Underwater gliders have become a focal point in marine research due to advancements in maritime technologies and the increasing demand for versatile autonomous underwater vehicles (AUVs) in applications such as oceanography, environmental monitoring, and marine surveillance. This study provides a comprehensive analysis of the critical parameters influencing the gliding behavior of a newly designed AUV model, simulated using ANSYS Fluent. In this study, two essential gliding parameters were investigated: the critical angle of attack and the optimum wingspan. The model was fully submerged, and a three-dimensional representation of the AUV was employed to replicate realistic underwater dynamics. Navier-Stokes equations, coupled with continuity equations, were numerically solved to ensure mass and momentum conservation across the simulated environment. The model was rigorously validated against published experimental data, thereby establishing reliability in the simulated outcomes. The results reveal an optimum angle of attack that significantly enhances the glider’s maneuverability, facilitating efficient ascent and descent adjustments by the automated control system to navigate precise underwater positions. These findings contribute valuable insights for designing AUVs with enhanced autonomous control and efficient gliding capabilities, aiding in the effective application of AUVs across a range of marine environments.
Electricity remains one of the most vital resources for industrial, domestic, and agricultural applications. However, electricity theft has emerged as a significant challenge, contributing to substantial power losses and severe economic repercussions for utility companies. This study examines the role of smart meters (SMs) in minimizing electricity theft and reducing energy losses by transitioning from traditional analogue meters to advanced SMs equipped with automated billing and metering systems. Data collected from the SM system in the Akre energy distribution network reveal that, following the implementation of SMs, overall electrical power losses were reduced by 17.1%, while theft incidents decreased by 96.4%. These results demonstrate that the deployment of SMs significantly contributes to lowering total power losses and yields considerable financial benefits for both utility providers (UPs) and consumers. Moreover, the system enhances the ability to remotely monitor and control customer meters, allowing continuous oversight of meter readings without requiring physical visits. This remote functionality strengthens theft prevention measures, improves grid reliability, and reduces operational costs. The findings highlight the potential of the SM system in advancing power efficiency and promoting a more secure and cost-effective energy distribution network.
The recognition of abnormal events in surveillance video streams plays a crucial role in modern security systems, yet conventional techniques such as Support Vector Machines (SVMs) and decision trees (DTs) exhibit limitations in terms of accuracy and efficiency. These traditional models are often hindered by high false alarm rates and struggle to adapt to dynamic environments with variable conditions, thus reducing their practical applicability. In response to these challenges, an innovative approach, termed Adaptive Regression for Event Recognition (ARER), has been developed, leveraging advanced regression algorithms tailored for video data analysis. The ARER model integrates deep learning techniques, allowing for more precise temporal and contextual analysis of video footage. This methodology is structured through a multi-layered architecture that progresses from basic motion detection to complex anomaly identification. Trained on an extensive dataset covering a range of environmental and situational variables, ARER demonstrates enhanced robustness and adaptability. Evaluation results indicate that the ARER model achieves a 0.35% improvement in detection accuracy and a 0.40% reduction in false positives when compared to SVMs. Additionally, system throughput is increased by 0.25%, and detection latency is reduced by 0.30% in comparison to DTs. These advancements highlight the ARER approach as a superior alternative for real-time monitoring, offering significant improvements in both reliability and performance for surveillance applications.
Separately excited direct current (DC) motors, renowned for their linear characteristics and controllability, are extensively employed in various industrial applications. Effective speed control of these motors can be achieved through multiple methods, with fuzzy logic being a particularly robust approach. This study focuses on evaluating the transient responses of current and voltage in relation to the rotational speed of a DC motor under two distinct control schemes: field control and armature control, both subjected to similar load disturbances. A simulation-based methodology was employed using a DC motor speed control system combined with a fuzzy logic controller (FLC) designed with the Mamdani min-max method. The system was implemented in Simulink. In this framework, the FLC processes speed error signals and field current ($I_f$) errors as inputs to generate a field voltage control signal, which is then utilized by the armature voltage (Va) regulator to modulate the armature voltage. The results demonstrate that the FLC effectively stabilizes motor speed, quickly and accurately following speed references, even under load disturbances. Moreover, the system effectively mitigates speed fluctuations induced by load variations. A comparison between the two control schemes reveals that the field control approach exhibits a slower response time, taking 2.93 seconds to reach a steady state, whereas the armature control achieves this in a significantly faster time of 0.144 seconds. These findings underscore the efficacy of fuzzy logic in maintaining stable and responsive speed control in DC motors, with the armature control method displaying superior transient performance.
Regulatory bodies in electrical engineering mandate the installation of power factor (PF) improvement systems to elevate PF values to between 0.9 and 0.96. Compliance is enforced by regional or local utility companies through penal rates and incentives for PF values nearing unity. Traditional power factor correction (PFC) systems often utilize microprocessor-based controllers for switching capacitor banks, which can result in under- or over-compensation of reactive power. This study developed an adaptive neuro-fuzzy inference system (ANFIS) utilizing a Sugeno-Takagi inference model based on the sub-clustering method to address the limitations of sensitivity and response time observed in existing microcontroller-based PFC systems. The proposed neuro-fuzzy (NF) controller comprises a five-layered model with two inputs, i.e., kilowatt (KW) and kilovolt-ampere reactive (KVAR), and one output (PF). A 25-rule set performance of the developed program was achieved, with significant improvements observed after 50 epochs, culminating in an error rate of 0.050691 recorded post the second epoch. The results demonstrated that the developed controller exhibits higher sensitivity and faster response time compared to existing PF controllers. Consequently, the implementation of the proposed controller is recommended for optimizing the switching of capacitor banks, thereby enhancing PF in manufacturing industries characterized by variable load conditions.
This study evaluates the usability and effectiveness of a newly developed special education (SPED) campus navigation system designed for students with visual impairment (SVI) at the Federal College of Education (Special), Oyo, Oyo State, Nigeria. The primary objective was to assess the system's capability to facilitate self-navigation for SVI and identify challenges encountered in a campus environment. A mixed-methods approach, combining quantitative data from questionnaires and qualitative insights from interviews, was employed. Twenty SVI, selected through purposive sampling, participated in the study, using the system over a five-week period. The findings indicate significant improvements in the orientation and mobility of SVI, resulting in increased confidence in navigating the campus. Participants reported that the navigation system effectively aided in locating key areas, detecting obstacles, and ensuring safety. However, several critical challenges were identified, such as the system's voice being drowned out in noisy environments and the frequent need for battery recharging every five days. Participants suggested enhancements, including the incorporation of volume control to accommodate various environmental conditions and regular device charging to prevent battery depletion. These improvements are deemed essential for enhancing the system's reliability and usability for SVI.
In recent decades, the demand for electricity has continuously increased. Power generation facilities are predominantly situated at substantial distances from consumption centers, necessitating transmission over extensive, high-voltage lines. Such configurations lead to significant energy losses and diminished capacity and capability of transmission systems. Consequently, enhancements in transmission line performance have become a focal point for power system operators. The integration of the flexible alternating current transmission system (FACTS) technology has emerged as a pivotal solution, facilitating dynamic control over power flow and amplifying the existing capacity of power lines without the need for constructing new infrastructure. Among various FACTS devices, the static synchronous series compensator (SSSC) plays a crucial role by injecting variable capacitive or inductive reactance as required, thereby optimizing power flow and enhancing voltage stability. This review paper meticulously examines the functionality of different FACTS technologies, with a specific focus on the SSSC. Comparative analyses of transmission line performance, uncompensated, compensated through traditional series capacitors, and enhanced via SSSC, were conducted. The findings underscore the versatility of SSSC in reducing transmission losses and stabilizing network operations. This investigation not only details the operational benefits of SSSC but also explores its potential in addressing contemporary challenges in power transmission systems.
In the pursuit of optimizing automotive suspension systems, a semi-active suspension system (SASS) utilizing continuous skyhook control has been developed to enhance vehicle ride comfort and handling. This system is specifically engineered to mitigate vibrations stemming from high-frequency road excitations. Central to this advancement is the introduction of an electrohydraulic (EH) damper, which is uniquely characterized by solenoid valves capable of adjusting the orifice size to modify damping characteristics. By tuning the damping ratio, the system effectively minimizes the positional oscillations of the sprung mass in response to road irregularities. The dynamic behavior of this damper is comprehensively modeled through a boundary model approach, ensuring precise simulation and prediction of performance. A full-scale quarter-car test platform was constructed to evaluate the dynamic response and the efficacy of various control strategies implemented within the SASS. The performance assessments were conducted using MATLAB Simulink to simulate the behavior of the system under skyhook control algorithms, which aim to maintain the chassis’s vertical stability during disturbances. Comparative tests involving a single EH damper have demonstrated a high level of correlation with the simulated models, achieving a 95% agreement level. These findings underscore the capability of the SASS to surpass traditional hydraulic dampers in terms of performance, cost-efficiency, and versatility in testing applications. The insights garnered from this study not only validate the functionality of the proposed system but also contribute significantly to the body of knowledge in vehicle dynamics and control. This research provides a foundational framework for future exploration and potential implementation of advanced damping systems in the automotive industry.
In the era of low-carbon travel, maglev cars emerge as a high-speed, environmentally sustainable solution, leveraging their frictionless, smooth operation. This study introduces a nonlinear dynamic model for the longitudinal dynamics of maglev cars, constructed via a data-driven approach. A nonlinear model predictive control (NMPC) strategy, incorporating rotational speed constraints, is developed to address the inherent instability of the open-loop system. The dynamic relationship between the driving force and the rotational speeds of magnetic wheels was quantified using the least squares method (LSM) based on tests conducted across varied rotational speeds. A single-degree-of-freedom model, integrating stiffness and damping characteristics, was subsequently formulated to describe the longitudinal motion of the maglev car. The model’s validity was confirmed through comparison with experimental outputs under varying conditions. Further, the stiffness and damping coefficients were derived from experimental data, enhancing the model’s precision. Control simulations and real-world experiments under diverse operational conditions demonstrated the efficacy of the NMPC in ensuring robust longitudinal tracking. This investigation substantiates the NMPC approach as an effective control strategy for enhancing the stability and performance of maglev transportation systems.
The quality of state estimation in uncertain systems exerts a significant impact on the performance of control systems. Within these uncertain systems, set-valued mappings introduce output uncertainties, complicating the design of observers. This study maps the output error of uncertain systems to the nonlinear terms of a framer , thereby extending the Luenberger framer. An interval observer design method for uncertain systems is proposed, leveraging monotone system theory to analyze the coherence of the error system. The effectiveness of the algorithm is validated through simulation examples.
Unmanned Aerial Vehicles (UAVs), have recently sparked attention due to its versatility in a wide range of real-life uses. They require to be controlled so as to conduct different operations and widen their typical roles. This study proposes an optimal robust deadbeat controller for the roll angle motion of tail-sitter vertically take-off and land vehicles, taking into consideration the systems’ intrinsic sensitivity to outside influences and fluctuation of their dynamics. Primarily, several assumptions are used to develop an appropriate transfer function that reflects the system physical attributes. The suggested controller is then formed in two sections: the first section addresses the nominal system’s unstable dynamics, and the second element imposes the desired deadbeat performance and robustness. The control system variables are optimized using the creative and efficient Incomprehensible but Time-Intelligible Logics optimization technique, ensuring that the specified robustness demand is satisfied correctly. Finally, simulation is used to evaluate the developed controller effectiveness, revealing beneficial stability and performance indicators for both nominal and uncertain regulated system featuring uniform, bounded, and feasible closed-loop outputs. The control unit performs well, with a rising time of 0.0965 seconds, a settling time of 0.1134 seconds, and an overshoot of 0.167%.
One significant benefit of the Maclaurin symmetric mean (MSM) is that it is a generalization of many extend operators and can consider the interrelationships among the multi-input arguments, such as multi-attributes or multi-experts in the multi-attribute group decision making (MAGDM). In the information fusion process, the Schweizer-Sklar T-norm (TN) and T-conorm (TCN), an important class of the TN and TCN, have more flexibility. We define SS operational rules of SFNs and extend SSTN, SSTCN to Spherical fuzzy values (SFVs) in order to fully utilize the advantages of SSTN, SSTCN, and MSM. Next, by combining the MSM with SS operational rules, we propose the spherical fuzzy Schweizer-Sklar weighted Maclaurin symmetric mean (SFSSWMSM) and spherical fuzzy Schweizer-Sklar Maclaurin symmetric mean (SFSSMSM) operators. This research examines their advantages and creates a novel approach based on these operators for particular MAGDM issues. Then, by comparing the suggested technique with current approaches in practical settings, its benefits and viability are demonstrated. Lastly, a few real-world examples are provided to demonstrate the applicability and benefits of the suggested approach in comparison to a few other approaches already in use.
Submit to JISC
Review for JISC
Propose a Special Issue 
Open Access
Journal Metrics
Journal Overview
Associated Journals