Taiwan Association of Engineering and Technology Innovation: E-Journals
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Evaluation of Passenger Thermal Comfort for Two Different Underground Metro Station Typologies in Istanbul
Get PDFThis study examines two different underground metro station typologies in Istanbul to evaluate passenger thermal comfort conditions. Long-term field measurements are conducted, and the relative warmth index (RWI) values are calculated to compare the stations’ thermal comfort conditions. The RWI method is employed due to transient environments in metro stations. Dry-bulb temperature and relative humidity data are simultaneously measured at 19 points in Şişli/Mecidiyeköy and 17 points in Gayrettepe station at one-minute intervals. The measurements are conducted every day throughout spring, summer, and autumn. The results show that the expected thermal comfort conditions at the stations are not met for all three seasons. Passenger thermal comfort is at its lowest level in summer, followed by autumn and spring. The RWI values for the platform and concourse levels at the cut-and-cover type are higher than at the bored-tunnel type station due to the lower train-induced air velocity in cut-and-cover type stations
Low Complexity High Throughput Low Density Parity Check Code Based on Compromised Iteration over 5G Out Door Channel
Get PDFThis paper presents a framework for determining an optimized decoding iteration value for low-density parity-check (LDPC) decoders, considering factors such as error rate performance, complexity, throughput, and latency. The compromised iteration is calculated at a specific signal-to-noise ratio (SNR). At this SNR, the error rate performance of the LDPC code meets the requirement for 5G by achieving a Bit Error Rate (BER) less than 10-4. The optimized decoding iteration value is determined for a given coding length, rate, and communication channel. The system has been evaluated using two channel models, additive white Gaussian noise and 5G channels. The results show that the proposed approach reduces the overall complexity and latency of the decoder by up to 50%, and enhances the decoder throughput by up to 99%. However, this improvement comes at the cost of a bit error rate degradation in a range between 0.1 and 0.6 dB
Performance-Based Design Response Spectrum Evaluation for a Peninsular Indian Site
Get PDFThe performance-based design response spectrum (PB-DRS) is perceived as the requisite of performance-based design of structures, systems, and components of nuclear facilities. In view of such requirement, this study evaluates PB-DRS carriers for a Peninsular Indian site. A probablistic seismic hazard analysis with multi-expert participation is deloyed to obtain seismic hazard results. Furthermore, PB-DRS from the uniform hazard response spectrum, regulatory guide 1.208, and ASCE 43-05 are respectively used to further evaluate and compare. The results reveal that PB-DRS from the uniform hazard response spectrum and regulatory guide 1.208 can be used for the performance-based seismic design, e.g., reactor buildings. Meanwhile, PB-DRS from ASCE 43-05 can be used for floor-molding components such as steam generators
A Systematic Review of Coal Mine Dust Suppression Methods Based on Numerical Simulations and Experimental Investigations
Get PDFLarge quantities of dust are generated during coal mining and transportation, posing a threat to workers’ health. Therefore, this article conducts a systematic review of the literature on coal mine dedusting. This study examines coal mine dust suppression methods by integrating numerical simulations and experiments, focusing on four aspects: the structural improvement of the dust remover, chemical modification, the optimization of the operating environment, and the ventilation system. The structural improvement of a dust remover primarily involves optimizing the nozzle’s structure and size, particularly the Laval structure. The findings indicate that alterations in the surface structure of the Laval nozzle’s contraction section have minimal effect on the airflow velocity. Chemical modification of the dust remover can enhance the wetting properties of coal dust and includes non-phytochemical and phytochemical modification. Molecular Dynamics (MD) simulations are frequently employed in chemical modification. The optimization of the operating environment for dust removers focuses predominantly on spray pressure optimization
Risk Management Framework-Based Failure Mode and Effect Analysis for AI Risk Assessment
Get PDFAs artificial intelligence (AI) technologies continue to spread into human life, developers must ensure benefits while minimizing the risk of adverse impacts. This study aims to evaluate risks in real-world AI applications using the AI Incident Database. It employs Failure Mode and Effect Analysis and the National Institute of Standards and Technology AI Risk Management Framework to identify failures, their causes and effects, and assess how current systems address them. A total of 100 incident reports were analyzed. The findings indicate frequent failures in autonomous systems and biased predictions. Seven cases were classified in the highest risk categories, including those involving physical harm and loss of life. Over 80% failures originated from algorithmic flaws or poor data quality. The method employed successfully evaluates the risks in current AI applications, revealing critical gaps in risk management and emphasizing the urgent need for targeted safeguards and proactive mitigation strategies
Lighting Design for Visual Comfort and Energy Efficiency Considerations: A Patient Room Case Study
Get PDFDiscomfort glare causes unease and distraction, significantly affecting patients, staff, and visitors. Achieving visual comfort is essential for glare reduction, as it is primarily influenced by artificial lighting in the workplace. This study examines the probability of visual comfort and the unified glare rating (UGR) as measures of discomfort glare. UGR calculations compare three types of artificial lighting sources in a hospital patient room, considering both visual comfort and energy efficiency. This study analyzes different lighting installations with a focus on surface properties and their relative height as critical factors for enhancing visuals and reducing energy consumption. The results show that increasing the reflection coefficient can reduce energy consumption while improving visual comfort. Although LED lighting generally outperforms traditional lamps, the latter can still achieve significant performance improvements with increased surface reflectance
Recursive Feature Elimination and Optimized Hybrid Ensemble Approach for Early Heart Disease Prediction
Get PDFEarly machine learning prediction improves patient health and prevents heart disease, one of the leading causes of morbidity worldwide. However, challenges such as noise and incomplete data often obscure patterns critical for accurate predictions, and single-classifier models may fail to capture data complexity. This study aims to develop a robust ensemble model leveraging advanced feature selection techniques to enhance prediction accuracy. Various machine-learning algorithms are examined. Recursive feature elimination is applied to remove irrelevant features, improving model performance. The hybrid ensemble method achieves 93.15% accuracy, 93.15% precision, and 92.97% recall, outperforming Principal Component Analysis and symmetrical uncertainty methods. This research sets a benchmark for future studies by leveraging hyperparameter tuning and advanced feature selection to optimize feature reduction and machine learning models
Multi-Target Robot Path Planning Using Enhanced Genetic Algorithms and Probabilistic Roadmaps
Get PDFPath planning receives considerable attention over the last two decades. This study proposes a hybrid approach that combines the probabilistic roadmap with an enhanced genetic algorithm (EGA), enabling path planning for both single and multiple targets. Compared with existing genetic algorithm (GA) methods, the proposed approach offers three main advantages: (1) it employs an environment representation based on image processing and morphological operations; (2) it introduces a new strategy for creating the initial population of the GA; and (3) it incorporates a novel operator to increase the quality of the generated paths. To demonstrate the effectiveness of the probabilistic roadmap and enhanced genetic algorithm (PRMEGA), multiple simulation experiments are performed, with results compared against the GA, artificial bee colony, and particle swarm optimization. The proposed approach outperforms existing methods by 25.5%, achieving near-optimal paths for both single and multiple targets in fewer generations while also reducing computation time by 14.1%
A New Aspect in Analysis and Improvement of Standalone Solar-Driven Absorption Refrigeration Systems
Get PDFSolar-driven absorption refrigeration systems (ARSs) are subjected to work under off-design conditions due to the driving temperature variation. In this study, a model of NH3/H2O ARS with 100-kW cooling capacity has been developed. Energetic and exergetic coefficients of performance (COP, ECOP), besides cooling production (Qeva), have been investigated at off-design conditions. The analysis indicates a reduction in the effectiveness of the generator and solution heat exchanger (SHX) under such conditions. A new method to improve the off-design system’s performance by modifying the generator and SHX heat capacities is suggested. Results revealed that an increase in heat capacities of the generator and SHX (UAgen, UASHX) effectively improves the system’s performance. Raising the values of UAgen and UASHX by 20% maintains the system’s COP, ECOP, and Qeva near their designed values under a wider range of driving temperatures (100 oC to 92 oC). Moreover, this adjustment helps decrease the system’s cut-in/off temperature
The Prediction of Low-Rise Building Construction Cost Estimation Using Extreme Learning Machine
Get PDFThis study aims to predict the possibility of low-rise building construction costs by applying machine learning models, and the performance of each model is evaluated and compared with ensemble methods. The artificial neural network (ANN) emerges as the top-performing individual model, attaining an accuracy of 0.891, while multiple linear regression and decision trees follow closely with accuracies of 0.884 and 0.864 respectively. Ensemble methods like maximum voting ensemble (MVE) improve the accuracy beyond individual models with an impressive accuracy rate of 0.924. Meanwhile, the stacking ensemble and averaging ensemble also demonstrate competitive performance with accuracies of 0.883 and 0.871, respectively. These findings can result in more informed decision-making, which is valuable for the real estate industry