International Journal of Electrical and Computer Engineering (IJECE)
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Development and testing of a dedicated cooling system for photovoltaic panels
Get PDFSolar energy is a viable alternative to fossil fuels, but its efficiency is limited by photovoltaic panel overheating, which causes a decrease in efficiency. This paper suggests a passive cooling method that incorporates aluminum heat sinks beneath the solar cells. This simple, low-cost device maximizes heat dissipation using natural convection. It requires no external energy. The goal is to provide a solution to the challenge of selecting an effective, sustainable, and flexible cooling system while considering technological, economic, and environmental constraints. Experimental results demonstrate that modules fitted with heatsinks experience an average 8.13 °C drop in temperature, as well as a 0.51 V rise in open-circuit voltage when compared to the reference panel. This increase demonstrates how well-designed passive solutions can dramatically improve the energy performance of solar panels. The study emphasizes the relevance of thermal design in photovoltaic system optimization and provides specific opportunities for the development of more efficient solar technologies, particularly in high-temperature situations
A solar-powered autonomous power system for aquaculture: optimizing dual-battery management for remote operation
Get PDFIn Indonesia, growing fish consumption demands necessitate expanded, yet sustainable, fish production without sacrificing quality. The process of feeding and the quality of the surrounding water are important factors influencing fish quality. To address this, Parahyangan Catholic University's Fishery 4.0 project pioneers a unique technology that integrates water quality monitoring with a fish feeding feature. The design and implementation of an independent, reliable power module, which is fundamental to the functionality of this system, is at the focus of this research. This study shows that a designed power module adapted to the specific needs of Fishery 4.0 is feasible. The system powers all modules with a 12 V battery and is recharged with a solar panel. The battery can be charged to 95% capacity, yielding 8550 mAh from a 9000 mAh capacity. A UC-3906 charger IC controls the charging process, deliberately managing the parameters required for optimal battery charging. Particularly, when exposed to ideal solar radiation, the charger recharges a 9 Ah battery from 30% to full capacity in about 10 hours and 10 minutes. This study proposes a novel to battery management, which is critical for the operation of aquaculture equipment at isolated locations
A memory improved proportionate affine projection algorithm for sparse system identification
Get PDFFor cluster sparse system identification, it is known that the cluster sparse improved proportionate affine projection algorithm (CS-IPAPA) outperforms the standard IPAPA. However, since CS-IPAPA does not retain past proportionate factors, its performance can be further improved. In this paper, a modification to CS-IPAPA is proposed by utilizing the past instant proportionate elements based on its projection order. Steady-state performance of the proposed memory cluster sparse improved proportionate affine projection algorithm (MCS-IPAPA) is studied by deriving the condition for mean stability. Different simulation setups show that the proposed algorithm outperforms different versions of IPAPA in terms of convergence rate, normalized misalignment (NM) and tracking, for different types of inputs like colored noise, white noise, and speech signal. By incorporating past proportionate factors, the proposed MCS-IPAPA significantly reduces computational complexity for higher projection orders
Enhancing diabetes prediction through probability-based correction: a methodological approach
Get PDFPredictive healthcare analytics demands accurate predictions from interpretable models for early diagnosis and intervention on diabetes prognosis, which remains a well-established challenge. This study presents a new probability-based correction method to enhance the performance of a model in diabetes prediction. Initial model comparisons are performed using the PyCaret framework to identify the baseline model. Logistic regression was selected due to its simplicity, interpretability, and its higher accuracy, which outperformed other models. To further facilitate future research in this field, this study was conducted using a noisy dataset without any changes or preprocessing steps other than those available in the dataset from the producer. This intentional decision meant that the new probability-based method could be evaluated in isolation without any additional modifications being applied. The proposed correction method adjusts predictions into borderline probability intervals to obtain more accurate classifications. This approach increased the model accuracy by 6% from 75% to 81%, thus proving successful in resolving the misclassification problem with higher risk. This approach outperforms state-of-the-art methods and demonstrates its generalizability in enhancing the certainty of downstream clinical decisions
Revolutionizing autism diagnosis using hybrid model for autism spectrum disorder phenotyping
Get PDFThe growing prevalence of autism spectrum disorder (ASD) necessitates efficient data-driven screening solutions to complement traditional diagnostic methods, which often suffer from subjectivity and limited scalability. This study introduces a hybrid ensemble model combining logistic regression (LR) and naive Bayes (NB) for ASD classification across four age groups (toddlers, children, adolescents, and adults) using behavioral screening datasets. By integrating statistical learning and probabilistic inference, the proposed model effectively captured behavioral markers, ensuring a higher classification accuracy and improved generalization. The experimental evaluation demonstrated its superior performance, achieving 94.24% accuracy and 99.40% area under the receiver operating characteristic curve (AUROC), surpassing those of individual classifiers and existing approaches. This artificial intelligence (AI)-driven framework offers a scalable, cost-effective, and accessible solution for both clinical and telemedicine-based ASD screening, facilitating early intervention and risk assessment. This study underscores the transformative potential of AI in neurodevelopmental diagnostics, paving the way for more efficient and widely deployable autistic screening technologies
Language model optimization for mental health question answering application
Get PDFQuestion answering (QA) is a task in natural language processing (NLP) where the bidirectional encoder representations from transformers (BERT) language model has shown remarkable results. This research focuses on optimizing the IndoBERT and MBERT models for the QA task in the mental health domain, using a translated version of the Amod/mental_health_counseling_conversations dataset on Hugging Face. The optimization process involves fine-tuning IndoBERT and MBERT to enhance their performance, evaluated using BERTScore components: F1, recall, and precision. The results indicate that fine-tuning significantly boosts IndoBERT’s performance, achieving an F1-BERTScore of 91.8%, a recall of 89.9%, and precision of 93.9%, marking a 28% improvement. For the model, M-BERT’s fine-tuning results include an F1-BERTScore of 79.2%, recall of 73.4%, and precision of 86.2%, with only a 5% improvement. These findings underscore the importance of fine-tuning and using language-specific models like IndoBERT for specialized NLP tasks, demonstrating the potential to create more accurate and contextually relevant question-answering systems in the mental health domain
A non-destructive approach for estimation of Hb, HCT and red blood cells using reflectance spectroscopic technique
Get PDFPaediatric haematology involves the use of non-invasive methods and technologies to evaluate haematological parameters in children. These techniques attempt to offer precise measurements of blood constituents without the necessity of intrusive procedures such as venipuncture or blood draws, which can be difficult and unpleasant for paediatric patients. The data gathered from the elbow will be given priority for further investigations to find haematological profiles. Estimates of haemoglobin, haematocrit, and red blood cell count were done and compared against the values obtained using conventional methods. This method achieves an accuracy of 75.56% with high precision and specificity which makes the method particularly beneficial for paediatric applications, potentially due to physiological differences or enhanced calibration for younger populations. The sensitivity varies with red blood cells (RBC) showing the lowest true positive detection rate. Future work could focus on improving the sensitivity of these parameters to enhance the accuracy. Conventional techniques cannot monitor continuously and remotely, which is crucial for a point-of-care screening device in the current era. The proposed non-destructive technique offers the benefits of infection control, pain reduction, and minimal operational cum maintenance expenses, all while being portable and child friendly
Instance segmentation for PCB defect detection with Detectron2
Get PDFPrinted circuit boards (PCBs) are essential in modern electronics, where even minor defects can lead to failures. Traditional inspection methods struggle with complex PCB designs, necessitating automated deep learning techniques. Object detection models like Faster R-CNN and YOLO rely on bounding boxes for defect localization but face overlap issues, limiting precise defect isolation. This paper presents a segmentation-based PCB defect detection model using Detectron2’s Mask R-CNN. By leveraging instance segmentation, the model enables pixel-level defect localization and classification, addressing challenges such as shape variations, complex structures, and occlusions. Trained on a dataset of 690 COCO-annotated images, the model underwent rigorous experimentation and parameter tuning. Evaluation metrics, including loss functions and mean average precision (mAP), assessed performance. Results showed a steady decline in loss values and high precision for defects like mouse bites and missing holes. However, performance was lower for complex defects like spurs and spurious copper. This study highlights the effectiveness of instance segmentation in PCB defect detection, contributing to improved quality control and manufacturing automation
Energy evaluation of dependent malicious nodes detection in Arduino-based internet of things networks
Get PDFDetection of malicious nodes in the internet of things (IoT) network consumes power, which is one of the main constraints of the IoT network performance. To evaluate the energy-security trade-off for malicious node detection, this paper proposes an Arduino-based system for dependent malicious nodes (DMN) detection. The experimental work using Arduino and radio frequency (RF) modules was implemented to detect dependent malicious nodes in an IoT network. The detection algorithms were evaluated in terms of energy efficiency. The experiment comprises a coordinator node with five sensor nodes and varying malicious nodes. The results assess the detection algorithms in terms of distinguishing between normal and malicious behaviors and their impact on energy efficiency. The experiment demonstrated that the detection system could identify the malicious nodes. Additionally, the effect of increasing the number of sensors or malicious nodes on the suggested detection algorithm’s energy usage is evaluated
Synthesis of nonlinear multilinked control systems of thermal power plants
Get PDFThe paper addresses the synthesis of nonlinear control laws for the technological parameters of drum boiler steam generators in thermal power plants, based on a synergetic control approach. The controlled system is considered to be multidimensional and highly interconnected. The inherent nonlinearity and interdependence of the technological parameters in thermal power plants necessitate the use of nonlinear control laws to achieve effective regulation. This approach enables the expansion of the range of permissible variations in regulator parameters, thereby ensuring the desired dynamic behavior of the controlled variables. An analytical method for synthesizing nonlinear vector control laws for steam generators is proposed. A methodology is developed for designing dynamic regulators capable of compensating for uncertain disturbances while accounting for control constraints. A Lyapunov function is constructed to describe the internal state dynamics of the control object. The proposed method for constructing the dynamic regulator ensures the asymptotic stability of the control system and stabilization of the controlled parameters over a wide range of load variations