Enhance System Performance Research Articles

Two-Stage Optimization Scheduling of Integrated Energy Systems Considering Demand Side Response

This study proposes a two-level optimization scheduling method for multi-region integrated energy systems (IESs) that considers dynamic time intervals within the day, addressing the diverse energy characteristics of electricity, heat, and cooling. The day-ahead scheduling aims to minimize daily operating costs by optimally regulating controllable elements. For intra-day scheduling, a predictive control-based dynamic rolling optimization model is utilized, with the upper-level model handling slower thermal energy fluctuations and the lower-level model managing faster electrical energy fluctuations. Building on the day-ahead plan, different time intervals are used for fast and slow layers. The slow layer establishes a decision index for command cycle intervals, dynamically adjusting based on ultra-short-term forecasts and incremental balance corrections. Case studies demonstrate that this method effectively leverages energy network characteristics, optimizes scheduling intervals, reduces adjustment costs, and enhances system performance, achieving coordinated operation of the IES network and multi-energy equipment.

Construction of an Adolescent Spinal Health Intelligent Early Warning and Intervention System under the Proactive Health Model

This study explores the construction of an intelligent early warning and intervention system for adolescent spinal health and its application under a proactive health model. The research demonstrates that through real-time monitoring and personalized interventions, the Spinal Health Index of adolescents in the intervention group significantly improved, increasing from 65 to 85 points, while the control group's index slightly decreased from 60 to 58 points. Personalized intervention strategies, such as the combination of exercise and nutritional interventions, were found to be the most effective, indicating that daily activity levels significantly impact spinal health. The system plays a crucial role in the management of adolescent spinal health by facilitating real-time monitoring, personalized interventions, and modifications in health behaviors. Despite limitations including constraints in sample size and geographical scope, a relatively short intervention period, and insufficient data diversity, future research can enhance universality and model generalizability by expanding sample sizes, prolonging the intervention period, and increasing data diversity. Looking forward, the integration of multimodal data, optimization of the user interface, and establishment of long-term tracking mechanisms will further enhance system performance and promote the improvement of spinal health management in adolescents.

Unlocking the potentials using humidifier-dehumidifier for proton exchange membrane fuel cell waste heat recovery

This study addresses the dual challenges of waste heat dissipation and operational longevity degradation in proton exchange membrane fuel cells (PEMFCs) by proposing a novel hybrid system that integrates PEMFC with a humidifier-dehumidifier (HDH) unit. The primary objective is to enhance the energy efficiency and sustainability of PEMFC systems by leveraging waste heat for freshwater generation. A comprehensive mathematical model based on thermodynamic and electrochemical principles is developed to capture the system's inherent irreversible losses. The performance metrics, including power density, energy efficiency, and exergy efficiency, are evaluated through systematic computational simulations. The proposed PEMFC/HDH hybrid system achieves a maximum power density of 4.8292 W cm−2 and an energy efficiency of 17.30 %, showing a 1.48 % improvement over standalone PEMFC systems. Parametric studies reveal that optimizing operating pressure significantly enhances system performance, while factors such as increased PEM thickness and thermodynamic loss composite parameter hinder it. Local sensitivity analysis identifies PEM thickness as the most critical parameter affecting performance. This research pioneers the integration of PEMFC and HDH for cogeneration, offering a viable path for performance enhancement and efficient waste heat utilization. The findings contribute to the strategic design and operational regulation of PEMFC-based hybrid systems for sustainable energy solutions.

Vanadium-dioxide-assisted multifunctional switchable terahertz metamaterial devices

A multifunctional, switchable terahertz (THz) metamaterial (MM) device with wideband absorption and polarization conversion capabilities has been developed, based on the insulator-metal phase transition of vanadium dioxide (VO2). In its metallic state, the device operates as a wideband absorber within the range of 2.56–6.74 THz, achieving a bandwidth of 4.18 THz and an absorption rate of ≥90%. The wideband absorption is insensitive to both oblique incident angles and polarization. When VO2 is in its insulating state, the device switches to a polarization converter, facilitating linear-to-cross polarization (LTX) conversion between 1.04 and 4 THz, and linear-to-circular polarization (LTC) conversion between 1 and 1.04 THz, with a conversion efficiency exceeding 90%. Additionally, the effects of incident angle and polarization angle on the device’s performance were analyzed. This THz device offers advantages of wide angle, wide bandwidth, and high efficiency, making it a valuable reference for research into new multifunctional THz devices. It has great potential applications in short-range wireless THz communication, ultrafast optical switches, high-temperature resistant switches, transient spectroscopy, and optical polarization control devices. In specific application scenarios, particularly in fields requiring efficient detection, transmission, and analysis—such as security and non-destructive testing, secure communication systems, imaging and sensing, multidimensional spectral analysis, pollutant detection, smart stealth coatings, dynamic optical control devices, and integrated optical systems—these devices offer multifunctional capabilities. They enhance system performance and flexibility, meeting diverse application needs.

Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm

This research aims to optimize the interference mitigation and improve system performance metrics, such as bit error rates, inter-carrier interference (ICI), and inter-symbol interference (ISI), by integrating the Redundant Discrete Wavelet Transform (RDWT) with the Arithmetic Optimization Algorithm (AOA). This will increase the spectral efficiency of MIMOOFDM systems for ultra-high data rate (UHDR) transmission in 5 G networks. The most important contribution of this study is the innovative combination of RDWT and AOA, which effectively addresses the down sampling issues in DWT-OFDM systems and significantly improves both error rates and data rates in high-speed wireless communication. Fifth-generation wireless networks require transmission at ultra-high data rates, which necessitates reducing ISI and ICI. Multiple-input multiple-output orthogonal frequency division multiplexing (MIMOOFDM) is employed to achieve the UHDR. The bandwidth and orthogonality of DWT-OFDM (discrete wavelet transform-based OFDM) are increased; however system performance is degraded due to down sampling. The redundant discrete wavelet transform (RDWT) is proposed for eliminating down sampling complexities. Simulation results demonstrate that RDWT effectively lowers bit error rates, ICI, and ISI by increasing the carrier-to-interference power ratio (CIR). The Arithmetic Optimization Algorithm is used to optimize ICI cancellation weights, further enhancing spectrum efficiency. The proposed method is executed in MATLAB and achieves notable performance gains: up to 82.95 % lower error rates and 39.88 % higher data rates compared to the existing methods. ConclusionThe integration of RDWT with AOA represents a significant advancement in enhancing the spectral efficiency of MIMOOFDM systems for UHDR transmission in 5 G networks. The proposed method not only enhances system performance but also lays a foundation for future developments in high-speed wireless communication by addressing down sampling issues and optimizing interference mitigation.

Augmenting human-guided progressive learning with machine vision systems for robust surface defect detection

Machine vision systems commonly utilize Convolutional Neural Networks (CNNs) for in-line surface defect detection of manufactured components. The prediction abilities of vision-based inspection systems deteriorate with time as the defect detection model trained on fixed image datasets fails to accommodate deviations. This paper proposes a human-guided progressive learning approach that systematically imparts learning of new features to the CNN-powered vision-based defect detection system. The approach augments the surface defect detection model with human intelligence, using an intuitive user interface to address model drift. The human expert monitors the trained model performance under specific conditions leading to the change of characteristics during implementation, identifies misclassifications, and initiates re-training. The algorithm accumulates misclassified data till a pre-defined threshold level is reached or a human expert terminates inspection. The misclassified results merge with the original datasets for progressive re-training using a strategy similar to the base model development. The present work utilizes pre-trained CNN Efficientnet-b0 to develop the surface defect detection model for tapered roller inspection through transfer learning. It is concluded that the progressive re-training improves defect detection performance and reduces misclassifications. The Matthews Correlation Coefficient (MCC) score, derived from the confusion matrix, showed improvement from 0.6 to 0.82 after four iterations. A cross-model benchmarking study is also performed to show the versatility of the proposed approach. The present work demonstrated that the human-guided progressive learning approach can provide adaptability to vision-based surface defect detection utilizing deep learning algorithms and enhance system performance during real-time implementation.

Cost Analysis of a Finite Capacity Queue with Server Failures, Balking, and Threshold-Driven Recovery Policy

The study is on the cost dynamics which is concerned with finite queues in particular to susceptibility to server outages, threshold-driven recovery and Catastrophic events, but main focus is on balking behaviour of the customers. A thorough investigation of the financial implications of interruptions and efficacy to recovery strategies is done and to alleviate the impact of breakdowns, disasters and customer refusals the queue system designs through empirical research is conducted. Various recovery policies have been compared with the existing thresholds, and the study further endeavors to categorize the cost-effective approach to mitigate the economic concerns of server failures and catastrophic incidents. The study brings discernments on the expected development in the management of the queuing system along with the assistance in complete decision-making in cost efficiency and enhancement of the complete system performance.

Enhancing Motor Imagery Classification in Brain–Computer Interfaces Using Deep Learning and Continuous Wavelet Transform

In brain–computer interface (BCI) systems, motor imagery (MI) electroencephalogram (EEG) is widely used to interpret the human brain. However, MI classification is challenging due to weak signals and a lack of high-quality data. While deep learning (DL) methods have shown significant success in pattern recognition, their application to MI-based BCI systems remains limited. To address these challenges, we propose a novel deep learning algorithm that leverages EEG signal features through a two-branch parallel convolutional neural network (CNN). Our approach incorporates different input signals, such as continuous wavelet transform, short-time Fourier transform, and common spatial patterns, and employs various classifiers, including support vector machines and decision trees, to enhance system performance. We evaluate our algorithm using the BCI Competition IV dataset 2B, comparing it with other state-of-the-art methods. Our results demonstrate that the proposed method excels in classification accuracy, offering improvements for MI-based BCI systems.

OPTIMIZED LOAD BALANCING TECHNIQUES IN CLOUD COMPUTING ENVIRONMENT: A SYSTEMATIC LITERATURE REVIEW AND FUTURE TRENDS

In today's rapidly evolving computing landscape, cloud computing has emerged as a pivotal paradigm that offers scalable and flexible resource provisioning to meet the demands of diverse applications. A critical aspect in this environment is load balancing, a technique that optimally distributes workloads across available resources to ensure efficient resource utilization, enhanced system performance, and seamless user experiences. This systematic literature review (SLR) delves into the realm of optimized load balancing techniques within cloud computing environments, encompassing various approaches, optimization methods, challenges, and future trends. The SLR reviews 50 research articles to track the evolution of load balancing techniques from traditional to modern dynamic strategies in cloud infrastructures. The review categorizes load balancing techniques into centralized, decentralized, and hybrid methods, noting their features and limitations. It highlights the importance of optimization methods, like meta-heuristics, machine learning, and swarm intelligence, in enhancing resource allocation and response times, and addresses challenges such as heterogeneity, scalability, and real-time adaptability.This review also identifies imminent challenges faced by load balancing in cloud computing environments. These challenges encompass the intricate interplay between resource allocation, fluctuating workloads, and dynamic system conditions. Additionally, the review offers insights into future trends that include harnessing the power of artificial intelligence and machine learning techniques, exploring fog and edge computing for load distribution, and exploring hybrid load balancing strategies to further enhance system efficiency. This review offers a comprehensive understanding of optimized load balancing in cloud computing, covering the evolution of techniques, optimization methods, and challenges. It also identifies emerging trends to guide future research and practice.In the subsequent sections, we delve deeper into the current state of research, outlining the proposed approach in alignment with the research objectives. Subsequently, we expound upon the experimental outcomes attained through the proposed framework, encapsulating both its methodology and results.

Spider Wasp Optimizer-Optimized Cascaded Fractional-Order Controller for Load Frequency Control in a Photovoltaic-Integrated Two-Area System

The integration of photovoltaic (PV) systems into traditional power grids introduces significant challenges in maintaining system stability, particularly in multi-area power systems. This study proposes a novel approach to load frequency control (LFC) in a two-area power system, where one area is powered by a PV grid and the other by a thermal generator. To enhance system performance, a cascaded control strategy combining a fractional-order proportional–integral (FOPI) controller and a proportional–derivative with filter (PDN) controller, FOPI(1+PDN), is introduced. The controller parameters are optimized using the spider wasp optimizer (SWO). Extensive simulations are conducted to validate the effectiveness of the SWO-tuned FOPI(1+PDN) controller. The proposed method demonstrates superior performance in reducing frequency deviations and tie-line power fluctuations under various disturbances. The results are compared against other advanced optimization algorithms, each applied to the FOPI(1+PDN) controller. Additionally, this study benchmarks the SWO-tuned controller against recently reported control strategies that were optimized using different algorithms. The SWO-tuned FOPI(1+PDN) controller demonstrates superior performance in terms of faster response, reduced overshoot and undershoot, and better error minimization.

Enhancing machine optimization through AI-driven data analysis and gathering: leveraging integrated systems and hybrid technology for industrial efficiency

Industrial automation has long been a driving force in enhancing manufacturing efficiency and productivity. However, traditional systems often rely heavily on human intervention, which can introduce errors and inefficiencies. This article explores the revolutionary potential of deep learning in transforming industrial automation by minimizing human involvement and optimizing operational performance. We present a comprehensive methodology for integrating deep learning models into automation systems, focusing on improving throughput and managing downtime and failures more effectively. The study employs advanced deep learning algorithms to analyse real-time data from industrial processes, enabling predictive maintenance and automated decision-making. Key findings reveal that incorporating deep learning significantly enhances system performance by reducing downtime, preventing failures, and increasing overall throughput. Additionally, the research highlights how minimizing human intervention can lead to more reliable and efficient automation systems. The implications of these findings suggest a paradigm shift in industrial automation, where intelligent algorithms drive process optimization and operational reliability. This shift promises to enhance manufacturing capabilities, reduce operational costs, and improve overall system resilience.

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm

The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.

Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs

This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.

Practical design and implementation of IoT-based occupancy monitoring systems for office buildings: A case study

This study introduces a scalable, cloud-based approach to occupancy monitoring designed to optimize HVAC operations in office buildings. It addresses the challenges of developing and implementing a multi-parameter IoT-based occupancy monitoring system by integrating various off-the-shelf sensors—CO2, infrared (IR), motion (PIR), and door status detection—into a cohesive system. Leveraging wireless LoRaWAN and novel cloud technologies, the system ensures easy installation, efficient maintenance, and robust data management. CO2-based occupancy detection models were trained using data from a reference office and validated in another office environment. Among the various models evaluated, the four best-performing ones—Decision Trees, Random Forest, LightGBM, and K-Nearest Neighbors—were selected for integration into a multi-parameter detection system. To further enhance system performance and identify optimal sensor combinations and configurations for cost-effective and accurate occupancy detection, a data fusion methodology was employed. This methodology, validated with ground-truth data from a test bed, tested the monitoring system in different office settings, ranging from single to quadruple-occupant rooms. Integration of additional parameters into the developed data fusion approach significantly improved system performance, achieving a True Positive Rate (TPR) of 95% compared to 81% with a simple baseline data fusion method. This approach also reduced false detections during unoccupied periods, as tested in multiple rooms within the studied building, thereby enhancing the system's reliability for integration into occupancy-aware HVAC control strategies.

Influence of straight and inclined baffles on enhancement of battery thermal management system performance

Battery thermal management systems (BTMSs) are used in electric vehicles (EVs) to regulate the heat generated by batteries while in use. Existing research on the improvement of BTMSs revealed that more research can be done by exploring different strategies to extend the service life and capabilities of EV batteries beyond the current limitations. In this study, effects of orientation of baffles; straight (90°), inclined (60° and 30°), height of baffles, thickness of baffles, positioning of baffles and number of baffles on the performance of conventional Z – Type BTMS were studied using Computational Fluid Dynamics (CFD) approach. The CFD approach was validated with existing experimental result from literature. Findings from the study showed that for straight baffle, the maximum temperature (Tmax) reduces as the height of baffles decreases. Furthermore, increasing the baffle thickness from 1 mm to 2 mm, produced reduction in Tmax by 0.26 K. For inclined baffles, by comparing the BTMS without baffles and BTMS with 2 baffles, Tmax and maximum temperature difference (ΔTmax) reduced by 0.82 K and 0.68 K, respectively at angle 60°, and reduced by 1.65 K and 1.43 K, respectively at angle 30°. The BTMS with 2 baffles, inclined at angle 60°, with height of 6 mm and thickness of 1 mm, yielded the optimum Tmax (Lowest) value of 333.15 K, a reduction by 2.65 K when compared to the conventional Z – Type BTMS. This was also accompanied with a slight increase in ΔP by 1.12 Pa. In conclusion, it can be said that findings from this study will be beneficial in enhancing the design of BTMSs through adequate selection and utilization of baffles orientation.

Underwater acoustic FBMC communication with multiple mode index modulation

In underwater acoustic communication (UWA), there is a significant presence of multipath and Doppler spread. The integration of orthogonal frequency division multiplexing (OFDM) with index modulation (IM), utilizing subcarrier index modulation to enhance system performance underwater, has garnered considerable interest from researchers. Multiple mode modulation can enhance utilization by transmitting symbols of different modes on different subcarriers. Inspired by the concepts of index modulation and multiple mode modulation, this paper proposes a method that combines multiple mode indexing with the filter bank multicarrier system (MM-FBMC-IM), enabling the transmission of different modulation orders on each subcarrier to increase system flexibility. Simulations demonstrate that under the same spectral efficiency, multiple mode indexing FBMC offers superior bit error rate (BER) performance compared to conventional FBMC and index modulation FBMC.

Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

AbstractA novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations.

RAM-YOLOv8: A multi-fault detection method for photovoltaic module

ABSTRACT Given the constraints in fault type identification, localization precision, and detection efficiency inherent in current photovoltaic fault detection methodologies, this paper introduces an innovative detection approach integrating RAM-YOLOv8 with the Swin Transformer. To mitigate the limitations in model extraction capabilities during the detection process and circumvent the vanishing gradient issue encountered during backward information propagation, this study introduces a residual aggregation module as a substitute for the original C2F module, thereby augmenting feature extraction efficiency. Furthermore, within the feature fusion network, the Swin attention mechanism fusion module is designed by integrating the Swin Transformer’s architectural concept to minimize the loss of weak defect information and suppress irrelevant background interference. The model achieves a detection accuracy of 88.5% on the dataset, representing a 7% improvement over the original YOLOv8 algorithm, with a detection rate of 83.33 FPS on an RTX2080 GPU. This method offers a robust and efficient fault detection solution for PV systems, rendering it a practical approach for enhancing system performance and reducing maintenance costs.

Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance

Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance