An active noise control system using giant magnetostrictive actuator has been proposed as a countermeasure for interior noise of the ultra-compact mobilities, which is a new transportation tools. In this paper, as bas...
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ISBN:
(数字)9784886864406
ISBN:
(纸本)9798350379105
An active noise control system using giant magnetostrictive actuator has been proposed as a countermeasure for interior noise of the ultra-compact mobilities, which is a new transportation tools. In this paper, as basic research toward the development of the proposed giant magnetostrictive actuator for active noise control, we conduct experiments on the material properties when terbium, dysprosium, and iron powder, which make up the giant magnetostrictive material, are mixed and the heat treatment conditions are changed. In this study, the heat treatment temperature was varied from $800^{\circ}\mathrm{C}$ to $1100^{\circ}\mathrm{C}$ in $100^{\circ}\mathrm{C}$ incrementsbased on the equilibrium state temperature of each material, and the heat treatment time was set in 30-minute increments from 1 hour to 3 hours. As a result, TbFe2 was confirmed after heat treatment at an electric furnace temperature of $1000^{\circ}\mathrm{C}$ for 3 hours, although its strength was insufficient.
This study introduces the Harmony Model, which examines factors influencing the success of online learning and the workload of lecturers. Drawing upon the Theory of Action Reason, it explores the impact of student sat...
This study introduces the Harmony Model, which examines factors influencing the success of online learning and the workload of lecturers. Drawing upon the Theory of Action Reason, it explores the impact of student satisfaction and lecturers' willingness to prepare course materials. The model emphasizes the challenge faced by lecturers in creating high-quality content and highlights the importance of balancing supporting factors and alleviating the associated burden. Strategies to enhance online learning effectiveness should address both lecturers' motivation and student satisfaction. Identifying determinant factors and analyzing their impact on material development can increase lecturers' willingness to provide appropriate course materials. Knowledge extraction processes and satisfaction-related factors play crucial roles. This study offers insights to enhance the online learning experience, optimize learning outcomes, and improve overall effectiveness.
Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environme...
ISBN:
(数字)9781839539954
Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications.
Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a c...
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ISBN:
(数字)9798331517601
ISBN:
(纸本)9798331517618
Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.
Only a few studies have so far focused on the addition of silver to SS316L alloys by conventional sintering methods. Unfortunately, the metallurgical process of silver-containing antimicrobial SS is greatly limited du...
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In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience clo...
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ISBN:
(数字)9798350308365
ISBN:
(纸本)9798350308372
In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience closer contact with nature near of big urban centers. Eventually, visitors get lost, and helping these people with velocity is important to avoid severe incidents. Normally, rescue operations mobilize firefighters, ex-pensive equipment like helicopters and public resources. Following that idea of reducing search time in rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). To find high-quality solutions, this paper proposes an Ant Colony Optimization algorithm enhanced with Reinforcement Learning to create an adaptive decision-making algorithm.
Managing the traffic system is a multifaceted challenge, imposing various solutions to prevent congestion. Traffic management aims to create an efficient control system with numerous advantages, including reduced fuel...
Managing the traffic system is a multifaceted challenge, imposing various solutions to prevent congestion. Traffic management aims to create an efficient control system with numerous advantages, including reduced fuel emissions, lower stress levels, and enhanced driving discipline. Max Pressure stands as one of the techniques for traffic control, designed to adapt and enhance network throughput by considering pressure, defined as the difference between upstream and downstream queue lengths. Recent developments have yielded improved performance in Max Pressure through learning. In this adaptive control mechanism, the concept of Max Pressure is integrated as a reward function within reinforcement learning. This study introduces the “Max Pressure learning” concept, revealing its potential for creating adaptable traffic controllers. By modifying the pressure equation and transforming it into a performance metric, the original Max Pressure concept effectively responds to diverse traffic conditions. This paper showcases research findings demonstrating the surprising effectiveness of pressure as a control mechanism in the context of the learning process. Ultimately, using pressure enables controllers to efficiently alleviate gridlock situations in traffic, highlighting its promising role in optimizing traffic management and enhancing overall traffic system performance.
Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher educ...
Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.
This article describes the estimation of a 3D point using a Kinect sensor and the Robot Operating system (ROS) along with You Only Look Once (YOLO) for object detection. The Kinect sensor provides RGB-D images, which ...
This article describes the estimation of a 3D point using a Kinect sensor and the Robot Operating system (ROS) along with You Only Look Once (YOLO) for object detection. The Kinect sensor provides RGB-D images, which are used to create a Point Cloud representing the geometry of the environment. ROS is used as a robotics development framework, while YOLO is employed to identify objects in the scene. The article presents the packages used, the datasets used for measurement, and the configuration of ROS and YOLO. Additionally, the functionalities of RViz, a 3D visualization tool used in the tests, are explored. Furthermore, it covers the methods employed, the acquired data, and an analysis of the error margin in relation to the measurement of the distance between the Kinect and the object. The findings and techniques presented in this study contribute to addressing the challenges faced in the RoboCup@Home competition, specifically in the context of object manipulation tasks.
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