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23,598 Article Results

The impact of age on second language acquisition: a critical review

10.11591/ijere.v13i5.27958
Manna Dey , Rizky Amelia , Ananda Setiawan
Age plays a significant role in second language acquisition (SLA). Research indicates that the ability to learn a second language declines with age. This study reviewed relevant studies on the impact of age on SLA in order to attain the best results as language learning methods should be tailored to the learner's age and specific needs. The results showed that younger learners are more proficient in acquiring a second language due to their brain's plasticity, which enables them to learn new information quickly. They can easily acquire the language’s pronunciation, grammar, and vocabulary through exposure and immersion. As learners age, their ability to learn a second language decreases. After adolescence, the brain becomes less plastic, and the acquisition of a second language becomes more challenging. Research also shows that language also has a positive impact on a country’s economic development, as well as improving the international relations of local entrepreneurs. However, adult learners can still learn a second language, but it may take more time and effort. Moreover, the motivation and learning strategies of language learners also play an essential role in SLA. Young learners may not have a strong motivation to learn a second language, while adult learners may have a higher motivation due to professional or personal reasons. Age is a crucial factor in SLA, but it is not the only determining factor. The learner’s motivation, learning strategies, and exposure to the second language also play a significant role in the acquisition of a second language.
Volume: 13
Issue: 5
Page: 3560-3570
Publish at: 2024-10-01

A novel distributed generation integrated MFUPQC for active-power regulation with enhanced power quality features

10.11591/ijeecs.v36.i1.pp26-40
Moturu Seshu , Kalyana Sundaram , Maddukuri Venkata Ramesh
The distributed generation (DG) scheme has become significant and advanced energy generation corridor for present power distribution system. This advanced DG scheme offers several merits such as flexible active power transfer, low transmission losses, maximize power efficiency, reduce transmission cost, expanding grid capacity, so on. It is motivated that, integration of such DG system in to multi-parallel feeder distribution system with enhanced power-quality features is considered as major problem statement. The proposed multi-functional unified power-quality conditioner (MFUPQC) device has robust design, reliable performance; specifically for addressing the voltage-current affecting PQ issues, regulation of active-power in multi-parallel distribution system. The fundamental goal of the MFUPQC device has been to operate as both a PQ improvement device and a DG integration device by implementing a new universal fundamental vector reference (UFVR) control algorithm. The suggested innovative control algorithm extracts the fundamental voltage and current reference signals with low computational response delay, simple mathematical formulations and without additional transformations which are also major problems identified in classical control schemes. This work focuses on design, operation and performance of MFUPQC device has been evaluated in both PQ and DG operations in a multi-parallel feeder distribution system through MATLAB/Simulink computing platform. The simulation results are illustrated with possible interpretation and analysis.
Volume: 36
Issue: 1
Page: 26-40
Publish at: 2024-10-01

Academic researchers, come on! Integrate social media in pedagogy

10.11591/ijere.v13i5.29651
Vipin Kumar Sharma , Suraj Begum
Teaching and learning have changed tremendously from traditional teaching to teaching integrating social media (SM) because of immense educational technological advancements and pedagogical innovation. Teachers, thus far, use conventional teaching as a seasoning tool and meet adaptability issues to the new learning environment. In this paper, we define SM, its related aspects, the vitality of social media networks (SMN), and how SM facilitates teaching and learning in tertiary education. It also contends with the benefits of their integration into pedagogy by teachers and students and answers a few fundamental questions on integrating SMN in teaching and learning. Accordingly, a few challenges do appear at different stages that scare us to reflect on SM as a distractor to pedagogy. To integrate and identify challenges in learning and teaching, we claim that a curriculum should be designed incorporating SMN as catalysts and managed as a learning community. The resources needed to operate SMN are also illustrated for both stakeholders to utilize and work together to collaborate for the learner-centered approach to teaching where learners use SMN to share, discuss course materials, post their assignments, get feedback, and interact beyond time, place, and location without any restrictions.
Volume: 13
Issue: 5
Page: 3284-3292
Publish at: 2024-10-01

Career-focused teaching and its effects on students’ biology-technical-vocational-fused skills

10.11591/ijere.v13i5.29131
Joelash R. Honra , Sheryl Lyn C. Monterola , Rosanelia T. Yangco
The K to 12 program in the Philippines, initiated in 2012, brought about challenges like job mismatch among senior high school (SHS) graduates. Addressing this issue requires integrating technical-vocational-livelihood (TVL) skills with core subject skills, particularly in biology. This study explores how the career-focused teaching approach (CFTA) nurtures biology-technical-vocational-fused skills (BTVFS). Using a pretest-posttest quasi-experimental design, two grade 11 classes (35 students each) participated-one exposed to CFTA and the other to conventional teaching. Quantitative data from a researcher-made BTVFS questionnaire were analyzed with an independent samples t-test, revealing significant differences in all BTVFS subcomponents; t(68)=3.670, p<0.036. Qualitative data from reflective journals aligned with BTVFS subskills (metacognition, communication, problem-solving, and collaboration). CFTA proved instrumental in enhancing the BTVFS of students, emphasizing its importance in the curriculum across SHS core subjects to mitigate job mismatch for K to 12 graduates.
Volume: 13
Issue: 5
Page: 3427-3434
Publish at: 2024-10-01

Enhancing the resistance of password hashing using binary randomization through logical gates

10.11591/ijece.v14i5.pp5400-5407
Muhamad Zaki Anbari , Bambang Sugiantoro
Digitalization in various sectors makes information security issues very crucial. Information security follows the authentication, authorization, and accounting (AAA) principle, where one of the most important parts is authentication. The most widely used authentication method is username-password. The best method to secure a user-pass is to convert the plaintext using a hash so that the converted plaintext cannot be recovered. However, with higher technology, hackers can crack the ciphertext using brute force. This research proposes a username-password scrambling algorithm before it is fed into the hash function to improve resilience from attacks. This algorithm is named logical gates (LG). It works by converting the user pass into binary form, adding salt, and scrambling it with certain logical gates before inserting it into the hash function. Testing is divided into two: time of execution and attack resistance. Time of execution results show that LG takes 0.0443432033 s, while without LG takes 0.01403197646 s. The resistance of attack results show that the plaintext of the hash amplified by LG cannot be cracked at all and increases the attack time by 321.3% at prefix and 161.3% at postfix, while without LG, the plain text can be found for a certain duration of time.
Volume: 14
Issue: 5
Page: 5400-5407
Publish at: 2024-10-01

A novel approach to simplified and secure message cryptography using chaotic logistic maps and index keys

10.11591/ijece.v14i5.pp5139-5152
Hussein Ahmad Al-Ofeishat , Jawdat S. Alkasassbeh , Khalaf Y. Alzyoud , Farouq M. Al-Taweel , Hisham Alrawashdeh , Ayman Y. Al-Rawashdeh
This paper proposes a novel method of message cryptography aiming to provide a simple, secure, and highly efficient approach to encryption and decryption. Unlike existing methods that rely on complex logical operations, our method utilizes simple rearrangement operations, reducing computational complexity while ensuring robust security. It employs a sophisticated, high-entropy private key designed to withstand hacking attempts. This key generates two chaotic keys using chaotic logistic map models, which are sorted to form two index keys essential for rearranging message blocks and characters during encryption and decryption. The process is facilitated by two simple operations, Get_index and Get_min, based on the index keys. These operations achieve streamlined procedures without compromising security. The method's speed is evaluated across various message lengths, demonstrating significant improvements in encryption time and throughput. The comparative analysis highlights the superior efficiency of this method compared to existing methods. Rigorous testing confirms that the proposed method meets stringent quality and sensitivity requirements, ensuring robust cryptographic standards. This innovative approach offers a promising solution for secure message encryption and decryption, combining simplicity, security and efficiency to meet the evolving needs of secure communication systems.
Volume: 14
Issue: 5
Page: 5139-5152
Publish at: 2024-10-01

Enhanced fault identification in grid-connected microgrid with SVM-based control algorithm

10.11591/ijeecs.v36.i1.pp115-126
Divya Shoba Nair , Thankappan Nair Rajeev , Sindhura Miraj
The penetration of renewable energy sources, electric vehicles (EVs) and load dynamics, and network complexities often lead to nuisance tripping in grid-connected microgrids. Traditional protection methods fail to discriminate fault and other dynamic volatilities in the system. The paper presents a novel two-level adaptive relay algorithm to avoid nuisance tripping in a grid-connected microgrid under varying grid dynamics. The novelty of the adaptive relay algorithm is that nuisance tripping is eliminated by precisely determining normal system-level dynamics at the first level using a phase deviation reference block. The first level determines the necessity for activating the second level, which consists of a detection scheme combining a multiclass support vector machine (SVM) and discrete wavelet transform (DWT). The hybrid DWT-SVM methodology ensures effective fault diagnosis, adapting to variations in energy sources, load fluctuations, and fault scenarios. Real-time hardware-in-the-loop (HIL) simulation validates the system’s effectiveness in dynamic microgrid environments. Extensive experiments on scenarios, including faults, fluctuations in renewable energy generation, and intermittent simulations of EV charging and capacitor switching, were conducted to test the efficacy of the adaptive relay algorithm. Finally, experiments using OPAL-RT HIL real-time simulator and the Raspberry Pi microcontroller validated the adaptive relay algorithm in a grid-connected microgrid under varying grid dynamics.
Volume: 36
Issue: 1
Page: 115-126
Publish at: 2024-10-01

Contemporary education: globalization and transformation process under the influence of artificial intelligence

10.11591/ijere.v13i5.29016
Yaroslav Tsekhmister , Tetiana Konovalova , Bogdan Tsekhmister , Tamara Pushkarova , Svitlana Nahorniak
This study is aimed to investigate the problem of globalization and transformation processes in contemporary education under the influence of artificial intelligence (AI) on the basis of systemic literature review, to examine AI implications in education and to outline the opportunities for AI research in future. To achieve the objective, we analyzed 159 articles published in Scopus, Web of Science Core Collection, EBSCO, PubMed, Index Copernicus, and Google Scholar databases between 2019 and 2023. The research was conducted in accordance with the PRISMA 2020 statement in four phases: reference search, selection of relevant papers, specific analysis, evaluation of publications’ contributions and preparing the review. The results show that a great number of scientific publications are related to AI research. We found that most of the articles were published in Asia, Europe, and North America in 2022. AI tools are most used while training medical students, information technologies (IT) specialists, engineers, business or management students. The analysis of publications showed that AI is used for data analytics, introduction of personalized learning, providing feedback, online learning, and automated assessment most often. We consider that AI-driven technologies currently play an important role in education and the number of publications testify that its implementations will extend in future. The study has contributions for outlining the role of AI-related tools in educational process.
Volume: 13
Issue: 5
Page: 3443-3455
Publish at: 2024-10-01

Development and assessment of solar radiation forecasting models based on operational data

10.11591/ijece.v14i5.pp4838-4845
Suwarno Suwarno , Catra Indra Cahyadi , Sukarwoto Sukarwoto , Janter Napitupulu
Operational forecasting of solar radiation is critical for better decision-making by solar energy system operators, due to the variability of energy resources and demand. Although the numerical weather forecasting (NWP) model can predict solar radiation variables, there are often significant errors, especially in direct normal irradiation (DNI), which are influenced by the type and concentration of aerosols and clouds. This paper presents an artificial neural network (ANN) based method to generate operational DNI forecasts using weather and aerosol forecast data from the European Center for medium-range weather forecasts (ECMWF) and Copernicus atmospheric monitoring service (CAMS) respectively. The ANN model is designed to predict weather and aerosol variables at a certain time as input, while other models use the DNI forecast improvement period before the instant forecast. The model was developed using North Sumatra location observations and obtained DNI forecasting results every 10 minutes on the first day with DNI forecasting compared to the initial forecasting which was scaled down with the R2, mean absolute error (MAE), and relative mean square error (RMSE) models were 0.6753, 151.2, and 210.2 W/m2, so that and provides good agreement with experimental data.
Volume: 14
Issue: 5
Page: 4838-4845
Publish at: 2024-10-01

New droop-based control of parallel voltage source inverters in isolated microgrid

10.11591/ijece.v14i5.pp4856-4868
Timilehin F. Sanni , Ayokunle A. Awelewa , Anthony U. Adoghe , Adeola Balogun , Tobi Somefun
Microgrids, featuring distributed generators like solar energy and hybrid energy storage systems, represent a significant step in addressing challenges related to the greenhouse effect and outdated transmission infrastructures. The operation and control of islanded microgrids, particularly in terms of grid voltage and frequency, rely on the synchronization of multiple parallel inverters connected to the distributed generators. However, to determine the necessary grid parameters for effective control, the presence of circulating currents from unbalanced grid voltages arises as a challenge. This situation necessitates the development of a new approach to achieve phase angle locking for grid synchronization, with the aim of maintaining the voltage within acceptable limits in islanded microgrids. This objective is realized through the creation of a microgrid network model, design of an adaptive filter, utilizing the double second-order generalized integrator–phase-locked loop (DSOGI-PLL), for dynamic voltage transformation. The design is evaluated by simulation using MATLAB/Simulink. The primary goal is to investigate the DSOGI-PLL-based droop control and compare its performance with the conventional synchronous reference frame–phase-locked loop (SRF-PLL) control approach. Notably, the DSOGI-PLL successfully eliminates the ripples in phase angle estimation, consequently enhancing the quality of voltage output in the microgrid.
Volume: 14
Issue: 5
Page: 4856-4868
Publish at: 2024-10-01

Deep reinforcement learning based quality of experience aware for multimedia video streaming

10.11591/ijece.v14i5.pp5209-5220
Manjunatha Peddareddygari Bayya Reddy , Sheshappa Shagathur Narayanappa
Video streaming involves the continuous delivery of video files from a server to a client, where multimedia streaming is employed for playback through an online or offline media player. Video streaming uses live broadcasts to enhance direct communication with community partners and customers. The existing methods have less video streaming quality and are unable to efficiently adapt to the dynamic conditions of the network. In this research, an adaptive bit rate (ABR) method depending on dynamic adaptive video streaming over hypertext transfer protocol or HTTP (DASH) based deep reinforcement learning (DRL) named DASH-based DRL is proposed to determine the following segment’s quality in DASH video streaming with wireless networks. The proposed algorithm significantly improves the quality of experience (QoE) performance by providing a highly stable video quality, reducing the distance factor, and enduring smooth streaming sessions. The performance of the proposed method is analyzed based on performance measures of performance improvement, QoE metrics, mean opinion score, normalized value of QoE, average of normalized value of QoE, switching quality, and rebuffering time. The suggested algorithm obtains a high average normalized QoE of 0.72, average switching quality of 0.15, and an average rebuffering time of 0.16 sec, which is comparatively superior to other algorithms like real-time streaming protocol (RTSP), HTTP live streaming (HLS) and reinforcement learning (RL).
Volume: 14
Issue: 5
Page: 5209-5220
Publish at: 2024-10-01

Performance evaluation of rank attack impact on routing protocol in low-power and lossy networks

10.11591/ijeecs.v36.i1.pp242-251
Laila Al-Qaisi , Suhaidi Hassan , Nur Haryani Zakaria
The internet of things (IoT) is a network of connected devices, enabling the exchange and collection of data from various environments. The routing protocol for low power and lossy networks (RPL) is a protocol for routing IPv6 over low-power wireless personal area networks, commonly used in IoT applications. However, RPL has several security and privacy issues that make it vulnerable to various attacks, including rank attacks (RA), which can lead to denial-of-service (DoS) scenarios. This research aims to address the impact of RA on RPL networks by conducting simulations using the Contiki/Cooja simulator with two topology types, random and grid, along with three RA scenarios and a normal network scenario. The study compares the performance of RPL network OF0 and MRHOF in terms of throughput, packet delivery ratio (PDR), hop count (HC) and delay. The results demonstrate that RA significantly degrades network performance and reduces network lifetime, thus draining its limited resources. Some possible solutions are also suggested to mitigate these attacks by focusing on core components of the network like objective function (OF) and node behavior. Future work will focus on studying security mechanisms for RPL against RA.
Volume: 36
Issue: 1
Page: 242-251
Publish at: 2024-10-01

Exploring the tree algorithms to generate the optimal detection system of students' stress levels

10.11591/ijeecs.v36.i1.pp548-558
Yuni Yamasari , Anita Qoiriah , Naim Rochmawati , Aditya Prapanca , Agus Prihanto , I Made Suartana , Tohari Ahmad
The significant changes in the world of education after the coronavirus disease 2019 (COVID-19) pandemic have increased students' anxiety levels. This anxiety can trigger stress which can interfere with students' academic performance. Therefore, this condition is a critical problem that needs to be addressed immediately. However, researchers have not previously conducted much research to detect post-COVID stress levels. Apart from that, the existence of a system capable of carrying out this detection is still lacking. Therefore, this research focuses on building a system for detecting student stress levels. First, an exploration of the tree algorithm was carried out to find the most optimal method for recognizing student stress levels. Then a detection system is built using this optimal method. The research results show that the tree ID3 (Iterative Dichotomiser 3) algorithm achieves the highest accuracy value of 95% compared to other tree algorithms with the scenario of dividing training data into test data of 80%:20%. Moreover, this telegram bot-based detection system works well in recognizing three categories of stress, namely: light-, moderate-, and heavy stress based on black-box testing techniques.
Volume: 36
Issue: 1
Page: 548-558
Publish at: 2024-10-01

System availability assessment and optimization of a series-parallel system using a genetic algorithm

10.11591/ijeecs.v36.i1.pp153-162
Priya Chaudhary , Shikha Bansal
To optimize the operational availability of the series-parallel system and provide useful insights for maintenance planning, the study attempts to investigate the availability of a ball mill unit. These four different components make up the ball mill production system: “drum,” “ring-gear,” “gearbox,” and “electric motor.” There is a chain mechanism connecting all four components. The “ring gear” and “electric motor” components are composed of two independent units, one of which serves the desired purpose and the other is maintained in cold standby. The “drum” and “gearbox” of the components each contain only one unit. Therefore, a novel mathematical model is designed and implemented in this work by assuming arbitrary repair rates and exponentially distributed failure rates using the Markov process and Chapman-Kolmogorov equations. This study explored the availability with a normalization method and used genetic algorithm techniques to optimize ball mill availability. Putting this article into practice is of great benefit when developing an appropriate maintenance program. Through this, the study achieves maximum production. To investigate the behavior of several performance characteristics of the ball mill production system, numerical results and corresponding graphs are also specifically created for specific values of subsystem parameters, such as failure rate, and repair rate to increase the system’s overall efficiency.
Volume: 36
Issue: 1
Page: 153-162
Publish at: 2024-10-01

Model predictive control with finite constant set for five-level neutral-point clamped inverter fed interior permanent magnet synchronous motor drive of electric vehicle

10.11591/ijece.v14i5.pp5038-5047
Tran Hung Cuong , An Thi Hoai Thu Anh
This paper uses the five-level neutral-point clamped (NPC) inverter to feed an electric vehicle's traction motor-interior permanent magnet synchronous motor (IPMSM). The model predictive control method controls the energy conversion process according to the model with two prediction steps. The advantage of this method is its fast response, which increases the ability to operate the converter with good voltage quality. Model predictive control (MPC) control is a closed-loop strategy with much potential when integrating multiple control objectives; the calculation process is compact without complex modulation. Within the scope of this article, the MPC strategy will be implemented with two control goals for NPC, including output load current and capacitor voltage balance with low switching frequency. The simulation results on MATLAB/Simulink software were performed to verify the proposed algorithm's effectiveness in minimizing the grid current's harmonics and ensuring an uninterrupted power supply.
Volume: 14
Issue: 5
Page: 5038-5047
Publish at: 2024-10-01
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