Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,939 Article Results

Isolation of hydrogen from water and its utilization as a co-fuel for trucks into fuel-efficient vehicles

10.11591/ijpeds.v16.i1.pp1-9
Sittichot Kradang-nga , Pongsakorn Kachapongkun , Thee Chowwanonthapunya
This research focused on the separation of hydrogen gas from water and its utilization as a supplementary fuel blended with the primary fuel of an internal combustion engine. The test was divided into two steps: evaluating the energy efficiency of the electrolyzer and conducting experiments on pickup trucks (common rail diesel engine, 2,499 cc) to determine energy savings and pollution emission. The results showed that the efficiency of the electrolysis system with an average electricity consumption of 125.74 W was 84.83 kWh/kgH2 and the theoretical efficiency of the electrolyzer in separating hydrogen gas from water was 45.97%. Results from the test on a pickup truck using 100% diesel fuel and hydrogen-diesel dual fuel with loads of 1,850 and 2,100 kg over a distance of 11 km showed that using a hydrogen-diesel dual system resulted in fuel savings of 27.8% and 16.70%, as compared to that of using pure diesel fuel system. Besides, levels of black smoke, PM2.5, and PM10 of the hydrogen-diesel dual fuel system were lower than those of the pure diesel fuel system.
Volume: 16
Issue: 1
Page: 1-9
Publish at: 2025-03-01

A novel technique for optimization of BLDC-based dual-motor electric vehicles using adaptive BFO-based PID controller

10.11591/ijpeds.v16.i1.pp10-24
Rajnish Kumar , Amitesh Kumar
This study addresses the imperative for electric vehicle (EV) propulsion systems to operate at higher speeds with effective motor control, given the rapid advancement of EV technology. Specifically focusing on electric 2-wheelers, we aim to enhance their maximum speed range from 45 km/hr to 110 km/hr by optimizing the control strategy of a widely used commercial e-bike from Vespa. Our approach explores the feasibility of employing a dual motor system instead of a single motor, coupled with optimization techniques for a proportional-integral-derivative (PID) controller governing a linear brushless DC (BLDC) motor. Implemented in MATLAB/Simulink, our method offers advantages such as consistent convergence, ease of implementation, and high computational efficiency. By employing bacterial foraging optimization (BFO) along with an adaptive BFO (ABFO) technique to optimize the PID controller, we achieve significantly faster response times compared to conventional BFO methods. These findings underscore the efficacy of our approach in enhancing the speed control and acceleration characteristics of EV propulsion systems, contributing to the ongoing evolution of electric mobility solutions.
Volume: 16
Issue: 1
Page: 10-24
Publish at: 2025-03-01

Control of shunt active power filter for power quality improvements with PV system using MPC approach

10.11591/ijpeds.v16.i1.pp278-286
Larouci Heguig , Nadhir Mesbahi , Yacine Guettaf
The major issue facing the electrical grid is the excessive use of non-linear loads, which pull distorted (non-sinusoidal) current from the grid. Considering this constraint, the objective is to remove any harmonic currents from the grid. The active filtering method has been selected, particularly focusing on the use of the shunt active filter, which provides numerous benefits. Therefore, in order to achieve effective harmonic compensation, a suitable and resilient control system is necessary for the shunt active filter. The system outlined in this study comprises a photovoltaic generator connected to the distribution electrical grid via a shunt active filter in order to simultaneously ensure the injection of renewable power generated by the photovoltaic generator into the grid and the improvement of the electrical energy quality. In this study, a model predictive current is introduced for shunt active power with fuzzy logic control to optimize the tracking of the maximum power point for the photovoltaic generator. The system was studied under various conditions, and the simulation was carried out using MATLAB/Simulink on the entire system.
Volume: 16
Issue: 1
Page: 278-286
Publish at: 2025-03-01

Battery management system employing passive control method

10.11591/ijpeds.v16.i1.pp35-44
Muhamad Aqil Muqri Muhamad Fahmi , Siti Hajar Yusoff , Teddy Surya Gunawan , Suriza Ahmad Zabidi , Mohd Shahrin Abu Hanifah
A battery management system (BMS) is essential for maintaining peak efficiency and longevity of rechargeable batteries. Conventional battery management system techniques often struggle to monitor, protect, and particularly have difficulties in balancing batteries. The project proposed has introduced a battery management system that employs passive control techniques to address excess energy and overcome these challenges. In the proposed design, a shunt resistor dissipates surplus energy from lithium-ion battery cells into heat following the proposed BMS design. This passive control technique is economically efficient, uncomplicated, and does not require an external power source. A prototype of the proposed BMS design was tested and was able to accurately monitor the battery, dissipate excess energy, and protect the battery while maintaining the cell charge balance. These findings suggest that the proposed BMS has the potential to improve both the effectiveness and longevity of rechargeable batteries.
Volume: 16
Issue: 1
Page: 35-44
Publish at: 2025-03-01

Battery management system using Jaya maximum power point tracking technique

10.11591/ijpeds.v16.i1.pp622-632
Muhammad Hasbi Azmi , Ayman Nurshazwan Abdul Rashid , Siti Zaliha Mohammad Noor , Muhammad Murtadha Othman , Suleiman Musa , Pusparini Dewi Abd Aziz
This paper introduces the development of a battery management system (BMS) utilizing the Jaya-based maximum power point tracking (MPPT) technique. Previous studies have combined various MPPT techniques with switching methods, each having its pros and cons. Traditional MPPT methods are common but have limited performance. Therefore, artificial intelligence (AI)-based approaches are introduced to enhance and reduce the limitations faced. The Jaya technique is straightforward and easy to implement, making it an attractive choice for MPPT in photovoltaic systems. It is recognized for its effectiveness in eliminating the worst solutions and identifying the best solution with only a few control parameters required for operation. The proposed work aims to develop a BMS using a DC-DC buck converter and the Jaya MPPT technique. The objective is to find the MPP to achieve the desired performance level and ensure the effectiveness of maintaining battery quality, preventing overcharging or undercharging. The system is modeled in MATLAB/Simulink. The findings indicate that the Jaya MPPT demonstrates a tracking speed of less than 1 second to locate the maximum power point (MPP). Furthermore, the BMS is capable of monitoring changes in state of charge (SoC) to determine whether the system is in charging or discharging mode.
Volume: 16
Issue: 1
Page: 622-632
Publish at: 2025-03-01

Harmonic reduction techniques in renewable energy distribution systems using cascaded multilevel inverters: a comparative analysis

10.11591/ijpeds.v16.i1.pp76-85
Nayana Gangadhara , Savita D. Torvi
Penetration of renewable energy in distribution generation increases power quality in the output. The harmonics inherent in the inverters are a major contributor to the power quality issues in the distribution system. Multilevel inverters are used to get rid of the harmonics inherent in the inverter output. Among the multilevel inverter topology cascaded multilevel inverters have taken center stage due to their simple topology and control with lesser components. This paper reviews different multilevel inverter topologies that have led to cascaded multilevel inverter topology and applies pulse width modulation (PWM) techniques to the topology. Phase disposition PWM technique is applied on the cascaded H-bridge multilevel inverter (MLI) topology for 5-level, 7-level, and 9-level inverter topologies. The total harmonic distortion (THD) obtained for these topologies is compared with and without the use of an LC filter in the inverter output. PWM techniques including phase disposition, for five-level, seven-level, and nine-level MLI methods are applied on the cascaded multilevel inverter and results are compared for harmonic reduction in the inverter output.
Volume: 16
Issue: 1
Page: 76-85
Publish at: 2025-03-01

TechTrolley-enhancing the retail experience

10.11591/ijeecs.v37.i3.pp1476-1486
Dhananjay Rajendra Chavan , Roshan Mahadev Sherekar , Sarthak Praveen Khudbhaiye , Jaya Zalte
In the modern era, convenience and efficiency have become essential aspects of daily life, and grocery shopping is no exception. The traditional shopping experience, characterized by long queues and time-consuming checkout processes, can be frustrating and inefficient. To address these challenges, the TechTrolley has emerged as an innovative solution, leveraging Bluetooth and radio frequency identification (RFID) technology to revolutionize the grocery shopping experience. With the help of TechTrolley, customer can seamlessly complete the shopping by scanning and purchasing the products, controlling the trolley with the use of controller integrated in application, getting details of the products and price in the application and over LCD display embedded on the trolley, complete the checkout process at billing counter. With the need to implement, we need an RFID tag, ESP32, LCD display, L298N motor driver and battery to implement the motion features of a trolley, database for storing the user and product details, a bridge network through router to establish the network between admin, user and the trolley in order to invoke the real time updates.
Volume: 37
Issue: 3
Page: 1476-1486
Publish at: 2025-03-01

Single-stage transformer less multilevel boost inverter with zero leakage current for PV system

10.11591/ijpeds.v16.i1.pp673-682
Dalya Hamzah Al-Mamoori , Shahrin Md Ayob , M. Saad Bin Arif
Transformer less inverters (TIs) are highly efficient and have a high power density, making them a popular choice for grid-connected solar PV applications. However, certain topologies can lead to high-frequency common-mode voltage (CMV), which can cause issues such as high leakage current, electromagnetic interference, and an absence of safety. Our newly developed inverter is designed to be more efficient, cost-effective, and compact than traditional types while also addressing the issue of leakage current. This architecture eliminates leakage current by directly connecting the grid's neutral terminal to the PV's negative polarity, resulting in a low leakage current. Moreover, the inverter increases output voltage using only one voltage source and a few power devices, making it a cost-effective solution. Its modular form allows for an increase in output levels, further enhancing its cost-effectiveness. We conducted a comprehensive mathematical examination, and the MATLAB/Simulink results demonstrate its ability to increase the output voltage, eliminate leakage current, and maintain acceptable output voltage THD and current waveforms. These results and the inverter's safety features showcase significant improvements over traditional inverters and provide a secure and reliable solution for grid-connected solar PV applications.
Volume: 16
Issue: 1
Page: 673-682
Publish at: 2025-03-01

Optimal placement of energy storage system in hybrid AC/DC microgrid to enhance stability

10.11591/ijpeds.v16.i1.pp195-203
Pagidela Yamuna , N. Visali
Nowadays, growing interest in sustainable energy solutions, hybrid AC/DC microgrids are becoming more and more recognized as a reliable and efficient option. In order to improve the stability of such microgrids advanced solutions for ESS placement are required due to the unpredictable nature of renewable energy sources and the complexity of load needs. The precision needed to maximize microgrid stability in the face of these obstacles is lacking. In this paper, an artificial neural networks (ANN)-based framework for the strategic allocation and sizing of ESS is proposed. This study uses ANN and the process is to determine the best locations and capacities for energy storage systems (ESS) to minimize system losses while accounting for variations in renewable generating and demand profiles. Simulation is carried on IEEE 12 bus system for studying the usefulness of the proposed method and stability is determined. The power flow datasets generated through simulation are utilized to train the ANN in order to determine the most appropriate placements for ESS. Furthermore, a series of simulations were performed to examine the impact of ESS characteristics on the performance of system loss under various circumstances.
Volume: 16
Issue: 1
Page: 195-203
Publish at: 2025-03-01

Authenticated image encryption using robust chaotic maps and enhanced advanced encryption standard

10.11591/ijeecs.v37.i3.pp1543-1554
Rupaliben V. Chothe , Sunita P. Ugale , Dinesh M. Chandwadkar , Shraddha V. Shelke
The ability of advanced encryption standard (AES) algorithm to protect information systems has given cryptography a new dimension. Recent encryption approaches to enhance randomness include the use of chaotic algorithms, which provide resistance to differential attacks. We have proposed the application of robust chaotic maps in the block cipher to design a secure authenticated encryption scheme to get advantages of both. The chaotic sequence is generated using hyperbolic tangent map and added to input image initially to increase randomness. The basic 256-bit AES key is generated using the robust Renyi modulo map. An additional 128-bit key enhances security. Instead of static values used in AES, dynamic initialization vector (IV), different for every image will be generated. The results are mathematically verified using various security parameters. The algorithm provides lower values of peak signal-to-noise ratio (PSNR) (7.81 to 9.10 dB) for encrypted images and higher dissimilarities between input and encrypted image histograms. Thus, it is highly resistant to statistical attacks. The experimental results and their comparison prove the superiority of our proposed cryptosystem against statistical, differential and brute-force attacks. Thus, the novel multi-chaotic AES-GCM (galois/counter mode) algorithm can be used for color image encryption in military and industrial applications demanding high data security and authentication.
Volume: 37
Issue: 3
Page: 1543-1554
Publish at: 2025-03-01

Machine learning model approach in cyber attack threat detection in security operation center

10.11591/csit.v6i1.p80-90
Muhammad Ajran Saputra , Deris Stiawan , Rahmat Budiarto
The evolution of technology roles attracted cyber security threats not only compromise stable technology but also cause significant financial loss for organizations and individuals. As a result, organizations must create and implement a comprehensive cybersecurity strategy to minimize further loss. The founding of a cybersecurity surveillance center is one of the optimal adopted strategies, known as security operation center (SOC). The strategy has become the forefront of digital systems protection. We propose strategy optimization to prevent or mitigate cyberattacks by analyzing and detecting log anomalies using machine learning models. This study employs two machine learning models: the naïve Bayes model with multinomial, Gaussian, and Bernoulli variants, and the support vector machine (SVM) model with radial basis function (RBF), linear, polynomial, and sigmoid kernel variants. The hyperparameters in both models are then optimized. The models with optimized hyperparameters are subsequently trained and tested. The experimental results indicate that the best performance is achieved by the RBF kernel SVM model, with an accuracy of 79.75%, precision of 80.8%, recall of 79.75%, and F1-score of 80.01%; and the Gaussian naïve Bayes model, with an accuracy of 70.0%, precision of 80.27%, recall of 70.0%, and F1-score of 70.66%. Overall, both models perform relatively well and are classified in the very good category (75%‒89%).
Volume: 6
Issue: 1
Page: 80-90
Publish at: 2025-03-01

Analysis of telehealth acceptance for basic life support training in sudden cardiac arrest in Pontianak

10.11591/csit.v6i1.p48-57
Ruhil Iswara , Sri Kusumadewi , Rahadian Kurniawan
Sudden cardiac arrest (SDA), which is one of the most prevalent causes of mortality, can be prevented by quickly conducting basic life support (BLS). In Pontianak City, the challenges associated with obtaining emergency health training, such as BLS, remain high. This study aims to evaluate user acceptance of telehealth as well as its effectiveness in BLS training. We will also discuss its impact on community knowledge and skills in managing cardiac arrest. We used the HOT-Fit method to analyze the level of acceptance of telehealth in BLS training. We collected data from 60 respondents who underwent telehealth-based BLS training. The results showed that participants' understanding and readiness in dealing with heart attack emergencies had increased significantly, by 90% and 92%, respectively. Analysis of the level of acceptance with HOT-Fit showed that system quality had the greatest influence on system use (0.611). Service quality exerted the most significant impact on user satisfaction (0.568). The net benefit was influenced by system use, user satisfaction, and organizational support, with user satisfaction having the greatest influence (0.600). Further research will be conducted on the utilization of augmented reality (AR) or virtual reality (VR) technology to implement telehealth for BLS training.
Volume: 6
Issue: 1
Page: 48-57
Publish at: 2025-03-01

Power of analytic tools in Oxygen Forensic® Detective based on NIST cybersecurity framework

10.11591/csit.v6i1.p8-19
Tole Sutikno , Iqbal Busthomi
The National Institute of Standards and Technology (NIST) cybersecurity framework is a systematic approach for assessing and improving cybersecurity procedures in digital investigations. Oxygen Forensic® Detective is a digital forensic software that integrates multiple analytic tools to assist investigators in extracting valuable insights from digital evidence. The analytic tools, including timeline, social graph, image categorization, facial categorization, maps, data search, key evidence, optical character recognition, statistics, and translation, assist investigators in thoroughly analyzing digital artifacts, establishing connections, and accurately classifying images with precision and effectiveness. By incorporating these analytical resources into Oxygen Forensic® Detective, a comprehensive strategy is established to effectively combat cyber threats. The NIST cybersecurity framework is incorporated into the tool, offering a methodical approach to identifying and reducing cybersecurity risks. Law enforcement agencies can enhance the productivity and effectiveness of their forensic methodologies by implementing these advanced technologies. This can result in successful prosecutions and improved cybersecurity practices.  Overall, the utilization of analytical tools in criminological inquiries has experienced a substantial rise in the contemporary digital era.
Volume: 6
Issue: 1
Page: 8-19
Publish at: 2025-03-01

Detection of android malware with deep learning method using convolutional neural network model

10.11591/csit.v6i1.p68-79
Reza Maulana , Deris Stiawan , Rahmat Budiarto
Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware.
Volume: 6
Issue: 1
Page: 68-79
Publish at: 2025-03-01

Secure e-voting system using Schorr's zero-knowledge identification protocol

10.11591/csit.v6i1.p20-27
Indah Octaviani Laleb , Daniel M.D.U. Kasse
In today's era of technological progress, the electoral system has changed significantly with the introduction of electronic voting (e-voting). The traditional voting system poses many vulnerabilities to manipulation, potential human error, and problems with voter privacy. These limitations can lead to reduced trust and participation in elections. E-voting has emerged to address this issue, aiming to improve the convenience, security, and privacy of voters. E-voting systems are evaluated on accuracy, security, privacy, and transparency; however, ensuring voter privacy while maintaining these principles remains a significant challenge. A potential solution to improving privacy in e-voting is Schorr's zero-knowledge identification protocol. This protocol allows voters to confirm their identity without revealing personal information, maintaining voter privacy throughout the process. By implementing these protocols, the e-voting system can strengthen security and privacy, making elections more transparent and trustworthy. As technology evolves, adopting solutions like Schorr's zero-knowledge identification protocol can help e-voting systems meet the growing demand for safe, fair, and private elections.
Volume: 6
Issue: 1
Page: 20-27
Publish at: 2025-03-01
1 2 3
...
1995 1996
Show 252 of 1996

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration