ITEGAM-JETIA

Speed Control of PMSM drive using Model Predictive Control Based Field Oriented Control

2025-04-01T21:29:51-03:00

This paper examines the enhancement of Permanent Magnet Synchronous Motor (PMSM) drives through the integration of Field-Oriented Control (FOC) with Model Predictive Control (MPC). The study aims to achieve high precision and dynamic response for PMSM drives under diverse operating conditions. The theoretical framework combines FOC and MPC principles, utilizing MPC's predictive capabilities to optimize d-q current references in real-time. The methodology encompasses the design and implementation of an MPC technique integrated with FOC, with key objectives including the minimization of torque ripples, the maintenance of system stability through robust control loops, and the optimization of PMSM drive performance across a wide speed range. The results indicate significant improvements in torque ripple reduction, dynamic response, and disturbance rejection, demonstrating the robustness and adaptability of the proposed control system. This approach effectively addresses key challenges and signifies advancements over traditional control methods, contributing to the field of electric drive control systems.

Optimal sizing of a hybrid microgrid system for a rural area of Algeria

2025-04-01T21:29:51-03:00

Renewable energy systems have replaced systems that use fossil fuels in many applications in different regions of the world. This is seen in the increasing use of solar and wind energy as the two most important sources for producing environment-friendly and economically convenient electrical energy. The fluctuating and unstable nature of renewable energy sources makes this type of energy complex to exploit, and related research has therefore mainly focused on Control and optimization. This work proposes an optimized configuration of two hybrid systems designed for a microgrid network with the aim to improve the power supply in isolated areas and provide a low cost, more reliable, and sustainable source of electricity for rural communities that may have limited access to traditional power grids. These hybrid setups consist of an initial system that caters for 10 houses which is then extended to serve 20 houses. Both setups utilize solar and wind energy sources, energy storage batteries, and a diesel generator. Real data collected in the Biskra region in the southeast of Algeria, is used. Particle Swarm Optimization algorithm is applied to achieve the optimal size of the hybrid system components through the weighted sum multi-objective approach, whereby three factors, namely, Cost of Electricity, Loss of Power Supply Probability, and Dummy Excess are combined into one objective function. Results of simulation show that the proposed approach achieves highly satisfactory values for the electricity prices in the 10- house and 20-house scenarios, with estimates of 0.15829 $/Kwh and 0.42112 $/Kwh, respectively.

Performance enhancement of wind energy conversion systems using PMSG with backstepping and ANN-based MPPT control

2025-04-01T21:29:50-03:00

This paper investigates the performance enhancement of wind energy conversion systems (WECS) using a Permanent Magnet Synchronous Generator (PMSG) specifically designed for standalone, fixed-pitch, variable-speed wind turbines. The study focuses on controlling the PMSG using Backstepping control which enables Maximum Power Point Tracking (MPPT) using the Tip Speed Ratio (TSR) method. Initially, a Proportional-Integral (PI) controller was implemented for regulating the generator speed. However, this approach encountered significant limitations, particularly in managing speed overshoot and responsiveness under fluctuating wind conditions. To address these issues, a neural network controller was introduced as a replacement for the conventional PI controller. This neural network controller provides an adaptive control mechanism capable of dynamically adjusting to change, thereby eliminating overshoot and greatly enhancing response speed and overall system stability. This work provides a reliable and efficient control solution where was the proposed control strategy was rigorously evaluated through numerical simulations in Matlab/Simulink, which confirmed its ability to stabilize the system and achieving both steady-state and dynamic optimization of wind energy conversion systems.

USE OF TARO BOGOR ( Colocasia esculenta ( l.) Schott ) AS A GROWTH MEDIUM FOR Aspergillus flavus

2025-04-01T21:29:50-03:00

Fungi are one type of microbe that plays an important role in human life, there are many groups of fungi that can be found in everyday life, one type of fungus that is pathogenic and produces aflatoxins, namely fungi with Aspergillus flavus species. Bogor taro has the potential to be used as a raw material because it has a carbohydrate content of 23.7% as an energy source, while potatoes have a total carbohydrate of 19.10%. So it can be known that the amount of carbohydrates in taro bogor is more than potatoes. Based on this background, researchers are interested in conducting research related to the use of taro bogor ( Colocasia esculenta ( L.) Schott ) as a growth medium for the fungus Aspergillus flavus. This type of research is experimental with Posttest Only Control Group Design research design, in this study the control group in the form of Aspergillus flavus planted on PDA medium while in the experimental group in the form of Aspergillus flavus planted on taro medium at a concentration of 2%. 4% and 6%. Conclusion based on the results of the paired T-test test, the p-value of taro medium is 2% and 4% less than 0.05 which means there is a significant difference with PDA medium, while in taro medium 6% the p-value of > 0.05 which can be concluded that there is no significant difference between taro medium 6% and PDA medium.

Modelling corona discharge threshold in composite dielectric properties of eggshell composite insulators: A VOLTAGE-BASED STUDY

2025-04-01T21:29:49-03:00

Corona and flashover threshold voltages are affected by the characteristics of insulator materials, such as chemical composition and thickness, as well as environmental conditions, including humidity and temperature. Eggshell powder is produced from the impact of crushed eggshells; this powder will be used as a substitute and filler (cement) used in the pavement mixture on solid insulators. This study aims to understand the difference in the response of flashover and corona phenomena to increased voltage in composite insulators. Flashovers tend to occur at low voltages and decrease drastically at high voltages, while coronas rarely appear at low voltages but increase and stabilize at high voltages. Flashovers are rare at voltages over 20 kV, but corona is still possible. This suggests that the design of insulators or conductors for high voltages needs to pay attention to the corona effect which can lead to material degradation over time, while the risk of flashover is lower. In this study, the voltage threshold for the emergence of corona is about 20 kV, where the electric field is strong enough to trigger an electrical discharge around the conductor. After crossing this limit, the corona remains at high voltage. Therefore, the design of the material or insulator must consider the constant presence of corona at high voltages to prevent material damage due to the discharge of electrical energy around the conductor.

Hopf bifurcation point identification in a power system with various static load models

2025-04-01T21:29:49-03:00

This paper covers the impact of voltage dependent load models on the small signal stability in Western States Coordinating Council (WSCC) system having Automatic Voltage Regulator (AVR) with increased generation and load scenarios. Earlier work on the same system using constant power type of load representation for locating the Hopf bifurcation point was done by the authors of this manuscript. This paper deals with the extension of the previous work to include various voltage dependent small signal load models such as Constant Current (CC), Constant Impedance (CZ), Industrial (IND) and large scale Electric Vehicle (EV) for evaluating damping ratios and sketching the locus of critical modes at stressed loadings by defining six cases. A thorough comparison of damping ratios of several complex modes obtained after including various voltage dependent load models in the linearization programmes with the constant power model presented in earlier work at the nominal loading is done. It is concluded that by including large scale EV load model at load buses, highest damping ratio of exciter mode corresponding to first generator of WSCC system was obtained when compared to remaining load models.

Hybrid FFT-ARMA-Burg Modeling and LSTM-Enhanced BBO Optimization for Fault Diagnosis in Induction Motors

2025-04-01T21:29:48-03:00

Induction motors (IM) are crucial in industrial systems, and fault diagnosis reliably and effectively is of importance for maintaining efficiency. The detection of a broken rotor bar(RBB) is one of the most challenging tasks in condition monitoring due to the complexity of fault features in motor current signals. This plans overcome by developing a hybrid diagnostic framework to enhance fault detection accuracy. The proposed approach fuses a hybrid spectral analysis technique that integrates the Fast Fourier Transform with an autoregressive moving average model estimated using Burg's method. This hybrid of FFT-ARMA-Burg enhances PSD representation. We employ biogeography-based optimization to optimally tune the parameters of the ARMA-Burg model for a better representation of fault-specific features. Further, this paper proposes an LSTM neural network that refines BBO-optimized parameters to improve fault frequency sensitivity. Experimental verification will demonstrate that the hybrid FFT-ARMA-Burg framework, combined with LSTM-enhanced BBO optimization, outperforms traditional motor current signature analysis (MCSA) and standalone ARMA-based methods in detecting broken rotor bars in squirrel cage induction motors. These findings confirm that the proposed methodology enhances broken rotor bar detection and supports predictive maintenance for improved reliability and efficiency in induction motors.

Enhancing sentiment analysis in tourism reviews: A comparative study of algorithms in ASPECT-BASED SENTIMENT ANALYSIS and EMOTION DETECTION

2025-04-01T21:29:48-03:00

Information technology now enables utilizing online review data to support the Sustainable Development Goals (SDGs). However, traditional sentiment analysis often cannot capture the complexity of sentiment. This research aims to combine Aspect-Based Sentiment Analysis (ABSA) and emotion detection for a more in-depth analysis of tourism reviews in Palangka Raya City and compare the performance of various algorithms. Review data was taken from Google Maps and analyzed using BoW, LDA, NRC Emotion Lexicon, machine learning, and deep learning algorithms such as Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Decision Tree (DT), and BERT. Most of the reviews are positive, with the dominance of the emotions of anticipation and joy. The combination of cross-validation with the best parameters from GridSearchCV resulted in the most significant increase in model accuracy. The SVM model performed better than other machine learning and deep learning algorithms, with accuracy and F1-score reaching 99.86%. The combination of ABSA and emotion detection improves the understanding of sentiment and emotion to support strategic decisions in tourism.

Navigation system and obstacle avoidance for mobile robot using Type-2 fuzzy logic in uncertain environments

2025-04-16T11:38:44-03:00

This paper introduces a reactive navigation strategy for wheeled mobile robots, utilizing type-2 fuzzy logic to manage uncertainty in dynamic environments. The approach incorporates two distinct type-2 fuzzy logic controllers, each tailored to address specific challenges in navigation. The first controller focuses on steering the robot toward its target by continuously adjusting its path in response to changing conditions. The second controller specializes in obstacle avoidance, enabling the robot to detect and maneuver around obstacles it encounters during its journey.

To evaluate the performance of the system, numerical simulations are carried out across various scenarios, including dynamic and cluttered environments, to demonstrate its robustness. Additionally, the results of the type-2 fuzzy logic approach are compared with conventional navigation techniques, such as rule-based or model-based methods. The comparison underscores the system’s greater adaptability and resilience. The study concludes that type-2 fuzzy logic provides an effective and flexible solution, significantly improving both path planning and real-time decision-making in unpredictable and complex environments.

Designing a smart home model using EasyVR and Arduino for voice control of devices

2025-04-01T21:29:47-03:00

This paper presents the design and implementation of a smart home model using easyVR and Arduino to voice control home appliances, without relying on traditional sensors. The system leverages easyVR, a voice recognition module, to process spoken commands, which are then interpreted by Arduino to control various smart devices, such as lights, TVs, and air conditioners. The proposed system aims to provide an affordable and efficient solution for home automation, offering a user-friendly, hands-free interface for users. The paper identifies the hardware and software components of the system, including the integration of easyVR with Arduino, and addresses the challenges of implementing accurate voice recognition in a real-world environment. A prototype is developed to demonstrate the functionality and effectiveness of the voice control system. The results show that the system responds reliably to voice commands, providing a seamless smart home experience. This paper demonstrates the potential of easyVR and Arduino in creating scalable and cost-effective voice-controlled smart homes.

A Comparative Analysis of Fast Charging Performance and Battery Life Against Charging Current Variations

2025-04-01T21:29:45-03:00

Meningkatnya permintaan untuk baterai pengisian cepat menghadirkan tantangan terkait kecepatan pengisian dan masa pakai baterai. Studi ini membandingkan kinerja baterai lithium-ion dan solid-state di bawah berbagai arus pengisian. Pada arus pengisian 50 A, baterai lithium-ion membutuhkan sekitar 140 detik untuk mengisi penuh, sedangkan baterai solid-state mencapai hal yang sama dalam waktu sekitar 130 detik. Namun, pada arus yang lebih tinggi (300 A), baterai lithium-ion secara signifikan mengurangi waktu pengisian menjadi 20 detik, sedangkan baterai solid-state mengisi daya dalam 18 detik. Meskipun demikian, pengisian arus tinggi mempercepat degradasi baterai. Baterai lithium-ion, misalnya, memiliki masa pakai sekitar 1.200 siklus pada 50 A, yang menurun secara drastis menjadi mendekati 0 siklus pada 300 A. Sebaliknya, baterai solid-state mempertahankan masa pakai yang lebih tinggi, berkurang dari 1.000 siklus pada 50 A menjadi sekitar 100 siklus pada 300 A. Baterai solid-state menunjukkan kinerja yang unggul, terutama dalam kondisi arus tinggi, dengan 600 siklus pada 150 A, dibandingkan dengan hanya 200 siklus untuk lithium-ion. Hasilnya menyoroti trade-off antara kecepatan pengisian dan masa pakai baterai, dengan pengisian daya yang lebih cepat dicapai dengan mengorbankan umur panjang baterai, terutama untuk lithium-ion. Teknologi solid-state memberikan solusi yang lebih seimbang, menawarkan waktu pengisian yang lebih cepat dan umur panjang yang lebih baik, sehingga cocok untuk aplikasi berdaya tinggi.

Quantitative analysis of subsurface structure cracking using pulsed eddy current nondestructive testing

2025-04-01T21:29:46-03:00

This paper presents a three-dimensional finite element method for the nondestructive evaluation of forward problems utilizing the pulsed eddy current technique. The method visualizes and maps the distribution of responses resulting from the interaction between eddy currents and defects, facilitating defect characterization. The study elaborates on the defect characterization process using the pulsed eddy current technique, which encompasses both numerical and experimental analyses. Initially, the variation in pulse width of the pulsed eddy current technique is discussed, along with its effectiveness. Subsequently, the investigation into the application of pulsed eddy current testing for defects is conducted through the mapping of magnetic field distributions, implemented via time-stepping three-dimensional finite element modelling, with features extracted from the mapping for the purpose of defect characterization.

Sub-Module Voltage Estimation of Modular Multilevel Converter in PV Applications Using a Sliding Mode Observer

2025-04-01T21:29:46-03:00

This paper presents a Sliding Mode Observer (SMO) for estimating sub-module (SM) voltages in Modular Multilevel Converters (MMCs) used in photovoltaic (PV) applications. MMCs are widely favored in medium- and high-voltage applications due to their modularity and scalability. However, traditional voltage sensing methods require numerous sensors, increasing system complexity, cost, and susceptibility to sensor failures. The proposed SMO offers a robust alternative, providing accurate voltage estimation despite parameter variations and external disturbances. The design and implementation of the SMO, along with its integration into the MMC control strategy, are demonstrated. The observer performance is validated through simulations and Matlab results, which demonstrate effective voltage estimation across different operating conditions. This method improves SM capacitor voltage balancing, enhances the reliable operation of MMCs in PV applications, and lowers costs.

The Incorporating ANFIS-based current regulation into a multi-active full bridge converter for reducing current ripple

2025-04-16T11:41:47-03:00

A multi-active full bridge converter is a type of DC-DC converter typically used in high-power applications like renewable energy systems, electric vehicles, or uninterruptible power supplies. Four switches (often MOSFETs or IGBTs) grouped in an H-bridge configuration make up the full bridge topology. The term "multi-active" implies that the power conversion involves a number of active components, which may indicate complicated switching management for improved efficiency and regulation. Changing the phase shift between two switch pairs is how phase-shifted full-bridge converters are controlled. Without altering the switching frequency, the output voltage can be controlled by adjusting the phase difference between the bridge's two legs (S1-S4 and S2-S3). To lower switching losses, soft switching strategies like ZVS or ZCS might be used.

improving overall efficiency. In this approach, the current through the switches is monitored and controlled to improve transient response and stability. Typically implemented in combination with voltage mode control, it ensures better regulation, especially in systems with fast load changes. The secondary side full bridge switches are controlled by current feedback phase delay controller which restricts the battery charge current. Each EV battery has its own charge current limit which is set in the current controller for battery protection. The controlled charge current improves the charge efficiency of the EV battery increasing the reliability of the cells and the overall circuit. The charge control which is a conventional PI controller is further updated with ANFIS for better stability in the EV battery charging current. A comparative analysis with PI and ANFIS controllers is proposed and the results are generated with same rating of EV batteries and system parameters.

Potential Adoption of Blockchain in Food Cold Supply Chain: A Bibliometric Study and Future Research Agenda

2025-04-01T21:29:44-03:00

The food cold chain represents a crucial aspect of the highly sensitive food industry. Insufficient supervision can give rise to a range of adverse consequences, including contamination, fraud, and food waste. In line with technological advancement, the implementation of cold chains is becoming increasingly integrated with digital solutions, with blockchain technology representing a notable example. However, some bibliometric analyses have not focused on the cold supply chain, limiting their scope to the supply chain in general. Consequently, further studies are required to gain a more comprehensive understanding of this complex and evolving field. The objective of this analysis is to provide a bibliometric examination of the potential implementation of blockchain technology in the context of food cold chains. This will facilitate the identification of the most recent developments, prevailing trends, and research gaps that require further investigation. This research uses the combination of PRISMA and bibliometric analysis method through Biblioshiny and Vosviewer using 201 scientific articles. The results show that this topic has an annual growth of 96.77%, and there are 3 interesting research clusters, namely 1) blockchain technology transformation in improving traceability and reducing food waste, 2) blockchain technology in improving the competitiveness of cold supply chain companies, and 3) blockchain that can improve food safety, leading to increased consumer confidence. Based on the major themes obtained and strategic diagram, there are various future research opportunities, including capability freight transportation, deteoritation, food contamination, storage and transportation, carbon emissions, competitiveness, delphi analysis, agricultural products and sales.

Long - Term Assessment of the Spatial Temporal Trends in selected Cloud Radiative Properties over the Three Distinct sites in Kenya

2025-04-24T21:39:19-03:00

The presence of clouds in the Earth’s atmosphere plays an important role in regulating the Earth’s energy budget. Increased anthropogenic activities and emissions can significantly lead to changes in cloud composition and cloud structure affecting the cloud properties causing alterations in climatic conditions over Kenya. Given this, the present study examined the spatial temporal radiative properties of clouds over Nairobi, Malindi and Mbita by paying a special consideration on cloud parameters such as; Cloud Effective Radius (CER), Cloud Optical Thickness (COT), Water Vapor (WV), Precipitation Rate (PR) and Cloud Albedo (CA). These cloud parameters were retrieved from MODerate resolution Imaging Spectroradiometer (MODIS) sensor, the Modern Era Retrospective analysis for Research and Applications, version2 (MERRA-2) model and Tropical Rainfall Measurement Mission (TRMM) between January 2005 and December 2020. Data retrieved on clouds radiative properties was utilized to estimate the trends and spatial variations and assess their statistical significance on climate over the study domain. The Spatial patterns of seasonal mean of cloud parameters from the sensors and the model were generally characterized with positive and negative trends over Kenya observed during the four seasons Spatial trends in the selected cloud properties were determined and observed to vary both seasonally and regionally, the study revealed patterns of trends in cloud radiative properties and forms a basis for further research on clouds over Kenya.

A Novel Hybrid Steganography Approach for Securing Text, Images, and Audio with Robust Encryption in Audio Steganography

2025-04-24T21:39:18-03:00

This research proposes an imaginative hybrid steganography technique meant to strengthen the protection of text, pictures, and audio in response to the critical problem of information security. The incorporation of strong encryption in audio steganography adds to the innovation. In response to rising dangers in digital communication, the study investigates the vulnerabilities of text, picture, and audio mediums. The inquiry looks into White Space and Least Significant Bit (LSB) approaches for text steganography, while Quantization Index Modulation (QIM) is combined with LSB for image steganography. A revolutionary hybrid solution for audio steganography develops, combining Adaptive QIM and LSB. Python implementations are provided to demonstrate the use and effectiveness of these strategies in maintaining data integrity and secrecy. The study broadens its reach by presenting a novel data security paradigm in audio files that combines AES encryption, Rubik’s Cube-like scrambling, and adaptive steganography. The method begins with strong AES encryption, which is then followed by a clever scrambling algorithm inspired by the Rubik’s Cube. The distinguishing characteristic is seen in the last stage, when adaptive QIM embeds scrambled data into audio files, thereby disguising encrypted material. This innovative solution not only strengthens data security through the complexities of AES and Rubik’s Cube-like scrambling, but it also pro- vides a full framework for safe data transfer, exemplifying the synergy of classical encryption and current steganography techniques. The results reveal a considerable improvement in information security across various digital forms, indicating a big step forward in reinforcing sensitive datatransfer.

A Tailored Compliance Solution for Securing Personal Data privacy under Law 18-07 in Algeria

2025-04-25T21:39:38-03:00

In an increasingly digitalized world, the management and protection of personal data has become a crucial issue for companies and governments. The rapid flow of information and the ubiquity of technology pose significant challenges to data security and privacy. In response to these concerns, the Algerian state enacted Law 18-07 on June 10, 2018, aimed at protecting individuals concerning the processing of their data. This law led to the creation of the National Authority for the Protection of Personal Data (ANPDP), responsible for supervising its application. This article proposes the design and implementation of a computer application dedicated to compliance with Law 18-07 for the protection of personal data. We first collected and analyzed the legal requirements for data protection and then modeled them using UML diagrams following the UP methodology. We have set up a database management system to ensure data protection and optimized management, incorporating cryptography techniques to enhance the security of the information. The development of this application was carried out with the Microsoft Visual FoxPro V.9 language, thus allowing the creation of a database and interfaces adapted to the needs of algerian companies to comply with Law 18-07 and guarantee the security of personal data.

Assessment of the effect of series passive filter on harmonic distortion-power losses relationship in an electricity distribution network

2025-04-25T21:39:37-03:00

Technological innovations in recent times have led to widespread use of non-linear loads in power networks. These loads generate harmonics which result in the distortion of the system’s voltage and current signals and consequently, leads to power loss. This research assessed the total harmonic distortion (THD) mitigation capability of series passive filters (SePF) in an electricity distribution network (EDN) with a high penetration level of non-linear loads and the resulting effect on power loss (PL). The EDN for 250-seater computer laboratory facility in Federal University of Agriculture, Abeokuta (FUNAAB), Nigeria was considered as a case study. The network modelled and simulated without and with SePF in MATLAB/Simulink environment (R2023a version). The system's voltage-current THD (THDV-I) and PL were evaluated to determine the relationship between the two parameters. The obtained results showed that when no filter was applied, the THDV-I and PL were 37.38% and 9,703 W, respectively. However, when SePF was used on the network, the THDV-I reduced to 4.90% and the PL minimised to 146 W. These results indicated that PL reduces with decrease in THDV-I. The series passive filter application on the considered facility in this research appropriately mitigated the observed THD and the associated PL.

Combination of minimum entropy deconvolution method and Van Cittert algorithm for features extraction of bearings

2025-04-25T21:39:37-03:00

Rolling bearings functionality has a primary importance for the correct operation of the rotating machines. In this paper, a monitoring technique based on deconvolution approach is proposed to restore the impulsive shape from the measured vibration signal. This latter is obtained from a convolution of real impulse signal and transmission function. The proposed procedure consists of two major steps; firstly, using the minimum entropy deconvolution (MED) to obtain the inverse filter, secondly introducing the iterative deconvolution algorithm to go back to the initial problem that is mathematically described by the convolution process to restitute the impulsive signal. The proposed procedure is applied to bearing diagnosis, and its effectiveness is validated by simulated and experimental data acquired from operational bearings. Moreover, the monitoring obtained results are satisfactory.

Solder Lead Waste Reduction of a Selected Mobile Phone Factory: A Six Sigma DMAIC Approach

2025-04-25T21:39:36-03:00

Six Sigma is an explicit statistical method of reducing variability and improving processes. Our research works with internal customers within the assembly lines. This study aims to identify and analyze the VOC of internal customers and show improvement based on the VOC. The Six Sigma DMAIC method was followed to conduct this research. Six mobile phone assembly lines were selected to collect wastage data for two months after identifying the problem. Collected data were analyzed in MS Excel, and this analysis showed that the wastage rates were 91.38% in July 2024 and 80.67% in August 2024. The average waste rate was 0.44 g/unit and 0.41 g/unit in July 2024 and August 2024. The root cause analysis, and cause and effect diagram illustrated that most of the reasons behind wastages were related to soldering operators. An awareness and training session was conducted. We collected data for the next month and found the wastage was reduced to 69.27% in September 2024, which is a 24.5% reduction considering August 2024 as the base. The average waste rate was reduced to 0.28 g/unit. The effectiveness of following the Six Sigma method to solve quality or wastage issues is evident hereafter accomplishing this research.

Enhancing Overall Equipment Effectiveness (OEE) of a Selected Machine in a Light Manufacturing Factory in Bangladesh

2025-04-25T21:39:35-03:00

Overall equipment effectiveness provides reliable and considerable performance indicators for machinery performance. This research aimed to measure the OEE of the selected machine, identify the reasons behind its poor performance, and take action to improve the machine's overall performance. A tire-curing machine was chosen for the research as the target machine for OEE analysis. After collecting data for a month, we did our calculation using Microsoft Excel and found OEE of just 63%, which is far below the industry standards. We investigated the causes of the low OEE and found that most affected the availability and performance metrics while quality was good. For the following month, several performance improvement activities were taken including maintenance improvements, process optimizations, and operator training. The new data collection and analysis phase was carried out after the implementation phase to measure the impact results of the interventions. The OEE of the machine improved from 63% to 73%, showcasing a significant increase in efficiency; that is, availability increased by 79% to 85%, while performance improved from 82% to 88%. This research also highlights the importance of OEE as it gives a comprehensive view of how machines and equipment may be tracked and improved.

Comparative study of 2D designs of 12/8 and 10/8 switched reluctance motors using ANSYS Maxwell

2025-04-25T21:39:35-03:00

The switched reluctance motor (SRM) is one of the common industrial applications where many fields of different models are used, but they differ in performance, thus the selection of the appropriate prototype must be determined according to the application's requirements. In this study, a comparative analysis was conducted between 12/8 and 10/8 switched reluctance motors (SRMs) using ANSYS Maxwell where the same dimensions, parameters, and operating conditions were adopted in the designs and only the number of stator poles were changed to evaluate the magnetic, mechanical, and electrical characteristics. Then we study their effect on the SRMs performance focusing on several aspects of torque, speed, various losses, electromagnetic analysis of intensity, and magnetic flux density. Electrical losses were reviewed, and the study revealed significant differences between the SRMs' electromagnetic performance, highlighting the importance of the SRM design to reduce losses and improve efficiency. The results show that the importance of choosing between the two SRMs depends on the application requirements, whether it requires high speed and efficiency or high torque. This study provides a comprehensive insight into the SRM designs deep analysis, supporting the drive for more efficient and sustainable technology in optimizing the electric motors' overall performance.

Monitoring and evaluation of water quality in rural Andean areas of Peru using wireless sensors with LoRa

2025-04-25T21:39:35-03:00

The objective of this research was to implement wireless sensors for remote monitoring and evaluation of water quality in five reservoirs in rural Andean communities in the district of Huancavelica-Peru, given the scarcity of information on water quality and its poor monitoring. It is important to monitor water quality for its safe management according to the sustainable development goals of the 2030 agenda, against natural or anthropogenic contaminants. The remote monitoring evaluated the performance of wireless sensors and water quality (temperature, pH and turbidity) for 10 days. The wireless sensors were calibrated with Hanna Instruments brand equipment achieving an R² of 98.98%, 96.81% and 89.82% for temperature, pH and turbidity respectively. The average amount of data received was 456/462, 8.73 km maximum communication distance, received signal strength RSSI (-93 to -122 dBm), signal to noise ratio SNR (9 to 13 dB), water temperature (-2.90 to 14.4 ºC), pH (6.60 to 8.24) and turbidity (0.34 to 4.98 NTU). The wireless sensors are highly effective in remote monitoring; the quality of the monitored water complies with Peruvian and World Health Organization regulations.

A Fast Enhanced Medical Image Encryption scheme Based on 2D-Chaotic Map and Improved Zigzag confusion

2025-04-25T21:39:34-03:00

The rapid growth of telecommunication systems has increased the need for the secure transmission of data images in the telemedicine context, ensuring confidentiality and reliability. Chaotic image cryptography, known for its ergodicity and sensitivity to initial conditions, is a robust solution against attacks on medical data in unsecured networks. This paper introduces a chaotic image encryption scheme utilizing the 2D-logistic sine-coupling map (2D-LSCM) to enhance the reliability and security level of medical encrypted images. 2D-LSCM has been used to generate chaotic matrices for achieving confusion and diffusion processes. In the confusion step, a variety of permutation operations are utilized, such as improved 2D zigzag transform, magic confusion, pixel confusion, and image rotation. We use also a pixel diffusion based on modulo arithmetic. Several simulations were carried out to prove the reliability and robustness of the proposed algorithm in protecting medical images. Additionally, we evaluate the system's performance and security, comparing it to other well-known chaos-based encryption schemes. The results obtained in the simulation demonstrated the high security of the cryptosystem, therefore our system can effectively secure multiple medical image formats and resist different security attacks.

Simulation of the elastic field of an interfacial dislocation in an anisotropic medium: Fourier series approach

2025-04-25T21:39:34-03:00

Interfacial dislocation networks, located at the interface between two crystals, significantly influence the mechanical, thermal and electrical behaviors of materials. Despite their importance, these phenomena have received relatively little attention in the scientific literature. This gap can be mainly explained by the difficulty of analyzing these complex systems under realistic experimental conditions, particularly using advanced techniques, which take into account the anisotropy of materials. This study focuses on the simulation of the elastic field (stresses and displacements) of a dislocation located at the interface of two infinite anisotropic media. Based on previous work in anisotropic elasticity, an analytical formulation based on Fourier series was used to numerically solve a system of 12 equations with 12 unknowns. The results obtained show the equistress curves for different crystalline systems (Al/Al, Cu/Cu and Al/Cu) considering both anisotropic and quasi-isotropic cases. The study highlights more pronounced stress dispersion in copper due to its hardness, as well as notable differences between isotropic and anisotropic cases, especially for heterogeneous materials such as Al/Cu. The conclusions highlight the importance of material heterogeneity in stress distribution and the relevance of the results for modeling crystal interfaces. This work offers promising perspectives for the optimization of materials in industrial fields such as aeronautics, electronics and renewable energies. It also provides a robust methodological framework for the study of complex crystalline materials.

Enhancing Incentive Schemes in Edge Computing through Hierarchical Reinforcement Learning

2025-04-25T21:39:33-03:00

Edge learning is a distributed approach for training machine learning models using data from edge devices. It preserves privacy by avoiding direct data sharing. However, existing systems struggle with resource inefficiency, malicious node participation and lack of long-term sustainability. These challenges reduce performance and discourage participation in edge learning. This paper proposes Chiron, a robustness-aware incentive mechanism designed to address these issues. Chiron employs a hierarchical reinforcement learning (HRL) framework to optimize resource allocation and ensure fair participation. The framework focuses on three key components: pricing strategy, resource distribution and malicious node detection. Chiron integrates system-level performance and model accuracy into its optimization goals, ensuring a balance between efficiency and effectiveness. The hierarchical structure includes three layers. The top layer determines the total incentive budget to achieve long-term sustainability. The middle layer allocates resources to minimize idle time and enhance efficiency. The bottom layer identifies and excludes malicious or lazy nodes that negatively impact the education process. By tackle both short-term and long-term objectives Chiron ensures fairness and performance stability. Extensive experiments validate Chiron’s capabilities using real-world datasets like MNIST and CIFAR-10. Chiron demonstrates robust byzantine resistance and supports sustainable edge learning by addressing critical gaps in current approaches. This work contributes to the advancement of edge learning by presenting a reliable and efficient solution for real-world applications. By integrating security, sustainability and performance, Chiron enables edge learning to be both practical and impactful in various domains

Enhancing real-time animation: Ensuring distinctiveness in crowd dynamics through physics-based collision avoidance

2025-04-25T21:39:33-03:00

Crowds are essential in daily activities, performing as dynamic systems of human interaction. They involve numerous individuals collecting in specified locations for diverse activities, whether in urban environments, public events, or social interactions. In virtual environments, animated crowds frequently display repetitive behaviors and a deficiency in movement diversity, leading to unrealistic simulations. It is imperative to provide distinctive and diverse actions for each character, avoiding visual duplication to augment realism. This paper introduces a method to enhance crowd variety by ensuring distinct and realistic character movements. We propose providing various motion types to prevent the duplication of cloned characters. Our approach creates a set of animations and utilizes techniques to control character velocity, ensuring distinctive and convincing movements. Furthermore, we present collision prevention methods based on Newton's Laws, the conservation of momentum, and the laws of kinetic energy. Ray-casting determines collision velocities by considering each character's mass and velocity without external forces. We implement a hybrid pool object approach and occlusion culling techniques to optimize real-time performance, increasing FPS (framed per second) and reducing computational load. These experiments evaluate the efficacy of our technique under different conditions. The results demonstrate the method's effectiveness and flexibility in dynamic environments.

Process optimization as a tool for analyzing performance indicators of additional taxi-out and taxi-in time of the Brazilian airspace control system

2025-04-25T21:39:32-03:00

This study aims to improve the efficiency of Brazilian air traffic by analyzing the Brazilian Airspace Control System (SISCEAB) performance indicators. The methodology used combined alternative data sources, namely BIMTRA, TATIC FLOW, and VRA, which were employed to examine the impact of different variations in taxiing times. Specifically, Additional Taxi-Out Time (KPI 02) and Additional Taxi-In Time (KPI 13) were analyzed to identify discrepancies among these data sources and determine the most precise combination. The results indicate that airport layout, gate distribution, and runway threshold selection significantly impacted taxiing times. Statistical analysis revealed substantial variations in unimpeded taxi times across different gates and runway thresholds, emphasizing optimizing operational flows. Based on these findings, integrating BIMTRA and VRA is recommended for more accurate KPI measurement. Therefore, this study contributes to implementing operational enhancements, optimizing airport operation flow, and leading to a more efficient management of Brazilian air traffic.

Quantum computing: Approaches, scalability, and the future of emerging technologies

2025-04-25T21:39:31-03:00

Quantum computing is an emerging field that promises to revolutionize science and technology by offering exponentially superior processing capabilities compared to classical computing. This paper analyzes three main approaches to quantum computer development: IBM’s scalable and modular systems, D-Wave’s practical solutions for optimization problems, and Nuclear Magnetic Resonance (NMR)-based computers designed for education and research. We explore the technological advancements, practical applications, challenges, and future prospects of these approaches, demonstrating how they collectively pave the way for a future where quantum computing becomes indispensable

Fuzzy irrigation model in protected crop based on expert knowledge

2025-04-25T21:39:31-03:00

A lógica difusa é um subcampo da Inteligência Artificial que permite que o conhecimento humano seja expresso de forma natural, através de variáveis e valores linguísticos, e de um processo de inferência muito semelhante ao que utiliza diariamente. A presente investigação utiliza critérios especializados para conceber e avaliar um modelo baseado num sistema fuzzy para prever o tempo de rega da cultura protegida de pepino (Cucumis sativus L.). As variáveis temperatura, humidade do solo e iluminação são utilizadas para a construção do modelo, que é acoplado a uma tecnologia IoT existente na empresa de várias culturas “Valle del Yabú”, servindo como um sistema de apoio à tomada de decisões. O protótipo é criado e simulado em MATLAB, sendo depois transferido para um Raspberry Pi 4 Modelo B, utilizando a linguagem de programação Python. Os testes realizados com uma base de dados recolhida durante um ciclo de cultivo mostram uma redução de 10,07% no consumo de água em comparação com a rega padrão atualmente implementada pela empresa.