bluuebunny/crossref_metadata_2025 huggingface dataset:
Prognostic Real Time Analysis of Induction Motor
Authors: Kumar C. S. Subash , Ravikrishna S. , Rajasekar P. , Venkatesan Mani
Published: September 2024

Variable speed induction motors controlled by variable frequency drives are used for a variety of industrial applications. Monitoring and prognostic occurrence of faults in induction motors is vital for reducing the downtime and accidents. The proposed work focuses on failures in induction motors owing to bearing misalignment and insulation failure in the stator that results in abnormal vibration and temperature rise in the motor. This research intends to improve the dependability and safety of industrial operations by identifying faults in their early stages using advanced methods such as vibration analysis and thermal monitoring. This work focuses on fault prognosis in induction motor through vibration data, which is analyzed using Daubechies orthogonal db10 wavelet transformation. The neural network algorithm optimizes the analyzed results to enable real time fault detection. The temperature of the stator is measured to estimate the expected lifetime of the insulator. The real time vibration and temperature data is measured and transferred to prognostic model build in MATLAB using ATMEGA 32 controller and the results are validated for good, allowable and not permissible conditions of motor based on ISO 10816 vibration levels for Class I motors. The improved accuracy and efficiency of real-time fault detection have the potential to reshape maintenance strategies and enhance the overall reliability of variable speed induction motors.
Research on Indoor 3D Reconstruction Technology Based on Semantic Visual Simultaneous Localization and Mapping
Authors: Liang Yu , Lijia Cao , Changyou Fu
Published: September 2024

In response to the challenge that traditional visual simultaneous localization and mapping (SLAM) systems, based on the assumption of a static environment, struggle to achieve real-time indoor 3D reconstruction in complex dynamic scenes, this paper proposes a real-time indoor 3D reconstruction algorithm based on semantic visual SLAM. By leveraging object detection to obtain 2D semantic information and providing prior information for geometric methods, the fusion of the two effectively suppresses dynamic features, reduces reliance on deep learning methods, and ensures the algorithm's real-time performance. Experimental results on dynamic scenes in the TUM RGB-D dataset show that our algorithm maintains nearly unchanged real-time performance while achieving an average performance improvement of approximately 97.56% and 97.31% on the TUM dataset and Bonn dataset, respectively, compared to the ORB-SLAM2 system. Moreover, our algorithm can reconstruct more intuitive indoor global Octo-map and semantic metric maps compared to sparse point cloud maps, effectively enhancing the scene perception capability of mobile robots and laying the foundation for performing advanced tasks. Furthermore, our algorithm demonstrates a 3.5-10.5 times improvement in real-time performance compared to other mainstream semantic SLAM systems. Experimental results on the NVIDIA Jetson AGX Xavier confirm that our algorithm can run in real time on low-power platforms such as mobile robots or drones. However, the drawbacks of our algorithm include lower reconstruction accuracy in low-texture and large-scale scenes and ineffective suppression of dynamic features in low-dynamic scenes. Future work will consider replacing and improving deep learning methods and integrating IMU and other sensors to enhance system usability.
Oil Pipeline Leak Detection in Iraqi Oil Fields based on 1DCNN
Authors: Al-Khalidi Mustafa Raad , Abdulsadda Ahmad Taha , Al Zuhryi Mudafeer Sadaq
Published: September 2024

The oil industry plays a crucial role in Iraq's economy. There's a growing need for technologies that can quickly detect leaks in oil pipelines because leaks can have serious ramifications, including monetary losses, endangerment to public safety, environmental degradation, and resource waste. Advances in technology and software have made it possible to detect leaks. Current approaches often require manual extraction of features, which can be slow and inefficient. This paper presents a new method that proposes using convolutional neural networks (CNNs) for automatic feature extraction. The Iraqi Ministry of Oil, specifically the Basra Oil Company, provided the dataset, such as total distance (km), pressure (bar), and flow rate (STB/d). We split the data into training (70%) and testing (30%) sets. then we calculate metrics such as confusion matrices, accuracy, precision, recall, and F-score to evaluate performance and calculate errors from the regression analysis (root mean square error, root mean absolute error, and relative error). Our contribution to this work is to use 1DCNN to identify leaks, pinpoint their location, and even predict the amount of spilled oil, unlike other research that only uses it to evaluate the presence or absence of a leak only. Additionally, we've created a user-friendly interface for the system. Finally, compare the proposed approach with conventional and alternative methods to show its efficiency. In the future, we plan to expand the system to assess pipeline corrosion and predict its remaining lifespan.
Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review
Authors: Utama Agung Bella Putra , Wibawa Aji Prasetya , Handayani Anik Nur , Chuttur Mohammad Yasser
Published: September 2024

This paper aims to explore the relationship between deep learning and forecasting within the context of the Sustainable Development Goals (SDGs). The primary objective is to systematically review 38 articles published between 2019 and 2023, following PRISMA guidelines, to understand the current landscape of deep learning forecasting for SDGs. Using data from 2019-2023 allows capturing the latest developments in deep learning forecasting for Sustainable Development Goals (SDGs), while excluding data before 2019 and after 2023 is based on the desire to avoid including potentially less relevant or unpublished research and to maintain focus on the most current and contextually relevant literature. The methodological approach involves analyzing the application of deep learning methods for forecasting within various SDG fields and identifying trends, challenges, and opportunities. The literature review results reveal the popularity of LSTM models, challenges related to data availability, and the interconnected nature of SDGs. Additionally, the study demonstrates that deep learning models enhance forecast accuracy and computational performance, as measured by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The findings underscore the importance of advanced data preparation techniques and the integration of deep learning with SDGs to improve forecasting outcomes. The novelty of this research lies in its comprehensive overview of the current landscape and its valuable insights for researchers, policymakers, and stakeholders interested in advancing sustainable development goals through deep learning forecasting. Finally, the paper suggests future research directions, including exploring the potential of hybrid forecasting models and investigating the impact of emerging technologies on SDG forecasting methodologies. Innovative methods for imputing missing values in deep learning forecasting models could be further explored to enhance predictive accuracy and robustness.
The Utilization of Fuzzy Logic Controllers in Steering Control Systems for Electric Ambulance Golf Carts
Authors: Chotikunnan Rawiphon , Chotikunnan Phichitphon , Imura Pariwat , Pititheeraphab Yutthana , Thongpance Nuntachai
Published: September 2024

This study investigates methods to improve steering control for electric ambulance golf carts by conducting a comparative analysis of fuzzy logic controllers. The research assesses four control systems, PD controller, fuzzy PD controller, fuzzy PD+I controller, and PBC and PD+I type fuzzy logic controller, to determine their effectiveness in enhancing steering control. Simulink simulations are employed to evaluate the performance of these controllers under various conditions. Results indicate that the PBC and PD+I type fuzzy logic controller demonstrates superior performance, showing significant reductions in both rise time and settling time with minimal overshoot compared to other controllers. The findings underscore the potential of fuzzy logic controllers in enhancing steering control for electric vehicles. Future research should explore alternative control strategies and assess controller robustness under diverse operating conditions.
Comparison of Convolutional Neural Networks and Support Vector Machines on Medical Data: A Review
Authors: Furizal Furizal , Ma'arif Alfian , Rifaldi Dianda , Firdaus Asno Azzawagama
Published: September 2024

Medical image processing has become an integral part of disease diagnosis, where technological advancements have brought significant changes to this approach. In this review, a comprehensive comparison between Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) in processing medical images is conducted. Automated medical analysis is becoming increasingly important due to issues of subjectivity in manual diagnosis and potential treatment delays. This research aims to compare the performance of Machine Learning (ML) in medical contexts using MRI, CT scan, and X-ray data. The comparison includes the accuracy rates of CNN and SVM algorithms, sourced from various studies conducted between 2018 and 2022. The results of the comparison show that CNN has higher average accuracy in processing MRI and X-ray data, with average values of 98.05% and 97.27%, respectively. On the other hand, SVM exhibits higher average accuracy for CT scan data, reaching 91.78%. However, overall, CNN achieves an average accuracy of 95.58%, while SVM's average accuracy is at 94.72%. These findings indicate that both algorithms perform well in processing medical data with high accuracy. Although based on these average accuracy rates, CNN demonstrates slightly better capabilities than SVM. Further research and development of more complex models are expected to continue improving the effectiveness of both approaches in disease diagnosis and patient care in the future.
Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measures for Real World Problem Solving
Authors: Palanisamy Sangeetha , Periyasamy Jayaraman
Published: September 2024

Similarity measures (SMs) are fundamental in various applications, including identifying patterns within medical data and aiding pattern recognition (PR) by quantifying the likeness between different patterns. Moreover, they play a crucial role in real-world problems such as Multiple Criteria Decision Making (MCDM), where decision-makers assess and compare alternatives based on multiple criteria simultaneously. Moreover, Cosine similarity is a measurement that quantifies the similarity between two or more objects. This study presents a comprehensive exploration of Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measures (IV IF CSMs) as a novel technique for assessing the degree of association between objects in realworld applications. By extending traditional cosine similarity measures (CSM) to interval-valued intuitionistic fuzzy sets (IV IFS), the proposed IV IF CSMs offer an effective framework for handling uncertainty, ambiguity, and imprecision in decision-making processes. The research demonstrates the practical utility of IV IF CSMs in addressing complex issues in PR, medical diagnosis (MD), and MCDM. In contrast to established methods like Singh’s, Xu’s, and Luo’s measures, our approach consistently generates higher similarity values, encompassing both membership (MF) and non-membership (NMF) with interval values.
Dynamic Performance Evaluation of a Brushless AC Motor Drive Using Different Sensorless Schemes
Authors: Sawy Mohamed A. El , Kamel Omar Makram , Mohamed Yehia S. , Mossa Mahmoud A.
Published: September 2024

The presented study concerns with evaluating the dynamic performance of an isotropic sinusoidal brushless motor drive while utilizing different sensorless schemes. Three estimation algorithms are considered: the first depends on extracting the speed and position via comparing two values of motor's voltage in two co-ordinate systems; the second extracts the speed and position signal via comparing two different values of motor's current defined in two co-ordinates; while the third depends on estimating the motor's flux and use it to get the speed and position. The vector control is adopted to manage the drive dynamics. The detailed mathematical derivations for all system components are presented to facilitate the performance analysis. The theoretical base of each sensorless scheme is also described in detail. The target of the provided comparative analysis is to outline the weakness and strength points of each adopted sensorless schemes while estimating the speed and rotor position for a wide operating speed range. The judgment is measured in terms of the speed and rotor position estimation errors and the dynamic response as well. The performance evaluation process is carried out using MATLAB/Simulink software in which all system parts are simulated using their mathematical models. The findings from the study state that when it comes to dynamic speed behaviour, the voltage-based sensorless technique dominates, while the current-based sensorless approach gives stability in speed estimate priority. Alternatively, the third adopted sensorless scheme offers an acceptable high-speed performance and respectable performance at lower speeds. Statistically, it is found that the voltage-based estimation technique gives respectively lower speed and position estimation errors with percentages of 35% and 10% lower than their values under the current-based estimation technique, and with percentages of 35% and 30% lower than their values under the third adopted scheme.
Optimization Combining with Digital Transformation of the Men's Shirts Processing at Small and Medium-Sized Garment Enterprises in Vietnam
Authors: Le Tieu-Thanh , Bui Phuong-Thao Thi , La Ngoc-Anh Thi , Dang Thai-Viet
Published: September 2024

Industry 4.0 has become a hype among the manufacturing industries across the globe. Recent developments require significant capital investments, but these technologies are yet to be established in developing countries such as Vietnam, especially the apparel industry.  Based on a survey of the current situation at small and medium-sized enterprises in Vietnam's textile industry, the paper proposes to apply technology, test and evaluate the effectiveness of applying and coordinating digital systems in management and chain supply. Multifaceted applications have been specifically explored including automatic equipment and digital systems, spanning the domains of automation, robotics, artificial intelligence, data analytics, and the Internet of Things (IoT). These technologies are posited as catalysts for transformative improvements in production efficiency and resource utilization. Furthermore, experimental results point out the symbiotic relationship between technology adoption and effective management strategies to achieve holistic operational enhancements.  As the Vietnamese textile industry strives for competitive excellence in the global arena, this research offers actionable insights for industry practitioners, policymakers, and researchers.
Adaptive Controller Based on Estimated Parameters for Quadcopter Trajectory Tracking
Authors: Srey Sophyn , Srang Sarot
Published: September 2024

This paper presents a trajectory control system design for a quadcopter, an unmanned aerial vehicle (UAV), which is based on estimated parameters that are assumed to exhibit random walk behavior. Initially, the rotational dynamic model of the UAV is formulated using the Newton Euler method in terms of angular velocity about the x, y, and z axes. This model is then simplified into three separated-first-order linear differential equations, with coefficients derived from the combined effects of inertia, aerodynamic drag, and gyroscopic effects, referred to as lumped parameters. A Proportional-Integral (PI) controller with feed-forward design is then developed to control this simplified model. To adapt the controller to the lumped parameters that exhibit random walk behavior, each simplified equation is restructured into a processing and measurement model. The states of these models are estimated by using the Unscented Kalman Filter (UKF). These estimated values are then utilized to adjust the PI gains and compensate the signal of the designed angular velocity controller, transforming it into an adaptive controller. The entire UAV controller comprises two main parts, an inner loop for adaptive angular rate control and an outer loop serving as an attitude-thrust controller. The proposed controller is simulated using Simulink, with circular and square trajectories. The simulation results demonstrate that the quadcopter successfully follows the desired circular and square paths. The steady-state error for the x and y axes in the square trajectory is less than 0.05 meters within 5 seconds, and for the z axis, it is less than 0.02 meters within 2.5 seconds. The controller gains do not require adjustment when changing trajectories. Moreover, the estimated parameters remain nearly constant at steady state.
Bifurcation Analysis of a Non-Linear Vehicle Model Under Wet Surface Road Condition
Authors: Kumar Abhay , Verma Suresh Kant , Dheer Dharmendra Kumar
Published: September 2024

The vehicles are prone to accidents during cornering on a wet or low friction coefficient roads if the longitudinal velocity (Vx) and steering angle (δ) are increased beyond a certain limit. Therefore, it is of major concern to analyze the behaviour and define the stability boundary of the vehicle for such scenarios. In this paper, stability analysis of a 2 degrees of freedom nonlinear bicycle model replicating a car model including lateral (sideslip angle β) and yaw (yaw rate r) dynamics only operating on a wet surface road has been performed. The stability is analysed by utilizing the phase plane method and bifurcation analysis. The obtained converging and diverging nature of the trajectories (β, r) depicts the stable and unstable equilibrium points in the phase plane. The movement of these points results in the transition of the stability known as bifurcation due to the change in the control parameters (Vx, δ). The Matcont/Matlab is utilized to obtain the bifurcation diagrams and the nature of bifurcations. The obtained results show that a saddle node (SNB) and a subcritical Hopf bifurcation (HB) exists for steering angle (±0.08 rad) and higher than (±0.08 rad) with Vx = (10-40) m/s respectively. The SNB and HB denotes the spinning of the vehicle and sliding of the vehicle respectively, thus generating a unstable behaviour. A stability boundary is obtained representing the stable and unstable range of parameters.
Comparative Electromagnetic Performance Analysis of Double Stator and Single Stator Superconducting Generators for Direct-Drive Wind Turbines
Authors: Elhindi Mohamed , Abdalla Modawy Adam Ali , Omar Abdalwahab , Pranolo Andri , Mirghani Abdelhameed , Omer Abduelrahman Adam
Published: September 2024

Superconducting synchronous generators, especially for 10-MW direct-drive wind power systems, are gaining prominence due to their lightweight, compact design, lowering energy generation costs compared to conventional generators. With the ability to generate high magnetic fields. various approaches are exist for designing such generators for example modular superconducting generators which allow for easier assembly, maintenance, and scalability by dividing the generator into smaller, interchangeable components and single stator which simplifying the generator's design and reducing manufacturing costs. This study introduces a novel concept of a double-stator superconducting generator alongside a conventional single-stator superconducting generator, aiming to investigate and contrast the electromagnetic performance of both machine types considering different number pole pairs. Booth of the machines has been designed and studied applying 2d finite element model (COMSOL Multiphysics). The compared machine parameters include: the flux linkage and electromagnetic torque. Our study and compression of the two machines reveal that the double stator superconducting generator is characterized by high electromagnetic torque compared to its single-stator counterpart. the analysis also reveals that increasing the pole pairs number leads to high electromagnetic torque and higher magnetic flux density.
Enhancing Hybrid Power System Performance with GWO-Tuned Fuzzy-PID Controllers: A Comparative Study
Authors: Singh Meetpal , Arora Sujata , Shah Owais Ahmad
Published: September 2024

This study explores the implementation of a novel control strategy within hybrid power systems, leveraging a Grey Wolf Optimization (GWO)-tuned Fuzzy Proportional-Integral-Derivative (Fuzzy-P.I.D.) controller to enhance the integration of renewable energy sources. By addressing the critical challenge of grid frequency deviations, this approach significantly bolsters the stability and efficiency of power flow, ensuring a more reliable electricity supply. Employing MATLAB simulations, the research underscores the superior performance of the GWO-tuned Fuzzy-P.I.D. controller, which necessitates fewer control interventions and yields lower oscillation frequencies than its conventional P.I.D. and Fuzzy-P.I.D. counterparts. The robustness of this optimized controller is further validated through extensive tests, demonstrating its resilience across a spectrum of parameter adjustments and operational scenarios, including the hypothetical removal of system components. The findings reveal that this advanced control method markedly surpasses traditional solutions in maintaining stable electricity flow and enhancing the system's overall resilience and adaptability to the variable nature of renewable energy. Thus, the GWO-tuned Fuzzy-P.I.D. controller emerges as a significant innovation in hybrid power system management, heralding a new era of optimization and efficiency in renewable energy integration.
Power Regulation of a Three-Phase L-Filtered Grid-Connected Inverter Considering Uncertain Grid Impedance Using Robust Control
Authors: Yay Socheat , Soth Panha , Tang Heng , Cheng Horchhong , Ang Sovann , Choeung Chivon
Published: September 2024

Uncertain grid impedance is often common in power distribution networks; therefore, it is crucial to design an efficient controller in this situation.  An issue that frequently occurs is the problem of unpredictable grid impedance, which can cause voltage fluctuations, power quality problems, and potential damage to equipment. This work provides a systematic control strategy to tackle these issues by supplying well-regulated power from a DC source to an AC power grid. A linear matrix inequality (LMI)-based robust optimal control is proposed in this paper to provide stability to the inverter system without offset error at the output side. The convergence time to steady state is minimized by solving the LMI problem to maximize the eigen value of the closed-loop system with the inclusion of the uncertainty of the filter parameter and grid impedance. Furthermore, the uncertainties in this study include the potential variation of values for the filters and the grid's impedance. These uncertainties occur because the grid impedance can fluctuate fast in the event of a fault or termination of a transmission line, while the filter's impedance can also be affected by changes in operating temperature. The simulation study of this proposed control includes a comparison between wide and narrow uncertainty ranges, as well as a performance comparison under uncertain parameters. Furthermore, this approach exhibits a lower power ripple in comparison to existing PI control method.
Enhanced Hybrid Robust Fuzzy-PID Controller for Precise Trajectory Tracking Electro-Hydraulic Actuator System
Authors: Ali Nur Husnina Mohamad , Ghazali Rozaimi , Tahir Abdul Wafi , Jaafar Hazriq Izzuan , Ghani Muhammad Fadli , Soon Chong Chee , Has Zulfatman
Published: September 2024

The Electro-Hydraulic Actuator (EHA) system integrates electrical and hydraulic elements, enabling it to generate a rapid reaction, a high power-to-weight ratio, and significant stiffness. Nevertheless, EHA systems demonstrate non-linear characteristics and modeling uncertainties, such as friction and parametric uncertainty. Designing a controller for accurate trajectory tracking is greatly challenging due to these limitations. This paper introduces a hybrid robust fuzzy proportional-integral-derivative (HFPID) and (HF+PID) controller. The controller is designed to effectively control a third-order model of an EHA system for trajectory tracking. It is a significant contribution to the development of an intelligent robust controller that can perform well in different environments. Initially, a mathematical model for the EHA system was created using a first-principle approach. Subsequently, the Ziegler-Nichols method was employed to fine-tune the PID controller, while a conventional Fuzzy Logic Controller (FLC) was constructed in MATLAB Simulink utilizing linguistic variables and rule-based control. Without further tuning, the FL and PID controller are combined as a hybrid controller with different structures: Hybrid Fuzzy-PID (HFPID) and Hybrid Fuzzy+PID (HF+PID) controller. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) are utilized as indices to assess the tracking accuracy and robustness of the four controllers. A greater value of MSE and RMSE indicates poorer performance of the controller. The results demonstrate that the HF+PID controller surpasses the other controllers by reaching the lowest MSE and RMSE values. It showcases the efficacy and accuracy in monitoring sinusoidal, multi-sinusoidal, and point-to-point trajectory tracking.  Future work should focus on implementing the designed controller on hardware for real-time performance and experimenting with various types of FLC or Hybrid controllers, such as self-tuning fuzzy-PID, to further explore their potential.
Aircraft Pitch Control via Filtered Proportional-Integral-Derivative Controller Design Using Sinh Cosh Optimizer
Authors: Abualigah Laith , Ekinci Serdar , Izci Davut
Published: September 2024

An innovative approach to controlling aircraft pitch is shown in this research. This approach is accomplished by adopting a proportional-integral-derivative with filter (PID-F) mechanism. A novel metaheuristic approach that we propose is called the sinh cosh optimizer (SCHO), and it is intended to further optimize the settings of the PID-F controller that is used in the aircraft pitch control (APC) configuration. An in-depth comparison and contrast of the recommended method is carried out, and statistical and time domain assessments are utilized in order to ascertain the success of the method. When it comes to managing the APC system, the SCHO-based PID-F controller delivers superior performance compared to other modern and efficient PID controllers (salp swarm based PID, Harris hawks optimization based PID, grasshopper algorithm based PID, atom search optimization based PID, sine cosine algorithm based PID, and Henry gas solubility optimization based PID) that have been published in the literature. When compared to alternative approaches of regulating the APC system, the findings demonstrate that the way that was presented is among the most successful as better statistical (minimum of 0.0033, maximum of 0.0034, average of 0.0034 and standard deviation of 5.1151E−05) and transient response (overshoot of 0%, rise time of 0.0141 s, settling time of 0.0230 s, peak time of 0.0333 s and steady-state error of 0 %) values have been achieved.
Adaptive Load Frequency Control in Microgrids Considering PV Sources and EVs Impacts: Applications of Hybrid Sine Cosine Optimizer and Balloon Effect Identifier Algorithms
Authors: Hassan Ahmed Tawfik , Banakhr Fahd A. , Mahmoud Mohamed Metwally , Mosaad Mohamed I. , Rashwan Asmaa Fawzy , Mosa Mohamed Roshdi , Hussein Mahmoud M. , Mohamed Tarek Hassan
Published: September 2024

The negative impacts of microgrids (µGs) on the load frequency highlight the importance of implementing a robust, efficient, and adaptable controller to ensure stability. This work introduces an adaptive load frequency control (LFC) for an isolated µG that includes a PV system and electric vehicles (EVs), which have a significant impact on frequency. This control utilizes a combination of sine cosine optimization (SCO) and balloon effect identifier (BEI) algorithms. The controller presented in this work transforms the LFC process into an optimization problem that is highly compatible with various random situations encountered in the control process. The suggested control method is a novel approach by utilizing SCO+BEI for adaptive LFC application, resulting in a highly efficient response. The effectiveness of the proposed adaptive controller is assessed under the conditions of 17 MW variable load, system parameters uncertainties, and installed PV systems of 6 MW.  MATLAB / Simulink package is rummage-sale as a digital test environment. According to simulation results, the proposed adaptive controller succeeds in regulating the frequency and power of an islanded µG. To measure the efficiency of the proposed control scheme, a comparison between other control techniques (such as adaptive controller using Jaya+BEI and classical integral controller) is done. The findings of the studied scenarios assured that the not compulsory control method using (SCO+BEI) has an obvious superiority over other control methods in terms of frequency solidity in case of random load instabilities and parameter uncertainties. Finally, it can be said that the proposed controller can better ensure the safe operation of the µGs.
Robust Voltage Control of a Single-Phase UPS Inverter Utilizing LMI-Based Optimization with All-Pass Filter Under System Uncertainty
Authors: Tang Heng , Choeung Chivon , Srang Sarot , So Bunne , Yay Socheat , Soth Panha , Cheng Horchhong
Published: September 2024

This paper proposes a systematic control design for a single-phase LC-filtered inverter considering uncertain system parameters. One major difficulty in controlling single-phase power converters is the lack of a direct conversion method for transforming single-phase signals into dq-frame signals. By employing an all-pass filter in this proposed approach, it is possible to control the output voltage in terms of DC quantity or the dq-rotating frame. Furthermore, voltage stability and harmonic distortion (THD) minimization of the uninterruptible power supply (UPS) are major concerns in inverter design. Therefore, this controller uses integral action to get rid of steady-state errors and stabilize the closed-loop system by the state feedback control. In order to enlarge and guarantee the stability range in the presence of potential parameter fluctuations, an uncertainty model is being considered. In this context, the uncertainty models refer to the potential model with variations in the filter's inductance and capacitance caused by operating temperature, aging, and various external factors. The efficacy of the control approach is assessed through simulations and experiments, with the objective of comparing its results with those of the PI control using a control board featuring a TMS320F28335 digital signal processor. Consequently, the proposed approach offers lower THD at every load step with lesser afford in performance tuning in comparison to the PI method.
Impact of Smart Greenhouse Using IoT for Enhanced Quality of Plant Growth
Authors: Ali Munawar , Gunawan Anak Agung Ngurah , Prasetya Dwi Arman , Ibrahim Mohd Zamri Bin , Diyasa I Gede Susrama Mas
Published: September 2024

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.
Investigating the Influence of Temperature on UAV Signal Quality
Authors: Abbas Ahmed Hussein , Abdulsadda Ahmad Taha , Hameed Hassanain Ghani
Published: September 2024

Advancements in drone technology make them important in many areas. military, industry, and disaster The efficacy of a drone's communication systems can be greatly impacted by temperature fluctuations, either from environmental conditions or mechanical problems in the drone's construction. This study gives an analysis and computational model of the impact of temperature on the performance of drone communication. Utilizing a one-dimensional convolutional neural network, we aim to forecast the signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). Following the initial stage of dataset creation in the drone laboratory, proceed to reprocess the dataset and divide it into a 70% training set and a 30% testing set. Subsequently, a graphical user interface (GUI) was developed using MATLAB App Designer to enhance user friendliness. The outcome suggests that the efficiency of the drone communication system  declines with rising temperatures.  Using 1DCNN is our contribution to this work; other studies depend only on simulation to assess performance. One benefit of 1DCNN is that the impact may be evaluated by automatically extracting important features from the input dataset. Using 1DCNN is our special addition to this project; other research evaluate the UAV communication system's effectiveness only through simulation. We propose in this work to optimize system characteristics for improved performance, including power transfer, by adding a feedback loop between the CNN result and the communication system. Furthermore, we investigate how different weather conditions, such wind and rain, affect UAV communication systems.
Comparative Study of ANN and SVM Model Network Performance for Predicting Brake Power in SI Engines Using E15 Fuel
Authors: Hofny Mohamed S. , Ghazaly Nouby M. , Shmroukh Ahmed N. , Abouelsoud Mostafa
Published: September 2024

Currently, artificial neural networks (ANNs) and support vector machines (SVMs) are the most common applications of machine learning approaches.  In this study, a comparative study of ANN and SVM is presented to evaluate the performance of each model in predicting the brake power (BP) of GX35-OHC 4-stroke, air-cooled, single cylinder gasoline engine with E15 (15% ethanol + 85% gasoline) fuel. Two models are compared based on experimental dataset that has single output (BP) and five inputs, engine speed (S), engine torque (T), intake air temperature (Tair), intake air flow (Qair), and fuel consumption (ṁ). The samples were split into three sets: Training set (70%), Validation set (15%), and the Test set (15%) based on 60 samples. The results are compared through different graphs such as target vs actual values, regression plots, histograms of prediction errors, residual plots, learning curves, and error distributions. The results showed that SVM model is indicated to have lower RMSE (0.0044) and higher EVS (0.9953), while ANN is shown to have lower value of MAPE (1.51%). These results have significant implications for the use of ANN and SVM models in real-world applications that need gradual comprehensibility and model generalization. In addition, work done with the models outlined above should try and test them in other engines and operating conditions to demonstrate the model’s and performance.
Novel Leak Detector Based on DWT an Experimental Study
Authors: Meftah Sabir , Bentoumi Miloud , Burhanuddin Dirman Hanafi , Bakhti Haddi , Chabira Chaima
Published: September 2024

We always face water leakage problems in underground distribution water networks (DWNs). Existing leak detectors suffer from false alarms due to poor leak signal quality affected by external noise, often collected by acoustic or vibratory sensors. This paper introduces a novel Discrete Wavelet Transform Detector (DWTD) that leverages precise pressure signals non-influenced by environmental noise. Using a prototype of a 100m PEHD pipeline and a diameter of 40mm, Data from two pressure transmitters were collected using a dSPACE MicroLabBox unit. The main idea is to apply the Discrete Wavelet Transform (DWT) with a DONOHO threshold law to cancel noises due to water turbulence fluctuations, ensuring high-quality signals for accurate leak detection and localization. As benchmarks to assess the quality of denoising signals three parameters were calculated, Signal to Noise Ratio (SNR 26.6763 dB), Normalized Cross-Correlation (NCC≈1), and Mean Square Error (0.20573 MSE 48.4761). The denoised temporal signals are obtained from the Inverse Discrete Wavelet Transform (IDWT). A Cross-correlation is employed to these signals to determine the leak’s location. The experimental validation involves positioning the first and second transmitters at specific distances on both sides of the leak position. This allows for comparison between the actual leak position in advance known and calculated positions at various points and leak sizes. With only a few exceptions where the maximum error rate reached 5 meters from the actual leak position, the detector's effectiveness was proven across tests involving four different leak sizes.
Applications of Multi-Objective OPF Solutions with Optimal Placement of Multiple and Multi-Type FACTS Units to IEEE System: Comparison of Different Approaches
Authors: Hakmi Sultan Hassan
Published: September 2024

Optimal power flow (OPF) problem and its implications for power system stability and efficiency is investigated in this study. OPF, a restricted optimization query with non-linearity and non-convexity, is one of the most challenging and fascinating problems in the recent power system. Based on these parameters, researchers have been working hard over the past few decades to identify the best solutions to the OPF issue that maintain system stability. This work presents multi-objective OPF solutions utilizing Newton's technique with numerous multi-type FACTS units. First, the GA is applied to identify the perfect size and location of the FACTS units. Next, the generator and FACTS settings are optimized. In this instance, four scenarios are taken into consideration and three OFs are employed to see how the OFs affect the positioning and dimensions of FACTS devices. The OF is suggested to consider the reduction of both generation costs and transmission losses while also optimizing the power transfer capacity of designated corridors. A full analysis relating to the IEEE-30 bus system is presented and analyzed.
Optimized Fault Detector Based Pattern Recognition Technique to Classify and Localize Electrical Faults in Modern Distribution Systems
Authors: Mishra Chandra Sekhar , Jena Ranjan Kumar , Sinha Pampa , Paul Kaushik , Mahmoud Mohamed Metwally , Elnaggar Mohamed F. , Hussein Mahmoud M. , Anwer Noha Mohammed
Published: September 2024

This research presents a method that integrates artificial neural networks (ANN) and discrete wavelet transform (DWT) to identify and classify faults in large power networks, as well as to pinpoint the zones where these faults occur. The objective is to enhance reliability and safety by accurately detecting and categorizing electrical faults. To manage the computational demands of processing the extensive and complex data from the power system, the network is divided into optimal zones, each made visible for fault detection. Niche Binary particle swarm optimization (NBPSO) is employed to place the fault detectors (FD) in each zone. This allows for precise measurement of fault voltage and current phasors without significant cost. The ANN module is tasked with identifying the fault area and locating the exact fault within that zone, as well as classifying the specific type of fault. Discrete Wavelet Transform is used for feature extraction, and a phase locked loop (PLL) is used for load angle computation. The proposed method's validity has been tested on the IEEE-33 bus distribution network.
Recent Developments and Future Prospects in Collision Avoidance Control for Unmanned Aerial Vehicles (UAVS): A Review
Authors: Harun Mohamad Haniff , Abdullah Shahrum Shah , Aras Mohd Shahrieel Mohd , Bahar Mohd Bazli , Ali@Ibrahim Fariz
Published: September 2024

The industry has been significantly enhanced by recent developments in UAV collision avoidance systems. They made collision avoidance controllers for self-driving drones both affordable and hazardous. These low-maintenance, portable devices provide continuous monitoring in near-real time. It is inaccurate due to the fact that collision avoidance controllers necessitate trade-offs regarding data reliability. Collision avoidance control research is expanding significantly and is disseminated through publications, initiatives, and grey literature. This paper provides a concise overview of the most recent research on the development of autonomous vehicle collision avoidance systems from 2017 to 2024. In this paper, the state-of-the-art collision avoidance system used in drone systems, the capabilities of the sensors used, and the distinctions between each type of drone are discussed. The pros and cons of current approaches are analyzed using seven metrics: complexity, communication dependency, pre-mission planning, resilience, 3D compatibility, real-time performance, and escape trajectories.
Design and Implementation of Smell Agent Optimizer for Parameters Estimation of Single and Double Diode in PV System: A Comparative Analysis
Authors: Elnaggar Mohamed F.
Published: September 2024

One of the most important and desirable options for moving toward clean electric energy sources is solar energy. Therefore, a PV system's characteristics play a significant role in determining how effective it is across a range of temperature and radiation scenarios. One can consider the PV model's parameter estimation to be a nonlinear optimization situation. This work makes use of a novel application of the smell agent optimizer (SAO) created to forecast the undefined parameters of the PV model's single- and two-diode equivalent circuits.  The goal of this effort is to create an accurate photovoltaic model that can accurately represent its performance under variable operating conditions. The square of the mean squared error between the actual measured curve and the current-voltage curve derived from the model defines the intended objective function. The suggested system is constructed and tested experimentally in a range of temperature and light conditions. Next, the MATLAB software is used to create the simulated PV model integrated with the SAO. The PV parameters are then predicted by comparing the experimental data with the convergence of the SAO based on the PV model. Based on the observed properties, the suggested approach for determining the parameters of an actual solar cell has been put into practice and contrasted with eight other optimization techniques. The outstanding efficacy of the method utilized compared with alternate methods is demonstrated by the statistical comparison of the ideal objective function resulting from the difference in the current-voltage curve produced from the optimized circuit model and the measurement.
Design and Implementation of Crowbar and STATCOM for Enhanced Stability of Grid-Tied Doubly Fed Induction Wind Generators
Authors: Elnaggar Mohamed F.
Published: September 2024

These days, one of the most used layouts in the wind power industry is a variable-speed doubly-fed induction wind generator (DFIWG). For providing active power (P) and reactive power (Q) control during grid failures, this research examines the DFIWG. The system's transient behavior is examined under normal and abnormal circumstances. Through control of rotor side (RSC) and grid side (GSC) converters, Q assistance for the grid, and power converter stress reduction, the suggested control approach achieves system stability while enabling DFIWG to operate smoothly during grid failures. The DFIWG is exposed to three- and two-phase faults to analyze the machine's performance. The crowbar and STATCOM tools are implemented to enhance the system performance under faults and compared with the base case. The implemented tools successfully suppress rotor and stator overcurrent, over voltage at the DC link (DCL), and power oscillations, as well as supporting the grid voltage understudied cases. The obtained results prove that both STATCOM and crowbar not only enhance the system's effectiveness and performance but also enable the system to achieve the fault ride-through capacity (FRTC). MATLAB/SIMULINK 2017b is used for time-domain computer simulations.
Control of a Multimode Double-Pendulum Overhead Crane System Using Input Shaping Controllers
Authors: Hussien Sharifah Yuslinda Syed , Jaafar Hazriq Izzuan , Ghazali Rozaimi , Ramli Liyana , Johari Mohd Khairul Azizat
Published: September 2024

This paper investigates the impact of higher derivative input shaping for minimizing both oscillations, namely hook and payload of a multimode double-pendulum overhead crane (MDPOC) system. The MDPOC has greater nonlinearities and stronger internal couplings, especially when involving two oscillation frequencies with multimode dynamic effects. With a suitable system’s natural frequency and damping ratio of the hook and payload oscillations, multimode zero-vibration (ZV-ZV), multimode zero-vibration derivative (ZVD-ZVD) and multimode zero-vibration derivative-derivative (ZVDD-ZVDD) shapers are successfully designed. More interestingly, two scenarios under a fixed cable length and a payload hoisting are considered which are closer to the real practical crane.  Thus, an average travel length (ATL)-based shaper method is also considered to further verify the effectiveness and robustness of efficient hook and payload oscillation control under payload hoisting. All the multimode input shaping is simulated using the Matlab software. The simulation results of multimode ZVDD-ZVDD shaper successfully reduced in the overall hook and payload oscillations by 97.9% and 97.2%, respectively, compared to the unshaped system, whereas the multimode ATL-ZVDD shaper reduced hook and payload oscillations by 94.8% and 94.0%, respectively. In fact, the multimode ZVDD-ZVDD and multimode ATL-ZVDD shapers demonstrate the superiority in minimizing the hook and payload oscillations compared to the multimode ZV-ZV, multimode ZVD-ZVD, ATL-ZV and ATL-ZVD shapers. This significant reduction in oscillations enhances the precision and safety of real-world crane operations in industrial settings. It has been proven that considering the additional derivative of input shaping results in a higher level of hook and payload oscillations reduction.