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Research Article
The Use of Artificial Intelligence in Assessing the Reliability of Electric Power Systems and Networks
Issue:
Volume 13, Issue 1, February 2025
Pages:
15-23
Received:
27 November 2024
Accepted:
12 December 2024
Published:
17 January 2025
DOI:
10.11648/j.jeee.20251301.12
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Views:
Abstract: Improving the reliability of power networks is a critical challenge, especially with the rise of renewable energy sources and the continuous growth in electricity demand. This article explores the use of artificial intelligence, specifically dynamic Bayesian networks (DBNs), to evaluate the reliability of electric power systems and networks, focusing on the IEEE 9-bus and IEEE 14-bus networks as case studies. To achieve this, a comprehensive study was conducted by simulating various operating scenarios using these networks as models. These networks were modeled using the simulation and analysis software PyAgrum. Key system variables, including nodes, lines, generators, and transformers, were integrated into the analysis, enabling the construction of conditional probability tables (CPTs) for each component. These tables accounted for both interdependencies and state transitions to reflect real-world dynamics accurately. Simulations performed using MATLAB enabled an in-depth analysis of reliability levels, revealing critical information on the availability rates of nodes, transformers, and generators. The analysis identified specific vulnerabilities within the network, such as node 2 in the IEEE 9-bus network achieving an availability rate of 65%, which indicates robust performance. Conversely, nodes 7 and 9 exhibited significantly lower availability rates of 20% and 40%, respectively, highlighting critical areas requiring immediate attention. Similarly, transformer 1 displayed a relatively high availability rate of 70%, indicating strong performance, whereas transformer 3 showed a notably low availability rate of 30%, suggesting an urgent need for upgrades or replacements. For generators, generator 1 had the lowest availability at 25%, representing a critical vulnerability, while generator 2, with a 55% availability rate, stood out as the most efficient and could serve as a benchmark for performance improvement efforts.
Abstract: Improving the reliability of power networks is a critical challenge, especially with the rise of renewable energy sources and the continuous growth in electricity demand. This article explores the use of artificial intelligence, specifically dynamic Bayesian networks (DBNs), to evaluate the reliability of electric power systems and networks, focusi...
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Research Article
Nested Hexagonal Split Ring Resonator-Based Metamaterial for Performance Enhancement in Multiband Antenna
Hassan Belaid*
Issue:
Volume 13, Issue 1, February 2025
Pages:
24-39
Received:
17 December 2024
Accepted:
8 January 2025
Published:
6 February 2025
Abstract: In this paper, we present a Nested hexagonal shaped split-ring resonator based negative epsilon metamaterials layered on 11 mm × 10 mm × 1.524 mm Rogers RO4350B dielectric substrate and designed to enhance the performance of multiband satellite antennas. Simulations using CST electromagnetic software show that the NH-SRR metamaterial manifests seven distinct resonance frequencies of S21spectrum at 2.37, 3.92, 5.4, 7.71, 8.58, 9.73 and 10.94 GHz, spanning S, C, and X-bands. The unit cell yields an effective medium ratio (EMR) of 12.66 and an electrical dimension of 0.087λ × 0.079λ when calculated at 2.37 GHz, which implies the effectiveness and compactness of the NH-SRR shaped metamaterial. The simulated outcomes also revealed that negative electric permittivity (є) response is attained within 4.16-5.75 GHz, 10.16-11.58 GHz and 14.46-16 GHz, with Near-Zero permeability property near the resonance frequencies. Our methodology involves using multiple electromagnetic software tools, including CST, HFSS and COMSOL for simulation results and design validation. A detailed numerical analysis was conducted to assess the impact of using this metamaterial as array cover above a Log Periodic Dipole Array (LPDA) Antenna on the performance metrics, demonstrated that the LPDA with metamaterial superstrate surpasses the conventional antenna in term of gain, return in loss and impedance matching, particularly at frequencies where negative permittivity and near-zero permeability properties are observed. These findings suggest that the NH-SRR metamaterial offers compactness, efficiency and scalability for applications in modern wireless communication and network systems.
Abstract: In this paper, we present a Nested hexagonal shaped split-ring resonator based negative epsilon metamaterials layered on 11 mm × 10 mm × 1.524 mm Rogers RO4350B dielectric substrate and designed to enhance the performance of multiband satellite antennas. Simulations using CST electromagnetic software show that the NH-SRR metamaterial manifests seve...
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Letter
Optimized Three Bit Counter Employing T Flip-Flop in Quantum-Dot Cellular Automata Technology
Javad Mohammadi*,
Mahdi Zare,
Masoumeh Molaei
Issue:
Volume 13, Issue 1, February 2025
Pages:
40-45
Received:
14 December 2024
Accepted:
13 January 2025
Published:
10 February 2025
Abstract: Finding new efficient low-cost methods to use CMOS technology is one of the main topics in this area due to the physical limitations of the present methods. The researchers are looking to find new solutions to overcome VLSI problems such as large area, high power consumption, low speed, and electrical current issues. Quantum-dot cellular automata is a new nano-scale technology that has overcome the limits of metal oxide technology and is considered as an advanced method in digital circuit designs. QCA has attracted the attention of many researchers due to its special features such as power consumption, high-speed computing operations, and small dimensions. Besides, the counter is a module that has wide applications in digital systems. In this study, an optimized counter has been proposed in Quantum-dot cellular automata which has utilized T Flip-Flop and improved the cell number and area parameters. The design of the proposed circuit has employed 108 cells. The simulation results of the circuit show 0.1 μm2 of area occupation. Also, the delay of circuit is 4.25 clock periods. This design has improved the cell number and area by 22% and 39%, respectively. The power or Complexity has reduced by 22% compare to the best prior design.
Abstract: Finding new efficient low-cost methods to use CMOS technology is one of the main topics in this area due to the physical limitations of the present methods. The researchers are looking to find new solutions to overcome VLSI problems such as large area, high power consumption, low speed, and electrical current issues. Quantum-dot cellular automata i...
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Research Article
Three-State Non-Stationary Hidden Markov Model for an Improved Spectrum Inference in Cognitive Radio Networks
Issue:
Volume 13, Issue 1, February 2025
Pages:
46-58
Received:
9 January 2025
Accepted:
27 January 2025
Published:
17 February 2025
Abstract: Spectrum manufacturers, operators and regulators are faced with the challenge of meeting the astronomical increase in demand by spectrum users due to the limited available radio spectrum already fixed for licensed or Primary Users (PUs). The emergence of Cognitive Radio Network (CRN) allows unlicensed or Secondary Users (SUs) to opportunistically access spectrum holes left unused by the PUs through spectrum sensing, management, sharing and mobility functionalities with the aid of algorithms and protocols. However, CRN suffers prolonged delay with negative impact on spectral efficiency. In order to improve the spectral efficiency, spectrum inference was introduced. Yet, inaccurate spectrum inference by existing mechanisms could not solve spectrum underutilization effectively due to persistent false alarm, interference and missed detection of PUs. Two-state Non-Stationary Hidden Markov Model (NSHMM) focused only on idle and busy states of PUs while previous work on three-state Stationary Hidden Markov Model (SHMM) did not consider the time-varying property of channel states obtainable in real scenarios where the state transition probability of a PU is time-varying. This work has proposed three-state NSHMM for spectrum inference in CRNs by formulating its parameters and modelling PU's dwell time distributions to realize the time-varying property of the stochastic PU behavior apart from the fuzzy state that takes care of noisy effects and undetermined or incomplete observations in the existing mechanisms where only idle and busy states were mostly recognized. The performance of the proposed mechanism was evaluated using Probability of Detection (PD), Prediction Accuracy (PA) and Spectrum Utilization Efficiency (SUE). The results were compared to the performance metrics obtained from spectrum inference of existing 2-state NSHMM and 3-state SHMM. The simulation results obtained revealed that the proposed three-state NSHMM spectrum inference mechanism gave the best performance with the highest PD, PA and SUE which curtailed PU collision because of its least possible chances of incorrect detection of primary users and least false alarm. The outstanding performance of the proposed NSHMM was due to its non-stationarity as well as the fuzzy state incorporated in the development of the mechanism. Therefore, the proposed three-state NSHMM for an improved spectrum inference in CRNs has grossly abated PU collision, false alarm and spectrum underutilization.
Abstract: Spectrum manufacturers, operators and regulators are faced with the challenge of meeting the astronomical increase in demand by spectrum users due to the limited available radio spectrum already fixed for licensed or Primary Users (PUs). The emergence of Cognitive Radio Network (CRN) allows unlicensed or Secondary Users (SUs) to opportunistically a...
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Research Article
Intellimice Classifier: Towards Smart Object Detection and Classification of Laboratory Mice Using Multi-Sensor Integration
Issue:
Volume 13, Issue 1, February 2025
Pages:
59-81
Received:
10 January 2025
Accepted:
24 January 2025
Published:
27 February 2025
Abstract: Laboratory mice (Mus musculus) play a crucial role in scientific research, where accurate classification and sorting are essential for ensuring reliable experimental results. This study presents an intelligent multi-sensor system for the automated classification and sorting of laboratory mice based on three key parameters: health status, gender, and weight. The system integrates thermal imaging cameras AMG8833 for monitoring the health status of mice, object detection algorithms (YOLOv8) for gender classification, and load cell HX711 sensors for weight measurement. The integration of these sensors leverages advanced sensor fusion techniques to improve classification accuracy and efficiency. Thermal imaging detects physiological anomalies to assess the health condition of the mice, while object detection algorithms identify gender characteristics in real-time with high precision. Additionally, load cell sensors provide accurate weight data for further categorization. The combined system eliminates the need for manual intervention, ensuring a non-invasive, efficient, and scalable approach to laboratory animal management. The proposed system performed evaluation through multiple test scenarios aimed at assessing the health of mice and classifying their weight. The detection of mice gender was evaluated using a dataset comprising over 6,722 images stored in the STASRG laboratory. The test results indicated that the accuracy of animal sorting across three parameters achieved a 100% success rate. The accuracy of gender sorting was 86.67%, while the accuracy of weight measurement exhibited a difference of approximately 0.1 gram. The overall response time for sorting was 19 seconds. This multi-sensor integration demonstrates the potential to enhance laboratory workflows, minimize human error, and promote the welfare of laboratory animals via automated, data-driven processes.
Abstract: Laboratory mice (Mus musculus) play a crucial role in scientific research, where accurate classification and sorting are essential for ensuring reliable experimental results. This study presents an intelligent multi-sensor system for the automated classification and sorting of laboratory mice based on three key parameters: health status, gender, an...
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Research Article
A High-Resolution Non-Volatile Floating Gate Transistor Memory Cell for On-Chip Learning in Analog Artificial Neural Networks
Issue:
Volume 13, Issue 1, February 2025
Pages:
82-91
Received:
2 February 2025
Accepted:
17 February 2025
Published:
27 February 2025
Abstract: This paper proposes a high-resolution non-volatile memory cell design that addresses the most substantial limitations associated with the effective implementation of analog long-term memory storage solution. Prior research efforts often suffer from limited resolution, hindering their ability to accurately represent fine-grained weight adjustments required for effective learning in analog neuromorphic systems. This work effort has been channeled toward crafting conductive circuit designs using 90 nm complementary metal-oxide semiconductor technology for on-chip learning applications in analog neuromorphic systems. The operational mechanism of the cell involves the storage of charge on the floating gate of the NM0 transistor. The writing process is accomplished through hot-electron injection, while the erasure of stored information is executed via gate oxide tunneling. An advantageous feature of this cell is its capability to facilitate simultaneous reading and writing of data. The reduction of errors that may arise due to oxide mismatch or charge trapping is achieved through feedback control incorporation during the writing phase. The memory reveals clear synaptic behavior characteristics in storing and retrieving analog information reliably including, good memory cell resolution, good charge retention rate, reliable operation in noisy environments, and high resolution with faster learning with a power consumption of 1.06 µW and an output current of 10 µA under a typical operating voltage of 1 V. This strategic implementation enhances precise and reliable weight updates within neuromorphic analog artificial neural networks, which is essential for ensuring accurate on-chip learning outcomes as well as minimizing power consumption.
Abstract: This paper proposes a high-resolution non-volatile memory cell design that addresses the most substantial limitations associated with the effective implementation of analog long-term memory storage solution. Prior research efforts often suffer from limited resolution, hindering their ability to accurately represent fine-grained weight adjustments r...
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