Engineering Science Letter
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Comparative Analysis of Arduino-microcontroller based on PWM and MPPT Solar Charge Controller for Stand-alone Solar PV System in Charging Stations of Electric Vehicles
Full text linkThis study especially focuses on Arduino-microcontroller based pulse width modulation (PWM) and maximum power point tracking (MPPT) solar charge controller design using buck topology for stand-alone solar photovoltaic (PV) application where the PV output voltage is larger than the load/battery voltage. It consists of the design calculation for solar charge controller, design simulation techniques, construction of the hardware circuit design and modified perturb and observe (P&O) MPPT algorithm by utilizing the variable duty cycle step size. In the PWM solar charge controller design approaches, the fixed duty cycle, D=89% is specified and in the MPPT approaches, the variable duty cycle step size is applied. The design approaches with simulation techniques are also performed by professional circuit IDE (Proteus). After completing the design approaches with simulation techniques, the construction of hardware circuit for both PWM and MPPT is also conducted in the research laboratory of Yangon Technological University (YTU). In this study, the real-time measurement (RTM) technique for practical measurements and their results are offered and compared with the results on simulation techniques. The practical operation, monitoring and recording of data are also executed by using Arduino microcontroller (ATmega328P) with Arduino Data Logger Shield. The comparison results on RTM practical approaches for both PWM and MPPT solar charge controllers are given and their performance specifications are also evaluated. Both of two approaches can safely be used to charge the 12V rechargeable battery in reality. However, RTM practical results confirm that the designed MPPT controller could be improved 9% efficiency than PWM technique. Therefore, the designed MPPT solar charge controller is more efficient than PWM technique and suitable for real world applications in 12V, 100Wp stand-alone PV system
RMSProp Optimizer and KAN Method-Based CNN on Rupiah Banknote Classification for Visually Impaired
Full text linkThe visually impaired refers to individuals who experience a loss of visual function. Approximately 4 million people, or about 1.5% of Indonesia's total population, are visually impaired. They rely on their sense of touch to recognize banknote denominations in financial transactions. However, damaged banknotes often hinder identification and increase the risk of fraud. Therefore, this study aims to develop a rupiah banknote denomination classification model to assist them in conducting independent transactions. The researchers developed a CNN-KAN model with the RMSProp optimizer using a private dataset comprising 800 images of Rupiah banknotes with denominations of IDR 1,000, IDR 2,000, IDR 5,000, IDR 10,000, IDR 20,000, IDR 50,000, IDR 75,000, and IDR 100,000 from the 2016, 2020, and 2022 emission years. The dataset encompasses variations in image perspectives, lighting conditions, and the physical state of banknotes, including both intact and damaged ones, with up to 30% of the samples comprising damaged banknotes. Data augmentation techniques were implemented to improve data diversity. The dataset was then utilized for training and testing with different split ratios: 50:50, 60:40, 70:30, 80:20, and 90:10. Performance evaluation was conducted using loss, accuracy, precision, recall, and AUC-ROC metrics. Experimental results indicate that the CNN-KAN model with the RMSProp optimizer achieved optimal performance. In the 90:10 data split scenario, the model achieved 100% accuracy, precision, and recall, with an AUC-ROC of 1 and a loss of 0.008. Therefore, the CNN-KAN model with the RMSProp optimizer has been proven effective for implementing Rupiah banknote denomination detection for the visually impaired in an automated system
Analysis of Driving and Inhibiting Factors in Used Cooking Oil Management Based on Social and Environmental Responsibility in Catering Businesses
Full text linkThe improper disposal of used cooking oil (UCO) poses significant environmental and health risks, particularly when not managed responsibly by food-related businesses. This study aims to identify and analyze the most influential driving and inhibiting factors affecting UCO management in catering businesses, with a particular focus on social and environmental responsibility. The research adopts the Analytic Hierarchy Process (AHP) to systematically assess the priority levels of multiple criteria across 15 catering companies. AHP was chosen due to its strength in structuring complex decision-making problems through pairwise comparisons and eigenvalue analysis. Data were collected using both quantitative questionnaires and qualitative open-ended responses, allowing for a comprehensive understanding of stakeholder perspectives. The results show that among the driving factors, the most dominant is internal policy or management commitment, followed by factors such as enhancing company reputation and increasing profit. Conversely, the top inhibiting factor is the lack of knowledge related to proper oil waste handling, with other significant barriers including high processing costs and insufficient infrastructure. These findings emphasize that managerial awareness, education, and access to resources are crucial for advancing sustainable practices. The study suggests that enhancing knowledge and reinforcing managerial commitment are key strategies to improve oil waste management practices in the catering industry
The Effect of Dividend Policy and Capital Structure on Firm Value: A Case Study in Vietnam
Full text linkDividend policy represents the portion of corporate profit that companies pay to investors. Capital structure is a crucial corporate policy that indicates the ratio of debt to total assets. This paper aims to investigate the effect of dividend payout and capital structure on firm value in Vietnam. The study utilizes data from publicly traded companies on the Vietnamese Stock Exchanges. The analysis methods used include pooled OLS, and fixed and random effects models. The results reveal that dividend payout policy has a positive effect on the corporate value of Vietnamese publicly traded companies. The proportion of debt usage also exerts a positive effect on firm value. The findings show that corporate size and firm profitability have a significant and positive relationship with firm value. The study's results are useful for planning corporate dividend and capital structure policies
Hysteresis Dynamic Behavior of Pneumatic Artificial Muscle
Full text linkThe purpose of this work paper is to analyze the effects of friction on the hysteresis dynamic behavior of pneumatic artificial muscle (PAM). Due to the structure of the PAM, the dynamic response is not only affected by the compressed air but also the friction phenomenon. Indeed, the pipe of the PAM called the bellow is made from rubber simultaneously reinforced by metal fibers that are wrapped around the bellow. Hence, the effect of the friction between fibers, between fiber and rubber will be considered in the nonlinear dynamic of the PAM. From dynamic analysis, the complex stiffness model will be attained and analyzed. Comparison between the analysis model and experimental results is realized subjected to harmonic displacement excitation. The results proved the effectiveness of the analysis model. The studied model is a suitable tool in the field of systems vibration analysis using PAM as an elastic element, actuator
A Variable Step Size of the LMS Algorithm for System Identification
Full text linkThe Least Mean Square (LMS) algorithm and adaptive filter techniques have been used in system identification.ย However, their effectiveness may degrade due to the learning gain in the LMS algorithm is a constant. In this paper, a variable step-size learning parameter (VSSP) is proposed to improve the performance of the unknown system identification. The proposed algorithm is implemented without any offline learning phase, while faster convergence can be achieved. Moreover, the computational complexities of different methods are compared. Comparative simulation results demonstrate the validity of the proposed method
A Systematic Review on Industrial 4.0 Readiness for Environmental Sustainability Considering Cultural Influences
Full text linkย This systematic literature review (SLR) investigates the development of sustainability guidelines tailored for Industry 4.0, emphasizing the influence of cultural factors and the multidimensionality of existing sustainability models. Industry 4.0 represents a pivotal shift in industrial operations, integrating advanced technological innovation with a human-centric focus to achieve sustainability goals. Cultural factors, however, play a crucial role in shaping the success of such initiatives. This research examines how the various dimensions of sustainability models are influenced by cultural considerations and provides a comprehensive analysis of their impact on Industry 4.0 readiness. The findings of the SLR underscore that aligning technology with local cultural values, engaging stakeholders across diverse societal levels, and fostering capacity-building through culturally relevant training and education are vital to enhancing the effectiveness of sustainability practices. The proposed guidelines advocate for incorporating cultural analysis during the early planning phases, developing culturally sensitive communication strategies, formulating adaptable and flexible policies, and promoting international collaboration that respects cultural diversity. This study contributes valuable insights into the dynamic interplay between cultural influences and sustainability within the Industry 4.0 framework, emphasizing the importance of culturally responsive strategies for achieving enduring sustainable outcomes
Business Intelligence Solutions for Enhanced Accounting Decision-Making in Digital Transformation
Full text linkIn the era of fast-growing digital transformation, integrating business intelligence (BI) solutions in accounting decision-making has become essential for companies. This research reviews the critical role of BI solutions in optimizing the accounting decision-making process amid digital transformation. The method used in this research is a descriptive method with a qualitative approach, while the data used is secondary data obtained from previous studies. The data was collected through literature study techniques and analyzed qualitatively. The results of this study show that Business Intelligence (BI) plays a vital role in optimizing financial data presentation, adaptability to business changes, and providing competitive advantage through informed decision-making, especially in today's digital era. Implementing BI in an economic environment can improve efficiency, provide deep insights, and strengthen a company's competitiveness. BI is a technology tool and a strategic foundation supporting a company's success in the ever-changing digital era through its adaptability and ability to process data for decision-making
Responsive Skin Application Techniques in Energy-Efficient Buildings
Full text linkEnergy efficient buildings are those that consume less energy with the measures taken during the design process, meet the energy they need from renewable sources, and make a minimum environmental impact by using the energy obtained most effectively. Within the scope of this study, energy-efficient buildings were selected because they emphasize environmental sensitivity in the design, construction, and use phases. The study aims to analyze the sensitive volumes through algorithmic graphic programs and categorize them according to the application technique. The study is based on written sources, internet databases, and photographs. Visuals and examples are used to give details. In energy-efficient building design, responsive skin algorithmic application techniques provide optimum energy in a short time and make a great contribution to the national economy
Applying Robust Fuzzy-stochastic Programming Model in Multi-objective Optimization for Flow-shop Scheduling with Parallel Machines: a Real-life Case Study in an Engine Component Industrial
Full text linkResources scheduling, jobs sequencing, and resources assignment are considered the brain activities that play an extremely important role to the business, such as flow-shop scheduling. Additionally, the internal manufacturing environment and the current market are drastically changing, in which attracted significant attention in scheduling under uncertainties. This research aims to investigate the multi-objective flow-shop scheduling with parallel machines under machine breakdown and operational uncertainty. A novel robust fuzzy stochastic programming (RFSP) model is presented to address the unexpected operational disruptions and the inherent uncertainty of processing time. The objective is to optimize the cost of late delivery and the efficiency of manufacturing evaluated by overall equipment effectiveness (OEE) whilst considering the CO2 emissions produced. Eventually, a proposed algorithm is applied with a real-life scheduling data from a German factory that faces a practical situations in machine breakdowns and operational risks with unrelated parallel machines. Thus, this study intends to provide the computational results and useful information for multiple decision processes