Bulletin of Electrical Engineering and Informatics
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Chaotic grey wolf optimization based framework for efficient task scheduling in cloud fog computing
Full text linkTask scheduling is an essential component of any cloud computing architecture that seeks to cater to the requirements of its users in the most effective manner possible. It is essential in the process of assigning resources to new jobs while simultaneously optimising performance. Effective job scheduling is the only method by which it is possible to achieve the essential goals of any cloud computing architecture, including high performance, high profit, high utilisation, scalability, provision efficiency, and economy. This article gives a framework based on chaotic grey wolf optimization (CGWO) for efficiently scheduling tasks in cloud fog computing. Task scheduling is done with CGWO, ant colony optimization (ACO), and min-max algorithms. CloudSim is used to implement task scheduling algorithms. Makespan time required by CGWO algorithm for 500 tasks is 73.27 seconds. CGWO is taking minimum resources to accomplish the tasks in comparison to ACO and min-max methods. Response time of CGWO is also 3745.2 seconds. CGWO is performing better in terms of Makespan time, response time and resource utilization among the methods used in the experimental work
Modified multilevel inverter based an active power filter using fuzzy controller for power quality enhancement
Full text linkPower electronics-based nonlinear loads generate significant current harmonics, adversely affecting the efficiency and reliability of distribution networks. Active power filters (APFs), leveraging power electronics technology, provide an alternative to passive filters in mitigating harmonics. Multilevel inverter-based (MLI) APFs, particularly for high-power applications, offer numerous advantages but often suffer from increased component count and control complexity. In this article, a novel five-level MLI topology is proposed, featuring a reduced number of switches compared to the traditional cascaded H-bridge topology with eight switches. This research reduces system cost and simplifies controller design. To further enhance system performance, a fuzzy logic controller (FLC) is implemented for DC-link voltage control. Harmonics are identified using the instantaneous p-q theory, and switching signals are generated through multicarrier pulse width modulation (PWM) techniques. Study conducted in MATLAB for a single-phase balanced system demonstrate the effectiveness of the proposed topology. Results reveal a reduction in total harmonic distortion (THD) of the source current from 34.15% to 2.31%, meeting the IEEE-519 standard. The findings validate the proposed APF's capability to enhance power quality by mitigating harmonics. By integrating advanced MLI technology with artificial intelligence-based control, this work offers a cost-effective, efficient solution to improve the performance of polluted distribution networks
A novel switched-capacitor multilevel inverter for efficient voltage level generation
Full text linkThis paper presents a novel single direct current (DC) source with switched-capacitor multilevel inverter (MLI) architecture capable of achieving seven output voltage levels using only eight switches, one diode, and two capacitors. The proposed topology (P) is compared with recent MLI configurations to assess its efficiency and performance. MATLAB/Simulink tools are utilized for simulation studies, and experimental validation is conducted to corroborate the theoretical findings. The investigation explores the impact of modulation index and switching frequency variations on the P output characteristics. Results indicate that the proposed MLI topology offers significant advantages in terms of component count reduction and simplicity while maintaining competitive performance compared to state-of-the-art alternatives. Additionally, the study provides insights into the influence of modulation index and switching frequency changes on the output voltage waveform, highlighting the adaptability and robustness of the P under varying operating conditions. This research contributes to the advancement of MLI designs by offering a streamlined and efficient solution suitable for various power electronic applications, including renewable energy systems and motor drives, where minimizing component count and complexity are crucial design considerations
Animate prime movers: an un-exploited resource towards achieving United Nations SDG 7-future research requirements
Full text linkRenewable energy is a prominent concept that encompasses various forms, such as solar photovoltaics, wind power, and geothermal energy. Although less familiar, animate energy resources, which include human beings and animals, may also be seen as being explored. While animal-based renewable energy generation may appear novel, different research articles, patents, and a couple of commercially available products have been developed. For the specific case of dairy farms, harnessing this resource can be coupled with appropriate exercise regimens for cows, which may lead to clean energy, animal welfare, and even potential benefits for human health. These efforts align with the sustainable development goals (SDG) of the United Nations, specifically SDG 3 and SDG 7. However, ethical concerns regarding the use of animals for energy production as well as the potential and clean nature of this resource need to be thoroughly investigated before it can be exploited on a larger scale. This research paper aims to identify deficiencies in the current relevant body of knowledge and to present requirements for future research efforts that may help tap into this resource. By exploring the potential of animate energy resources, we may contribute towards sustainable energy production while promoting animal welfare and human health
Design of environmental detector system application aims to promote awareness of pollution on campus
Full text linkPoliteknik Negeri Medan (POLMED) was involved in the UI GreenMetric world rankings. The UI GreenMetric committee assessed green campus activities and environmental sustainability. The UI GreenMetric aims to raise awareness about sustainable campus greening, and social impacts of these endeavors. Based on the concept, an environmental detection system (EDS) was developed using internet of things (IoT) technology. The EDS can detect and monitor environmental parameters remotely such as carbon dioxide (CO2), noise levels, light intensity, air temperature, relative humidity, and dust particle density in real-time via the internet. Measurements of environmental parameters were conducted at one location in POLMED. The average CO2 level was 485 ppm. The average noise level was 53.40 dB. The average light intensity was 129 lux. The average air temperature was 26.60 °C. The average of relative humidity was 63.8% RH. The average of PM2.5 dust particle densities was 23 µg/m3. The average of PM10 dust particle densities was 29 µg/m3. Based on these results, the air quality has begun to be polluted because this value is already above the threshold clean quality air set by the Government of the Republic of Indonesia (310–330 ppm)
Combination of item response theory and k-means for adaptive assessment
Full text linkThis study focuses on developing an adaptive assessment system for basic programming courses using a combination of item response theory (IRT) and the K-mean. The main objective is to enhance the precision of assessments by adapting the difficulty of questions to students' cognitive levels while grouping them based on both cognitive and affective characteristics. The key contribution is the creation of a more personalized assessment framework, addressing the shortcomings of traditional assessments, which often fail to accommodate varying student abilities. Methodologically, the study employs IRT to dynamically assess students' abilities, and students are categorized into different groups based on their answer patterns using K-means. The research design involves a student motivation survey and a programming skills test. Data is collected through the Google Quiz platform and analyzed using R Studio Software to apply the algorithms. The results demonstrate that combining IRT and K-means successfully adjusts the difficulty of questions and more accurately clusters students, providing more relevant feedback. In conclusion, this method enhances adaptive assessments' effectiveness and fosters personalized learning experiences. The findings have implications for broader application in courses with diverse student competencies
A novel MPK optimization framework for financial data analysis incorporating complexity and uncertainty management
Full text linkIn a competitive environment, the ability to scale quickly and successfully is a critical need. This research proposes a new framework using multi-objective complexity prediction model (MPK) for financial data analysis, including complexity and uncertainty management. This model integrates input, uncertainty, and output optimization functions (OOFs) (input optimization function (IOF), uncertainty optimization function (UOF), and OOF) to predict complex output values under dynamic business conditions. Model evaluation is carried out using performance metrics, namely mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R² score. The evaluation results show that this model has an MSE value of 20.112, an RMSE of 2.267, and an MAE of 2.351, reflecting a low prediction error rate and high accuracy. In addition, the R² value of 0.884259 indicates that this model is able to explain around 88.4% of the variability in the output data, indicating its ability to capture complex data patterns. Thus, the proposed MPK model is effective in predicting output values in complex business scenarios and can be applied for strategic decision-making under conditions of uncertainty
Autoregressive integrated moving average-long short-term memory optimized hybrid model for cybercrime forecasting
Full text linkCybercrime represents a growing global threat with adverse impacts on citizen security, the digital economy, and quality of life. In this context, an optimized hybrid model was developed that combines autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) for the monthly forecast of cybercrime complaints, applying the cross industry standard process for data mining (CRISP-DM) methodology and applying Python based data science techniques. The model combines the capabilities of the ARIMA statistical approach to capture linear components with the power of LSTM neural networks to address nonlinear temporal relationships. The architecture was trained on a set of 60,378 official records of complaints registered by the National Police of Peru between 2018 and 2023, achieving a mean absolute percentage error (MAPE) of 10.73%, which represents a significant improvement over the singular ARIMA and LSTM predictive models. Compared to previous studies in crime, health, and agriculture, this approach showed a greater ability to generalize over complex time series. It is concluded that the application of the proposed model is a relevant contribution for the police and other security agencies to anticipate crime trends and design preventive and effective strategies to combating cybercrime
System dynamics modeling for strategic management of information technologies in universities
Full text linkThis study seeks to answer the question: how can system dynamics (SD) modeling contribute to the strategic management of information technology (IT) in universities? The objective of the research is to analyze the importance of incorporating IT into university strategic management through the application of SD methodology. To this end, a model was designed that integrates variables related to resource allocation, the quality of the educational process, and the interaction between institutional actors. The methodology made it possible to simulate technological implementation scenarios and examine their effects on operational efficiency and academic performance. The results show that the strategic integration of IT promotes better resource planning, optimizes the interaction between administrative and academic processes, and contributes to raising the quality of teaching. In conclusion, the proposed model demonstrates that SD is an effective tool for anticipating and understanding the internal dynamics of universities, facilitating more efficient strategic management in today's digital context
Parameter tuning of PIDG controller on maximum photovoltaic power point for battery charging system
Full text linkMaximum photovoltaic power point (MPVPP) based on DC-DC buck converter is supplied by photovoltaic module. A controller method is needed to control the signal that it drives the switching component of DC-DC buck converter. The previous researcher conducts proportional integral derivative (PID) controller applying the DC-DC buck converter, but only its parameters (proportional, KP, integral, KI, and derivative, KD) are studied. This paper presents MPVPP based on PID with gain (PIDG) controller on the DC-DC buck converter by tuning the parameters of KP. KI and KD and adding a gain, G connected to PIDG controller for charging 12 V, 7 Ah battery. The DC-DC buck converter is designed for the output voltage of 14.7 V and output power of 150 W and modelled using Simulink MATLAB. The simulation results show that the parameters of KP=0.0032, KI=1, and KD=4×10-7 are suitable to control the switching component. The gain, G gives significant effect on the settling time and the time to reach their steady state value of output voltage of 14.7 V. The battery SOC can increase 1.36% per second, if the initial SOC is 25%, thus it needs arround 55 seconds to reach the fully charging condition