International Journal of Advances in Applied Sciences
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Local area network architecture design at Nurul Jalal Islamic Boarding School, North Jakarta
Get PDFA computer network is a telecommunications network that allows computers to communicate with each other by exchanging data. At Nurul Jalal Islamic Boarding School, they have taken advantage of advances in computer network technology, but have not been fully connected properly. Therefore, in this research, a local area network (LAN) architecture that is connected to speedy internet will be built and developed. The design of this network architecture includes designing a connection to the internet speedy network and designing a computer lab architecture that is connected to the network at the Nurul Jalal Islamic Boarding School. This study aims to design the existing technology with the system and add technologies and systems that do not yet exist so that they can be integrated into a computer network connected to the speedy internet network. It is hoped that this research will help teachers and students of Nurul Jalal Islamic Boarding School in exploring information so that it can help effective and efficient learning
Artificial intelligence assisted insights into Bali’s destination image: sentiment and thematic analyses of TripAdvisor reviews
Get PDFThis study applies sentiment and thematic content analyses based on natural language processing (NLP) to gain valuable insights into the perceived image of Bali as a tourist destination. This study addresses the gap in how to realize the benefits of big data analytics in applied research, by using more approachable tools for researchers with limited programming skills and coding experience. A total of 6,800 TripAdvisor reviews of Bali’s top 12 tourist attractions between May 2019 and April 2023 were scrapped. The authors used Bardeen.ai for data mining and Atlas.ti for qualitative data analyses. Sentiment analysis revealed an overwhelmingly positive sentiment (70.4%) towards Bali’s tourist attractions, indicating a positive destination image. Post-pandemic tourists tend to express more positive sentiments in their reviews compared to pre-pandemic. Thematic content analysis indicated that positive sentiments are strongly related to satisfaction, positive experiences, enjoyment, and excitement, while environmental concerns and dissatisfaction are potentially harmful to Bali’s destination image. The study provides valuable insights into tourists’ emotional sentiments, perceptions, and thematic patterns of behavior, which can inform tourism marketers and destination strategists, and contribute to the larger discussion of utilizing big data analytics in tourism marketing research
Optimization of mechanical properties of Al7150/Si3N4/C composites using artificial neural network
Get PDFThe study aims to investigate and predict the effect of reinforcements such as silicon nitride (Si3N4) and graphene (C) in aluminum 7150 matrix. Al7150/Si3N4/C hybrid composite is fabricated by a stir casting technique and subsequently T6 heat treated for applications such as body stringers, spar chords, seat tracks, and stringers of wing surfaces of aircraft. A feedforward propagation multilayer neural network was developed for modeling and prediction of hardness, tensile strength, and tensile elongation. The results show that the addition of fillers and T6 heat treatment enhances the mechanical properties of the Al7150/Si3N4/C composite. The artificial neural network (ANN) model suggested for Al7150 composites demonstrates beneficial results when compared to experimental measurements. The prediction model, which has a mean absolute percentage error of 0.64%, 0.3%, and 2.49% for hardness, tensile strength, and tensile elongation can accurately predict the effect of reinforcement contents and T6 heat treatment on mechanical properties of Al7150/Si3N4/C composites
A review of the state of art and prospects in energy storage systems for energy harvesting applications
Get PDFDue to the increasing trend in worldwide energy consumption, many new energy technology systems have emerged in the past decades. The implementation of energy storage system (ESS) technology in energy harvesting systems is significant to achieve flexibility and reliability in fulfilling the load demands. In this paper, several types of energy storage technologies available in the market are discussed to view their benefits and drawbacks. The main aim of this review is to provide a platform for readers especially those who seek to know more about ESS at a glance, to decide which ESS technology is best suited for any specific applications. This review would serve as a base for the initial state to make the right decision by referring to the criterias and characteristics of energy resources to get the optimal ESS technology. A comprehensive comparison among the various types of ESS technologies is outlined and elaborated to provide a better and clearer picture to the readers. Last but not least, the relevant recommendations and alternative choices for services related to the harvesting of solar PV energy are described too. It is hoped that the findings of this review article may be helpful to all readers interested in ESS technology
Modelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia’s meteorological condition
Get PDFSolar photovoltaic (PV) panels performance is influenced by various external factors such as precipitation, wind angle, ambient temperature, wind speed, transient irradiation, and soil deposition. Soiling accumulation on panels poses a significant challenge to PV power generation. This paper presents the development of an artificial neural network (ANN)-based soil deposition prediction model for PV systems. Conducted at a Malaysian solar farm over three months, the research utilized power output data from the inverter as model output and meteorological data as input variables. The model employed the Levenberg-Marquardt backpropagation method with Tansig and Purline activation functions. Performance assessment via statistical comparison of experimental and simulated results revealed a coefficient of determination (R2) value of 0.68073 for the ANN architecture of 5 input layers, 30 hidden layers, and 1 output layer (5-30-1). Sensitivity analysis highlighted relative humidity and wind direction as the most influential parameters affecting PV soiling rate. The developed ANN model, combined with sensitivity analysis, serves as a robust foundation for enhancing the efficiency of smart sensors in PV module cleaning systems
Review on electrical submersible pump failures detection and monitoring system
Get PDFAn artificial lift is a technique for pumping fluids in the petroleum industry today. Most artificial lift that is used nowadays is an electrical submersible pump (ESP). ESP is very convenient and reliable for lifting production. It applies under offshore and onshore industries, where it has high displacement capacity and flexibility to handle various sizes and flow rates under different well conditions. Due to severe operating conditions, ESP may experience fatigue failure, an undesirable malfunction resulting in a shortened service life. Early failure detection on ESP is imperative to prevent a major failure in the pump and reduce the cost of the damage. This study provides an in-depth examination of condition monitoring and early failure detection, with a focus on mechanical and electrical failures. This paper begins with a description of the ESP working principle followed by an analysis of ESP fault and numerous ESP performance monitoring techniques, including the application of early detection. Finally, the authors summarize ESP’s failures, including detection methods for each failure. Besides, recommendations for future research to increase ESP’s lifetime are also discussed. This review revealed that the lifetime of existing ESP can be extended if extensive monitoring of ESP conditions and advanced fault detection methods are applied
Implementation approach in legal research
Get PDFThe use of a research approach in legal research will determine the results. There are three categories of approaches in legal research. The normative approach is the most widely used. This research aims to examine how the approach should be implemented in legal research. This research uses a conceptual approach which is still within the scope of the normative approach. Research data was collected by searching articles published in 23 law journals. The results of this research show that the approach to legal research is the use of perspective in discussing legal issues. There are three legal research approaches, namely normative, empirical, and philosophical approaches with all their variants. The normative approach reviews legal problems from a positive law perspective. The empirical approach examines legal problems as a cultural reality. A philosophical approach examines legal problems from an ideal perspective. The approach to legal research should be applied according to the type of research, research data, and level of research. The normative approach is the most widely used. This is because law is mostly understood as a set of rules. Sequentially, of the 256 articles studied, 70% of legal research used a “normative approach”, 19% empirical, and 11% philosophical
Intelligent control strategies for grid-connected photovoltaic wind hybrid energy systems using ANFIS
Get PDFThis study proposes intelligent control strategies for optimizing the grid integration of photovoltaic (PV) and wind energy in hybrid systems using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS control aims to enhance grid stability, improve power management, and maximize renewable energy (RE) utilization. The hybrid system's performance is evaluated through simulations, considering various environmental conditions and load demands. Results demonstrate the effectiveness of the proposed ANFIS-based control in dynamically adjusting the power output from PV and wind sources, ensuring efficient grid-connected operation. The findings underscore the potential of intelligent control strategies to contribute to the reliable and sustainable integration of RE into the grid
An innovative fast iterative process algorithm computerization for intermittency LSSPV generation reconfiguration
Get PDFThe recent implementation of solar photovoltaic (SPV) power generation in low-voltage distribution networks has increased due to its environmentally friendly technology, low cost, and high efficiency. However, SPV generation carried both the availability of uncertainty and intermittency on power energy exceeding voltage range, increased losses during reverse power flow action, and energy transmission problems. This paper presents a new capabilities methodology with accurate analysis to simulate the intermittent nature of SPV energy including normal generators associated with uncertain customer demand of high resolution with 1-minute temporal resolution using a fast iterative process algorithm (FIPA) simulated by Python programming. The primary goal is to address the unpredictable nature of SPV using computer operation technology connected to a real network with a fast iteration process. The result shows that in 0-10% of standard generators, grid energy (GE) is still required in daily supply, and the intermittent nature influences voltage violations and losses. Besides, the prediction typical SPV method (zero fluctuation) can serve as guidelines for engineers to design the photovoltaic (PV) module reducing its fluctuating nature and battery installation area. The research provides utilities with accurate information to plan for various difficulties at different levels of PV penetration while reducing time, effort, and resource utilization
Phasor measurement unit application-based fault allocation and fault classification
Get PDFThis paper makes a contribution to the field of fault location finding in a new way that helps in the improvement of grid reliability. This paper proposes a study-based approach for fault allocation and fault type classification that uses the study of voltage and current frequency during the abnormal condition. Although, ideally frequency of voltage and current are the same in the abnormal condition they may differ from each other. This difference in frequency is separately measured by the phasor measurement unit (PMU) block at MATLAB/Simulink platform. The PMU (PLL-based, positive-sequence) block is inspired by the IEEE Std C37.118.1-2011. In this approach, we measure the line voltage and current frequency variation with the help of installed PMU after this we present this measurement in characteristics form with the help of the scoping tool in MATLAB/Simulink and study them one by one, and proposed a conclusion for fault location identification and fault type classification. The proposed approach is able to identify the source side and load side fault location and also able to classify faults into two categories namely symmetrical and asymmetrical. The proposed approach is tested on two MATLAB/Simulink models and observed satisfactory