000 enterprises and searched for ergonomic activity in the business arena. The second questionnaire was sent to a narrower group of ergonomically active enterprises, which provided answers to 100 open and closed questions. The survey was conducted between 2020 and 2022. Main findings: All three research assumptions were successfully achieved. Businesses were found, investigated and conclusions reached. Selected results from the questionnaire survey are presented in tabular form. The surveyed respondents, representatives of various business sectors of micro, small, medium and large enterprises, unequivocally stated that, in their opinion, ergonomics helps to modernise machinery and technology. In addition, ergonomic activity brings them closer to complying with current standards and legislation, thus, having a positive impact on quality, health and safety aspects. Ergonomic activity also gives entrepreneurs the opportunity to increase productivity, efficiency and work performance. Seeking ergonomic inspiration also contributes to increasing the potential of the knowledge economy.

Analysis of Salt Marketing Strategy of Local Farmer Groups (Case Study of Labuhan Bontong Village, Tarano District)

Labuhan Bontong Village, Tarano Sub-District is one of the largest salt-producing villages in Sumbawa Regency. However, marketing issues such as limited market access, competition with imported salt, and unstable prices continue to hinder the local salt industry in this area. This research aims to determine the marketing strategies carried out by the local farmer group in Labuhan Bontong Village, Tarano Sub-District. The research uses a qualitative descriptive approach with data collection techniques including observation, interviews, and documentation. The theory used in this research is the Marketing Mix 7P theory. The results of the research show that the marketing strategy of salt by the local farmer group only shows a few strategies such as salt production carried out near the coast and the presence of physical evidence in the form of machine technology that can lift seawater into salt plots, while some do not show any strategies yet, such as pricing that still depends on time/season, promotion still through word of mouth, and the marketing process still uses sacks.

HELIOS Approach: Utilizing AI and LLM for Enhanced Homogeneity Identification in Real Estate Market Analysis

The concept of homogeneity in the real estate market is a well-known analysis aspect, yet it remains a significant challenge in practical implementation. This study aims to fill this research gap by introducing the HELIOS concept (Homogeneity Estate Linguistic Intelligence Omniscient Support), presenting a new approach to real estate market analyses. In a world increasingly mindful of environmental, social, and economic concerns, HELIOS is a novel concept grounded in linguistic intelligence and machine learning to reshape how we perceive and analyze real estate data. By exploring the synergies between human expertise and technological capabilities, HELIOS aims not only to enhance the efficiency of real estate analyses but also to contribute to the broader goal of sustainable and responsible data practices in the dynamic landscape of property markets. Additionally, the article formulates a set of assumptions and suggestions to improve the effectiveness and efficiency of homogeneity analysis in mass valuation, emphasizing the synergy between human knowledge and the potential of machine technology.

Application of two-lever baglog pressing machine technology to improve the production of oyster mushroom cultivation

Application of two-lever baglog pressing machine technology to improve the production of oyster mushroom cultivation

Frequency stability of new energy power systems based on VSG adaptive energy storage coordinated control strategy

A self-adaptive energy storage coordination control strategy based on virtual synchronous machine technology was studied and designed to address the oscillation problem caused by new energy units. By simulating the characteristics of synchronous generators, the inertia level of the new energy power system was enhanced, and frequency stability optimization was achieved. This strategy is integrated with the frequency response model of the new energy power system to improve the system's frequency regulation capability and achieve more stable and efficient operation. From the results, the damping of the system increased, the oscillation frequency decreased after a duration of about 15 s, and the system stability improved by 76.09%. The proposed strategy based on virtual synchronous generator adaptive energy storage coordination control strategy was improved by 83.25%. In addition, the proposed strategy has improved stability indicators and system completion efficiency by 40.57% and 22.21% respectively, both of which are better than the comparative strategies. As a result, this strategy significantly enhances the frequency regulation capability of the system, which has a positive effect on achieving efficient operation of the new energy power system and maintaining the stability of the power system.

An Energy-saving Data Transmission Approach based on Migrating Virtual Machine Technology to Cloud Computing

Introduction: Over the past few years, researchers have greatly focused on increasing the electrical efficiency of large computer systems. Virtual machine (VM) migration helps data centers keep their pages' content updated on a regular basis, which speeds up the time it takes to access data. Offline VM migration is best accomplished by sharing memory without requiring any downtime. Objective: The objective of the paper was to reduce energy consumption and deploy a unique green computing architecture. The proposed virtual machine is transferred from one host to another through dynamic mobility. Methodology: The proposed technique migrates the maximally loaded virtual machine to the least loaded active node, while maintaining the performance and energy efficiency of the data centers. Taking into account the cloud environment, the use of electricity could continue to be critical. These large uses of electricity by the internet information facilities that maintain computing capacity are becoming another major concern. Another way to reduce resource use is to relocate the VM. Results: Using a non-linear forecasting approach, the research presents improved decentralized virtual machine migration (IDVMM) that could mitigate electricity consumption in cloud information warehouses. It minimizes violations of support agreements in a relatively small number of all displaced cases and improves the efficiency of resources. Conclusion: The proposed approach further develops two thresholds to divide overloaded hosts into massively overloaded hosts, moderately overloaded hosts, and lightly overloaded hosts. The migration decision of VMs in all stages pursues the goal of reducing the energy consumption of the network during the migration process. Given ten months of data, actual demand tracing is done through PlanetLab and then assessed using a cloud service.

Application of two-lever baglog pressing machine technology to improve the production of oyster mushroom cultivation

The baglog pressing process in the Ngadi Oyster Mushroom Cultivation Community Group in Gunungronggo Village is still done manually, by compressing the media in a plastic bag with a bottle or plastic pipe several times until it is solid. This work requires quite a lot of labor and a long time. The level of success of this process really depends onthe skills and accuracy of the workers. The results of compacting the media in this way are sometimes unsatisfactory, because there are still air spaces in the media, so oyster mushrooms cannot grow. Problems that can be identified in SMEs are the division of labor duties for the printing and steaming processes, as well as the lack of experienced workers who can compact baglog properly. So it is necessary to apply a two-lever baglog pressing machine which aims to increase production capacity. The results of the activity show an increase in baglog production capacity, from 60 baglog/day with 2 workers to 300 baglog/day with a baglog press machine, with a printing process time of 5 hours/day. Production increased 500% compared to before using the tool. This capacity meets the needs for the steaming process, where the steamer capacity owned by SMEs is 215 baglogs/process/day

РОБОТИЗАЦИЯ ПРОИЗВОДСТВА СЕЛЬСКОХОЗЯЙСТВЕННОЙ ТЕХНИКИ

Increasing labor productivity at agricultural engineering enterprises in modern conditions is impossible without the use of robots, which should replace human labor in technological operations with dangerous and harmful production processes. (Research purpose) The research purpose is evaluating the use of robotics by enterprises of the agricultural machinery industry in the production. (Materials and methods) Conducted an assessment of the use of robotics tools when visiting enterprises in the agricultural engineering industry. (Results and discussion) It has been established that for a sustainable presence and strengthening of their positions in the market, companies producing agricultural machinery are increasing the volume of research and development work on the designs and production technologies of modern machines. It was noted that an increase in labor productivity in modern conditions is impossible without the use of robots, which are designed to replace human labor at work with harmful and dangerous production factors in procurement, when cleaning blanks, with thermal and physical methods of surface treatment of products made of various materials, when painting, as well as in order to reduce defects. It was determined that the use of robotization tools by enterprises of the agricultural machinery industry does not exceed three percent of the total volume of technological operations performed. Robotics tools are used mainly in welding operations. Loading and unloading, replacement of working tools on numerically controlled machines, as well as many other technological operations remain practically not covered. (Conclusions) The conducted studies have shown that machine-building companies have large reserves for using robotics tools in performing technological operations in order to reduce production costs and increase labor productivity. As measures to support the introduction of robots into production, grants are allocated by the Russian Digital Economy and Skolkovo foundations, preferential loans through the industrial development Fund "Digitalization of Industry".

Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction

Hydropower stations are important infrastructures for generating clean energy. However, they are vulnerable to natural disasters such as earthquakes, which can cause severe damage and even lead to catastrophic failures. Therefore, it is essential to develop effective strategies for maximizing hydropower station safety against earthquakes. To evaluate the potential shear rate of surrounding rock layers, the shear wave velocity (Vs) parameter can be used as a useful tool. This parameter helps to determine the velocity at which shear waves travel through the rock layers, which can indicate their stability and susceptibility to earthquakes. This study will investigate the significance of the Vs parameter in evaluating the potential shear rate of rock layers surrounding hydropower stations and how it can be used to ensure their safety and efficiency in earthquake-prone regions. Furthermore, a novel approach is proposed in this research, which involves using extreme learning machine (ELM) technology to predict Vs and enhance the seismic safety of hydropower stations. The ELM model predicts the Vs of the soil layers around the hydropower station, a crucial factor in determining the structure’s seismic response. The predicted Vs is then used to assess seismic hazard and design appropriate safety measures. The ML-ELM model outperformed both the ELM and empirical models, with an RMSE of 0.0432 μs/ft and R2 of 0.9954, as well as fewer outlier data predictions. This approach shows promise for predicting Vs in similar environments, and future research could explore its effectiveness in other datasets and practical applications.

Pengungkapan Kinerja Lingkungan Instansi Pemerintah Daerah Melalui Laporan Keberlanjutan Berbasis Global Reporting Initiative

Laundry washing is a crucial process in daily life. Although automatic washing machines have simplified the washing process, utilizing advanced technology like fuzzy logic control can enhance the performance of smart washing machines to achieve optimal washing results. This research aims to implement fuzzy logic control using the Mamdani method on smart washing machines to improve efficiency and washing quality. The research methodology consists of several stages. Firstly, analyzing variables that affect the washing process, such as laundry load, fabric thickness, dirt level, and water temperature. Next, designing a Mamdani fuzzy logic control system by determining membership functions for each variable and creating fuzzy rules to link input and output variables. Lastly, evaluating washing results based on predefined input variables. The research findings demonstrate that implementing Mamdani fuzzy logic control on smart washing machines can significantly enhance washing quality and adaptively determine washing parameters to achieve cleaner washing outcomes. Therefore, integrating Mamdani fuzzy logic control into smart washing machines has the potential to improve overall washing performance. In conclusion, utilizing Mamdani fuzzy logic control on smart washing machines is an effective step towards enhancing efficiency and effectiveness in laundry washing. This research contributes to the development of more adaptive and environmentally-friendly smart washing machine technology and plays a crucial role in advancing fuzzy logic control technology for other smart household applications.

Identification of Bloodstains by Species Using Extreme Learning Machine and Hyperspectral Imaging Technology.

How to identify bloodstains and obtain some potential evidence is of great significance for solving criminal cases. First, the spectral data of different species of bloodstain samples (human blood and animal blood) were acquired by using a hyperspectral imager. Then, an extreme learning machine (ELM) algorithm was used to build the training models of different species of bloodstain samples. Meanwhile, two traditional support vector machine and random forest classification algorithms were also compared with the ELM algorithm. The prediction results showed that the precision, sensitivity, specificity, and F1 score of the ELM algorithm were the highest. This indicates that hyperspectral technology, together with an ELM algorithm, could identify bloodstain species rapidly, non-destructively, and accurately. It has provided a new technical reference for bloodstain detection and identification.

IoT based solar power forecasting using SSA-ELM technique

Abstract The optimizing of renewable energy use and grid integration relies on accurate solar power predictions. In order to predict the amount of power that solar photovoltaic (PV) systems would produce inside an IoT framework, this study suggests a new method that integrates Singular Spectrum Analysis (SSA) with Extreme Learning Machine technology. The SSA algorithm makes sense of solar power data by separating it into its component parts, such as trend, seasonality, and noise. The ELM model, a quick and effective feedforward neural network with a single hidden layer, takes these broken-down parts as input characteristics. In order to enhance the accuracy of solar power forecasts, the suggested strategy combines the decomposition skills of SSA with the predictive capability of ELM. Data acquired by solar PV sensors is input into the IoT-based forecasting model, which then undergoes preprocessing with SSA, feature extraction, model training with ELM, and performance evaluation. The SSA-ELM methodology has been successfully tested on real solar power data and has shown promising results in terms of accuracy measures such as low mean absolute error and mean absolute percentage error. By implementing the suggested method, accurate projections of solar output can be made, leading to better energy management, lower costs, and the smooth incorporation of renewables into smart grids. A dependable and computationally efficient method for solar forecasting in Internet of Things applications is provided by the combination of SSA and ELM.

A Framework of Digital Twins for Improving Respiratory Health and Healthcare Measures

The investigation describes an inventive use of digital twin technology and LSTM-based machine learning models for real-time patient lung disease monitoring and nutrition planning. The suggested application uses various patient healthcare data, treatment processes, dietary habits, and real-time sensor information to construct digital twins, which are virtual reproductions of specific patients. The LSTM model is trained on this large dataset to predict patient health improvements and dietary needs. For each patient’s digital twin, the program provides personalized treatment plans and nutritional advice, enabling proactive interventions and optimizing patient care. Using performance measures, the trained LSTM model achieves high scores for accuracy (92%), precision (89%), recall (93%), and F1 score (91%), proving its usefulness in generating credible health predictions. Patient feedback on the program shows that patients (98.8%) agree on the accuracy and importance of health feedback, as well as the convenience of access to health information (95.4%). The application’s response rate study reveals an average response rate of 85.87%, assuring prompt feedback. To secure patient information, the study emphasizes data privacy and security, adopting multilayered authentication and data encryption. The outcomes of this study demonstrate the application’s potential to revolutionize patient-centered healthcare by providing data-driven, personalized solutions to patients and healthcare professionals.

Comparative Study: Use of Learning Languages in Mastering Printing Machine Operation Competencies (Case Study of Indonesian and Malaysian Printing Study Programs)

The development of printing machines has undergone an extraordinary transformation along with advances in technology. One of the biggest advances in printing machine technology is the transition from traditional printing methods to digital printing. The production process becomes faster and more efficient. Today's printing machine technology is equipped with advanced features that improve print quality. Modern machines are equipped with intelligent control systems with operating language and instructions in English, human involvement in the production stage is absolute. This research uses descriptive qualitative analysis methods. The comparative study used two countries, Indonesia and Malaysia. As a result, Malaysian students who use English as the medium of instruction have the ability to understand printing machine operating instructions better than Indonesian students. Operating a printing machine has similar abilities to students from two countries. This study underscores the importance of integrating English into engineering curricula, with recommendations for improving Indonesian students' command of English.

State-of-Art Review of Renewable Energy-Based Systems for Electrical Machine Applications: Prospects and Challenges

Abstract Renewable energy sources (RES) are a game-changer since they produce no greenhouse gases (GHG). There is an increasing amount of interest in it across the world. The intermittent nature of RES makes it difficult to incorporate them into the existing power grid infrastructure and for use in electrical machines for industrial and domestic applications. As the number of RES grows along with different machine variants, innovative methods of interconnecting them are being explored. This study gives a thorough analysis of net zero RES (wind, solar and hydro) for electric machine applications considering their efficiency, feasibility, and reliability coupled with the trend of connection topologies. The Paradigm shift in material technology for RES application is also examined from the 1950s till the present paving the way for innovative design of novel electrical machines with optimal output performances and cost minimization. Modelling and optimization approaches were captured with the benefits and drawbacks highlighted. For electric machines to function at peak efficiency over time, energy harvesting from these sources becomes significant and must be controlled and maintained risk-free. In addition, the challenges, and prospects, associated with the operation of an Electric Machine technology in a clean environment have all been highlighted.

Revolutionizing Computational Efficiency: A Comprehensive Analysis of Virtual Machine Optimization Strategies

This study undertakes a systematic exploration of contemporary virtual machine optimization strategies, aiming to unravel the intricate dynamics that shape computational efficiency in virtualized environments. The research synthesizes findings from a diverse array of recent studies published from 2020 onwards, encompassing themes such as dynamic resource allocation, machine learning integration, security fortification, and technology synergies. Through rigorous thematic analysis, a comprehensive framework is developed, providing a holistic view of the interrelated strategies that drive the evolution of virtual machine efficiency. Key insights underscore the significance of dynamic resource allocation in responding to fluctuating workloads, the pivotal role of machine learning in predictive resource provisioning, and the robustness of encryption-based security measures. Technology synergies, particularly the integration of containers and virtual machines, emerge as a crucial avenue for enhancing overall system efficiency. Quantum-inspired algorithms further add an avant-garde dimension to the discourse, showcasing potential breakthroughs in computational optimization. The study concludes by offering practical recommendations for practitioners, emphasizing the implementation of dynamic resource allocation, exploration of machine learning-driven solutions, enhancement of security measures, and adoption of technology synergies. Acknowledging context-specific limitations, this research lays a foundation for future investigations into emerging trends, providing valuable insights for organizations seeking to optimize virtualized systems.

Editorial: Machine intelligence and technology: clinical applications in neurology

Editorial: Machine intelligence and technology: clinical applications in neurology

Innovative Achievements of Agricultural Engineering Scientific Institutions in the Context of the Development of Digital Systems in Agriculture

The article deals with the results of the fundamental and applied research work carried out in 2023 by scientific agricultural engineering institutions of the Ministry of Education and Science of Russia guided by the Department of Agricultural Sciences of the Russian Academy of Sciences (RAS) in the field of energy supply, energy saving, renewable and alternative energy in the agricultural sector; development of energyresourcesaving, environmentally friendly machine technologies, robotic technology and digital systems for the production of high-quality and competitive crop and livestock products; technologies and automated means of technical service, restoration and improvement of the machinery reliability using nano-technologies, polycomposite and nanomaterials.

Aggregated model of an active distribution system for coordinated TSO–DSO fast frequency control

The growing integration of distributed energy sources (DERs) challenges the traditional independent operation of transmission system operators (TSOs) and distribution system operators (DSOs). The coordination between TSOs and DSOs becomes essential, as DERs not only offer local ancillary services but also enable the provision of these services at the distribution network level following disturbances in the transmission system. To enhance power system analyses in TSO–DSO coordination, the availability of reduced-order or aggregated models proves crucial, avoiding detailed modeling and reducing computational burden. Addressing the lack of reduced-order models capturing the dynamics of diverse distributed technologies and their control systems within a unified single-device model, this paper introduces a single equivalent device based on virtual synchronous machine (VSM) technology. This device effectively subsumes the frequency response of various DERs and loads. The proposed aggregation methodology undergoes validation on systems of different sizes and compositions of DERs. Results from simulated scenarios consistently demonstrate the accuracy and generalizability of the proposed aggregated model, irrespective of system size and included technologies.