International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, DOI: 10.31695/IJASRE
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An Adaptive Soft Computing Model for Flux Estimation and Torque Control of Induction Motors
Get PDFInduction motors are widely utilized in various industries because of simplicity in service, durability, and low cost, leading to the displacement of DC motors. However, control system challenges have hindered their full potential for high performance. Many low-performance drives utilize scalar control, which adjusts only the stator magnitudes to uphold a persistent stator flux. Vector control emerged to address this limitation by enabling induction motor control analogous to DC motors. Subsequently, Direct Torque Controlled (DTC) induction motor drives were developed. Unlike vector-controlled drives where stator currents serve as control variables, DTC controls stator flux linkages. DTC employs a Reference Flux (RF) estimator to determine RF based on motor speed and a PI controller to estimate reference torque using speed error as input. The calculated stator flux angle determines the sector number for generating switching signals, traditionally done through a RF estimator. This estimator has been enhanced with fuzzy logic to ensure adaptability, and further improved with an ANFIS-based approach to combine fuzzy logic and artificial neural networks. Performance evaluation involves metrics utilizing speed and torque errors to gauge controller effectiveness. Comparing the fixed RF estimator to the ANFIS-tuned RF estimator with manually tuned speed PI, there is a performance improvement of 72.73%, 72.749%, and 46.636% for ISE, ITSE, and ITAE, respectively.
Keywords - Direct torque controller, reference flux, induction motor, ANFIS, vector controller
The Power Factor Improvement Modelling with the Runge-Kutta 4th Order, Analytical, and Experimental Method
Get PDFThe Power factor improvement modeling is conducted by modeling electrical circuits that have a low power factor, then the power factor will be improved by pairing capacitors in series and in parallel. The capacitance values of the connected capacitors are varied for maximum power factor value. The power factor improvement modeling was analyzed using the Runge-Kutta 4th Order (RK-4) method and the analytical calculations were then compared with the experimental method. The study shows the relationship between the value of the power factor to the capacitance value and the relationship between active power, apparent power, and reactive power to the capacitance value of capacitors installed in series or parallel in the circuit. The results of calculations using the RK-4 method, analytical methods and experimental data show the same pattern, with a relative error below 8%. The results of the analysis show that the parallel installation of capacitors is the most appropriate power factor improvement model
A Forty-Two Points Second Order Rotatable Design in Three Dimensions Constructed using Trigonometric Functions Transformations
Get PDFIn this study, a new forty-two point’s second order rotatable design is constructed using transformations of some trigonometric functions. This design permits a response surface to be fitted easily and provides spherical information contours besides the economic use of scarce resources in relevant production processes
A Study of an Intrusion Detection System in IoT Environment
Get PDFThe fast development and widespread use of IoT gadgets in recent years have altered many aspects of our lives, from wearable technology and smart homes to healthcare systems and industrial automation. However, the extraordinary expansion of interconnected IoT devices also brings various security issues that open them to cyber threats. Intrusion Detection Systems (IDS) have become essential elements of IoT security systems to reduce these risks. To thoroughly grasp the subject’s state, this literature review will examine the current research and technological developments in IoT IDS. This literature review covers the architecture of the IoT, various security issues and attacks in IoT, security mechanisms for IoT, IoT IDS, the role of AI in IoT data security, and hybrid IoT IDS
Big Data in Industry 4.0 and 5.0 for Operational Efficiency and Decision-Making
Get PDFToday's smart manufacturing is influenced by two distinct paradigms: Industry 4.0 heralds the shift toward process automation and digitization while Human centricity is emphasized in Industry 5.0. The entrance of Industry 4.0 has revolutionized the manufacturing and industrial sectors by integrating AI tools and data-driven approaches. A pivotal element of the transformation is the utilization of Big Data, which significantly enhances operational efficiency and decision-making processes. This paper explores the impact of Big Data on Industry 4.0, focusing on how it drives improvements in operational efficiency and supports informed decision-making. Through analysis of experience and current applications, the study demonstrates how Big Data analytics facilitates predictive maintenance, optimizes production processes, and enables real-time monitoring of systems. The findings highlight that leveraging large datasets allows for more precise forecasting, reduced downtime, and enhanced resource management. Furthermore, the paper addresses the challenges associated with Big Data integration, including data security, privacy concerns, and the need for AI analytical skills. By providing a nuanced understanding of these dynamics, the paper offers valuable insights for industries aiming to harness the full potential of Big Data within the Industry 4.0 framework
Class Prediction of High-Dimensional Data with Class Imbalance: Breast Cancer Gene Expression Data
Get PDFBreast cancer remains a leading cause of mortality among women worldwide, with early and accurate diagnosis being critical for effective treatment. Gene expression profiling has emerged as a powerful tool for understanding the molecular mechanisms of cancer and for developing predictive models. However, the high dimensionality and class imbalance inherent in gene expression data pose significant challenges for developing robust predictive models.
This study aims to develop and evaluate a predictive model for classifying breast cancer subtypes using high-dimensional gene expression data, addressing the challenges of class imbalance. The objective is to improve the accuracy and reliability of breast cancer subtype prediction to facilitate better diagnostic and treatment strategies.
A comprehensive dataset of breast cancer gene expression profiles was utilized, comprising numerous gene expression levels across multiple samples. To address class imbalance, resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-sampling, were employed. The machine learning algorithms employed included Support Vector Machines (SVM), Random Forests, and Neural Networks. The algorithms were trained and evaluated using cross-validation to identify the most effective model. The performance of these models was assessed based on metrics such as accuracy, precision, recall, and F1-score. The results indicate that the use of SMOTE in combination with SVM provided the most balanced and accurate predictions, with an F1-score significantly higher than models without resampling. The Random Forest algorithm also showed promising results, particularly in handling the high-dimensionality aspect of the data.
The study demonstrates that addressing class imbalance through advanced resampling techniques can significantly enhance the predictive accuracy of models trained on high-dimensional gene expression data. The findings underscore the potential of machine learning models, particularly SVMs and Random Forests, in improving breast cancer subtype classification
Bee Visits Improves Production Parameters of Maize in Northern Côte d’Ivoire (West Africa)
Get PDFMaize (Zea mays) is one of cereal rich in nutrients and antioxidants essential for human health. Many studies showed that this speculation is pollinated indirectly by wind. However, we observed a strong presence of bees on the male flowers of maize as soon as they open. The high activity of bees on these flowers, causes an intense release of pollen, which could impact positively the fruit set. This study was carried out in the botanical garden of the university Peleforo Gon Coulibaly of Korhogo located in northern Cote d'Ivoire. Two varieties of maize (EV8728-SR and LG501) were used for this study. For each variety, 15 plants were protected from insect visits and 15 other plants were still unprotected. Ear mass, seed mass and seed number resulting from each treatment were analyzed. The findings showed that the average number of seeds per ear resulting from unprotected plants (V1: 445.8 ± 17.92 and V2: 442.27 ± 6.8) was significantly higher compared to the protected plants (V1: 242.13 ± 4.39 and V2: 208.6 ± 9.52) for each variety. The average mass of seeds resulting from unprotected plants (V1: 205.31 ± 2.23g and V2: 169.45 ± 5.15g) was also higher compared to the protected plants (V1: 101.79 ± 1.93g and V2: 77.49 ± 1.70g). Whatever the maize variety, the germination rate of seeds resulting from unprotected plants (V1: Tg = 96.67% and V2: Tg = 100%) was higher compared to the protected plants (V1: Tg = 66.67% and V2: Tg = 56.67%). This study are very relevant for farmers and scientists because, it showed clearly bee contribution in the maize production improvement
Cassava cell wall disruption methods: A review
Get PDFCyanogenic compounds such as linamarin, lotaustralin and linustatin are found in roots of cassava (Manihot Esculenta, Crantz). Those compounds are located in cell vacuoles while their hydrolytic enzyme, the linamarase is present in cell wall. Once the cell wall is disrupted, the cyanogenic compounds are hydrolyzed by the linamarase, producing glucose, acetone and hydrocyanic acid under favorable conditions. The main condition for cyanide production and elimination is the disruption of cassava cell wall, to promote the contact of cyanogenic glycosides and the linamarase. The cell wall disruption can be obtained by mechanical methods that are: shear grating, grating by pounding and grating by impacting. Cell wall disruption can also be done by solid-state fermentation or submerged fermentation that are chemical methods. It can finally be done by the combination of mechanical method for cassava division, followed by fermentation in hot water
Biogas Potential of Wastewater Sludge from a Palm Oil Refinery Plant
Get PDFAn agri-food company specializing in the processing of crude palm oil into refined oil generates physico-chemical and biological sludge from its SN Cie-coded wastewater treatment plant. This sludge is therefore a management issue. The aim of this study is to valorize the sludge from a wastewater treatment plant located in an industrial zone in Vridi/Abidjan (Côte d'Ivoire) into biogas. The physico-chemical characteristics of the sludge and its methanogenic potential were assessed, followed by optimization of biogas production. These results revealed that the biological sludge (73.08% MS) contained more organic matter than the physico-chemical sludge (55.55% MS). The relatively average organic matter content of the physico-chemical sludge necessitated the use of cow dung as an inoculum to enhance biogas production. Biodegradability tests carried out with different ratios of Substrate/Inoculum led to the conclusion that the presence of a significant quantity of microorganisms is necessary for optimum biogas production. As a result, 6510mL of biogas were produced from the SN Cie wastewater treatment plant's biological sludge, with a methane composition of 71.33%, which is required for power generation.
 
Determination of Activity Optimum Inulinase from Bacteria Screened from Dadih (Curd)
Get PDFDadih (curd) contains Lactic Acid Bacteria (LAB), most of them are probiotics. Probiotics favour nutritional supplements called prebiotics. One compound that belongs to the prebiotic group is inulin. Inulinase hydrolyses inulin into fructose and FOS. The aim of the study was to determine the optimum activity of inulinase from bacteria screened from dadih (curd) at variation of pH (4.5; 5; 5.5; 6; 7; 8), temperature (20°C, 30°C, 37°C, 45°C) and variation of substrate concentration (0.5%; 1%; 1.5%; 2%; 2.5%). Determination of inulinase activity was determined using Dinitrosalicylic Acid (DNS) reagent method. Absorbance was measured using spectrophotometer at 488 wavelengths. The results obtained from this study were the optimum activity of inulinase (bacterial inulinase degrading inulin) at pH 5.5; temperature 37°C and substrate concentration 2.5%