Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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Development and Evaluation of a High-Performance Electrochemical Potentiostat-Based Desktop Application for Rapid SARS-CoV-2 Testing
Get PDFThe COVID-19 pandemic has necessitated the development of rapid and trustworthy diagnostic tools. Reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard for detecting SARS-CoV-2 but has cost and time constraints. The sensitivity, specificity, and low cost of electrochemical biosensors make them an attractive alternative for virus detection. This study aims to develop and evaluate a high-performance desktop application for an electrochemical potentiostat-based SARS-CoV-2 test device, with a user-friendly interface that automatically interprets results, to expedite the testing process and improve accessibility, particularly in resource-limited settings. The application was built with the Electron framework and the HTML, CSS, and JavaScript programming languages. Our findings indicate that the developed electrochemical potentiostat-based desktop application demonstrates high accuracy compared to commercial software, achieving rapid detection within 30 seconds. The graphical user interface was found to be straightforward and user-friendly, requiring minimal training for efficient system operation. Our electrochemical potentiostat-based desktop application represents a valuable tool for rapid SARS-CoV-2 testing, particularly in settings with limited resources. This research contributes to developing rapid and reliable diagnostic tools for SARS-CoV-2 and potentially other pandemic-causing viruses, addressing the pressing need for improved public health surveillance and response strategies
Optimal Control of Switched Capacitor Banks in Vietnam Distribution Network Using Integer Genetic Algorithm
Get PDFIn distribution network, power and energy losses can be reduced by using switched capacitor banks. The capacitor banks can be switched on or off based on voltage profile or power factor or using timers. Due to variation of load, it is necessary to control the capacitor banks switching in function of load curve. This paper presents the application of an integer genetic algorithm to determine the optimal number of banks corresponding with hourly load to minimize total active power losses of distribution feeders. The problem constraints include voltage profile and heat conditions which are taken into account to the objective function by a penalty function. In this application, the structure of chromosomes is a set of numbers of the capacitor banks hourly connected to the grid. The proposed formulation is validated by a feeder. The result shows that in some cases, the active power losses at maximum compensation are greater than the ones of optimal control compensation, and the voltage reaches a higher level than the maximum voltage limit. The optimal control of switched capacitor banks can reduce power and energy losses as well as ensure maximum voltage profile within the limit
An Enhanced Cluster-Based Routing Model for Energy-Efficient Wireless Sensor Networks
Get PDFEnergy efficiency is a crucial consideration in wireless sensor networks since the sensor nodes are resource-constrained, and this limited resource, if not optimally utilized, may disrupt the entire network's operations. The network must ensure that the limited energy resources are used as effectively as possible to allow for longer-term operation. The study designed and simulated an improved Genetic Algorithm-Based Energy-Efficient Routing (GABEER) algorithm to combat the issue of energy depletion in wireless sensor networks. The GABEER algorithm was designed using the Free Space Path Loss Model to determine each node's location in the sensor field according to its proximity to the base station (sink) and the First-Order Radio Energy Model to measure the energy depletion of each node to obtain the residual energy. The GABEER algorithm was coded in the C++ programming language, and the wireless sensor network was simulated using Network Simulator 3 (NS-3). The outcomes of the simulation revealed that the GABEER algorithm has the capability of increasing the performance of sensor network operations with respect to lifetime and stability period
VGG19+CNN: Deep Learning-Based Lung Cancer Classification with Meta-Heuristic Feature Selection Methodology
Get PDFLung illnesses are lung-affecting illnesses that harm the respiratory mechanism. Lung cancer is one of the major causes of death in humans internationally. Advance diagnosis could optimise survivability amongst humans. This remains feasible to systematise or reinforce the radiologist for cancer prognosis. PET and CT scanned images can be used for lung cancer detection. On the whole, the CT scan exhibits importance on the whole and functions as a comprehensive operation in former cancer prognosis. Thus, to subdue specific faults in choosing the feature and optimise classification, this study employs a new revolutionary algorithm called the Accelerated Wrapper-based Binary Artificial Bee Colony algorithm (AWBABCA) for effectual feature selection and VGG19+CNN for classifying cancer phases. The morphological features will be extracted out of the pre-processed image; next, the feature or nodule related to the lung that possesses a significant impact on incurring cancer will be chosen, and for this intention, herein AWBABCA has been employed. The chosen features will be utilised for cancer classification, facilitating a great level of strength and precision. Using the lung dataset to do an experimental evaluation shows that the proposed classifier got the best accuracy, precision, recall, and f1-score
The Impact of Semantic Web and Ontology to Improve E-government Services: A Systematic Review
Get PDFThe semantic web and ontology are extensions of the World Wide Web that aim to make data more interconnected and machine-readable. This systematic review examined researched publications published between 2018 and 2023 and indexed in Google Scholar and Science Direct. 23 records were chosen and classified into six groups: information and retrieval, knowledge archiving, interoperability, enhancing public and employee services, barriers to ontology application, and use of ontologies to apply the regulations. This study explored the semantic web and ontology used to enhance e-government services. It found that improving ontology for searched and information retrieval processes could help computers retrieve accurate results, improve interoperability and integration, and provide a knowledge base for terms used in building databases and applications. However, more research is needed to effectively integrate ontology into existing systems and extend these approaches to real-world contexts. Future research should focus on presenting an applied model of e-government technology and conducting continuous evaluations
Optimal operation mode of wind turbines in distribution grid to minimize energy loss considering power generation probability
Get PDFThis research considers a distribution grid where wind turbines were connected. The aim of this reasearch is to determine the optimal operation mode and setting parameter of wind turbines to minimize the energy loss of this distribution grid. To obtain the above purpose, we proposed an algorithm based on PSO algorithm. The suggested algorithm was coded in MATLAB and it was verified via a sample distribution grid. Results indicated the optimal operation mode and setting data in which the energy loss in the tested grid is minimal. Moreover, this research also compared the testing results with three cases of the generation at wind turbine node including average power, rated power and power probability distribution. Comparing results indicated that we cannot use average power generation or rated power of wind turbine to determine the optimal operation mode and setting data of wind turbines instead of we use the power distribution probabilit
Machine Learning Centered Energy Optimization In Cloud Computing: A Review
Get PDFThe rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing
Automatic Caption Generation for Aerial Images: A Survey
Get PDFAerial images have attracted attention from researcher community since long time. Generating a caption for an aerial image describing its content in comprehensive way is less studied but important task as it has applications in agriculture, defence, disaster management and many more areas. Though different approaches were followed for natural image caption generation, generating a caption for aerial image remains a challenging task due to its special nature. Use of emerging techniques from Artificial Intelligence (AI) and Natural Language Processing (NLP) domains have resulted in generation of accepted quality captions for aerial images. However lot needs to be done to fully utilize potential of aerial image caption generation task. This paper presents detail survey of the various approaches followed by researchers for aerial image caption generation task. The datasets available for experimentation, criteria used for performance evaluation and future directions are also discussed
Characterization of Short-Term Wind Power Variations and Estimation of Reserve Requirements for High Wind Generation Shares
Get PDFThe need to deal with variability in wind power output is one of the greatest challenges connected with adopting a considerable amount of wind power into power grids. Power system operators need to acquire more information on this variability, which can be utilized in the mitigation of high ramping events, especially when these events synchronize with a large error in the prediction, ensuring flexibility and reliability in the power system besides the economic considerations. The paper analyses short-term variability in output power using actual data obtained from aggregated wind farms from 2015 to 2020, where power ramping characteristics are described using a variety of measurements. The use of the standard deviation of short-term wind power variation as a reserve measure will be investigated in detail since there is no consensus about the ideal confidence level value as a multiplier of σ, which ranges from 3 to 6 times σ. The paper addresses how large this confidence level should be, as well as developing a data-driven approach for estimating this reserve with increasing wind shares and evaluating the proper distribution of short-term wind variation. The results illustrate that the stochastic variations in wind power can retain many of their characteristics from year to year, even when the share of wind capacity is raised.
A Review of Energy Management of Renewable Multisources in Industrial Microgrids
Get PDFThis review aims to consolidate recent advancements in power control within microgrids and multi-microgrids. It specifically focuses on analyzing the comparative benefits of various architectures concerning energy sharing and demand cost management. The paper provides a comprehensive technical analysis of different architectures found in existing literature, which are designed for energy management and demand cost optimization. In summary, this review paper provides a thorough examination of power control in microgrids and multi-microgrids and compares different architectural approaches for energy management and demand cost optimization