Indonesian Journal of Electrical Engineering and Computer Science
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Inertia factor and crossover strategy based particle swarm optimization for feature selection in emotion classification
Get PDFEmotion recognition using electroencephalography (EEG) is a better choice because it can’t be easily mimicked like facial expressions or speech signals. The emotion of EEG signals is not the same and vary from human to human, as everyone has different emotional responses to similar stimuli. Existing research has achieved lesser classification accuracy as it relies on whole feature subsets that include irrelevant features for classifying emotions. This research proposes the inertia factor and crossover strategy (IFCS)-based particle swarm optimization (PSO) algorithm to select relevant features for classification, which removes irrelevant features and enhances classification performance. Then, the self-attention with gated recurrent unit (SA-GRU) method is developed to classify the valence and arousal emotion classes, which focuses much on the significant parts of emotions and reaches high classification accuracy. The proposed IFCS-PSO and SA with GRU method achieved an accuracy of 98.79% for the valence class and 98.03% for the arousal class of the DEAP dataset, outperforming traditional approaches such as convolutional neural networks (CNN)
Wirelength estimation for VLSI cell placement using hybrid statistical learning
Get PDFOptimizing wirelength involves predicting the total length of wires needed to connect different components within a chip during cell placement. It is a fundamental challenge in very-large-scale integration (VLSI) of integrated circuit (IC) design, as it directly impacts the overall performance and manufacturability of chips. Accurate wire-length estimation in the early stages of the design process is critical for guiding subsequent optimization tasks. This paper proposes a novel hybrid linear regression wirelength (hybrid-LRWL) method that combines the strengths of existing methods rectilinear Steiner minimal tree (RSMT) for low-degree nets and a statistical learning-based approach for high-degree nets. Additionally, it compares the performance of three well-established wirelength estimation techniques: half-perimeter wirelength (HPWL), rectilinear minimum spanning tree (RMST), and RSMT. The methods were evaluated using the International Symposium on Physical Design (ISPD) 2011 benchmark suite, considering accuracy and computational efficiency. The experimental results demonstrated that the proposed hybrid method achieves superior accuracy, with a mean error of less than 0.05% in total wirelength, closely approximating RSMT results. The proposed method reduces computational time up to 3.6 times faster than traditional RSM-based methods. The results establish a strong framework for accurate and efficient wirelength estimation in VLSI design for modern, high-performance ICs
Optimizing timing closure and enhancing efficiency in RTL design: a focus on physical design tasks for I2C design blocks
Get PDFAchieving precise timing closure in integrated circuit (IC) design is a significant challenge, especially with today's rapid technology advancements and intricate design specifications. Even with intense post-synthesis optimization, timing violations persist particularly in multi-corner, multi-mode designs. This research work emphasizes the necessity for power-efficient methods and streamlined approaches to boost timing closure and physical verification. Modern IC design thrives on effective physical design optimization strategies, usually tackled top-down. Clock tree synthesis (CTS) is transformative which effectively addresses clock deviation, latency, transition time, and insertion delay. This investigation mainly focuses on improving timing closure for inter integrated circuit (I2C) design blocks using custom-designed ccopt_spec and mmmc.tcl files to support multi-corner, multi-mode settings and significantly reduces register-to-register path violations from 80 to. 0. Additionally, the development and the usage of mmmc.tcl and global files are highlighted as critical components in the design process
Performance of rocket data communication system using wire rope isolator on sounding rocket RX
Get PDFThe rocket experiment (RX) ballistic rocket requires a reliable data communication system capable of withstanding intense vibrations and shocks during flight. This study investigates the application of wire rope isolators (WRI) to damper mechanical disturbances and protect the rocket's communication system. Installation of WRI position and direction in this experiment with compression position. A series of vibration tests were conducted using 4 WRI installed in the rocket’s 30 kg data communication compartment, vibration test results frequency between 4 Hz and 1500 Hz with acceleration of 8.37 g to 20.37 g, higher "g" readings on the test object sensor compared to vibration machine readings are usually caused by phenomena such as resonance, differences in dynamic response, non-linear behavior, sensor placement location, and swing effects when the vibration machine oscillates. This is a natural mechanical response to external vibrations during testing. While the results of flight tests rocket RX has an acceleration of 8 g to 9.3 g. The results showed that the WRI dampers are effective in protecting the data communication system and ensuring the uninterrupted transmission of flight data to the ground control station (GCS)
A framework for security risk assessment of blockchain-based applications
Get PDFBlockchain technology has revolutionized various industries by enabling decentralized, transparent, and tamper-resistant digital transactions. However, despite its benefits, blockchain-based applications are vulnerable to security threats such as smart contract exploits, 51% attacks, Sybil attacks, and private key compromises, posing significant risks to their integrity and reliability. Traditional security frameworks lack a comprehensive approach to systematically assess and mitigate these risks across different blockchain layers. To address this challenge, this paper proposes the blockchain cybersecurity risk assessment model (BCRAM), a structured framework designed to identify, analyze, evaluate, and mitigate security risks in blockchain systems. The methodology involves categorizing threats, assessing risks using quantitative and qualitative techniques, and validating the model through a case study on Ethereum. Results demonstrate that implementing BCRAM led to a 65% reduction in smart contract exploits, a 70% decrease in phishing incidents, and an 85% improvement in distributed denial of service (DDoS) resilience, proving its effectiveness. This research offers a standardized risk assessment approach, providing valuable insights for developers, security analysts to enhance blockchain security
Optimizing energy efficiency in wireless sensor networks with integration of Calinski-Harabasz index in K-means clusterings
Get PDFThe optimization of energy consumption and the assurance of efficient data transmission are critical factors in enhancing the longevity and performance of wireless sensor networks (WSNs). This study introduces an advanced clustering technique aimed at prolonging the network's lifespan while facilitating reliable data delivery. By integrating the Calinski-Harabasz index into the traditional K-Means clustering approach, the methodology evaluates the quality of clusters and determines the optimal number of clusters, which leads to better node organization within the network. Moreover, the selection of routing pathways from cluster heads to the base station is strategically optimized to conserve energy. Simulation results demonstrate that this novel dual enhancement technique surpasses traditional K-Means in multiple areas, including power consumption, network reliability, and successful data delivery. Consequently, the suggested advancements in cluster formation and routing substantially enhance the performance of energy-limited wireless sensor networks, boosting their robustness and reliability in practical applications
Influences of the Sm3+ -Eu3+ codoped Ba2Gd(BO3)2Cl phosphors on the commercial white light emitting diodes
Get PDFThe color quality of current commercial white light emitting diodes (wLEDs) suffers low performance owing to the lack of the red-emission component. Developing quality and stable red-emission phosphors is feasible among various approaches to obtain the red spectral supplement for the w-LEDs in the pursuit of color quality improvement. In this paper, the Sm3+-Eu3+ codoped Ba2Gd(BO¬3)2Cl (BGBC:Sm-Eu) red phosphor was proposed for using in commercial w-LEDs. Its luminescence and influences on w-LED properties were simulated and presented. The solid-phase method was utilized for the fabrication of the phosphor. The results indicated that the phosphor emitted the strong emission in orange-red region with a peak centering at 593 nm. It can be caused by the proficient power shift between Sm3+ and Eu3+. In the w-LED package, the presence of BGBC:Sm-Eu phosphor stimulated the scattering efficiency to promote the blue-light conversion and extraction. The orange emission spectrum of the w-LED increased with the higher BGBC:Sm-Eu doping amount. The luminous strength of the w-LED was enhanced and so was the color temperature uniformity. The color rendering properties declined with high BGBC:Sm-Eu phosphor concentration owing to the red-light dominance over the light spectrum. The BGBC:Sm-Eu phosphor is a promising red phosphor for improving commercial w-LED color-temperature stability and luminosity. It also helps to obtain full-spectrum w-LED with high color rendition when combined with other blue-to-green luminescent materials
Path planning of an elongated undulating fin using mutant particle swarm optimization
Get PDFThis paper proposes a mutant particle swarm optimization algorithm (M-PSO) to optimize the power energy of a bio-mimetic robotic fish that comprises sixteen undulating fin-rays equipped to a fish robot. The main objective is to obtain the shortest path for the fish robot to achieve the desired position while minimizing power consumption. The proposed MPSO is a recent generation of particle swarm optimization (PSO) that employs the removal of the worst particles to accelerate the swarm, enabling particles to escape local minima and improve the propulsive efficiency of the fish robot. Simulation results demonstrate that the developed M-PSO consumes less energy and requires less time compared to the original PSO and genetic algorithm (GA). Moreover, the M-PSO was tested on a robotic fish navigating an unknown environment characterized by complex spatiotemporal parameters, showcasing its superiority over other methods in all case studies
Artificial neural network based load flow analysis of radial distribution system in Kurdistan region
Get PDFToday electric energy is the most commonly used source in the world. Power flow (load flow) analysis is conciderd as the backbone of any power system analysis and design; they have a great necessity for operating systems, future planning, fault analysis, and contingency analysis. For better utilization of electrical power, off-line modeling and simulation of power systems using powerful software are essential and significant task especially in developing countries and regions. Therefore, this paper performs a comparison study of conventional and non-conventional load flow techniques for a 24-Bus radial distribution system in the governorate of Sulaymaniyah. The conventional power flow techniques include the Newton-Raphson (NR), and Gauss-Seidel (GS) techniques, while the nonconventional load flow technique utilizes the artificial neural network (ANN). Modeling, simulation, and analysis of the 24-Bus feeder are performed using MATPOWER simulation tool. The MATPOWER and neural network techniques are implemented independently, and it has been proved that ANN model efficiently estimated the power flow analysis for the system mentioned above, the high regression values of nearly 0.999 indicates that the ANN model can be used as an efficient tool to perform power flow analysis
An innovative approach to Raga pattern identification
Get PDFRaga is a fundamental element of Indian classical music (ICM), crucial for identifying the unique characteristics of a given song. Recognizing the embedded Raga allows for various applications, including music therapy, and leveraging the therapeutic effects of different Ragas. The use of mathematical techniques such as fast fourier transform (FFT) and fundamental frequency measurement (FFM) in calculating note values has proven effective for Raga pattern recognition. Both methods yield nearly identical results, facilitating accurate identification of Ragas. Once identified, these Ragas can be used for specific therapeutic purposes, harnessing their healing potential