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检索条件"任意字段=International Conference on Computer and Automation Engineering, ICCAE"

共 41680 条记录，以下是41-50 订阅

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international conference on computer Vision, Robotics, and automation engineering, CRAE 2024

International Conference on Computer Vision, Robotics, and A...

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international conference on computer Vision, Robotics, and automation engineering, CRAE 2024

ISBN: (纸本)9781510682283

The proceedings contain 43 papers. The topics discussed include: deep learning-based license plate information recognition in harsh environments;research on quality prediction of resistance spot welding based on knowledge graph;deep learning-based recognition of autism using facial datasets;HRPD: a lightweight high-resolution projector deblurring network;3D visualization of large scale point clouds on transmission lines;model reconstruction and digital exhibition based on photogrammetry: a case study of bronze cultural relics in Hubei provincial museum;enhancing small object detection in UAV images with DSD-YOLO;machine vision based sorting method for precision industrial components;and the optimal control of automated sorting robot movements based on PLC logic controller.

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Self-Organization of Wireless Sensor Networks for Wildfire Detection Using Shapley Values

Self-Organization of Wireless Sensor Networks for Wildfire D...

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international conference on computer and automation engineering, iccae

作者： Garrison Greenwood Electrical & Computer Engineering Department Portland State University Portland OR USA

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Wildfires have devastating impacts on the environment, economy and public health. Early detection can help mitigate damage but detection can be challenging in remote and inaccessible areas. Wireless sensor networks provide an effective solution. These networks consist of inexpensive nodes that monitor the environment and transmit the sensed data over wireless links to a base state. Sensor nodes are usually randomly distributed via air and must self-organize to create the network. This paper proposes a new self-organizing method that forms a hierarchical sensor network suitable for wildfire detection. Clusters are formed without a predefined structure or the need for localization. Shapley values are used to select cluster heads as new clusters are dynamically created during self-organization. Routing paths for aggregated data are naturally defined as clusters are created. Simulation results are presented to show the effectiveness of this approach.

关键词： Wireless communication Location awareness Wireless sensor networks Wildfires automation Simulation Routing Public healthcare Monitoring

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2020 international conference on Mechanical automation and computer engineering, MACE 2020 - Preface

2020 International Conference on Mechanical Automation and C...

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2020 international conference on Mechanical automation and computer engineering, MACE 2020

The proceedings contain 641 papers. The topics discussed include: engineering safety early warning platform based on BIM and Internet of things;harmless treatment and comprehensive utilization of dairy farming waste based on artificial intelligence;research on intelligent management of campus water supply system under the background of big data;research on intelligent control system of high power soft starter based on discrete frequency conversion technology;design of digital business center of enterprise project management system based on information technology;digital watermarking algorithm for multi-sampling compressed sensing in measurement domain;analysis of the influence of big data background on the spread of large-scale sports events;construction of college English teaching resource database under the background of big data;research on the medical English teaching under the condition of medical literacy based on computer-aided technology;and research on the development path of Russian teaching innovation in newly-elevated undergraduate colleges in contemporary old industrial bases based on the analysis of big data.

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2020 international conference on Mechanical automation and computer engineering, MACE 2020 - Big Data Technology

2020 International Conference on Mechanical Automation and C...

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2020 international conference on Mechanical automation and computer engineering, MACE 2020

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Developing Multi-Factor Authentication and Biometric Verification Protocols for Enhancing Data Security in IoT Healthcare Devices

Developing Multi-Factor Authentication and Biometric Verific...

引用

international conference on computer and automation engineering, iccae

作者： Hamdan Alshehri Department of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

This paper introduces a Multi-Factor Authentication System for enhancing data security in IoT healthcare devices. The system comprises the Multi-Factor Authentication Algorithm (MFAA), Biometric Template Protection Algorithm (BTPA), Dynamic Risk-Based Authentication Algorithm (DRBAA), Adaptive Biometric Fusion Algorithm (ABFA), and Context-Aware Access Control Algorithm (CAACA). Through a comparative analysis, the proposed system consistently outperforms existing methods, demonstrating its potential for robust, context-aware, and adaptive user authentication. The ablation study delves into the individual contributions of each algorithm, emphasizing their collective strength in creating a secure and versatile authentication framework. The proposed system stands as a promising solution for addressing the evolving challenges of data security in the dynamic landscape of IoT healthcare.

关键词： Biometrics Multi-factor authentication Adaptive systems Heuristic algorithms Data security Medical services Internet of Things Risk management Protection Testing

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Momordica Charantia Size Classification Using Mask Region-Based Convolutional Neural Network

Momordica Charantia Size Classification Using Mask Region-Ba...

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international conference on computer and automation engineering, iccae

作者： Charles Ivan Matthew C. Nangit Julian Marc B. Surara Analyn N. Yumang Electronics and Computer Engineering School of Electrical Mapúa University Manila Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Bitter melon (Momordica charantia), also known locally as Ampalaya, holds significant economic and medicinal value, but its size classification remains a challenge due to its irregular morphology. Proper size classification is essential for maintaining packaging standards and ensuring efficient market distribution. Thus, this study presents a deep learning-based approach for automated bitter melon size classification using the Mask R-CNN model for instance segmentation, the Harris Corner Detection algorithm for contour detection, and the Douglas-Peuker algorithm for contour simplification. A hardware prototype was developed using a Raspberry Pi 4 Model B and a camera module to capture images for real-time processing. The system achieved an overall accuracy of $\mathbf{9 7. 9 1 \%}$ in classifying the sizes of bitter melons. The model displayed a high classification accuracy, demonstrating its potential for improving agricultural automation and postharvest processes.

关键词： Instance segmentation automation Accuracy Prototypes Morphology Packaging Real-time systems Classification algorithms Corner detection Standards

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An IoT-based Water Salinity Monitoring and Time-Dependent Automatic Feeding System for Mud Crab Aquaculture

An IoT-based Water Salinity Monitoring and Time-Dependent Au...

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international conference on computer and automation engineering, iccae

作者： Jemuel R. Ricohermoso Peter Paul A. Javier Jessie Jaye R. Balbin School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

This study presents an IoT-based water quality monitoring and automatic feeding system for indoor mud crab aquaculture. The system integrates an ESP8266 micro controller with dedicated sensors to continuously measure pH, salinity, temperature, and water levels while implementing an automated feeding mechanism. The accuracy and reliability of the system were improved through sensor calibration techniques and proactive measures, such as sensor drift mitigation and keeping spare sensors for replacements. The results show improved crab growth rates, reduced crab mortality, and reduced manual labor due to the system's implementation. In addition, the study shows that IoT-based aquaculture improves the sustainability and efficiency of an indoor crab farm.

关键词： Temperature measurement Temperature sensors Salinity (geophysical) Water quality Sensor systems Sensors Calibration Temperature control Monitoring Aquaculture

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Weather Forecasts-Based Machine Learning Models to Predict Wildfire Characteristics

Weather Forecasts-Based Machine Learning Models to Predict W...

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international conference on computer and automation engineering, iccae

作者： Zina Abohaia Abeer Elkhouly Mai Elbarachi School of Computer Science University of Wollongong in Dubai Dubai UAE School of Engineering University of Wollongong in Dubai Dubai UAE

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Wildfires are increasingly severe global challenges, driven by environmental, economic, and health impacts. This study investigates the use of weather forecasts into machine learning models to predict wildfire characteristics across seven Australian regions. Various machine learning models were evaluated for their ability to predict wildfire attributes, including fire area, brightness, and radiative power. Results show that mean fire brightness predictions achieved moderate success nationally ( $\mathbf{R}^{2}$ : 0.504), while fire area and radiative power remained challenging due to their complexity. Region-specific analysis revealed better predictive performance in New South Wales and Queensland, with lower accuracy in regions like Northern Territory and Tasmania, emphasizing the need for region-specific approaches. This study highlights the potential of integrating real-time weather data into predictive frameworks and calls for enhanced feature engineering and regional modeling to improve accuracy, and wildfire management strategies.

关键词： Wildfires Accuracy Shape Biological system modeling Brightness Weather forecasting Predictive models Data models Complexity theory Risk management

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Automatic Question Generation From Handwritten Lecture Notes Using Trocr Text Recognition and T5 Language Processing

Automatic Question Generation From Handwritten Lecture Notes...

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international conference on computer and automation engineering, iccae

作者： Rommel John H. Ronduen Jan Adrian C. Manzanero Analyn N. Yumang School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

This research involves the creation and evaluation of a system that allows for text extraction and automatic question generation (AQG) using a T5 and TrOCR pipeline. With the use of a Raspberry Pi 5, web camera, and a touchscreen display, factoid- type questions are created from image captures of single-column handwritten notes that only contain textual information. The T5 large language model (LLM) used was finetuned using the Stanford Question Answering Dataset (SQuAD) for facilitating question generation. The system had a word error rate (WER) of 0.40, a ROUGE-1 score of 0.358, and a question validity rate of 68%. This research helps to promote the ease of creation of learning materials in learner education.

关键词： Error analysis Large language models Pipelines Optical character recognition Education Touch sensitive screens Image capture Cameras Question generation Question answering (information retrieval)

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Utilizing Binary Segmentation to Enhance Object of Interest Details in Images Synthesized Using Generative Adversarial Networks

Utilizing Binary Segmentation to Enhance Object of Interest ...

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international conference on computer and automation engineering, iccae

作者： Toka Ossama Barghash Mohammed Abdel-Megeed Salem Department of Computer Science and Engineering German University in Cairo Cairo Egypt

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Image synthesis, once considered a distant fantasy, has emerged as a rapidly evolving field, driven by advancements in generative models and computational power. Many researchers in academia and corporations have tackled this problem and achieved impressive results. Among the widely adopted models in this domain is the Generative Adversarial Network (GAN). In this work, we integrate Binary Segmentation with GANs to enhance the quality of the object of interest in generated images. The binary segmentation model guides the generative model to focus on the region of interest within the image rather than the background producing more detailed representations of the target object. Our approach was trained and validated using labeled samples from the well-known CUB dataset. For testing, we generated images corresponding to text descriptions from the dataset. Evaluation of these images demonstrated the effectiveness of our model, achieving a comparable Inception Score of $5.243\pm 0.090$ with significantly fewer training epochs (200 epochs).

关键词： Training Solid modeling Image segmentation Image resolution Image synthesis Computational modeling Interference Generative adversarial networks Data models Testing

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