Object tracking is a technique for finding moving objects of interest and estimating their trajectoryor path with regard to time in a series of images. It involves object representation, detection,and tracking. It becomes an important field of study due to the need in video surveillance, trafficmonitoring, live sport video analysis and many other applications. In this paper, both static camera-based and dynamic camera-based object tracking techniques have been developed. The static camera-based object tracking was developed with NI LabVIEW, and Shape adaptive mean-shift algorithmhas been used for tracking. In case of dynamic camera-based object tracking, an optimal Fuzzy-PIDcontroller has been designed to adjust the position of the pan/tilt mechanism so as to trace the object’strajectory. Genetic algorithm (GA) was used to find the optimal values of the operating ranges (scalingfactors) of the membership functions. The performance of the system has been tested by differenttrajectories like step, sinusoidal, circular and elliptical at different frequencies 1, 50 and 100 rad/sec.The system has best performance at low frequencies and when the frequency or speed of the objectincreases, the system performance decreases which complies for real systems. The simulation resultsdemonstrate that GA tuned Fuzzy-PID controller has given us the best results in terms of reducedsteady-state error, faster rise time and settling time, and object position stabilization than PID,Fuzzy and Fuzzy-PID controllers, which shows that optimal Fuzzy-PID controller designed is moreappropriate and efficient.Keywords: Object tracking, LabVIEW, Fuzzy-PID, Pan/Tilt system, Genetic algorithm

In engineering applications, the bearing faults diagnosis is essential for maintaining reliability andextending the lifespan of rotating machinery, thereby preventing unexpected industrial productiondowntime. Prompt fault diagnosis using vibration signals is vital to ensure seamless operation ofindustrial system avert catastrophic breakdowns, reduce maintenance costs, and ensure continuousproductivity. As industries evolve and machines operate under diverse conditions, traditional faultdetection methods often fall short. In spite of significant research in recent years, there remains apressing need for improve existing methods of fault diagnosis. To fill this research gap, this researchwork aims to propose an efficient and robust system for diagnosing bearing faults, using deep andShallow features. Through the evaluated experiments, our proposed model Multi-Block Histogramsof Local Phase Quantization (MBH-LPQ) showed excellent performance in classification accuracy, andthe audio-trained VGGish model showed the best performance in all tasks. Contributions of this workinclude: Combine the proposed Shallow descriptor, derived from a novel hand-crafted discriminativefeatures MBH-LPQ, with deep features obtained from VGGish pre-trained of Convolutional NeuralNetwork (CNN) using audio spectrograms, by merging at the score level using Weighted Sum (WS).This approach is designed to take advantage of the complementary strengths of both feature models,thus enhancing overall bearing fault diagnostic performance. Furthermore, experiments conductedto verify the approach’s performance is assessed based on fault classification accuracy demonstrateda significant accuracy rate on two different noisy datasets, with an accuracy rate of 98.95% and 100%being reached on the CWRU and PU datasets benchmark, respectively.Keywords: Bearing fault diagnosis, Vibration signals, Transfer learning, Shallow descriptor, Deep features,MBH-LPQ, VGGish, CNN

The goal of natural language processing (NLP), which has recently gained popularity, is to improve the ca-pacity of computers to comprehend and interact with human language. Consequently, to converse usingnatural language, it is crucial that spoken language be grammatically correct, especially for Geéz language.Geéz language sentences must follow certain norms of agreement in terms of number, person, gender, tense,and other factors to be considered grammatically correct. If the input sentence in Geéz language is improper,then it can have problems with subject-verb agreement, object-verb agreement, adjective-noun agreement,and adverb-verb agreement. The goal of the proposed work is to provide a neural machine translation ap-proach for detecting and correcting grammar errors in Geéz sentences. We have prepared manually 11,490Geéz parallel corpuses (Geéz language grammatically incorrect and grammatically correct sentences). Afterwe have prepared a parallel Geéz sentence, we have used normalization, tokenization, padding, and one hotencoding as preprocesses. We have used two deep learning algorithms, including a bidirectional long short-term memory encoder-decoder and a long short-term memory encoder-decoder, for training the proposedmodel. Keras and TensorFlow were used for importing the required libraries, and we used the Python 3.7 en-vironment for implementation. Two test cases are used for the evaluation technique. The first one is for thelong short-term memory encoder-decoder model, and the second one is for the bidirectional long short-termmemory encoder-decoder model. Finally, the bidirectional long short-term memory encoder-decoder modelachieved best results with an accuracy of 82%, recall of 82%, precision of 85%, and F1-measure of 83% withbalanced error type classes.CCS Concepts: • Computing methodologies → Machine translation;Additional Key Words and Phrases: NMT, GSD, GGEH, NLP, DL, LSTM, BILSTM

Accurate regulation of the liquid level in a quadruple tank system (QTS) is not easy and imposes higherrequirements on control strategies, so the design of controllers in these systems is challenging dueto the difficulty of dynamic analysis of its nonlinear characteristics and parametric uncertainties.To overcome these problems in liquid level regulation and increase the robustness to the pumpcoefficients, this article proposes and investigates the use of an optimal hybrid fractional-ordertype-2 fuzzy-PID (OH-FO-T2F-PID) regulator using a combination of two bio-inspired evolutionaryoptimizers, namely augmented grey wolf optimizer and cuckoo search optimizer, which gives rise tothe new hybrid A-GWOCS algorithm. This control mechanism was chosen to facilitate the convergenceof the water liquids in the two tanks as quickly as possible to the corresponding required values. Inaddition, a collaborative optimization technique with several objectives is used to adjust the regulatorparameters. The capability and efficiency of the suggested regulator is first investigated throughcomputer simulation results and then confirmed by real-time control experimental results on the QTSbased on dSPACE 1104 computation engine. The findings showed that the suggested OH-FO-T2F-PIDregulator significantly outperformed both the optimized ADRC and the OH-FO-T1F-PID regulators.Specifically, it reduced the rising time by 17.02% and 95.21%, respectively, and the settling time by25.13% and 74.28%. Additionally, the designed OH-FO-T2F-PID regulator successfully eliminatedthe steady-state error and overshoot, enabling precise regulation of the QTS, and maintenance theliquid level at the desired set point under a wide range of working situations. The robustness of therecommended regulator is also studied by considering − 50% disturbance in the QTS parameters, andthe findings showed that the OH-FO-T2F-PID regulator is less susceptible to variations in parameters.Keywords: Quadruple tank system (QTS), Optimal hybrid fractional order type 2 fuzzy PID regulator,Hybrid A-GWOCSO algorithm, Multi-objective optimization, dSPACE 1104 computation engine

Composite insulators demonstrate superior electrical performance in contrast to standard insulators.Nevertheless, the deterioration of composite insulator and the challenges in identifying defects arethe primary drawbacks of these insulators. This study investigates the effect of water droplets onthe electrical behavior of composite insulators, which are widely used in high-voltage applications.Using COMSOL software, a Finite Element Model (FEM) was developed to simulate the electric fielddistribution on the surface of a composite insulator in the presence of water droplets. The resultsindicate that the existence of water droplets increases the electric field intensity by approximately33.33% when the number of droplets increases from two to six. The simulations also reveal that waterdroplets significantly increase the electric field’s intensity, which affects the electric field and potentialdistribution on the insulator’s surface. Furthermore, the conductivity of water droplets was found tohave a negligible impact on the electric field distribution along the insulator. To systematically evaluatethe influence of various factors, Response Surface Methodology (RSM) was employed in combinationwith Analysis of Variance (ANOVA) to analyze the interactions between water droplet number,pollution, and applied voltage. The statistical analysis demonstrated that the maximum electric fieldintensity increased by nearly 38.3% as water droplet conductivity rose from low to high levels. RSMwas used to generate a second-order polynomial model that describes the relationship between thesefactors and the electrical performance of the insulator, allowing for the identification of significanttrends and interactions. The findings provide valuable insights for the design and development ofcomposite insulators that are more resilient to environmental factors, enhancing their overall electricalperformance.Keywords: ANOVA, Composite insulator, Electric field, FEM, Water droplet

This study investigates the performance of hemispherical solar stills (HSS) enhanced with date kernelsand olive kernels as heat storage materials to improve water distillation efficiency. By utilizing thesenatural and sustainable materials, the research highlights an alternative to synthetic options. Rigorousexperimentation and detailed analysis under identical conditions reveal that both kernels significantlyimprove heat retention and water production rates. The HSS with date kernels (HSSDK) achieved adaily water productivity of 6.66 kg/m2 day, representing an efficiency increase of 10.87%, while theHSS with olive kernels (HSSOK) produced 8.00 kg/m2 day, enhancing efficiency by 13.54%. The cost perm3 of distilled water for HSSDK is approximately USD 4.65, while HSSOK costs USD 3.89, comparedto USD 7.83 for the conventional CHSS system. These results demonstrate that the inclusion of heatstorage materials has significantly reduced the cost of water production, with reductions of about 40%for HSSDK and 50% for HSSOK compared to the conventional system. These results are attributedto the high thermal conductivity and specific heat capacities of the kernels, enabling effective heatstorage and gradual release. This study demonstrates the potential of agricultural by-products ascost-effective and sustainable solutions for solar water distillation. Further research is recommendedto optimize the quantities and configurations of these materials, as well as to explore their integrationwith other renewable energy systems to enhance overall efficiency and sustainability.Keywords: Hemispherical solar still, Date kernels, Olive kernels, Heat storage materials, Distillation efficiency

Increasing greenhouse gas (GHG) emissions and environmental issues have heightened the demandfor renewable energy sources (RES) and prompted a swift transition to electric vehicles (EVs) in thetransportation sector. This shift underscores the need to address the challenges of electricity supplyand continuity for electric vehicle charging stations (EVCS). This study aims to determine the mostsuitable hybrid systems to ensure the electricity supply to EVCSs in the Çukurova region of Adana,Turkey. Six different scenarios involving components such as photovoltaic (PV) panel, wind turbine(WT), biomass generators (BG), electrolyzer (Elz), hydrogen tank (HT), fuel cell (FC), batteries (Bat),inverter (Inv), and the grid were analyzed using HOMER Pro microgrid analysis tool version 3.14.2software. The optimization results indicated that the most feasible system was Scenario 4, comprisingthe PV, BG, Elz, HT, FC, Inv, and grid components. This scenario’s total net present cost (NPC) was$611,283.50, with a levelized cost of energy (LCOE) of $0.0215. The annual energy productionand consumption were 1,507,169 kWh and 1,420,714 kWh, respectively. The fact that the energygenerated from exceeds the energy sourced from the grid reduces the payback period of the system.These findings highlight the economic and sustainable potential of renewable hybrid systems forenhancing the performance of EVCS in solar-rich regions.Keywords: Energy cost efficiency, Renewable energy integration, Electric vehicle charging stations, Hybridsystems, Optimization, Energy sustainability

This paper presents a novel approach to modeling and controlling a solar photovoltaic conversionsystem(SPCS) that operates under real-time weather conditions. The primary contribution is theintroduction of an uncertain model, which has not been published before, simulating the SPCS’sactual functioning. The proposed robust control strategy involves two stages: first, modifying thestandard Perturb and Observe (P&O) algorithm to generate an optimal reference voltage usingreal-time measurements of temperature, solar irradiance, and wind speed. This modification leadsto determining and linearizing the nonlinear current-voltage (I-V) characteristics of the photovoltaic(PV) array near standard test conditions (STC), resulting in an uncertain equivalent resistance used tosynthesize an overall model. In the second stage, a robust fixed-order H∞ controller is designed basedon this uncertain model, with frequency-domain specifications framed as a weighted-mixed sensitivityproblem. The optimal solution provides the controller parameters, ensuring good reference trackingdynamics, noise suppression, and attenuation of model uncertainties. Performance assessments atSTC compare the standard and robust P&O-MPPT strategies, demonstrating the proposed method’ssuperiority in performance and robustness, especially under sudden meteorological changes andvarying loads. Experiment results confirm the new control strategy’s effectiveness over the standardapproach.

This paper introduces the Efficient Metaheuristic BitTorrent (EM-BT) algorithm, aimed at optimizingthe placement and sizing of photovoltaic renewable energy sources (PVRES) and capacitor banks(CBs) in electric distribution networks. The main goal is to minimize energy losses and enhance voltagestability over 24 h, taking into account varying load profiles, solar irradiance, and temperature effects.The algorithm is rigorously tested on standard distribution networks, including the IEEE 33, IEEE69, and ZB-ALG-Hassi Sida 157-bus systems. The results reveal that EM-BT outperforms establishedmethods like Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale OptimizationAlgorithm (WOA), demonstrating its effectiveness in reducing energy losses and maintaining stablevoltage profiles. By effectively combining PVRES and CBs, this research highlights a robust approach toenhancing both technical performance and operational reliability in distribution systems. Additionally,the consideration of temperature effects on PVRES efficiency adds depth to the study, making it avaluable contribution to the field of power system optimization.Keywords: Efficient Metaheuristic BitTorrent (EM-BT) algorithm, Photovoltaic renewable energy sources(PVRES), Capacitor banks (CBs), Energy loss minimization, Particle Swarm Optimization (PSO), Grey WolfOptimizer (GWO), Whale Optimization Algorithm (WOA), Operational reliability

This study suggests an enhanced version of the adaptive fuzzy fast terminal synergetic controller(AF-FTSC) for controlling the uncertain DC/DC buck converter based on the synergetic theory ofcontrol (STC) and newly developed terminal attractor technique (TAT). The benefits of the proposedSC algorithm involve the features of finite-time convergence, unaffected by parameter variations, andchattering-free phenomenon. A type-1 fuzzy logic system (T1-FLS) make the considered controllermore robust and is utilized to estimate the undefined converter nonlinear dynamics without resortingto the usual linearization and simplifications of the converter model. Taking a switching DC-DC buckconverter as a demonstration, the suggested AF-FTSC is thoroughly analyzed and executed on adSPACE ds1103 controller board. The outcomes of the experiment confirm the competence andapplicability of the suggested regulator.Keywords: Synergetic control, Fuzzy logic system, Fast terminal method, Finite-time convergence, DC/DCbuck converter