International Journal of Electrical and Computer Engineering (IJECE)
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Efficiency enhancement of off-grid solar system
Get PDFThis paper presents the design and implementation of a sensor-enabled off-grid solar charge controller aimed at maximizing the utilization of renewable energy. The proposed system integrates solar and load power sensors to minimize solar energy wastage. A microcontroller is employed to efficiently monitor and regulate battery voltage, solar power generation, and load demand. This system is designed to optimize solar energy usage, reduce dependency on the electrical grid, and lower electricity bills. Additionally, a main supply controller board with a display is introduced, along with a smart scheduler for appliance management. Prior to deployment, total solar power wastage was recorded at 93.1 watts per day. After implementing the proposed solution, wastage was reduced to 13.1 watts per day—reflecting an 85.92% reduction. These results confirm the system’s effectiveness in reducing energy loss, increasing self-consumption, and promoting energy sustainability in off-grid environments. It is important to note that this value may vary based on factors such as temperature, cloud cover, fog, and irradiation levels
Optimal investment framework of static VAr compensators in distribution system based on life cycle cost
Get PDFThe distribution system planning and operating present significant challenges due to low voltage, high impedance, and large load density, which lead to substantial power losses and low voltage quality. To address this challenge, the paper proposes an optimal framework for the simultaneous determination of the placement and sizing of static VAr compensators (SVCs) in DSs. The proposed model is formulated as an optimization problem that minimizes the life cycle cost, while accounting for the varying lifespans and investment times of SVCs. The framework incorporates hourly load variation and employs full alternating current (AC) power flow analysis to improve the accuracy of results. Additionally, it considers the dependency of the reactive power injected by SVCs on the DSs and incorporates the discrete rated capacities of SVCs to ensure practical feasibility and enhance the accuracy of compensation power, effect of DSs. The proposed approach is validated using a modified 33-bus IEEE test system implemented in the general algebraic modeling system (GAMS). Numerical results from multiple case studies confirm the feasibility and high performance of the proposed model
FinFET technology: a comprehensive review on materials, structures, fabrication, and device performance
Get PDFAs semiconductor devices become smaller, FinFETs have replaced traditional planar MOSFETs. Planar devices face issues like weak electrostatic control and high leakage current at small sizes. FinFETs solve these problems with a three-dimensional structure and multigate design. This improves gate control and reduces short-channel effects. This paper explains FinFET design, materials, and fabrication methods. It highlights how fin geometry affects current flow and device performance. Gate-source voltage (VGS) and drain-source voltage (VDS) are important parameters. These control the device operation in the lin-ear, saturation, and pinch-off regions. Performance factors such as on/off current ratio (ION /IOFF), subthreshold swing (SS), and drain-induced barrier lowering (DIBL) show that FinFETs work well for low-power and high-speed uses. Achieving uniform doping below 5 nm remains difficult. Atomic layer deposition (ALD) helps improve doping control. In summary, FinFETs play a key role in modern semiconductor design by improving scalability and efficiency
Tiny machine learning with convolutional neural network for intelligent radiation monitoring in nuclear installation
Get PDFThis study focuses on developing an intelligent radiation monitoring system capable of operating on a low-power single-board computer (Raspberry Pi) for deployment in remote monitoring stations within nuclear facility environments. The proposed system utilizes a radionuclide identification method based on tiny machine learning (TinyML) with a convolutional neural network (CNN) architecture. The radionuclide dataset was acquired through measurements of standard radiation sources, with variations in distance, exposure time, and combinations of multiple sources-including Cs-137, Co-60, Cs-134, and Eu-152. The radiation intensity data from detector measurements were structured into a response matrix and subsequently converted into a grayscale image dataset for model training. Keras is used to design and train machine learning models, while Tensor Flow Lite is used to model size reduction. Experimental results demonstrate that the developed model achieves an accuracy of 99.338% for Keras model trained on computer and 84.568% after deployment on the Raspberry Pi. Furthermore, this study successfully designed and embedded the TinyML model into an environment radiation monitoring system at the PUSPIPTEK nuclear installation
Evaluating plant growth performance in a greenhouse hydroponic salad system using the internet of things
Get PDFHydroponic salad cultivation is becoming increasingly popular. However, a common challenge is the lack of time to maintain hydroponic vegetables due to other responsibilities. This study presents a hydroponic system based on the internet of things (IoT) technique, designed to save time by enabling remote control through a mobile application connected to a NodeMCU microcontroller. Various sensors are integrated with the NodeMCU for real-time monitoring and automation. The study also explores the use of RGB LEDs, which significantly accelerated plant growth and reduced cultivation time. A comparative experimental design was employed to evaluate the growth rate of green oak salad vegetables under two different greenhouse systems. The primary factor compared was the greenhouse system type, with plant growth rate as the outcome variable. Each treatment was replicated 10 times. F-tests were used to statistically determine significant differences in growth rates between the two systems across measured intervals. Results showed that the automated greenhouse system produced the highest leaf width and plant weight values. The use of RGB LEDs reduced the cultivation period from 45 days to 30 days, enabling more planting cycles and ultimately increasing overall yield
Enhanced embedded system for various synthetic electrocardiogram generation using McSharry’s dynamic equation
Get PDFn electrocardiogram (ECG) is a signal that describes the heart’s electrical activity. Signal processing techniques are necessary to extract meaningful information from ECG signals. Researchers often use large databases like the PhysioNet database to evaluate the performance of algorithms. However, these databases have limitations concerning the lack of temporal or morphological variations. This study addresses this limitation by introducing a synthetic ECG capable of producing both normal 12-lead ECG signals and abnormal ECG signals and implementing it into the microcontroller. The primary contribution involves developing a synthetic ECG model using McSharry's dynamic equation model and implementing it using Mikromedia 5 for STM32F4 Capacitive as a microcontroller. This model enables users to set the desired heart rate and accurately replicates ECG waveforms using parameters , , and , each determines the peak’s magnitude, the peak’s time duration, and the angular velocity of the trajectory. The synthetic ECG was evaluated qualitatively and quantitatively, demonstrating waveform similarity to the ECG signals. This study implies that the synthetic ECG model serves as a valuable tool for researchers and practitioners in electrocardiography. It enables the generation of normal and abnormal ECG signals, aiding in algorithm development and potentially enhancing the understanding and diagnosis of heart conditions
An efficient Radix-4 butterfly structure based on the complex binary number system and distributed arithmetic
Get PDFComplex number arithmetic is pivotal in various applications, requiring the selection of an efficient multiplier for high-performance computations. Fast Fourier transform (FFT)-based multipliers are widely employed for computing complex number products, but their reliance on using dedicated multipliers and treating the real and imaginary parts as two entities significantly add to the cost and complexity of the system. Distributed arithmetic (DA) is a technique that replaces complex multiplications with a bit-level shift and addition mechanism. The complex binary number system (CBNS) utilizes binary arithmetic, which treats the real and imaginary parts as a single entity, which can simplify complex number arithmetic and computations. This paper introduces an approach integrating the CBNS with DA in a Radix-4 decimation in time FFT 8-bit and 16-bit butterfly structure. The proposed design significantly reduces arithmetic computations and eliminates dedicated multipliers, demonstrating a reduction in power consumption, area size, and lookup tables, as well as increasing overall clock performance compared to the conventional FFT architecture on Artix-7, Kintex-7, and Virtex-7 field-programmable gate array chips
Hybrid deep learning for estimation of state-of-health in lithium-ion batteries
Get PDFLithium-ion (li-ion) batteries have a high energy density and a long cycle life. Lithium-ion batteries have a finite lifespan, and their energy storage capacity diminishes with use. In order to properly plan battery maintenance, the state of health (SoH) of lithium-ion batteries is crucial. This study aims to combine two deep learning techniques (hybrid deep learning), namely convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), for SoH estimation in li-ion batteries. This study contrasts hybrid deep learning methods to single deep learning models so that the most suitable model for accurately measuring the SoH in lithium-ion batteries can be determined. In comparison to other methodologies, CNN-BiLSTM yields the best results. The CNN-BiLSTM algorithm yields RMSE, mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) in the following order: 0.00916, 0.000084, 0.0048, and 0.00603. This indicates that CNN-BiLSTM, as a hybrid deep learning model, is able to calculate the approximate capacity of the lithium-ion battery more accurately than other methods
Energy analysis of active photovoltaic cooling system using water flow
Get PDFAn active water-cooling system is one of several technologies that has been proven to be able to reduce heat losses and increase electrical energy in photovoltaic (PV) module. This research discusses a comparative experimental study of three pump activation controls in cooling of PV module with the aim of evaluating specifically the PV output power, net energy gain, water flow rate, and module temperature reduction. The three pump activation controls being compared are continuously active during the test, active based on setpoint temperature, and active by controlling the pump voltage using pulse width modulation (PWM) control in adjusting water flow rate smoothly. The results show that controlling the pump voltage using PWM in the PV cooling process produces energy of 437.95 Wh, slightly lower than the others and the average module cooling temperature is 35.24 °C, higher of 1-3 °C than the others. Nevertheless, PWM control of cooling pump has resulted the percentage of net energy gain of 9.94%, greater than other controls, and with an average flow rate of 2.17 L/min, more efficient than the others. Thus, this control is quite effective as it can produce higher net PV energy yield and lower water consumption
Refining thyroid function evaluation: a comparative study of preprocessing methods in diffuse reflectance spectroscopy
Get PDFThyroid dysfunction, comprising conditions such as hyperthyroidism and hypothyroidism, represents a substantial global health challenge, necessitating timely and precise diagnosis for effective therapeutic intervention and patient welfare. Conventional diagnostic modalities often involve invasive procedures, that could cause discomfort and inconvenience for individuals. The non-invasive techniques like diffuse reflectance spectroscopy (DRS) can offer a promising alternative. This study underscores the critical role of preprocessing methods in enhancing the accuracy of thyroid hormone functionality through a non-invasive approach. In the proposed study the spectral data acquired from the DRS setup are subjected to different preprocessing techniques to improve the efficacy of the prediction model. Thirty individuals with thyroid dysfunction were included in the study, and preprocessing methods such as baseline correction, multiplicative scatter correction (MSC), and standard normal variate (SNV), were systematically evaluated. The study highlights that SNV preprocessing outperformed other methods with a root mean square error (RMSE) of 0.005 and an R² of 0.99. In contrast, MSC resulted in an RMSE of 0.87 and an R² of 0.86, while baseline correction showed a RMSE of 0.84 and an unusual R² of 1.09, indicating potential issues. SNV proved to be the most effective technique