Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Revitalizing cultural heritage: Strategies for teaching Indonesian traditional music in elementary schools
Get PDFThis study focuses on the integration of Indonesian traditional music education at the elementary school level as a strategy for promoting culture in Indonesia. It recognizes the significance of social capital, cultural capital, and knowledge capital as the foundation for fostering a comprehensive and systematic approach to cultural development. The research employs interview, observation, and literature review methods to gather data, guided by three key questions: (1) the current state of traditional music teaching in Indonesian elementary schools, (2) the strategies employed in integrating traditional music education, and (3) the outcomes of the Archipelago Traditional Music pre-congress in 2021 within the paradigm of elementary school music education. The analysis is primarily based on the implementation of previous policies and programs, particularly those managed by the Ministry of Education and Culture. The study's findings reveal several important aspects, including: (1) the need for statutory regulations supporting the conservation of traditional music programs, (2) fostering active collaboration between teachers and traditional artists or musicians through open participation, (3) providing necessary facilities and infrastructure to create an ecosystem conducive to traditional music education at the elementary school level, (4) implementing training programs to enhance the traditional music competence of elementary school teachers, and (5) involving the traditional music community in the design of the curriculum for traditional music education in elementary schools. These results underscore the importance of integrating traditional music education at the elementary school level to promote Indonesian culture. By implementing the suggested strategies, policymakers and educators can contribute to the preservation and development of traditional music, creating a stronger cultural foundation within Indonesian society
Stability Analysis of Cyber-physical System Under Transmission Delay
Get PDFWith the intimate integration of power grids and cyber networks, limited bandwidth and packet delay have a rapidly expanding negative impact on power system performance. The presented multi-area interconnected power system consists of four areas, each including thermal and hydro-generation plants. This paper investigates the stability analysis problem for cyber-physical systems with a round-robin communication protocol under mixed cyberattacks and load changes. The objective is to stabilize a multi-area interconnected power system (MAIPS) using a static feedback controller while minimizing the defined performance function. Then, the stability of the MAIPS is characterized when the system is subjected to a transmission delay while considering predetermined limits for the duration and the frequency of the delay. Our findings indicate that time delays can influence system stability and that choosing an appropriate sampling interval is necessary to ensure the stability of the system. Finally, an illustrative example of three areas of interconnected power systems with several scenarios is presented to verify the effectiveness of the proposed method
Optimal Pneumatic Actuator Positioning and Dynamic Stability using Prescribed Performance Control with Particle Swarm Optimization: A Simulation Study
Get PDFThis paper introduces an optimal control strategy for pneumatic servo systems (PSS) positioning using Finite-time Prescribed Performance Control (FT-PPC) with Particle Swarm Optimization (PSO). Pneumatic servo systems are widely used in industrial automation, as well as medical and cybernetics systems that involve robotics applications. Precision in pneumatic control is crucial not only for the sake of efficiency but also safety. The primary goal of the proposed control strategy is to optimize the convergence rate and finite time of the prescribed performance function in error transformation of the FT-PPC, as well as the Proportional, Integral and Derivative (PID) controller as the inner-loop controller for this system. The study utilizes a dynamic model of a pneumatic proportional valve with a double-acting cylinder (PPVDC) as the targeted plant and performs simulations with a multi-step input trajectory. This offline tuning method is essential for such nonlinear systems to be safely optimized, avoiding major damage to the real-time fine-tuned works on the controller. The results demonstrate that the proposed control strategy surpasses the performance of FT-PPC with a PID controller alone, significantly improving the system's performance, including suppressing overshoot and oscillation in the responses. Further validation through the actual system of PPVDC using the fine-tuned values of FT-PPC and PID with PSO is a future task and more challenging to come, as hardware constraints may vary with different environments such as temperatures
Optimization model for determining global solar radiation in the northeastern states of Nigeria using both meteorological and satellite imagery data
Get PDFThis study presents an optimization model for determining global solar radiation in the northeastern region of Nigeria using a combination of meteorological data and satellite imagery. Ten recent models were chosen from the literature review and optimized to select the one that best fits the study region. Two models were developed to provide accurate solar radiation predictions, which can be used to improve the planning and implementation of a solar energy project in the region. The model integrates the Angstrom-Prescott model with various climate parameters such as Temperature (∆T), relative humidity (RH), location latitude (Φ), solar declination angle (δ), and the number of days in a year (n) with satellite image data to determine the global solar radiation. The finding of optimization models shows that the model10 performed very well with minimum error as Mean Base Error (0.028), Mean Percentage Error (- 0.001), Root Mean Square Error (0.098), and coefficient of determination R2 (0.994), which suggested as the optimized model for determining of global solar radiation in northeastern Nigeria. The two models were developed, that is, proposed Model1 and proposed Model2. Proposed Model1 slightly overestimated the global solar radiation with Mean Base Error (-0.863), Mean Percentage Error (-0.039), Root Mean Square Error (2.990), and coefficient of determination R2 (0.745), while proposed Model2 performed better with Mean Base Error (-0.005), Mean Percentage Error (0.0003), Root Mean Square Error (0.02) with the coefficient of determination R2 (0.985). The proposed models were validated using the suggested optimized model10 and satellite data model, which show that the proposed model can accurately determine global solar radiation in the northeastern region of Nigeria. This study's findings will benefit the region's solar energy project developers, researchers, and policymaker
Modified Fractional Order PID Controller for Load Frequency Control of Four Area Thermal Power System
Get PDFThis paper presents the development of a modified Fractional Order Proportional Integral Derivative (FOPID) controller to mitigate frequency deviation in a four-area thermal power system. Change in load demand and noisy power system environment can cause frequency deviation. Reducing high-frequency deviation is very paramount in load frequency control. This is because large frequency deviation can cause the transmission line to be overloaded, which may damage transformers at the transmission level, damage mechanical devices at the generating stations and also damage consumer devices at the distribution level. The conventional PID has been widely used for this problem. However, the parameter values of the various generating units of the power system like generators, turbines and governors keep changing due to numerous on/off witching in the load side. As such, it is essential that the control strategy applied should have a good capability of handling uncertainties in the system parameters and good disturbance rejection. Fractional order PID controller is known to give a higher phase margin resulting in very good disturbance rejection, robustness to high-frequency noise and elimination of steady-state error. A four-area power system was designed, and FOPID was used as the supplementary controller to mitigate frequency deviation. Ant Lion Optimizer (ALO) algorithm was used to optimize the gains of the FOPID controller by minimizing Integral Square Error (ISE) as the objective function. Results obtained outperformed other designed methods available in the literature in terms of reducing frequency deviation, tie-line power deviation and area control error
Forging a learner-centric blended-learning framework via an adaptive content-based architecture
Get PDFThe covid-19 pandemic was reported with significant negative impact on global education with shocks that disrupted the learning processes via the closure of traditional classrooms/schools from 2020 to March 2022. These effects have continued to ripple across even with advances in media literacy. The Nigerian frontier has also witnessed a paradigm shift in the adoption/integration of the information and communication tech as tools for both digital revolution and advancement of alternative education delivery. Today’s education which aspires for growth and progressive development is assured of positive changes if priority for educational values and ICT is harnessed. Past educational theories seem not to cope with the ever-changing, information society. Nigeria must develop strategies to address education reforms with frameworks to bridge these gaps vid post covid-19 era. Our study implements a hybrid a(synchronous) learning framework for Nigerian Tertiary education. Result shows improved learner cognition, engaged qualitative learning, and a learning scenario that ensures a power shift in the educational structure that will further equip learners to become knowledge producer, help teachers to emancipate students academically, in a framework that measures quality of engaged student’s learnin
Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges
Get PDFThe main objective of this study is to demonstrate the integration of weather forecasts which can lead to a significant reduction in energy costs and carbon emissions while ensuring the reliability of the microgrid operation. By serving a small area or a particular building, the incorporation of weather forecasts can considerably increase the efficiency of microgrid energy management. The planning and operation of microgrids can be greatly improved by using weather predictions, which give useful information about upcoming weather conditions. By forecasting future energy demand and supply based on meteorological conditions, Microgrid Energy Management (MEM) is utilized to optimize the energy management decisions in microgrid systems. Making better choices regarding energy generation, storage, and consumption may be aided by the incorporation of weather forecasts, which can offer a more precise and trustworthy estimate of the energy demand and supply. This strategy can result in increased energy efficiency, decreased energy prices, and decreased carbon emissions, all of which are important goals for contemporary power systems. A promising approach for raising energy effectiveness and lowering greenhouse gas emissions in contemporary power networks is MEM. The incorporation of weather forecasts into MEM can improve decision-making regarding energy management by giving a better insight of future energy demand and supply. This essay examines the advantages and disadvantages of using weather forecasts in MEM through the presentation of a case example. By providing valuable information about future weather conditions, weather forecasts this review explain the Optimized Renewable Energy Integration, Improved Energy Storage Utilization, Load Shifting and Demand Response, Efficient Grid Management for reducing reliance on fossil fuels and lowering energy cost and carbon emissions. In order to address the issues related with MEM employing weather forecasts, this study offers potential fixes for increasing the accuracy of weather forecasts and emphasizes the necessity for more research in this area
Real-Time Obstacle Detection for Unmanned Surface Vehicle Maneuver
Get PDFThe rapid advancement and increasing demand for Unmanned Surface Vehicle (USV) technology have drawn considerable attention in various sectors, including commercial, research, and military, particularly in marine and shallow water applications. USVs have the potential to revolutionize monitoring systems in remote areas while reducing labor costs. One critical requirement for USVs is their ability to autonomously integrate Guidance, Navigation, and Control (GNC) technology, enabling self-reliant operation without constant human oversight. However, current study for USV shown the use of traditional method using color detection which is inadequate to detect object with unstable lighting condition. This study addresses the challenge of enabling Autonomous Surface Vehicles (ASVs) to operate with minimal human intervention by enhancing their object detection and classification capabilities. In dynamic environments, such as water surfaces, accurate and rapid object recognition is essential. To achieve this, we focus on the implementation of deep learning algorithms, including the YOLO algorithm, to empower USVs with informed navigation decision-making capabilities. Our research contributes to the field of robotics by designing an affordable USV prototype capable of independent operation characterized by precise object detection and classification. By bridging the gap between advanced visualization techniques and autonomous USV technology, we envision practical applications in remote monitoring and marine operations with object detection. This paper presents the initial phase of our research, emphasizing significance of deep learning algorithms for enhancing USV navigation and decision-making in dynamic environmental conditions, resulting in mAP of 99.51%, IoU of 87.80%, error value of the YOLOv4-tiny image processing algorithm is 0.1542
Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique
Get PDFDeveloping effective strategies to contain the spread of infectious diseases, particularly in the case of rapidly evolving outbreaks like COVID-19, remains a pressing challenge. The Susceptible-Infected-Recovery (SIR) model, a fundamental tool in epidemiology, offers insights into disease dynamics. The SIR system exhibits complex nonlinear relationships between the input variables (e.g., population, infection rate, recovery rate) and the output variables (e.g., the number of infected individuals over time). We employ Recurrent Neural Networks (RNNs) to model the SIR system due to their ability to capture sequential dependencies and handle time-series data effectively. RNNs, with their ability to model nonlinear functions, can capture these intricate relationships, enabling accurate predictions and understanding of the dynamics of the system. Additionally, we apply the Pontryagin Minimum Principle (PMP) based different control strategies to formulate an optimal control approach aimed at maximizing the recovery rate while minimizing the number of affected individuals and achieving a balance between minimizing costs and satisfying constraints. This can include optimizing vaccination strategies, quarantine measures, treatment allocation, and resource allocation. The findings of this research indicate that the proposed modeling and control approach shows potential for a comprehensive analysis of viral spread, providing valuable insights and strategies for disease management on a global level. By integrating epidemiological modeling with intelligent control techniques, we contribute to the ongoing efforts aimed at combating infectious diseases on a larger scale
Exploring diverse learning styles in elementary schools: A study on student preferences
Get PDFThis study aimed to describe the learning style preferences of elementary school students in 5 districts in Yogyakarta. The population is 4th-grade and 5th-grade elementary school students in 5 districts in Yogyakarta. The sample was 992 students. Data was collected with a survey by questionnaire of learning style preferences. The results showed that the majority of elementary students have visual-kinesthetic (25.06%), the majority of students in Sleman district have kinesthetic (33.33%), the majority of students in Bantul district have visual-kinesthetic (31.93%), the majority of students in Gunungkidul district have the kinesthetic (27.45%), and the majority of students in Kulonprogo district have the visual-kinesthetic (29.36%). The elementary school students in Yogyakarta Province have more than one preference, a combination of two learning styles: visual-kinesthetic and auditory-kinesthetic. It can be concluded that the majority of students in Yogyakarta tend towards two learning styles, i.e., visual and kinesthetic