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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

2024 International Conference on Decision Aid sciences and Applications, DASA 2024

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

ISBN: (纸本)9798350369106

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. © 2024 IEEE.

关键词： Underground corrosion

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Short-term Wind Power Ramp Forecasting Using Sequential Approach

Short-term Wind Power Ramp Forecasting Using Sequential Appr...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Gopalakrishnan, Leechita Barrera, Julian L Cardenas Fatima, N. Sabiyath B.S. Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India University of New Brunswick Dept. of Electrical and Computer Engineering Fredericton Canada

ISBN: (纸本)9798331533311

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine's SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps. © 2024 IEEE.

关键词： Wind forecasting

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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22nd International Learning and Technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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Sustainable Innovative New Advanced Materials for Underground Infrastructure Projects

Sustainable Innovative New Advanced Materials for Undergroun...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

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

ISBN: (纸本)9798350369106

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. © 2024 IEEE.

关键词： Corrosion resistance

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Nova: Revolutionizing Agile Project Management with Scrum and Kanban Integration for Enhanced Team Collaboration and Productivity 5

Nova: Revolutionizing Agile Project Management with Scrum an...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Manaswini, Poluri Shaik, Sajjad Ashwani, Siddhant Balakrishnan, Roshni M. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova,"a novel project management tool, embodies these methodologies to offer a user-friendly interface that supports the dynamic needs of modern software development teams. This paper explores Nova's unique approach that integrates the foundational principles of Agile which are Scrum, and Kanban, thereby facilitating a seamless and effective management platform. Through a comparative analysis with traditional project management tools, this study evaluates how Nova enhances team collaboration, project tracking, and outcome predictability. Emphasizing its ease of use and adaptability, the research highlights Nova's potential to improve project delivery times and overall team productivity. © 2024 IEEE.

关键词： Lean production

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An Extended Reality Platform Powered by Large Language Models: A Case Study on Teaching Dance Costumes 7

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

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

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

ISBN: (纸本)9798331521578

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. © 2025 IEEE.

关键词： Hosiery manufacture

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Stability Analysis and Design of a Lightweight Concrete Canoe 2

Stability Analysis and Design of a Lightweight Concrete Cano...

引用

2nd International Conference in civil engineering for a Sustainable Planet, ICCESP 2024

作者： Keng, Azan Noor Raveendra, Velkuru Lalith Kumar Madhu, Ragala Meera Elayaraja, Srikanth Adhitha, R. Sivanantham, Pradeep Dept. of Civil Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9780784486047

ISBN: (纸本)9780784486047

This project focused on the development of a lightweight canoe made of carbon fiber, looking into its stability, design, and production. The objective was to make a breakthrough in concrete technology and structural engineering through the production of this lightweight concrete canoe. All this was achieved through an increase in sustainability and adding beneficial attributes to concrete produced using Class-C fly ash, P250 superplasticizer, rice husk ash, micro-silica, microspheres, and cenosphere. The comprehensive design mixture would be developed and cast in cubes for testing for compressive strength, accompanied by a variety of tests on lightweight prisms, panels, and cylinders. There are seven different mortar mixtures that have been developed for different ingredient combinations. Hull size for the canoe was finalized to be 6 m in length, 0.54 m wide, and 0.3 m high, keeping in mind parameters such as passenger capacity and the pattern of sitting arrangement. The study also utilizes Maxsurf software to evaluate the hull design for better maneuverability, speed, stability, and tracking. Stability analysis indicated that the canoe supported up to 48.2° of heel angle when it had a payload capacity of 150 kg and 52.6° of heel angle when the canoe was empty. This difference is caused by the uneven distribution of the weight as more passengers are loaded, thereby sinking the canoe deeper and decreasing its stability. This paper evaluates the potential of applying cenosphere in Portland cement for newer construction techniques that are more sustainable and resource efficient. © ASCE.

关键词： Carbon fibers

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Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features 15

Pattern Recognition in Lung Disease Classification: Leveragi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

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

ISBN: (纸本)9798350370249

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. © 2024 IEEE.

关键词： CNNs Deep learning Early detection Feature extraction GLCM Local Phase Quantization Lung disease diagnosis Medical imaging Texture analysis

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Tasnim, Nowshin Noor, Kazi Rifah Islam, Mursalina Huda, Mohammad Nurul Sarker, Iqbal H. Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh Edith Cowan University PerthWA6027 Australia

ISBN: (纸本)9798350372229

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology. © 2024 IEEE.

关键词： CT scans deep learning healthcare analytics image processing lung cancer prediction

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