Enhance System Performance Research Articles

A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors

The field of indoor localization is fast developing and has important ramifications for a number of areas, such as smart infrastructure development, healthcare settings, industrial automation, and military operations. Advances in a range of technologies, each suited to certain use cases and objectives, have been fueled by the capacity to precisely locate objects or people inside places. Prominent indoor localization technologies like Bluetooth, Wi-Fi, ultra-wideband (UWB), ZigBee, and RFID-based systems are examined in this review, along with hybrid solutions that combine several technologies to get around their individual drawbacks and enhance system performance. The field still faces several obstacles in spite of these developments. Widespread acceptance is hampered by persistent problems such as signal interference, high energy consumption, and restricted scalability. The deployment of these systems is further complicated by elements like cost-effectiveness, privacy issues, and compatibility in a variety of situations. This study also examines potential avenues for future research to improve the precision, dependability, and versatility of indoor localization technology in order to overcome these obstacles. Designing systems with increased resilience to environmental changes, utilizing edge computing for real-time processing, and integrating artificial intelligence for predictive modeling are all promising areas of emphasis. This study attempts to help academics and practitioners navigate the changing terrain of indoor localization by offering a comprehensive picture of the field’s present status and future directions.

Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies

Accidents involving electric wheelchairs are a growing concern, with users frequently encountering obstacles that lead to collisions, tipping, or loss of balance. These incidents underscore the need for advanced safety technologies tailored to electric wheelchair users. This research addresses this need by developing a driving assistance system to prevent accidents and enhance user safety. The system incorporates ultrasonic sensors and a front-facing camera to detect obstacles and provide real-time warnings. The proposed system operates independently of stable server communication and employs embedded hardware for fast object detection and environmental recognition, ensuring immediate guidance in various scenarios. In this research, we utilized the existing yolov8 model as is. But we attempted to improve performance by hardware acceleration of convolutional neural networks, supporting various layers such as convolution, deconvolution, pooling, batch normalization, and others. Thus, the YOLO model was accelerated during inference on the specialized hardware in our experiments. Performance was evaluated in diverse environments to assess its usability. Results demonstrated high accuracy in detecting obstacles and providing timely warnings. Leveraging hardware acceleration for YOLOv8 delivers faster, scalable, and robust object detection, making it a great platform for enhancing driving safety on edge and embedded devices. These findings provide a strong foundation for future advancements in safety assistance systems for electric wheelchairs and other mobility devices. Future research will focus on enhancing system performance and integrating additional features to create a safer environment for electric wheelchair users.

Control of a Doubly Fed Induction Generator for Variable Speed Wind Energy Conversion Systems using Fuzzy Controllers optimized with a Genetic Algorithm

This paper presents a comprehensive study of a wind turbine system operating under variable wind conditions, utilizing a Doubly Fed Induction Generator (DFIG) connected to the grid. The DFIG is controlled via a rotor-side transducer, allowing for independent regulation of the conductors to manage both active and reactive power flows effectively. The control strategy focuses on generating reference voltages for the rotor to ensure that active and reactive power align with the desired targets, optimizing the tracking of the maximum power point to maximize electrical output. The research analyzes the system's dynamic performance under fluctuating wind conditions, emphasizing control strategies for managing active and reactive energy. A notable innovation is the integration of fuzzy logic and genetic algorithm into the control strategy for the wind turbine's switching mechanism, which enhances system performance and efficiency. Simulation results demonstrate that this approach provides higher efficiency, improved performance, and greater stability compared to the traditional Proportional-Integral (PI) controllers. Advanced artificial intelligence methods, such as fuzzy genetic algorithm control, were employed and the proposed system's effectiveness was validated with Matlab/Simulink simulations.

Enhancing Free Space Optical System Performance through Fog and Atmospheric Turbulence using Power Optimization

Free Space Optical (FSO) communication is gaining traction as a pivotal technology for next-generation communication systems, offering extremely high data rates, unlicensed bandwidth, and rapid transmission capabilities. However, its performance is significantly hindered by atmospheric factors such as turbulence and fog. This paper presents a comprehensive model for FSO communication designed to optimize performance under various atmospheric conditions. We assess channel capacity across a spectrum of weak to strong atmospheric turbulence using a Gamma-Gamma channel distribution. To mitigate channel losses, our system employs Wavelength Division Multiplexing (WDM) and multi-beam Multiple Input Multiple Output (MIMO) technologies. Results indicate that the integration of diversity techniques and WDM substantially enhances system performance in adverse weather conditions. Furthermore, power optimization is achieved through the implementation of optical amplifiers and feedback mechanisms from the receiver to the transmitter to adjust the transmitter power in accordance with received Bit Error Rate (BER). The proposed power-optimized WDM MIMO system demonstrates a remarkable BER of 1.48884e-15, while extending the transmission link distance to 2500 meters with a Q factor of 21.5 even under strong atmospheric turbulence conditions.

Hardware-assisted virtualization extensions for LEON processors in mixed-criticality systems

The increasing complexity of real-time embedded critical systems has driven the adoption of new methodologies to mitigate high development costs. One of the most common approaches is the implementation of mixed-criticality systems, characterized by integrating applications with different levels of criticality on the same processing unit. In these systems, applications run on a separation kernel hypervisor, a software element that controls the execution of the different operating systems, providing a virtualized environment and ensuring the necessary spatial and temporal isolation. This paper presents the design and implementation of hardware virtualization extensions for LEON processors, whose use is widespread in the field of space systems. These extensions enable the execution of virtualized applications with minimal transitions to the hypervisor, enhancing system performance. Our proposed solution defines a specific execution mode and duplicates control and status registers for the exclusive use of virtualized applications. In addition, the functionality of the hardware and software interrupt signals has been extended, allowing developers to select which ones are handled by the hypervisor and which ones by the guest operating systems directly. We have implemented the proposed extension using the LEON version 3 processor’s original VHDL code, and validated it using exhaustive tests to evaluate performance and resource consumption. The results show that the proposed modifications allow virtualized applications to execute without hypervisor intervention, matching the performance when non-virtualized while significantly outperforming existing para-virtualization solutions. Resource consumption increases by 6% to 14%, depending on the FPGA, which is low when compared to available resources. Power consumption increases by only a few milliwatts, which can be considered negligible.

Small-data-trained model for predicting nitrate accumulation in one-stage partial nitritation-anammox processes controlled by oxygen supply rate.

Nitrate (NO3--N) accumulation is the biggest obstacle for wastewater treatment via partial nitritation-anammox process. Dissolved oxygen (DO) control is the most used strategy to prevent NO3--N accumulation, but the performance is usually unstable. This study proposes a novel strategy for controlling NO3--N accumulation based on oxygen supply rate (OSR). In comparison, limiting the OSR is more effective than limiting DO in controlling NO3--N accumulation through mathematical simulation. A laboratory-scale one-stage partial nitritation-anammox system was continuously operated for 135 days, which was divided into five stages with different OSRs. A novel deep learning model integrating Gated Recurrent Unit and Multilayer Perceptron was developed to predict NO3--N accumulation load. To tackle with the general obstacle of limited environmental samples, a generic evaluation was proposed to optimise the model structure by leveraging predictive performance and overfitting risk. The developed model successfully predicted the NO3--N accumulation in the system ten days in advance, showcasing its potential contribution to system design and performance enhancement.

Comparative evaluation of voltage conversion and encoder pulse detection methods for controlling a model high-bay warehouse

This study evaluates five control methods for the Fischertechnik Training Factory Industry 4.0–24 V system, focusing on accuracy, efficiency, and performance in managing voltage differences and encoder pulse detection. The proposed method introduces a novel approach by integrating a dedicated pulse-counting Arduino with the TBD62783APG chip, enhancing precision and reliability beyond traditional methods. Despite requiring additional hardware, this method demonstrated exceptional control and dependable operation, showcasing the efficacy of specialized integrated circuits in industrial automation applications. The presented evaluation enhances control strategies for industrial automation, emphasizing efficient voltage conversion and precise encoder pulse detection to enhance system performance and reliability, laying the groundwork for future advancements in remote control capabilities and complex manufacturing systems.

Error-related potentials during multitasking involving sensorimotor control: an ERP and offline decoding study for brain-computer interface

Humans achieve efficient behaviors by perceiving and responding to errors. Error-related potentials (ErrPs) are electrophysiological responses that occur upon perceiving errors. Leveraging ErrPs to improve the accuracy of brain-computer interfaces (BCIs), utilizing the brain's natural error-detection processes to enhance system performance, has been proposed. However, the influence of external and contextual factors on the detectability of ErrPs remains poorly understood, especially in multitasking scenarios involving both BCI operations and sensorimotor control. Herein, we hypothesized that the difficulty in sensorimotor control would lead to the dispersion of neural resources in multitasking, resulting in a reduction in ErrP features. To examine this, we conducted an experiment in which participants were instructed to keep a ball within a designated area on a board, while simultaneously attempting to control a cursor on a display through motor imagery. The BCI provided error feedback with a random probability of 30%. Three scenarios–without a ball (single-task), lightweight ball (easy-task), and heavyweight ball (hard-task)–were used for the characterization of ErrPs based on the difficulty of sensorimotor control. In addition, to examine the impact of multitasking on ErrP-BCI performance, we analyzed single-trial classification accuracy offline. Contrary to our hypothesis, varying the difficulty of sensorimotor control did not result in significant changes in ErrP features. However, multitasking significantly affected ErrP classification accuracy. Post-hoc analyses revealed that the classifier trained on single-task ErrPs exhibited reduced accuracy under hard-task scenarios. To our knowledge, this study is the first to investigate how ErrPs are modulated in a multitasking environment involving both sensorimotor control and BCI operation in an offline framework. Although the ErrP features remained unchanged, the observed variation in accuracy suggests the need to design classifiers that account for task load even before implementing a real-time ErrP-based BCI.

Compact Semi-Elliptical Quad Array Antenna for UWB MIMO Systems

Ultra-Wide Band (UWB) systems are increasingly favored for their ability to deliver high data rate transmissions, making them integral to various modern applications. This demand, however, presents significant challenges in antenna design. Compared to conventional narrow-band antennas, UWB antennas offer distinct advantages, including lower cost, reduced power consumption, higher efficiency, and the capability to achieve broad bandwidth. This paper presents the development of a semi-elliptical quad array antenna designed specifically for UWB MIMO systems. The proposed antenna is fabricated on a cost-effective FR-4 Epoxy substrate with dimensions of 52 × 48 × 1.6 mm and is fed using an Asymmetric Coplanar Strip (ACS) line. Each element of the 2 × 2 MIMO array is oriented orthogonally at the ports, and the antenna elements are isolated using a decoupling via structure placed between the antenna elements to mitigate mutual coupling and enhance system performance. The investigated antenna radiates across the 3–12 GHz frequency range with good reflection coefficient characteristics < −20 dB and good isolation characteristics below 30 dB . It generates an acceptable omnidirectional power pattern, with a maximum peak gain of 5 dB at 8.2 GHz. Good performance with a Diversity Gain (DG) of 9.99 and an Envelope Correlation Coefficient (ECC) < 0.005 is achieved, making it well-suited for MIMO applications.

Leveraging Asynchronous Processing Tools in Salesforce: A Comprehensive Analysis

This article provides a comprehensive analysis of asynchronous processing tools within the Salesforce platform, exploring their significance in enhancing system performance, scalability, and user experience. It delves into a wide array of server-side and client-side asynchronous processing options, including Asynchronous Apex, Platform Events, Asynchronous Flows, and Lightning Actions, offering detailed insights into their functionalities, use cases, and implementation considerations. The article presents a comparative analysis of these tools, discussing their performance metrics, implementation complexities, and scalability factors. It also outlines best practices for implementing asynchronous processing in Salesforce, addressing key aspects such as resource optimization, platform limit mitigation, and data integrity. Furthermore, the article examines the challenges and limitations associated with asynchronous processing in Salesforce, and explores future trends and developments in this domain. By providing a thorough examination of Salesforce's asynchronous processing capabilities, this article serves as a valuable resource for developers, architects, and decision-makers seeking to leverage these tools effectively in building robust, efficient, and scalable Salesforce applications.

Hybrid multi-objective optimization of µ-synthesis robust controller for frequency regulation in isolated microgrids

Frequency regulation in isolated microgrids is challenging due to system uncertainties and varying load demands. This study presents an optimal µ-synthesis robust control strategy that regulates microgrid frequency while enhancing system performance and stability—a proposed fixed-structure approach for selecting performance and robustness weights, informed by subsystem frequency analysis. The controller is optimized using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) under inequality constraints, employing a Pareto front to identify optimal solutions. Comparative analyses demonstrate that the MOPSO-optimized controller achieves superior robustness and performance, tolerating up to 236% uncertainty compared to 171% for conventional µ-synthesis controllers. Additionally, it significantly reduces frequency deviation and enhances transient response. Nyquist stability analysis confirms robustness across renewable energy uncertainties. The results highlight the proposed controller’s effectiveness in isolated microgrid frequency regulation, with future work focused on discrete-time implementation for practical digital signal processing (DSP) applications.

Secrecy-Constrained UAV-Mounted RIS-Assisted ISAC Networks: Position Optimization and Power Beamforming

This paper investigates secrecy solutions for integrated sensing and communication (ISAC) systems, leveraging the combination of a reflecting intelligent surface (RIS) and an unmanned aerial vehicle (UAV) to introduce new degrees of freedom for enhanced system performance. Specifically, we propose a secure ISAC system supported by a UAV-mounted RIS, where an ISAC base station (BS) facilitates secure multi-user communication while simultaneously detecting potentially malicious radar targets. Our goal is to improve parameter estimation performance, measured by the Cramér–Rao bound (CRB), by jointly optimizing the UAV position, transmit beamforming, and RIS beamforming, subject to constraints including the UAV flight area, communication users’ quality of service (QoS) requirements, secure transmission demands, power budget, and RIS reflecting coefficient limits. To address this non-convex, multivariate, and coupled problem, we decompose it into three subproblems, which are solved iteratively using particle swarm optimization (PSO), semi-definite relaxation (SDR), majorization–minimization (MM), and alternating direction method of multipliers (ADMM) algorithms. Our numerical results validate the effectiveness of the proposed scheme and demonstrate the potential of employing UAV-mounted RIS in ISAC systems to enhance radar sensing capabilities.

SoC–Based Inverter Control Strategy for Grid‐Connected Battery Energy Storage in AC Microgrid

The successful integration of battery energy storage systems (BESSs) is crucial for enhancing the resilience and performance of microgrids (MGs) and power systems. This study introduces a control strategy designed to optimize the operation of BESSs. This control strategy optimizes the BESS operation by dynamically adjusting the inverter’s power reference, thereby, extending the battery cycle life. This approach incorporates a droop control mechanism that adjusts control actions in response to state‐of‐charge (SoC) fluctuations of the BESSs, thereby, enhancing system performance. The effectiveness of this SoC–based control strategy is demonstrated through Matlab/Simulink. It shows its capabilities in regulating power, voltage, grid synchronization, and stability. The paper utilizes a modified CIGRE MG benchmark for system evaluation. It presents case studies to demonstrate the effectiveness of the proposed control modifications. Additionally, a comparative analysis with a power–voltage (P–V) control strategy is presented. This analysis highlights the advantages of the proposed strategy in ensuring stable voltage regulation within the MG. This research provides a robust foundation for future developments in optimizing BESS integration. It offers a roadmap to advance the efficiency, reliability, and longevity of battery‐based solutions in the evolving landscape of sustainable energy systems. Additionally, it sheds light on the potential for scalability and applicability across diverse MG configurations and operational scenarios.

High-Gain Observer in Fuel Cell and Cascaded DC-DC Boost Converter System

In this study, we propose using a high-gain observer (HGO) in fuel cell systems and the FC-cascade boost converter to achieve precise state estimation and improve control accuracy. The HGO offers significant advantages, including robustness to disturbances, simplicity of design, and reduced dependency on costly sensors, thereby enhancing system performance and reducing operational costs. Our results demonstrate the superior accuracy and reliability of this approach, confirming its potential as a promising solution for fuel cell applications.

A Novel Fuzzy Logic EMS for Hybrid Microgrids with Photovoltaic, Wind, Fuel Cell, and Energy Storage Integration

This paper presents an innovative Energy Management Strategy (EMS) for a hybrid microgrid that combines two main renewable energy sources (RESs), photovoltaic (PV) and wind turbine (WT) generators working at the maximum power point (MPP) to extract the maximum available energy, and an energy storage system (ESS) based Lithium batteries to ensure DC-bus stability. This system includes a backup device based on Fuel Cell (FC) to assure system dependability and minimize blackouts, as well as power electronic converters for flexible system parts and a residential community as DC loads. The proposed EMS is based on the advanced Fuzzy Logic Controller (FLC), which regulates voltage and manages power flow inside the microgrid. MATLAB/Simulink simulations were conducted to assess the performance of innovative microgrid stability strategy. A comparative analysis with conventional EMS approaches demonstrates the superior effectiveness and viability of the proposed FLC technique in energy management within microgrids. The simulation-based comparison indicates that the proposed optimization method enhances system performance and DC-bus stability.

Influence of Sacrificial Reagents on the Photoelectrocatalytic Performance of Titanium Dioxide Nanotube Arrays for Hydrogen Production

AbstractPhotoelectrochemical water splitting offers a synergistic method for producing hydrogen using solar energy and an external bias with no emissions. Since the water oxidation process is a limiting factor in the hydrogen production rate, the use of efficient hole scavengers is crucial for enhancing system performance. This study evaluated the performance of titanium dioxide nanotubes (TiO2 NTs) using different hole scavengers in comparison to sodium sulfate. All the evaluated hole scavengers achieved higher photocurrent densities than sodium sulfate with performance ordered as follows: glycerol &gt; glucose &gt; triethanolamine &gt; methanol &gt; sodium sulfite &gt; ethanol &gt;&gt; sodium sulfate. Regarding the hydrogen production rate, glycerol achieved 0.292 mL cm−2 h−1, a value 32.5 times higher than that obtained with sodium sulfate (0.009 mL cm−2 h−1). Structural characteristics, such as the presence of hydrogen atoms adjacent to alcohol groups (α‐H), the carbon‐to‐oxygen atom ratio (C:O), solvent polarizability, and redox potential were fundamental for understanding the role of hole scavengers. These findings validate the enhancement of the photoelectrocatalytic process by hole scavengers, supported by the thermodynamically favorable electron transfers facilitated by organic molecules with higher polarization, a low C:O ratio, and rich presence of α‐H.

Bearing fault signal detection based on non-negative matrix factorization and dual-feedback segmented unsaturated tristable stochastic resonance system

Abstract Stochastic resonance is widely used in bearing fault detection due to its ability to enhance weak signals. This paper proposes a fault detection method that combines noise reduction with stochastic resonance. Firstly, a segmented unsaturated potential function based on the classic potential function is constructed, and a dual-feedback structure is introduced to feed the system output back to the input, thereby enhancing system performance. Secondly, the theoretical expressions for the Mean First Passage Time (MFPT) and the Spectral Amplification (SA) of the Dual-feedback Segmented Unsaturated Tristable Stochastic Resonance (DSUTSR) system are derived and analyzed. Additionally, a numerical simulation comparison using the fourth-order Runge-Kutta method is performed between the DSUTSR system and its predecessor systems to verify the improvements brought by the dual-feedback structure. Subsequently, Non-negative Matrix Factorization (NMF) is introduced as a noise reduction method, with cross-validation used to determine the decomposition rank of NMF to guide the decomposition of the fault signal matrix. Finally, the combination of NMF and the DSUTSR system is used to detect bearing fault frequencies under white noise and Lévy noise backgrounds. Experimental results demonstrate the superiority and effectiveness of the proposed method in fault signal detection. This system holds significant potential for future weak signal detection, effectively enhancing and identifying fault signals hidden within noisy backgrounds.

Pre-design of bottom ash cooling using CFD simulation: a case study of the coal generator power plant in the PT. BEST Tanjung Enim of South Sumatera

The manual removal of bottom ash from the boiler is a hazardous activity due to the extremely high temperatures involved, ranging from 700 to 800°C. The objective of this research is to develop a cooling device to facilitate the bottom ash removal process for workers at PT. BEST Tanjung Enim and to measure the reduction in ash temperature achieved by cooling device. The design was created using computer aided design software and Computational Fluid Dynamics (CFD) simulations. Subsequently, a hypothesis is proposed, defining the bottom ash material as Silicon Dioxide (SiO₂) and equalizing the density of the bottom ash. To ascertain the temperature drop, variations in the screw conveyor’s rotation speed were tested at 40, 33, and 12 rpm, paired with cooling water velocities of 0.5, 0.75, and 1 m/s respectively. The findings of study indicated that a screw conveyor speed of 12 rpm combined with a cooling water velocity of 0.5 m/s yielded the lowest bottom ash temperature, reaching 402°C, thus significantly reducing the need for manual handling when the ash temperature remains at 800°C. Further study should explore the application of Discrete Element Method (DEM) simulations for modelling bulk material behaviour and the integration of additional cooling media to enhance system performance.

Potentials of Genetic Algorithm in the Performance of Load Frequency Control using FACT Devices, AVR and SMES for Hybrid Power System in Deregulated Environment

In this article, the interconnection of two areas of a multi-unit hybrid interconnected power system is studied under a deregulated environment. For emergency requirements and energy-efficient building systems, the hybrid power system is studied which consists of several co-generating units thermal, hydro, nuclear, diesel, and gas power. To control the active power of the interconnected system, the Automatic Generation Controller (AGC) control loop is investigated, whereas, to control voltage, a reactive power control loop named automatic voltage regulator is applied. Effective governance requires the use of additional reliable fast energy exchange devices named superconducting magnetic energy storage (SMES) in both areas. The FACT devices considered are thyristor control series compensator (TCSC), Thyristor controlled phase shifter (TCPS), Unified power flow controller (UPFC), and Static synchronous series compensator (SSSC). For tuning of SMES, a Genetic Algorithm (GA)-based proportional integral derivative with a filter (PIDF) controller is applied to optimize integral square error (ISE). The frequency, tie-line power, and voltage profile of both the areas with various options of SMES connected to the system are studied in terms of overshoot, undershoot, rise time, and settling time. A system without SMES and with SMES in both areas are investigated. It is observed that GA-based PIDF controllers considerably enhanced system performance with SMES and SSSC gives better performance compared to other FACT devices. The response among all the FACT devices, SMES with SSSC gives a better response in terms of the settling time of frequency for two areas is settled at 8.25sec and 7.89sec. The modeling of an interconnected hybrid power system with FACT devices, SMES, and Automatic Voltage Regulator (AVR) is done in MATLAB 2016b Simulink.

Optimal DG and Capacitor Allocation Along with Network Reconfiguration Using Grey Wolf Optimizer Algorithm

This study introduces a multi-criteria optimization approach using the Grey Wolf Optimizer (GWO) algorithm to determine the optimal capacity, locations of DG units and capacitor banks, and network reconfiguration in distribution systems. The objective function incorporates six key performance metrics: real losses, imaginary losses, voltage variation, voltage stability index, section load ability, and current balancing index. The proposed GWO technique was evaluated on IEEE 33- and 69-bus systems and benchmarked against Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results demonstrate that GWO effectively optimizes the placement and sizing of DGs and capacitor banks under diverse conditions, enhancing system performance and reducing active power losses.