Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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A Hybrid PSO-GCRA Framework for Optimizing Control Systems Performance
Full text linkOptimization is essential for improving the performance of control systems, particularly in scenarios that involve complex, non-linear, and dynamic behaviors. This paper introduces a new hybrid optimization framework that merges Particle Swarm Optimization (PSO) with the Greater Cane Rat Algorithm (GCRA), which we call the PSO-GCRA framework. This hybrid approach takes advantage of PSO's global exploration capabilities and GCRA's local refinement strengths to overcome the shortcomings of each algorithm, such as premature convergence and ineffective local searches. We apply the proposed framework to a real-world load forecasting challenge using data from the Australian Energy Market Operator (AEMO). The PSO-GCRA framework functions in two sequential phases: first, PSO conducts a global search to explore the solution space, and then GCRA fine-tunes the solutions through mutation and crossover operations, ensuring convergence to high-quality optima. We evaluate the performance of this framework against benchmark methods, including EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO. Comprehensive experiments are carried out using metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and convergence rate. Â The proposed PSO-GCRA framework achieves a MAPE of 2.05% and an RMSE of 3.91, outperforming benchmark methods, such as EMD-SVR-PSO (MAPE: 2.85%, RMSE: 4.49) and FS-TSFE-CBSSO (MAPE: 2.98%, RMSE: 4.69), in terms of accuracy, stability, and convergence efficiency. Comprehensive experiments were conducted using Australian Energy Market Operator (AEMO) data, with specific attention to normalization, parameter tuning, and iterative evaluations to ensure reliability and reproducibility
Autonomous Driving Model with Collision Prediction for Urban and Extra-Urban Environments
Full text linkThis study introduces an architecture for an autonomous vehicle control system based on a collision detector and geometric modeling of trajectories. The goal is to develop a robust and reliable control model that can navigate metropolitan environments, often crowded with pedestrians and bicycles, as well as suburban areas, where traffic patterns can fluctuate. We have created a modular control unit that includes a collision predictor, which interacts closely with the decision module. The executed algorithm demonstrates the effectiveness of our system by ensuring the safety and comfort of the passengers. It can identify potential collisions from a distance and initiate braking preventively, following precise guidelines for deceleration and acceleration. To validate our methods, we are looking at simulations of realistic case studies. The research conducted underscores a crucial advancement in the development of safer and more flexible autonomous driving technologies
Revitalization of Sumatra batik motifs from tradition to innovation
No full textSumatran batik has motifs that have deep meanings adopted from everyday life, and many are already rarely produced.The problem in this study is how the process of revitalizing Sumatran batik will be downstream into the creative industry. The purpose of this study is to create a digital revitalization model for rare Sumatran batik motifs, with a focus on their integration into the local creative industry, which can increase cultural awareness and increase market potential in the creative industry. The method used is descriptive qualitative with a visual language approach. The sample in this study is a digital batik with a sample of the Tarok-tarok Boraspati produced by MSMEs. Results show that digitizing batik motifs not only maintains visual authenticity but also enables scalable and market-relevant innovation, contributing to cultural preservation and creative economic growth. The main result is a structured batik composition that maintains the identity of the main motif while enabling wider commercial applications. This approach offers a replicable model for other cultural regions that aim to combine heritage with innovation and increase global cultural visibility through the creative industry
Maintaining service quality: Examining the impact of the HESQUAL scale on student satisfaction in HEIs
No full textThis study aims to examine the effect of service quality and technology support on student satisfaction through student perceived value. Research in Indonesia has yet to extensively investigate service quality in the education sector using the Higher Education Service Quality (HESQUAL) framework, which provides a more accurate measurement for higher education settings. Previous studies have often utilized SERVQUAL indicators, which are not fully suitable for evaluating quality in the context of higher education institutions (HEIs). Moreover, the role of technology support in shaping student perceived value within HEIs has not been thoroughly explored in prior research. A quantitative approach was adopted in this study, employing a non-probability sampling method using purposive sampling. The sample consisted of 246 postgraduate students from a public university in Indonesia. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (SEM-PLS) to examine the relationships among the variables. The findings reveal that service quality, as measured by HESQUAL, has a positive and significant effect on student satisfaction, but does not significantly influence student perceived value. In contrast, technology support has a positive and significant impact on both student perceived value and student satisfaction. These results highlight the critical role of service quality in enhancing student satisfaction within HEIs. The novelty of this study lies in its application of the HESQUAL framework as a context-specific instrument for measuring service quality in higher education, thereby addressing a methodological gap in Indonesian educational research where this approach has been scarcely utilized
Exploring social media use and its impact on knowledge and behavior during Covid-19 in China
Full text linkIn An extended Cognitive Mediation Model (CMM) was constructed to examine the public's knowledge acquisition and preventive behavioral intentions during the COVID-19 pandemic in the context of social media in China. This extended CMM incorporated three additional variables compared with the original CMM: risk perception, interpersonal communication, and behavioral intention. The motivations for social media use, including surveillance gratification, guidance, anticipated interaction, and risk perception, were positively associated with social media attention, elaboration, and interpersonal communication. Elaboration and interpersonal communication were positively associated with factual and structural knowledge acquisition, which in turn positively influenced behavioral intention. Differential mediation effects were observed: significant indirect effects from motivations to factual knowledge involved elaboration and the combination of attention and elaboration, while all mediation effects from motivations to structural knowledge were significant. Furthermore, mediation paths from motivations to behavioral intention were primarily significant when involving elaboration and structural knowledge, but not factual knowledge. Theoretical and practical implications are discussed
Optimizing Virtual Classrooms: Real-Time Emotion Recognition with AI and Facial Features
Full text linkOnline education, especially post-COVID, faces the challenge of maintaining student engagement, particularly at the college level. A key factor in effective learning is understanding students’ emotional states, as they influence comprehension and participation. To address this, we propose an intelligent system that classifies students’ emotions by analyzing facial expressions, allowing teachers to adapt their methods in real-time. Our system utilizes the Learning Focal Point algorithm to improve emotion classification accuracy, focusing on key facial regions related to emotional expressions. The methodology involves preprocessing facial images, extracting features, and classifying emotions using the algorithm. Trained on a diverse dataset, the system performs well under various conditions, with a classification accuracy of 94% based on a well-known database. Although the system shows significant improvements over traditional methods, factors like image quality and internet connection can impact accuracy in realworld applications. Ultimately, our approach enhances remote learning by providing real-time emotional feedback, fostering a more responsive and student-centered environment
Robust Multi-State EEG Cognitive Classification via Optimized Time-Domain Features and CatBoost
Full text linkThis study introduces a novel framework for classifying multi-state cognitive processes using electroencephalogram (EEG) signals. By integrating optimized time-domain feature extraction with ensemble learning techniques, the proposed method achieves exceptional accuracy in distinguishing eight distinct cognitive states. The preprocessing pipeline employs finite impulse response (FIR) bandpass filtering (0.5–45 Hz) and Independent Component Analysis (ICA) for artifact removal, while feature extraction leverages Hjorth parameters and statistical measures. A comparative analysis of classification algorithms reveals CatBoost as the top performer, achieving 93.4% accuracy, followed by Neural Network (91.3%), SVM (89.7%), and AdaBoost (88.9%). CatBoost excels in discriminating complex states with computational efficiency, processing times ranging from 18 ms (SVM) to 32 ms (CatBoost), supporting real-time applications. The framework demonstrates robustness under varying signal quality, maintaining 91% accuracy at 10 dB SNR. These advancements set new benchmarks for EEG-based cognitive monitoring, with implications for adaptive systems requiring real-time neural feedback
Enhanced Advanced Multi-Objective Path Planning (EAMOPP) for UAV Navigation in Complex Dynamic 3D Environments
Full text linkUnmanned Aerial Vehicles (UAVs) have emerged as vital tools in diverse applications, including disaster response, surveillance, and logistics. However, navigating complex, obstacle-rich 3D environments with dynamic elements remains a significant challenge. This study presents an Enhanced Advanced Multi-Objective Path Planning (EAMOPP) model designed to address these challenges by improving feasibility, collision avoidance, and path smoothness while maintaining computational efficiency. The proposed enhancement introduces a hybrid sampling strategy that combines random sampling with gradient-based adjustments and a refined cost function that prioritizes obstacle avoidance and path smoothness while balancing path length and energy efficiency. The EAMOPP was evaluated in a series of experiments involving dynamic environments with high obstacle density and compared against baseline algorithms, including A*, RRT*, Artificial Potential Field (APF), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Results demonstrate that the EAMOPP achieves a feasibility score of 0.9800, eliminates collision violations, and generates highly smooth paths with an average smoothness score of 9.3456. These improvements come with an efficient average execution time of 6.6410 seconds, outperforming both traditional and heuristic-based methods. Visual analyses further illustrate the model's ability to navigate effectively through dynamic obstacle configurations, ensuring reliable UAV operation. Future research will explore optimizations to further enhance the model's applicability in real-world UAV missions
Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier
Full text linkThis article presents a wavelet analysis-singular value decomposition (WA-SVD) based method for precise fault localization in recent power distribution networks using k-NN Classifier. The WA-SVD leverages the slime mould algorithm (SMA) and graph theory (GT) in enhancing the overall accuracy of fault localization. To validate the proposed methodology, extensive tests are conducted on various benchmark systems, including the IEEE 33-bus radial distribution system, the IEEE 33-bus meshed loop unbalanced distribution system, the IEEE 33-bus system with integrated renewable energy sources, and the IEEE 13-bus feeder test system. The results demonstrate a high fault classification accuracy of 99.08%, with an average localization error of just 1.2% of the total line length. The k-NN classifier exhibited a precision of 98.2% and a recall of 99.2%, underscoring the reliability and sensitivity of the proposed method. Additionally, the computational efficiency of the algorithm is evidenced by an average processing time of 0.0764 seconds per fault event, making it well-suited for real-time applications
Art therapy facilities as a supportive environment for teen mental health recovery
Full text linkThe prevalence of mental health issues among adolescents in Indonesia has significantly increased; however, this rise has not been accompanied by adequate recovery facilities, particularly for this vulnerable age group. Existing mental health recovery facilities predominantly emphasize conventional psychotherapy methods. In contrast, art therapy provides a more engaging and interactive approach by utilizing art as a medium to facilitate the recovery process. Therefore, this study aims to identify the essential facilities required in an art therapy center, particularly in supporting the recovery of mental health disorders among adolescents in Indonesia. The research approach was conducted using an inductive qualitative method, including observation and field surveys at established art therapy facilities. This method, is analyzes data, essentially involving producing an overall summary of the content of the data set. The findings indicate that art therapy is a deeply personal experience for each individual, as it involves expressing emotions. Because of this, the approach should be personalized to each person's unique needs and supported with a variety of art media and appropriate facilities to support a meaningful and effective creative process. Due to the complexity of various methods that can be used in art therapy, further research is needed to explore the relationship between art therapy methods and the design of specialized art therapy spaces