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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest 8

Borneo Smart Forest Information System for Management of Dip...

引用

8th IEEE Information Technology International Seminar, ITIS 2022

作者： Wati, Masna Puspitasari, Novianti Hairah, Ummul Widians, Joan Angelina Septiarini, Anindita Tjikoa, Ade Fiqri Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Kalimantan's tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan's rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation. © 2022 IEEE.

关键词： Information systems

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Sustainable Making Decision in Design of Concrete Tunnels Using Durable FRP Reinforcement

Sustainable Making Decision in Design of Concrete Tunnels Us...

引用

Decision Aid sciences and Application (DASA), International Conference on

作者： Basil Ibrahim Hend Elzefzafy Hamdy M. Mohamed dept. of Civil and Building Engineering University of Sherbrooke Quebec Canada College of Engineering Applied Science University Bahrain

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper explores the innovative use of Fiber-Reinforced Polymer (FRP) reinforcement in the design of concrete tunnels, emphasizing the material's unique properties and advantages over traditional reinforcement methods. FRP bars offer enhanced durability and corrosion resistance, making them particularly suitable for underground applications where environmental conditions can lead to significant degradation of conventional materials. The study highlights moreover the mechanical properties of FRP and the implications of these properties for structural design, including considerations for load-carrying capacity and bonding behavior with concrete. Through a comprehensive review of existing design codes and standards, the research highlights the challenges and opportunities associated with integrating FRP reinforcement in tunnel construction. The findings underscore the importance of adapting design guidelines to accommodate the unique characteristics of FRP, paving the way for more sustainable and efficient tunnel designs.

关键词： Resistance Codes Design methodology Corrosion Concrete Mechanical factors Polymers Bonding Standards Bars

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An Extended Reality Platform Powered by Large Language Models: A Case Study on Teaching Dance Costumes

An Extended Reality Platform Powered by Large Language Model...

引用

IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Silvia Garzarella Giacomo Vallasciani Pasquale Cascarano Shirin Hajahmadi Elena Cervellati Gustavo Marfia Dept. of the Arts University of Bologna Bologna Italy Dept. of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

In this paper, we present an Extended Reality (XR) platform powered by Large Language Models (LLMs), designed to support education in dance history and cultural heritage. The platform here applied to a case study on a dance costume, allows users to interact with 3D models, annotate them, and explore their historical and cultural contexts collaboratively. Using features like image-to-LLM queries, users can gain insights into costume details and performance history, and the annotation tools enable uploading multimedia resources, thereby simulating an archival research environment. By integrating LLMs, the platform provides tailored information and context on demand, enriching the user experience with detailed explanations about objects, such as costume construction, usage, and cultural significance.

关键词： Solid modeling Three-dimensional displays Extended reality Annotations Large language models User experience Cultural differences History Context modeling

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Practical Implementation of Q-Learning and Object Detection for Mobile Robot Path Planning

Practical Implementation of Q-Learning and Object Detection ...

引用

2024 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2024

作者： Valcourt, John P. Chandler, Franya M. Avrelus, Chamma Lee, Jou-Yi Güllü, Ali Ihsan Shah, Syed H. Yuan Ze University Dept. of Computer Science and Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Ece Taoyuan Taiwan Yuan Ze University Mechanicai/Industrial Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Engineering Taoyuan Taiwan Gebze Technical University Defense Electronics Engineering Kocaeli Turkey

ISBN: (纸本)9798350362572

Expanding on an earlier study that assessed the performance of a Q-learning approach for solving the path planning problem for mobile robots, this research implemented the RL approach in a real-world setting employing the RoboMas-ter EP Core. The robotic system also included object detection and recognition through the robot's sensors and a pre-trained YOLOv9 model. The robot navigated to predefined target points while avoiding stationary obstacles. The Q-learning algorithm was trained using the Google Colaboratory platform. Experi-ments conducted at various speeds identified an optimal speed and high success rates in obstacle avoidance and target region accuracy were achieved. Additionally, the object detection sys-tem demonstrated strong performance in real-time applications. Despite these successes, challenges such as high friction and multitasking inefficiencies were identified. Future research should address these limitations by enhancing control systems and the robot's multitasking capabilities, as well as using a computer with better processing power to improve overall system performance further. © 2024 IEEE.

关键词： Robot programming

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Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features

Pattern Recognition in Lung Disease Classification: Leveragi...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： M Ramya Sree Mettu Siddhartha Poli Vamsi Vardhan Reddy Tripty Singh Rekha R Nair Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction.

关键词： Visualization Accuracy Quantization (signal) Pneumonia Lungs Computational modeling Feature extraction Convolutional neural networks X-ray imaging Medical diagnostic imaging

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Navigable Areas Segmentation Method for Unmanned Surface Vehicles in Paddy Fields 11

Navigable Areas Segmentation Method for Unmanned Surface Veh...

引用

11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Machida, Kentaro Nakamura, Tetsuya Cai, Jingyong Nguyen-Quang, Thinh Pham, Duc Anh Huy-Dung, Han Nguyen, Huy Hoang Ookawa, Taiichiro Nakajo, Hironori Tokyo University of Agriculture and Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan Yukimai Design Co. Ltd. Tokyo Japan School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Tokyo University of Agriculture and Technology Dept. of Biological Production Tokyo Japan

ISBN: (纸本)9798350326147

An Unmanned Surface Vehicle (USV) capable of automatic navigation in paddy fields could enhance paddy rice cultivation efficiency. However, flexible navigation is challenging with rule-based methods. This study introduce a deep neural network-based navigable area binary segmentation method, utilizing images from a front camera mounted on a USV. Evaluation with a newly created dataset confirmed that this method could flexibly segment navigable areas, even in rice fields where rule-based methods struggle. © 2023 IEEE.

关键词： Unmanned surface vehicles

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Smart Attendance Management System Using Face Recognition

Smart Attendance Management System Using Face Recognition

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Shivam Rawat Shweta Goyal Nikita Rawat Dept. Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Electrical Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Managing attendance in educational institutions is often a time-consuming and error-prone task, with traditional methods like roll calls or sign-in sheets being inefficient and susceptible to proxy attendance. This paper presents a Smart Attendance Management System that leverages facial recognition technology to automate the attendance process. By using cameras to capture images of students, the system recognizes their faces and marks their attendance in real time. Developed in Python, the system employs the K-Nearest Neighbours (KNN) algorithm for accurate face recognition. Once attendance is recorded, it is stored in a CSV file and can be visualized using the flask web application. The proposed system significantly reduces manual effort, ensures accuracy, and operates efficiently in real-time environments.

关键词： Visualization Image recognition Accuracy Face recognition Nearest neighbor methods Real-time systems Registers Usability Faces Monitoring

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Deep Learning-based Secure Machine-to-Machine Communication in Edge-Enabled Industrial IoT 3

Deep Learning-based Secure Machine-to-Machine Communication ...

引用

3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Mankodiya, Harsh Jadav, Nilesh Kumar Tanwar, Sudeep Gupta, Rajesh Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad382481 India U. V. Patel College of Engineering Ganpat University Dept. of Computer Engineering Mehsana India

ISBN: (纸本)9781665462006

A still in-progress technology of the Industrial Internet of things (IIoT) involves the use of machine-to-machine (M2M) for communication. It can serve the whole purpose of automation in industries by integrating it with smart systems. However, hindrances in such a system lie in the security area. Traditional systems can be easily compromised by developing competent algorithms. The modern system with robust security sometimes fails to sustain the network traffic and load at deeper layers in the communication systems. To overcome the aforementioned issue, we propose including a trained neural network that can interpret incoming machine data streams in this study. It isolates malicious machines from normal machines by identifying attacks based on machine requests. Hence, this can prevent security breaches early. The performance of the trained neural networks is evaluated considering standard metrics such as F1-score and accuracy scores. The proposed model significantly improves the detection rate of malicious machine requests. © 2022 IEEE.

关键词： Internet of things

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Sustainable Innovative New Advanced Materials for Underground Infrastructure Projects

Sustainable Innovative New Advanced Materials for Undergroun...

引用

Decision Aid sciences and Application (DASA), International Conference on

作者： Hamdy M. Mohamed Basil Ibrahim Hend ElZefzafy College of Engineering Applied Science University Bahrain dept. of Civil and Building Engineering University of Sherbrooke Quebec Canada

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The utilization of Fiber-Reinforced Polymer (FRP) bars in underground infrastructure projects, such as tunnels, presents a sustainable and innovative solution to address the challenges posed by harsh environmental conditions. This paper discusses the properties of FRP materials, including their corrosion resistance, lightweight nature, and mechanical strength, which make them ideal for applications in tunnel construction. The study highlights the benefits of using FRP as reinforcement, including reduced maintenance costs and enhanced durability over traditional steel reinforcements. Additionally, it explores the acceptance and application of FRP within the construction industry, emphasizing the need for updated guidelines and standards to facilitate wider adoption. Through a review of existing literature and practical applications, this research provides insights into the advantages of incorporating FRP in tunnel structures, paving the way for future studies and advancements in the field.

关键词： Resistance Costs Corrosion Maintenance Polymers Steel Sustainable development Standards Guidelines Bars

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Implementation of Dynamic Spectrum Access Techniques in Cognitive Radio Networks under Generalized Fading Conditions

Implementation of Dynamic Spectrum Access Techniques in Cogn...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Shobhit Garg Amulya Sharma Divyansh Devgan Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Management Studies Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This paper is organized as follows: Section II provides a background on our research work based on DSA in CRNs using CR under different fading conditions. c) Performance: We also study how effective adaptive algorithms along with the machine learning can prove in achieving higher spectrum sensing accuracy and decision making on run time. By conducting simulations and extensive performance evaluation, we show that the traditional solutions fall short when compared to our AI-based methodology in terms of accuracy especially under harsh fading conditions. Finally, sensitivity analysis highlights the control parameters that need to be submitted to further optimization for better system performance. With all above analysis, our results indicate that DSA can alleviate spectrum underutilization problem and enhance communication reliability in CRNs. Thus, with proper adaptation strategies and machine learning algorithms, the DSA systems provide an appealing opportunity to meet the continuously increasing requirements of bandwidth-consuming applications in current days wireless networks. The results of this research are helpful in providing effective value input to Advances in the understanding and design of robust, efficient spectrum management approaches for dynamic and diverse wireless settings.

关键词： Fading channels Wireless sensor networks Accuracy Sensitivity analysis System performance Wireless networks Machine learning Sensors Reliability Optimization

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