Taiwan Association of Engineering and Technology Innovation: E-Journals
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Smart Streetlight Energy Saving System Based on mmWave Radar
Get PDFStreetlights serve as fundamental infrastructure to meet the lighting needs of people on every road. However, their extensive deployment often results in unnecessary energy waste, with many streetlights maintaining high brightness despite minimal usage during the night. This study aims to develop a smart energy-efficient streetlight system that automatically adjusts lighting levels based on the absence of vehicles and pedestrians, detected after a 3-minute countdown. Specifically, the study utilizes mmWave radar to collect point cloud data, which undergoes denoising through Doppler, DBSCAN, and XYZ techniques. Additionally, the mmWave radar assists in training an LSTM model to identify pedestrian pathways. The implementation of the proposed system significantly reduces energy consumption and annual costs by automatically dimming or turning off streetlights in areas with minimal pedestrian activity during nighttime
Precision Geolocation of Medicinal Plants: Assessing Machine Learning Algorithms for Accuracy and Efficiency
Get PDFThis study investigates the precision geolocation of medicinal plants, a critical endeavor bridging ecology, conservation, and pharmaceutical research. By employing machine learning algorithms—gradient boosting machine (GBM), random forest (RF), and support vector machine (SVM)—within the cross-industry standard process for data mining (CRISP-DM) framework, both the accuracy and efficiency of medicinal plant geolocation are enhanced. The assessment employs precision, recall, accuracy, and F1 score performance metrics. Results reveal that SVM and GBM algorithms exhibit superior performance, achieving an accuracy of 97.29%, with SVM showing remarkable computational efficiency. Meanwhile, despite inferior performance, RF remains competitive especially when model interpretability is required. These outcomes highlight the efficacy of SVM and GBM in medicinal plant geolocation and accentuate their potential to advance environmental research, conservation strategies, and pharmaceutical explorations. The study underscores the interdisciplinary significance of accurately geolocating medicinal plants, supporting their conservation for future pharmaceutical innovation and ecological sustainability
Iris Recognition Scheme Based on Entropy and Convolutional Neural Network
Get PDFThis study presents an advanced iris image segmentation approach to overcome vibration and occlusion from the lashes. The proposed scheme removes the surrounding areas of the iris image to recover the region of interest (ROI) containing the iris images. The entropy function and mathematical morphology are employed as the foundation of the proposed scheme. Initially, the entropy function is applied to the binarization image. Subsequently, the ROI is cropped and extracted from the binary image using the dilation method. Furthermore, a convolutional neural network (CNN) is used in the recognition phase. The database of the Indian Institute of Technology Delhi (IIT Delhi) serves as a test. The results yield a high level of accuracy—up to 93% during segmentation. Using half of the dataset during the recognition phase results in an accuracy of 98.8%, while using the complete database produces an accuracy of 97.5%
Influence of Surface Roughness on Durability of New-Old Concrete Interface
Get PDFThe bond zone between old and new concrete is greatly affected by environmental factors. This study investigates the impact of surface roughness on durability using as-cast surface (CS), drilled holes surface (DS), and grooved surface (GS). After a 28-day water-curing, specimens undergo a 5% NaCl solution immersion for 30 and 60 days; exposure to temperatures of 200 ℃ and 500 ℃; and a water permeability test. Slant shear and splitting tensile tests assess durability. Results show that CS exhibits the greatest decrease in resistance to sodium chloride solution and temperature, while DS and GS show less pronounced effects. At 500 ℃, CS and DS specimens fail, whereas GS retains 50% and 75% of its shear and tensile strengths, respectively. GS has the lowest water permeability (7 × 10-11 m/s), followed by DS (1.2 × 10-10) and CS (1.5 × 10-10). Overall, surface roughness enhances durability and mitigates environmental effects
A Novel Approach to Construct Finite Automata Using Grid and Product Automata
Get PDFThis research aims to utilize an organized grid-based strategy to make the development of complex Finite Automata easier. Product Automata are used to merge many automata into a single automaton to integrate various computing processes. Combining these methodologies provides novel methods of improving the scalability and efficiency of automata building, broadening the field of research for automata theory and its applications. The scalability of this developed automated system will benefit sectors such as automotive production and logistics. The results indicate considerable improvements in construction time, memory usage, scalability, and resilience compared to older approaches. The performance of the developed method, as measured by construction time, memory utilization, scalability, robustness, application range, and complexity, is 25% to 50% higher than that of traditional methodologies in the literature
Effect of Mean Flow on the Transmission Loss of a Doubly Tuned Flow Reversal Muffler
Get PDFThe same-end-inlet-outlet (SEIO) muffler, also referred to as a flow reversal muffler under flow conditions, features inlet and outlet pipes positioned on the same side of the chamber. Recently, a parametric expression has been developed to determine the end correction for double tuning of the SEIO muffler. This study extends the development of the SEIO muffler by experimentally validating the derived end correction expressions. Additionally, the tuning of the muffler is assessed with a mean flow using 3-D computational fluid dynamics, solving the linearized Navier-Stokes equation. This investigation explores the impact of flow conditions (Mach number 0.05 and 0.1) and temperature conditions (T = 733 K and 953 K) on the transmission loss (TL) of a doubly tuned muffler. The findings reveal that the muffler maintains its double tuning, even in the presence of mean flow at elevated temperatures, albeit with somewhat of a reduction in performance
An Efficient Application of Modified YOLOv5 in Basketball Player Detection and Analysis
Get PDFEffective analysis helps players evaluate their performance, make necessary adjustments, and develop diverse game strategies. Moreover, the analysis provides viewers with different perspectives, enhancing their understanding of the game. This study aims to develop a basketball player detection and analysis system to assist in analyzing on-court situations. The system uses perspective transformation to obtain player tracking information on the top view image in basketball games. The system uses a modified you only look once (YOLO) v5 model that replaces the backbone of YOLOv5s with the MobileNetv3-small architecture for player detection. Compared to the original YOLOv5, the modified YOLOv5 reduces parameters from 7.02 × 106 to 3.5 × 106, a decrease of 49.8%. The number of frames obtainable per second increases from 12.4 to 17.5, an improvement of about 41.1%. Finally, the system performs perspective transformation and tracks the detected player positions onto the top-view court image using the YOLOv5 model
Advanced Gallbladder Segmentation in Dynamic Ultrasound Imaging Using Fully Convolutional Networks
Get PDFThis study develops an advanced technique for segmenting the gallbladder from dynamic B-mode ultrasound images to enhance the accuracy and efficiency of volumetric analysis in medical diagnostics. Using a Wi-Fi probe, volumetric data are captured and processed into labeled images for training a fully convolutional network (FCN) model with specifications including an epoch of 9, a batch size of 3, and a learning rate of 0.001. Performance metrics such as global accuracy, mean accuracy, and Intersection over Union (IoU) are evaluated. The MobileNetV2 architecture achieves a maximum mean IoU of 0.690 and a mean Boundary F1 (BF) score of 0.990, while the ResNet50 architecture demonstrates significant effectiveness. This study substantiates the effectiveness of the MobileNetV2 architecture for precise gallbladder segmentation in dynamic B-mode ultrasound imaging
Locomotion Control of a Bipedal Wheeled Robot Using Virtual Model Control and Linear Quadratic Regulator Techniques
Get PDFThis paper aims to develop a balance control technique and investigates its impact on the stability and disturbance rejection capability of a bipedal wheeled robot. The bipedal wheeled robot is equivalent to a wheeled inverted pendulum nonlinear model with a legs-airframe centroid variable rod. The nonlinear model is linearized and decoupled into two subsystems: straight-line control using linear-quadratic regulator (LQR) for balance and speed, and steering control employing proportional integral derivative (PID). Height control adjusts the virtual force with PID-Feedforward, while hip torque is determined by virtual model control (VMC). MATLAB simulation confirms effective control of height, linear motion, and steering, with decoupling enhancing steering performance
Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds
Get PDF The study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggests that the computer-based system serves as a decision-making tool for classifying pulmonary diseases, especially in resource-limited settings