Improve System Efficiency Research Articles

Data-Driven Decision-Making in Transportation Management Using AI

The rapid growth of data generation and the advancements in artificial intelligence (AI) have opened up new opportunities for data-driven decision-making in transportation management. This article presents a comprehensive review of the applications of AI in transportation, focusing on machine learning techniques, optimization algorithms, and predictive analytics. The article proposes a novel AI-based decision-making framework that integrates data preprocessing, AI modeling, and performance evaluation to address complex transportation challenges. A case study on traffic congestion management is conducted to demonstrate the effectiveness of the proposed framework in reducing travel times and improving system efficiency compared to traditional methods. The results highlight the potential of AI in optimizing transportation operations and supporting informed decision-making. However, the article also discusses the limitations and challenges of implementing AI-based decision-making in transportation, such as data quality, privacy concerns, and computational requirements. Future research directions, including transfer learning, integration with emerging technologies, and explainable AI, are identified to facilitate the widespread adoption of AI-based decision-making in transportation management. The findings of this article contribute to the growing body of knowledge on data-driven intelligent transportation systems and provide valuable insights for researchers, practitioners, and policymakers in the field.

A Quantified Analysis of Lean First Then Automate for a Synergetic Effect

ABSTRACTLean manufacturing has become a buzzword for the past few years due to its capability to reduce waste and make the system more efficient. To meet the numerous demands of consumers, companies are moving toward Industry 4.0, which can be accomplished through automation. Prior research demonstrated a conceptual integration of lean tools' effects into automation, but it lacked a quantitative analysis. The purpose of this study is to ascertain the effects of implementing lean tools prior to automation. To get real‐time data for the analysis of applying lean and automation, an empirical study was conducted on a manufacturing line. Using ARENA simulation software, four distinct simulation models were created using the data: the current assembly line model, the model with a lean tool (Inline Quality Check), the model with only automation, and the model with lean applied first and automation applied later. The productivity of the assembly line is used to compare the results. The comparative analysis demonstrated that applying lean tools prior to automation increases productivity in automated lines. This study could assist businesses understand that, before implementing automation, they should use lean technologies to improve system efficiency. An effective system can become considerably more efficient by being automated.

Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013–2023)

EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.

Artificial Intelligence-Based Direct Power Control for Power Quality Improvement in a WT-DFIG System via Neural Networks: Prediction and Classification Techniques

This paper discusses the improvement of power quality injected into the AC grid. This approach is achieved by enhancing the quality of injected power signals and mastering the active and reactive power exchanged between the DFIG based wind turbine (WT-DFIG) and the electrical grid, resulting in an improvement of the overall system performance and efficiency. This study includes all WT-DFIG operating modes, successively and continuously, as well as all local reactive power compensation modes. Therefore, novel control strategies are proposed in this paper for wind energy conversion systems based on artificial intelligence techniques. These techniques include Neural Network Prediction (PNN-DPC) and Classification (CNN-DPC). They aim to eliminate the drawbacks and difficulties associated with conventional Direct Power Control (C-DPC), while retaining its advantages. The paper also provides a thorough explanation of the mathematical models for neural network techniques and WT-DFIG system models. The MATLAB/Simulink environment is used to investigate the performance of the proposed techniques under different conditions and operating modes related to different scenarios. The results reveal a significant reduction in the ripple of the generated active power and the compensated local reactive power, better quality of the generated signal currents and a remarkable reduction in the current total harmonic distortion (THD). Furthermore, compared to C-DPC, PNN-DPC achieves a reduction of 72.07% in active power ripples, 77.07% in reactive power ripples, and 76.79% in current Total Harmonic Distortion (THD). CNN-DPC shows similar improvements with 72.04%, 77.13%, and 76.54% of reductions respectively. In addition, CNN-DPC slightly outperforms PNN-DPC. Nevertheless, both proposed control techniques show significant improvements in all characteristics compared to other methods. Consequently, the proposed control strategies indicate that artificial intelligence has the potential to improve the power quality and performance of wind power conversion system.

Experimental investigation of high-temperature latent heat storage packed bed using alloy-based phase change materials

This study explores the effectiveness of a high-temperature latent heat thermal energy storage (LTES) system incorporating Al-Si-based microencapsulated phase change material (MEPCM) composite pellets within a cylindrical packed bed. A parametric analysis was conducted to examine the impact of varying pellet sizes (1, 3, and 5 mm) and airflow rates (20–50 L min−1) on the efficiency of heat storage and discharge. The experimental approach included controlled charging and discharging cycles at temperatures ranging from 500 to 800 °C, with Reynolds numbers between 17.6 and 261. The findings indicate that the system achieved a maximum round-trip efficiency of 0.93, with no substantial gains observed beyond a Reynolds number of 150. Additionally, the results reveal the importance of minimizing heat loss to improve system efficiency, particularly during the discharge phase. These insights are crucial for optimizing the design and operational parameters of high-temperature LTES systems to enhance energy storage efficiency.

A IMPORTÂNCIA DO ATERRAMENTO ELÉTRICO NA SEGURANÇA E EFICIÊNCIA DE SISTEMAS ELÉTRICOS

The following work will propose the electrical grounding, the voltage and the way in which electrical grounding is used and its proper installation tends to promote an impact on user safety, minimizing accidents, as most users do not know what the system is or even what the Fundamentals are for, the general objective of this article is to understand electrical grounding and how its performance contributes to user safety and as a secondary objective: Importance of grounding, types of grounding, materials and devices, basic principles and technical standards. The methodology used was bibliographic research, using sources such as books, magazines, theses, and publications on the internet, allowing the researcher to access existing material on the subject, being of the qualitative type. The results and discussion demonstrate the importance of electrical grounding for the safety and protection of electrical infrastructure, thus providing important details that can improve system efficiency and maintain safety, as well as regulatory standards. The conclusion is that electrical grounding plays a crucial and important role in safety and protection, so it is better to use the TN-S grounding system because the protective conductor will always be at zero potential.

Advancing Sustainable Mobility: A Data Acquisition System for Light Vehicles and Active Mobility

Active mobility and light vehicles, such as e-bikes, are gaining increasing attention as sustainable transportation alternatives to internal combustion solutions. In this context, collecting comprehensive data on environmental conditions, vehicle performance, and user interaction is crucial for improving system efficiency and user experience. This paper presents a data acquisition system designed to collect data from multiple sensor platforms. The architecture is optimized to maintain low power consumption and operate within limited computational resources, making it suitable for real-time data acquisition on light vehicles. To achieve this, a data acquisition module was developed using a single-board computer integrated with a custom shield, which also captures data related to the assistance of an e-bike motor through a wireless interface. The paper provides an in-depth discussion of the architecture and software development, along with a detailed overview of the sensors used. A demonstrator was created to verify the system architecture idea and prove the potentialities of the system overall. The demonstrator has been qualified by professional and semi-professional riders in the framework of the Giro-E, a cyclist event which took place in May 2024, on the same roads of the Giro d’Italia. Finally, some preliminary analyses on the data acquired are provided to show the performance of the system, particularly in reconstructing the user behavior, the environmental parameters, and the type of road.

The Application of Near Field Communication (NFC) in Public Transportation: Current Practices, Challenges, and Future Prospects

In the field of public transportation, the introduction of NFC technology can realize contactless payment and automatic ticket checking functions, significantly improving system efficiency and passenger experience. The article first reviews the development history of the public transportation system and the basic principles of NFC technology, and then analyzes the technical architecture and working principle of the NFC payment system. Through the cases of public transportation systems in many cities around the world, the practical application and effect of NFC technology are demonstrated. This article also verifies the transaction speed, stability and user satisfaction of NFC technology in the public transportation system through a series of experimental evaluations. The results show that NFC technology has excellent performance in terms of processing speed, system stability and user satisfaction, especially during peak hours, its advantages are more obvious. At the same time, the research also pointed out the limitations of current NFC technology applications and the challenges faced in practical applications. In the future, NFC technology will become more complete and is expected to provide passengers with a more personalized and intelligent service experience.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Six Pulse Type Segmented Thyristor Controlled Reactor with Fixed Capacitor for Reactive Power Compensation

This article presents a Six Pulse Type Segmented Thyristor Controlled Reactor (TCR) integrated with a Fixed Capacitor (FC) for reactive power compensation. The primary objective is to improve voltage stability and power factor in electrical networks, addressing issues related to reactive power imbalance and harmonic distortion. The proposed configuration combines the advantages of segmented TCR and FC, providing a flexible and efficient approach to reactive power management. The novelty of this work lies in the segmentation of the TCR, which enhances the dynamic control of reactive power by allowing more precise regulation and reduced harmonics compared to conventional TCR systems. This segmented approach also minimizes switching losses and thermal stress on thyristors, leading to enhanced reliability and longevity of the system. Additionally, the integration of a fixed capacitor optimizes the overall power factor correction, contributing to improved system efficiency. Key findings from simulation and experimental results demonstrate that the Six Pulse Type Segmented TCR with FC significantly reduces reactive power, stabilizes voltage levels, and effectively suppresses harmonics within permissible limits, adhering to IEEE standards. The system shows a marked improvement in power quality, making it a viable solution for industrial applications where reactive power control is critical. This innovative approach not only provides superior compensation characteristics but also offers a scalable and adaptable framework for modern power systems, highlighting its potential to enhance operational performance and energy efficiency in various electrical grids.

Voltage Deviation Improvement in Microgrid Operation through Demand Response Using Imperialist Competitive and Genetic Algorithms

In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject of extensive research in the field of microgrid operational optimization. The energy demand is considered a crucial element for energy management due to its fluctuating nature over the day. The use of demand response strategies for energy management is one of the most important factors in dealing with renewables. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) have gained great attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper, both ICA and GA, as well as their hybrid application, are used to significantly enhance the voltage regulation in microgrids. The integration of optimization techniques with demand response strategies improves the overall system efficiency and stability. The results proved that the hybrid method provides valuable insights for optimizing energy management systems.

Balancing Efficiency and Accuracy: Enhanced Visual Simultaneous Localization and Mapping Incorporating Principal Direction Features

In visual Simultaneous Localization and Mapping (SLAM), operational efficiency and localization accuracy are equally crucial evaluation metrics. We propose an enhanced visual SLAM method to ensure stable localization accuracy while improving system efficiency. It can maintain localization accuracy even after reducing the number of feature pyramid levels by 50%. Firstly, we innovatively incorporate the principal direction error, which represents the global geometric features of feature points, into the error function for pose estimation, utilizing Pareto optimal solutions to improve the localization accuracy. Secondly, for loop-closure detection, we construct a feature matrix by integrating the grayscale and gradient direction of an image. This matrix is then dimensionally reduced through aggregation, and a multi-layer detection approach is employed to ensure both efficiency and accuracy. Finally, we optimize the feature extraction levels and integrate our method into the visual system to speed up the extraction process and mitigate the impact of the reduced levels. We comprehensively evaluate the proposed method on local and public datasets. Experiments show that the SLAM method maintained high localization accuracy after reducing the tracking time by 24% compared with ORB SLAM3. Additionally, the proposed loop-closure-detection method demonstrated superior computational efficiency and detection accuracy compared to the existing methods.

Optimizing thermal performance in internal passage cooling with extended rib: Applying response surface method with artificial neural networks

Efficient cooling technology is crucial for high-temperature gas turbines which are significant applications in thermal processes particularly in industries such as power generation aerospace and marine propulsion. Enhancing cooling efficiency in these systems not only improves energy utilization but also plays a critical role in reducing fuel consumption increasing operational reliability and minimizing environmental impacts. This research focuses on optimizing the thermal performance of internal passage cooling channels with extended ribs using a combination of the response surface method and artificial neural networks. By varying the extended rib length and rib width the study aims to enhance heat transfer and minimize flow loss leading to improvements in overall system efficiency. Latin Hypercube Sampling is employed to strategically select variables and automated numerical simulations are conducted to perform a thorough parametric analysis. A steady-state k-ω shear stress transport turbulence model is applied in the simulations and the numerical results are validated against experimental data ensuring accuracy. The combined response surface method–artificial neural network approach integrated with TensorFlow-based modeling allows for the prediction of heat transfer and friction factor ultimately generating a comprehensive response surface to identify the optimal design point. Applying the optimized design leads to a 23.99 % improvement in the thermal performance factor compared to the reference case. The findings of this study highlight a significant enhancement in the thermal performance of the cooling channel structure particularly in high heat flux environments. This research provides valuable insights into the design of more efficient thermal management systems for gas turbines and has broader implications for other high-temperature thermal processes. The proposed optimization methodology offers a reliable approach for improving cooling technologies contributing to the development of the next generation of energy-efficient high-performance gas turbines and other industrial applications where effective thermal management is essential.

Numerical investigation on scroll compressor with intermediate discharge valve for VRF annual performance promotion

Over-compression of scroll compressor in variable refrigerant flow (VRF) system is a common problem in improving system efficiency because it operates in part load conditions for most of the year. By introducing intermediate discharge valves (IDVs) to allow gas bypass from compression chamber to discharge side during compression process, the over-compression loss can be eliminated. In this study, the characteristics of compression process in IDV compressor are analysed and the effects of IDV port location and port size on compressor efficiency are discussed based on the validated high fidelity scroll compressor model. It is found that the IDVs could be able to adjust gas bypass process in different compression ratio conditions and maintain high efficiency over wider compression ratio range than that of non-IDV compressor. In addition, the VRF system model is integrated to verify the system performance improvement by IDV compressor. It is found that VRF system performance is significantly improved in cooling conditions with the IEER enhanced by 23.9 %, while limited improvement is obtained in heating conditions as the operating compression ratios are relatively higher.

Digital Engineering in Photonics: Optimizing Laser Processing

This article explores the transformative impact of digital engineering on photonic technologies, emphasizing advancements in laser processing through digital models, artificial intelligence (AI), and freeform optics. It presents a comprehensive review of how these technologies enhance efficiency, precision, and control in manufacturing processes. Digital models are pivotal for predicting and optimizing thermal effects in laser processing, thereby reducing material deformation and defects. The integration of AI further refines these models, improving productivity and quality in applications such as micromachining and cladding. Additionally, the combination of AI with freeform optics advances laser technology by enabling real-time adjustments and customizable beam profiles, which enhance processing versatility and reduce material damage. The use of digital twins is also examined as a key development in laser-based manufacturing, offering significant improvements in process optimization, defect reduction, and system efficiency. By incorporating real-time monitoring, machine learning, and physics-based modeling, digital twins facilitate precise simulations and predictions, leading to more effective and reliable manufacturing practices. Overall, the integration of digital twins, AI, and freeform optics into laser processing marks a significant progression in manufacturing technology. These advancements collectively enhance precision, efficiency, and adaptability, resulting in improved product quality and reduced operational costs. The continued evolution of these technologies is expected to drive further advancements in manufacturing practices, offering more robust solutions for complex production environments.

Efficient utilization of enriched oxygen gas in residential PEMFC-CHP system with hydrogen energy storage

The oxygen byproduct generated from water electrolysis can be utilized on-site in residential combined heat and power (CHP) systems to enhance economic efficiency. This study explores solutions for optimizing the use of enriched oxygen gas (EOG) and determining the ideal oxygen fraction to maximize economic viability. A novel EOG supply system is designed to regulate oxygen fraction, oxygen excess ratio (OER), and humidity. A dynamic model of a PEMFC integrated with the EOG supply system is developed using MATLAB/Simulink® software and partially validated. The dual effects of EOG on system efficiency and remaining useful lifetime (RUL) are analyzed using a comprehensive economic optimization algorithm, leading to the identification of optimal oxygen fractions for various hydrogen production modes. Additionally, a feedforward static decoupling controller is designed to independently control the OER and oxygen fraction. The results indicate that, under rated conditions (10 kW), system power increased by 15 %, along with a 10 % improvement in system efficiency, from 48 % to 58 %. In conclusion, the efficient use of enriched oxygen gas demonstrates significant economic feasibility and practical viability, making it highly suitable for distributed power generation applications.

An on-demand collaborative edge caching strategy for edge–fog–cloud environment

In this paper, we tackle the critical challenges of content edge caching, such as limited storage capacity, content popularity prediction, dynamic user demand, and user privacy, issues that most existing studies only address partially. We present an innovative Genetic Algorithm-based On-demand Collaborative Edge Caching mechanism (GAOCEC), which introduces a multi-tiered caching architecture integrating cloud, fog, and edge computing. To enhance caching efficiency and minimize system cost, a novel on-demand caching quota mechanism is proposed that dynamically allocates cache resources to edge servers. To strengthen user privacy protection during content popularity prediction, a CNN-BiLSTM-based Federated Learning algorithm (CBFL) is presented that ensures high prediction accuracy without the need to upload local data to the cloud. We also refine the genetic algorithm for content placement by fine-tuning various parameter sets to identify the optimal balance between latency reduction and caching cost. Our experimental results validate the effectiveness of our approach, demonstrating increased cache hit rates, decreased content response times, and an overall improvement in system efficiency. This work provides a comprehensive, adaptive, and privacy-preserving solution for the edge–fog–cloud environment.

Optimization of Coreless PCB Coils Based on a Modified Taguchi Tuning Method for WPT of Pedelec

The printed circuit board (PCB) winding coil offers advantages such as small size, high precision, high repeatability, and low cost, making it conducive to the miniaturization of electronic equipment and a popular choice in wireless power transmission systems. This paper aims to clarify the correlation between induction parameters and inductive capabilities using the orthogonal array of the modified Taguchi method for Pedelec applications. The conventional Taguchi method typically achieves only local optimization; however, this paper considers practical application conditions and combines experimental data to establish the initial values of the orthogonal array, thereby achieving global optimization. Additionally, the tuning process of the Taguchi method replaces physical experiments with simulations, enhancing optimization speed and reducing hardware implementation costs. The performance index for the proposed modified Taguchi tuning method is selected as a combination of the quality factor (Q) and coupling coefficient (k) to minimize AC resistance and improve system efficiency. To validate the proposed method, the designed coils were implemented and tested in a WPT system based on S–S compensation with a half-bridge topology. The experimental results demonstrate that the optimized PCB coil parameters derived from the proposed tuning method accurately validate the method’s effectiveness and accuracy. From the measured results with the proposed modified tuning method, the system efficiency is increased by 43.87% and the system transmitting power is increased by 28.51%.

Improving fog resource utilization with a dynamic round-robin load balancing approach

In fog computing, load balancing is an important research problem. It focuses on efficiently assigning tasks to fog nodes and minimizing delay in real-time applications. The traditional round-robin algorithm assigns tasks in a rotating manner among fog nodes, but it can send tasks to the cloud too early, leading to increased delays. To solve this problem, this paper introduces an improved round-robin algorithm that takes a dynamic approach to balancing the use of fog resources. The new model aims to improve load balancing in fog computing by distributing tasks more evenly among fog nodes, reducing dependence on cloud computing, and making better use of fog resources. The improved algorithm helps fog computing systems run more efficiently, reduces delays in real-time applications, and lowers the costs associated with cloud use. The results show that the proposed load balancing algorithm is key to optimizing fog resource use, improving system efficiency, and reducing task completion times in distributed computing systems.

DBDAA: A real-time approach to Dynamic Banker’s Deadlock Avoidance Algorithm with optimized time complexity

Effective resource allocation is crucial in operating systems to prevent deadlocks, especially when resources are limited and non-shareable. Traditional methods like the Banker’s algorithm provide solutions but suffer from limitations such as static process handling, high time complexity, and a lack of real-time adaptability. To address these challenges, we propose the Dynamic Banker’s Deadlock Avoidance Algorithm (DBDAA). The DBDAA introduces real-time processing for safety checks, significantly improving system efficiency and reducing the risk of deadlocks. Unlike conventional methods, the DBDAA dynamically includes processes in safety checks, considerably decreasing the number of comparisons required to determine safe states. This optimization reduces the time complexity to O(n) in the best-case and O(nd) in the average and worst-case scenarios, compared to the O(n2d) complexity of the original Banker’s algorithm. The integration of real-time processing ensures that all processes can immediately engage in safety checks, improving system responsiveness and making the DBDAA suitable for dynamic and time-sensitive applications. Additionally, the DBDAA introduces a primary unsafe sequence mechanism that enhances the acceptability and efficiency of the algorithm by allowing processes to participate in safety checks repeatedly after a predetermined amount of system-defined time. Experimental comparisons with existing algorithms demonstrate the superiority of the DBDAA in terms of reduced safe state prediction time and increased efficiency, making it a robust solution for deadlock avoidance in real-time systems.