Indonesian Journal of Electrical Engineering and Computer Science
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Load frequency control for multi-area power system with two-source using sliding mode control
Get PDFA consistent electrical supply relies on the stability of power systems. In changing load conditions, control methods like load frequency control (LFC) are essential for safeguarding its stability. Conventional methods of LFC frequently encounter uncertainties in the system, external disruptions, and nonlinearities. This article introduces a more sophisticated method for managing load frequency and improving LFC in power systems through the utilization of sliding mode control (SMC). SMC provides strong stability and resilience against nonlinearities and disturbances, making it a promising method to overcome the drawbacks of traditional control techniques. We offer an in-depth examination of the second-order-integral SMC (SOISMC) method specifically designed for LFC, covering the creation and execution of the control algorithm. The method being suggested utilizes a sliding/gliding surface to maintain the system trajectories as continuous on the surface even with changes in parameters and external disturbances. Simulation results show big enhancements in frequency stability and system performance when compared to conventional proportional-integral-derivative (PID) controllers. The article also features a comparison between SOISMC and other contemporary control methods, emphasizing its strength in terms of resilience and flexibility
Design of face recognition based effective automated smart attendance system
Get PDFThe issue of automatic attendance marking has been successfully resolved in recent years through the utilization of standard biometric approaches. Although this strategy is automated and forward-thinking, its use is hindered by time constraints. Acquiring a thumb impression requires the individual to form a line, which might lead to inconvenience. The innovative visual system utilizes a computer and camera to detect and record students’ attendance based on their facial features. This article presents a face recognition based automatic attendance system. This system includes- image acquisition, image enhancement using histogram equalization, image segmentation by fuzzy C means clustering technique, building classification model using K-nearest neighbour (KNN), support vector machine (SVM) and AdaBoost technique. For experimental work, 500 images of students of a class are selected at random. Accuracy of KNN algorithm in proposed framework is 98.75%. It is higher than the accuracy of SVM (96.25%) and AdaBoost (86.50%). KNN is also performing better on parameters likesensitivity, specificity, precision and F_measure
BFT water color classification in tilapia aquaculture using computer vision
Get PDFBiofloc technology (BFT) is one of the most promising aquaculture cultivation methods in the modern aquaculture era because of its high efficiency level, especially in water and fodder use. Usually, the general condition of the biofloc can be known from the color of the water. By utilizing the vision sensor, BFT color identification can be done automatically, which helps cultivators find out their BFT system’s condition. In this research, a classification was made for the watercolor of the BFT Tilapia system based on the microbial community color index (MCCI) value and the initial cultivation conditions where algae and nitrifying bacteria had not developed significantly. The color classifications of the bioflocs are clear, green, browngreen, green-brown, and deep-brown. Clear color is the new classification to indicate BFT water conditions in the initial cultivation phase. Further, two computer vision algorithm methods are introduced to classify the color of BFT system water. The first method combines the B/W algorithm and MCCI calculations, while the second algorithm uses the Manhattan distance algorithm approach. From the experiments that have been carried out, both computer vision algorithms methods for classifying biofloc colors have shown promising results
Development of character extraction techniques to detect chicken gender based on egg shape
Get PDFThis research investigates the differentiation of chicken sex based on egg shape images by developing an innovative eccentricity shape feature extraction method. The goal is to determine the sex of chickens before hatching, by identifying the sex of the egg prior to incubation. Images of eggs are captured using a smartphone camera, creating a dataset of 150 images each of male and female eggs, with expert assistance. The research aims to accurately identify male and female eggs, aiding breeders in sorting them. The research introduces a unique method to expand the eccentricity value range, enhancing the precision of egg shape analysis. Characteristic extraction results include: area = 1290194, eccentricity = 6.56, contrast = 0.03, correlation = 0.99, energy = 0.44, and homogeneity = 0.98, with a previous value of 0.72. For Feature Selection, the values obtained are: eccentricity = 0.901188049, Area = 0.73, Energy = 0.03, Contrast = 0.01, Homogeneity = 0.01, and Correlation = 0.01. These findings demonstrate significant improvements in differentiating chicken sex from egg images, showcasing the effectiveness of the newly developed eccentricity shape feature extraction method
An approach for loss minimization and capacity savings in residential microgrid networks in Oman
Get PDFIn this paper, an approach for end-user-based energy saving and loss reduction technique in residential networks has been proposed. The proposed approach is applied to an Oman case study of community microgrid networks by connecting automatically switched capacitors to improve power factor and analyzed for capacity saving and loss minimization. The proposed approach can reduce the cost of electrical bills in the total community microgrid by minimizing losses and the capacity investment cost saving of all equipments in the transmission and distribution line from the generation to the end user. In addition, this study focuses on the healthy conclusion that average kVA capacity could be saved to an extent of 12.22% and in economic terms, approximately USD 3.68 per hour in the microgrid. This proposed technique can be implemented as a model community project for other similar residential community systems
Microservices caching for container-based IoT system in the edge and cloud
Get PDFMicroservices enable agile development by dividing internet of things (IoT) programs into autonomous components, ensuring fault tolerance and parallel operation for enhanced productivity. Their adaptability across diverse service types and applications improves IoT system performance. On the other hand, the container is the preferred solution for microservices-based enterprises. To improve the effectiveness of the deployment system presented in our paper 1, we developed a new caching technique to significantly optimize the performance of the deployment system and automate the sharing and re-using of ready-to-run microservices that have been packaged as Docker images. The new caching techniques are seamlessly integrated with our deployment system to optimize the microservices caching of the IoT application by utilizing Docker-based container virtualization and Redis for consistent data sharing. In addition, DevOps and versioning tools such as GOCD and GitHub are integrated into our system to enhance the automatic deployment of the microservices resulting in self-contained, portable, and repeatable IoT microservices. The effectiveness of the proposed techniques is evaluated via various experiments implemented in various working environments where the results show reduced deployment time and the effort required to re-execute the microservices, in addition to the reduction of burden and error that occur when adopting a manual deployment
Design of a segmented current steering digital to analog converter using PMOS cascode current source in UMC 65 nm technology
Get PDFDigital to analog converters (DAC) are the fundamental data converters used in the digital data transmission. In this paper 8-bit DAC is proposed using current cells with LSB current of 2 µA and full-scale voltage of 420 mV. Current cells mean the current sources designed using the MOSFETs. When it comes to mixed-signal and analog integrated circuits, current cells are the fundamental building blocks that are available. The optimized performance of current source is obtained with the proper biasing circuit. The performance of these current mirrors is evaluated in terms of key parameters such as output impedance, transconductance and linearity. The simulations for testing these parameters are performed using Virtuoso Cadence tool in umc 65 nm technology. After transistor characterization, various types of current sources are designed, and for each current mirror, PVT analysis is carried out for comparison and Monte-Carlo analysis is carried out to find the mismatch in current mirrors. Then different digital blocks are designed, that are D-Latch, Binary-thermo decoder and Row-column decoder which are required for designing of current steering DAC. Creating an 8-bit segmented current steering DAC by combining the ideas of 4-bit unary weighted and 4-bit binary-weighted DACs is the aim of this research. Performance measurements such as signal-to-noise ratio (SNR), effective number of bits (ENOB), spurious-free dynamic range (SFDR), differential non-linearity (DNL), and integral non-linearity (INL) are calculated to assess the proposed 8-bit segmented DAC. The analysis and designing of current mirrors in advanced CMOS technologies are critical for the development of high performance integrated circuits. An 8-bit DAC implemented using ILSB current of 2 µA with an accuracy of ±2%
Weierstrass scale space representation and composite dilated U-net based convolution for early glaucoma diagnosis
Get PDFGlaucoma is one of the common causes of blindness in the current world. Glaucoma is a blinding optic neuropathy characterized by the degeneration of retinal ganglion cells (RGCs). Accurate diagnosis and monitoring of glaucoma are challenging task through eye examinations and additional tests. To achieve accurate diagnosis of glaucoma with higher sensitivity and specificity, novel method called Weierstrass scale space representation and composite dilated U-net based convolution (WSSR-CDC) is introduced. At first, the Weierstrass transform scale space representation is employed to enhance image structures at various scales with higher accuracy of region of interest (ROI) detection using Euler’s identity. Next, CDC model is utilized with several layers. In input layer, preprocessed input images are taken as input. Fragment derivative are formulated for every preprocessed input. Log cosh dice loss function and optic cup to disc ratio are computed for segmented glaucoma detected results. With this, the accurate diagnosis of glaucoma is made with minimal error. The WSSR-CDC method was evaluated using the glaucoma fundus imaging dataset with several factors. The results show that the WSSR-CDC method outperforms conventional techniques, improving accuracy by 24% and sensitivity by 18%. It demonstrates promising results in fast, accurate, diagnosis of glaucoma
Modelling and simulation of maximum power point tracking on partial shaded PV based-on a physical phenomenon-inspired metaheuristic algorithm
Get PDFMaximum power point tracking (MPPT) is a technique to optimize the photovoltaic (PV) current generation, so it can improve the efficiency of solar energy harvesting. MPPT works by searching the voltage which generates the maximum power, called the maximum power point (MPP). MPP value changes by the fluctuance of ambient temperature and solar insolation level depicted by the I-V curve. Searching the MPP will be more complex if the partial shading is happened. The effect of partial shading will rise to more than one local MPPs. In this research, an optimization algorithm is modeled and simulated the MPPT technique in partial shading. The optimization uses the new metaheuristic algorithm which inspired from a physical phenomenon, called Archimedes optimization algorithm (AOA). The AOA uses mathematical modeling which has convergence capabilities, balanced exploration, and exploitation and is suitable for solving complex optimization technique, like MPPT. The research used varies partial insolation percentage. The implementation of MPPT-AOA compared to other metaheuristic algorithms to analysis its performance in the aspect of PV system parameters and tracking process parameters. The simulation result shows that the AOA can enrich the MPPT technique and improve the solar energy harvesting which is superior to other algorithms
A hybrid intelligent model for prediction of coronary artery diseases using TabNet and multiclass SVM
Get PDFCardiovascular disease is one of the significant fatality-causing diseases in this era by affecting the heart and blood vessels. Cardio diseases are classified into coronary heart disease (CHD), heart failure, valve disease, and arrhythmias. Medical diagnosis of heart disease and treating the patient is a challenging process, where early detection can lead to decreased fatality. In this research, hybrid model-based prediction of CHD detection is developed by TabNet and multiclass support vector machine (SVM). We created our datasets for experimentation by visiting the hospitals in the Mysore and Mandya regions of Karnataka, India. Datasets consist of 16 features; the features are pre-processed to normalize, encode, and handle missing values to extract the aggregate features using TabNet, and the multiclass SVM model is trained to classify the disease based on the classes. The proposed hybrid model prediction performance was evaluated using various metrics such as accuracy, recall, precision, and F1-score