Efficient System Research Articles

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.

Enhancing Hadoop distributed storage efficiency using multi-agent systems

<p>Distributed storage systems play a pivotal role in modern data-intensive applications, with Hadoop distributed file system (HDFS) being a prominent example. However, optimizing the efficiency of such systems remains a complex challenge. This research paper presents a novel approach to enhance the efficiency of distributed storage by leveraging multi-agent systems (MAS). Our research is centered on enhancing the efficiency of the HDFS by incorporating intelligent agents that can dynamically assign storage tasks to nodes based on their performance characteristics. Utilizing a decentralized decision-making framework, the suggested approach based on MAS considers the real-time performance of nodes and allocates storage tasks adaptively. This strategy aims to alleviate performance bottlenecks and minimize data transfer latency. Through extensive experimental evaluation, we demonstrate the effectiveness of our approach in improving HDFS performance in terms of data storage, retrieval, and overall system efficiency. The results reveal significant reductions in job execution times and enhanced resource utilization, there by offering a promising avenue for enhancing the efficiency of distributed storage systems.</p>

A non-isolated PFC bridgeless SEPIC-Cuk converter with adaptive PI controller for induction motor

In general, the induction motor (IM) is extremely nonlinear in nature and frequency dependent. In most cases, the power generated by the IM has a low power factor (PF), which exhibits detrimental effect on the extent to which the whole transmission and distribution system functions. Since there exists more current harmonics as an outcome of minimized PF, the efficiency of the power system suffers due to transmission line heating and voltage distortion characteristics. Therefore, this paper proposes a power factor correction (PFC) method to overcome the aforementioned issues. Here, by the utilization of AC-DC bridgeless SEPIC-Cuk converter, the power quality is improved by reducing reactive power consumption and enabling better control of voltage and current outputs. To maintain the stable DC link voltage with reduced ripples, the adaptive proportional-integral (PI) controller is used in this work. The three-phase voltage source inverter (VSI) transitioning function is controlled by cascaded fuzzy logic (CFL) controller, which is also utilized for regulating the speed of the three-phase IM. Implementing the proposed control strategy improves power quality significantly by reducing total harmonic distortion (THD). The proposed system is simulated in the MATLAB platform and the attained outcomes, it is clear that the proposed system is highly effective.

Optimizing ELD in power systems applying GWO: A Practical Approach

Addressing the Economic load Dispatch (ELD) Problem in power systems is crucial for minimizing generation cost and transmission losses while meeting the load demand. This research explores the application of Grey Wolf Optimization (GWO) to solve the ELD problem, leveraging ‘GWO’s inspiration from grey wolf social behavior. Through simulation, ‘GWO’s superior convergence speed and solution quality compared to traditional techniques is demonstrated. The finding highlights ‘GWO’s effectiveness in enhancing the economic and operational efficiency of power systems, offering promising avenues for sustainable energy management strategies.

The Role of Anomaly Detection in Industry 4.0: A Survey of Techniques and Applications

The integration of IIoT devices into Industry 4.0 marks a major shift towards smarter and more interconnected industrial processes. However, this progress also introduces intricate security vulnerabilities, specifically stemming from the emergence of anomalies that have the potential to undermine the dependability and efficiency of these advanced systems. Within the realm of Industry 4.0, this research undertakes a comprehensive examination of suitable anomaly detection techniques for IIoT devices. The study systematically analyzes the efficacy, scalability, and flexibility of various detection techniques, such as machine learning algorithms, hybrid approaches, and statistical models, in identifying and mitigating possible risks to IIoT environments. The investigation uncovers valuable insights into the performance of these techniques across various operational scenarios, shedding light on their advantages and constraints. This research examines the practical consequences of implementing these methods in real-life situations, emphasizing the crucial significance of anomaly detection in upholding the durability and dependability of Industry 4.0 systems. Through an extensive comparative examination, this research seeks to offer guidance to researchers, professionals, and policymakers in choosing and executing efficient anomaly detection approaches, thus promoting the progress and safeguarding of IIoT ecosystems.

Plasma-catalytic CO2 methanation over Ni supported on MCM-41 catalysts: Effect of metal dispersion and process optimization

Catalytic carbon dioxide (CO2) conversion technologies can be important components in carbon capture, storage and utilization for CO2 mitigation and possible future economic activity and have gained significant attention globally in past decades. Electrified non-thermal plasma (NTP) catalysis enables CO2 hydrogenation into value-added chemicals under mild conditions. If the hybrid process is coupled with renewable energy and green hydrogen, it can be the promising solution to address the energy and carbon emission challenges. To enhance the energy efficiency of NTP-catalytic systems, bespoke catalyst design and process optimization are necessary. Here, using Ni catalysts supported on mesoporous MCM-41 and NTP-catalytic CO2 methanation as the model systems, the effects of Ni metal dispersion, argon (Ar) addition and residence time on the NTP catalysis were also studied. The findings show that (i) increased metal dispersion alone did not lead to significant enhancement in the performance of NTP catalysis (e.g., CH4 production rate: 31.4 × 10−5 mol/(s·gNi) for 42.6 % Ni dispersion vs. 26.8 × 10−5 mol/(s·gNi) for 25.1 % dispersion), (ii) Ar addition to the system led to the decreased methane production rate (e.g., CH4 selectivity decreased by ∼19 % due to the increase in Ar addition to the system from 5 to 50 mL/min), and (iii) optimization of the residence time could improve the performance of NTP-catalytic CO2 methanation (i.e., an extension of the residence time to 0.69 s resulted in the higher CO2 conversion of 72.7 % and CH4 selectivity of 95.9 % at 9.6 kV than that at 0.49 s and 11 kV).

Thermal analysis for SWCNTs and MWCTs due to coaxially stretchable disks with applications of viscous dissipation and thermal radiation: Artificial neural network analysis

Owing to exclusive thermal prospective of nanoparticles, diverse applications of such materials have been reported in heat transfer systems. The carbon nanotube (CNTs) is the modified class of nanoparticles which is preserve more stable thermal properties. The decomposition of CNTs with base materials offer special applications in enhancing thermal efficiency of energy systems, nuclear processes, sensors, diagnostic imaging etc. Following to such motivations in mind, the objective of current work is to present artificial neural network analysis for heat transfer problem due to suspension of carbon nanotubes-based nanofluid. Both single-walled and multi-walled carbon nanotubes are utilized to analyse the thermal impact of water base fluid. The source of flow is coaxially stretchable disks. The applications of porous medium phenomenon are reported to inspect the inertial effects. The problem is further updated by utilizing the radiative phenomenon, external heat generation and dissipation and slip constraints. The transport system is expressed via coupled nonlinear equations which are tackled via numerical computations.

Analysis, design, and control of standalone PV based boost DC-AC converter

This paper presents a new control scheme for a boost DC–AC converter which is used for solar power applications. The proposed DC-AC converter configuration can produce an AC voltage level across the output or load side greater than input DC voltage based on the operating duty cycle. Generally, the conventional DC-AC converter or voltage source inverter (VSI) generates AC voltage which is less than input DC voltage. Maintaining a constant voltage across the load with improved dynamic performance is challenging for anyone for the solar photovoltaic (PV) system. A dual-loop sliding mode control is proposed for the boost VSI to address the above issues. The proposed controller has robust in nature against the wide fluctuation in the plant or load parameters. The design, analysis and control of the boost DC-AC converter are briefly discussed in this paper. This topology can be broadly used in solar powered uninterruptible power supply (UPS) where boosting operation is essential for low voltage solar PV system. This topology eliminates the DC boosting power processing stage which leads an improved efficiency of the overall system. The MATLAB/Simulink results are presented to highlight the above issues.

Comprehensive review and analysis of photovoltaic energy conversion topologies

Energy conversion is a pivotal process with widespread applications, spanning renewable energy systems, electric vehicles, and industrial power grids. Selecting the right energy conversion topology is critical for optimizing system performance, efficiency, and reliability. This comprehensive review paper provides a thorough overview of energy conversion topologies used in photovoltaic (PV) panel systems, as well as their applicability in diverse domains. Furthermore, the paper conducts a detailed analysis of commonly employed energy conversion topologies. Each topology is meticulously examined based on its operating principles, advantages, drawbacks, and typical use cases. This comprehensive review serves as an invaluable resource for researchers, engineers, and practitioners engaged in the dynamic field of energy conversion, offering insights into both wind energy and photovoltaic panel systems.

Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased

<p>Brain tumor is an uncontrolled growth of abnormal cell in the brain. Early diagnosis of brain tumor has a crucial step in this type of cancer, which is fatal. Magnetic resonance imaging (MRI) is one of the examination tools to examine brain anatomy in clinical practice. The high resolution and clear separation of the tissue enable medical experts to identify brain tumor. The earlier of brain tumor is detected, the wider of treatment options. However, manually analysed of brain anatomy on MRI images are time-consuming. Computer-aided diagnosis with automated way is helpful solution to help management with unreliable degrees of automation to trace various tissue boundaries. This study proposes convolutional neural network (CNN) with its excellences to automated features extraction in convolution layer. The popular architectures of CNN, i.e., visual geometry group16 (VGG16), residual network-50 (resNet-50), inceptionV3, mobileNet, and efficientNetB7 in medical image processing are compared to brain tumor classification task. As the results, VGG16 outperformed other architectures of CNN in this study. VGG16 yields 100% accuracy, precision, sensitivity, specificity, and F1-score for testing set data. The results show the excellent performance in classifying brain tumor and no tumor from MRI images that demonstrate the efficiency of system suggested.</p>

Speed control of PM brushless DC motor using sensorless hybrid controller

In most industrial processes, rising productivity necessitates increased demand on electrical motors in order to minimize costs and improve drive system efficiency. The sensorless technique is preferred. Brushless DC (BLDC) motors compete with a wide range of different motor types in the motion control industry. The nonlinearity of BLDC motor characteristics is challenging to manage with a traditional proportional integral derivative (PID) controller. The PID controller's fuzzy logic allowed it to tune itself while operating online. Hybrid approaches outperform stand-alone algorithms because they can overcome their weaknesses without losing their advantage. The purpose of this study is to present a fuzzy PI+D controller that is simulated over a wide range of reference speeds, loads, and parameter variations. This paper presents a model of a sensorless BLDC motor with a speed controller. The responses of the rotor speed, electromagnetic torque, stator back electromotive force (EMF), and stator currents are effectively monitored. The findings from the simulation indicate that the hybrid controller presented in this study exhibits resilience to rapid load torque and parameter fluctuation. Furthermore, it demonstrates superior dynamic performance and exhibits notable enhancements in speed tracking and system stability. The performance of the system is enhanced through the utilization of the proposed hybrid intelligent controller.

Energy and environmental assessment of a novel multigeneration plant powered by infectious medical waste

This study highlights the energy and environmental impacts of a multigeneration system for infectious medical waste -to-energy (IMWtE). Thermal performance and life cycle assessment (LCA) are investigated via the energy conservation, ReCiPe method, SimaPro database, and situation of environmental issues in Thailand and worldwide. A lifespan of 20y is considered in this study. The energy results show that the multigeneration system can produce a net power of 15.80 kWe from an organic Rankine cycle (ORC), 4.62kW of cooling power from an absorption chiller, and 13.21kW of heating power from a drying room. An overall system efficiency of 13.40% is directly driven from an infectious medical waste (IMW) of 81.98kg/s. According to the LCA results, the construction phase has the highest emissions (81%), followed by the operation phase (6%) and the landfill (1%). The recycling process reduces the environmental impact by 12%. The total lifetime midpoint impact is 3.88E-01 kg emission-eq/kWh. The endpoint includes 4.33E-07 DALY/kWh of human health, 1.50E-06 Species·y/kWh of ecosystem, and 3.81E-03 USD/kWh of resources. The LCA single score is approximately 6.08E+02 Pt/lifespan or 5.63E-04 Pt/kWh.

Battlefield Vitality: Soldier Health Monitoring and Position Tracking System Using BLYNK IoT Platform

Abstract: Soldiers, pivotal in defending national security, often face injuries and disappearances during missions. To ensure their well-being and track their locations, the System for Tracking Soldier Position and Monitoring Health employs IoT technology and the Blynk platform. Integrating NodeMCU8266, PULSE oximeter, LM35 temperature sensor, NEO 6M GPS, and a push button, this system monitors vital health metrics and relays real-time GPS coordinates. Instant updates on soldiers' conditions and precise locations are transmitted, aiding swift interventions. The GPS module communicates coordinates and Google Maps links, facilitating seamless tracking. This system's efficiency lies in promptly relaying crucial health data, ensuring rapid responses and bolstering soldier safety.

Captionify: Bridging the Gap Between Vision and Language with Neural Networks

Abstract: This research presents a unique approach that combines Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs) to generate picture captions. The model makes use of the CNNs' ability to extract complex spatial features from pictures and the LSTM's ability to create and expand on logical textual descriptions. This combination improves the resilience and efficiency of the captioning system by successfully addressing the two difficulties of linguistic description and visual understanding. Comparative tests show that the model performs better than existing approaches in generating accurate and contextually relevant captions. This development highlights the promise of the CNN-LSTM architecture in smoothly integrating visual input with textual interpretation in addition to pushing the envelope of image captioning systems.

Enhancing Smart Contact through Java Spring Boot Integration

Abstract: In the rapidly evolving landscape of digital interaction, effective contact management systems are pivotal for both personal and professional endeavors. This paper explores the integration of Java Spring Boot framework to enhance the efficiency and functionality of smart contact management systems. Leveraging the robust features of Spring Boot, developers can streamline the development process, improve scalability, and ensure seamless deployment. Through a comprehensive analysis, this research elucidates the design, implementation, and benefits of integrating Spring Boot in the development of smart contact management systems, promising a transformative approach to contact organization and accessibility.

Thermal management lithium-ion battery for electric vehicles with using cold plate

Lithium-ion batteries are widely employed as the primary energy storage solution for electric vehicles (EVs). Effective thermal management of these batteries within EVs is important to ensure their safety, optimal performance, and longevity. Lithium-ion batteries are sensitive to fluctuations in temperature, and if not managed correctly, these temperature variations can lead to issues like overheating, reduced efficiency, and even safety risks. This study aims to investigate the effectiveness of a cooling method for a prismatic NMC battery used in electric vehicles. It emphasizes the significance of maintaining the battery's temperature within a specific range, typically between 15 °C and 35 °C, to prevent capacity loss and extend the battery's life. The design of the minichannel cold plate allows for precise thermal control with the 7 °C temperature difference. It efficiently absorbs heat generated during battery operation and releases it, preventing the battery from reaching undesirable temperature levels. Furthermore, the study underscores the importance of optimizing the contact area between the coolant and the battery stack for superior performance of the liquid cooling system. At higher discharge rates of the battery, a greater flow rate is required to achieve efficient cooling, with certain limitations due to the cooling system's efficiency. Overall, the study highlights the advantages of employing a liquid cooling system for proficient temperature regulation and effective heat dissipation in battery stacks for EVs.

Improving IT Control Compliance using Artificial Intelligence

In an era where digital transformations are not just trends but necessities, the reliability and security of information systems have become paramount. The complexity of modern IT environments, coupled with increasing regulatory demands, poses significant challenges for organizations worldwide. IT control compliance, particularly in the context of Sarbanes-Oxley Act (SOX) IT General Controls (ITGC), requires rigorous and continuous monitoring to ensure that financial reporting and data integrity are maintained. However, traditional compliance efforts are often hampered by manual processes that are both time-consuming and error-prone. These methods struggle to keep pace with the dynamic nature of IT advancements and the evolving landscape of cyber threats. This gap not only increases the risk of non-compliance but also places a heavy burden on the resources of the organization. With the advancement of Artificial Intelligence (AI), there is promising potential to revolutionize these processes. AI technologies, through their capability to analyze large volumes of data and recognize patterns quickly, can automate complex tasks, enhance decision-making, and significantly improve the efficiency and effectiveness of compliance systems. By integrating AI into IT control frameworks, organizations can address the perennial challenges of compliance more effectively. AI-driven systems can reduce human error, streamline control processes, and ensure more robust governance and risk management frameworks. This paper explores the integration of AI technologies into IT control frameworks. It discusses how leveraging AI not only aids in maintaining compliance with existing regulations but also enhances the organization’s ability to adapt to new regulatory changes swiftly, thus safeguarding against compliance risks and fostering a culture of continuous improvement in governance practices.

Sub-System Architecture for millimeter-wave massive MIMO systems

In this paper, we study the hybrid beamforming design for millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) systems. The designing of hybrid beamforming for orthogonal frequency-division multiplexing (OFDM) systems is tasking since its analog beamforming is shared among all subcarriers. We adopt a two-step technique for designing the analog and digital beamforming separately in order to maximize the average achievable energy and spectral efficiency of frequency-selective mmWave mMIMO-OFDM systems. Firstly, the analog beamforming design is based on the viewpoint of sub-systems (SS) and the goal is to optimize the array gain and radio frequency chains. Secondly, the digital beamforming design is carried out by using the regularized channel diagonalization (RCD) and block diagonalization (BD) solutions. On the other hand, the BD solution is modified for single-user. Thus, we propose the use of SS-RCD for multi-user and SS-BD for single-user hybrid beamforming designs. The solutions provide interference suppression but differ in low-SNR performance when communicating to many mobile users via data streams. Simulation results present that our hybrid beamforming design outperforms several other designs.

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

As the oil and gas industry increasingly explores deeper and more remote offshore sites, the maintenance of subsea infrastructure becomes paramount. The use of Artificial Intelligence (AI) in subsea maintenance offers promising solutions to enhance safety and efficiency in these challenging environments. This review explores strategic approaches to integrating AI into subsea maintenance operations. AI facilitates predictive maintenance by analyzing vast amounts of data collected from sensors and historical maintenance records. Machine learning algorithms can detect patterns and predict equipment failures before they occur, enabling proactive maintenance scheduling. This predictive capability reduces downtime and minimizes the risk of accidents by addressing potential issues before they escalate. AI-enabled autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) play a crucial role in subsea inspections and repairs. These AI-enhanced robots can navigate complex subsea environments, perform inspections, and execute maintenance tasks with greater precision and efficiency than human divers. By reducing the need for human intervention in hazardous environments, AI-driven AUVs and ROVs significantly improve safety. Furthermore, AI algorithms can optimize maintenance schedules based on factors such as equipment condition, environmental conditions, and operational requirements. By dynamically adjusting maintenance plans, operators can maximize equipment uptime while minimizing costs and risks. This proactive approach ensures that maintenance activities are conducted at the most opportune times, reducing the likelihood of unplanned downtime and improving overall efficiency. Moreover, AI facilitates condition-based maintenance strategies, where equipment health is continuously monitored in real-time. Sensors installed on subsea infrastructure collect data on factors such as temperature, pressure, and vibration, which is then analyzed by AI algorithms to assess equipment condition. By detecting early signs of degradation or malfunction, AI enables timely interventions, preventing costly breakdowns and ensuring optimal performance. In addition to predictive and condition-based maintenance, AI-driven analytics offer insights into operational performance and asset integrity. By analyzing data from various sources, including sensors, historical records, and operational logs, AI can identify trends, anomalies, and optimization opportunities. These insights enable operators to make data-driven decisions that enhance overall system reliability and efficiency. Strategic approaches to implementing AI in subsea maintenance require collaboration between technology providers, operators, and regulatory bodies. Establishing industry standards and guidelines for AI applications in subsea operations is crucial to ensure safety, reliability, and interoperability. Furthermore, investing in research and development to enhance AI algorithms and robotics technology is essential to unlock the full potential of AI in subsea maintenance. AI-enhanced subsea maintenance offers significant benefits in terms of safety and efficiency. By leveraging predictive analytics, autonomous robotics, and real-time monitoring, operators can optimize maintenance activities, reduce downtime, and minimize risks. Strategic approaches to integrating AI into subsea operations require collaboration, investment, and a commitment to advancing technology to meet the challenges of offshore environments.

Floating solar power plant planning based on electricity price fluctuations in Towuti, south Sulawesi

A company located in South Sulawesi, intends to mitigate emissions from the Power Plant by constructing an environmentally friendly alternative, namely the Floating Solar Power Plant (FSPP), in Towuti. The development plan involves a feasibility analysis for each configuration of the FSPP, considering technological, economic, and environmental criteria. The selection of the preferred configuration is determined using the Analytical Hierarchy Process (AHP) method, based on input from experts. According to this method, the chosen configuration is the on-grid non-battery Floating Solar Power Plant, with a Net Present Value of Rp. 1,431,159,245,752, an Internal Rate of Return of 9.46%, a Payback Period of 8.916 years, a Profitability Ind ex of 1.299, a system efficiency of 21.56%, a carbon footprint of 35,003 kg CO2, and an emission reduction of 9,136,808,273 kg CO2. Decision analysis, involving open and abandonment scenarios based on fluctuations in industrial electricity prices, was conducted using Monte Carlo simulation, yielding a volatility value of 31.2%. The real options analysis recommends abandonment in the twentieth year, with a decision success potential of 38%, based on the results.