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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47833 条记录，以下是1041-1050 订阅

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Automated Wearable Device to Detect Drowning Incidents in Water Bodies 2

Automated Wearable Device to Detect Drowning Incidents in Wa...

引用

2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Basthikodi, Mustafa Shetty, Shreya S. Yashmitha, B. Shetty, Shravya R. Prakruthi, P.D. Computer Science and Engineering Department Sahyadri College of Engineering and Management Mangalore India Information Science and Engineering Department Sahyadri College of Engineering and Management Mangalore India

ISBN: (纸本)9798350383867

Drowning is a leading cause of unintentional death worldwide, with a significant number of fatalities occurring in natural bodies of water, swimming pools, and other aquatic environments. Timely and accurate detection of drowning incidents is critical for preventing tragic outcomes. This provides an overview of challenges, methods, and technologies associated with drowning detection. In the proposed work, we have developed a wearable device supplemented by web application that aims to detect drowning incidents effectively. The system incorporates sensors designed to detect distress signals or abnormal movement patterns underwater, enhancing safety by promptly alerting nearby individuals or authorities to the situation. The sensors are utilized to monitor critical parameters such as SpO2, heartbeat, water presence, as well the temperature and humidity levels of the individual. Key components of this system include the ESP8266 NodeMCU and the Arduino Uno board R3, which provide wired connectivity and microcontroller capabilities, respectively. By continuously monitoring these vital signs and environmental factors, the system can accurately identify potential drowning incidents and trigger timely alerts. © 2024 IEEE.

关键词： Wearable sensors

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Disease Recognition in ViticultureCNN-SVM Framework for Accurate Grapevine Disease Diagnosis 4

Disease Recognition in ViticultureCNN-SVM Framework for Accu...

引用

4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rana, Gautam Singh, Rahul Chauhan, Rahul Singh Pokhariya, Hemant Chhabra, Amisha Chitkara University Vlsi Centre of Excellence Chitkara University Institute of Engineering and Technology Punjab India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350343670

This research paper introduces a novel methodology that employs Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to classify grapevine leaf diseases. A complete dataset consisting of photos of grapevine leaves exhibiting different illnesses and healthy conditions was gathered and subjected to preprocessing techniques to assure consistency and improve the resilience of the model. The design of the Convolutional Neural Network (CNN) comprised of three convolutional layers, succeeded by three max-pooling layers, and culminated in a solitary fully connected layer. The SVM classifiers were trained for disease categorization using extracted characteristics. The experimental findings provided empirical evidence supporting the efficacy of the suggested methodology, as it achieved a classification accuracy over 95% when applied to several grapevine diseases. This outcome highlights the potential of the strategy for accurate and reliable disease diagnosis in the field of viticulture. © 2024 IEEE.

关键词： and Downy Mildew Anthracnose Botrytis Bunch Rot Convolution neural network food security Leafroll Disease

来源：评论

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Automated Skin Disease Detection Using GoogLeNet and MobileNet to Enhance the Diagnostic Accuracy with Convolutional Neural Networks 2

Automated Skin Disease Detection Using GoogLeNet and MobileN...

引用

2nd international conference on advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Santhiya, S. Parvathi, M. Jayadharshini, P. Harish, S.V. Navin, E. Indrajith, N. Kongu Engineering College Erode India Department of Artificial Intelligence India Nandha Engineering College Erode India Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798331541217

Skin diseases affect human health through physical discomfort, emotional distress, and social stigma The high clinical variability and that the diagnostic procedures are subjective and may change over time make it difficult to detect accurately. This paper suggests applying CNN architectures, such as GoogLeNet and MobileNet, for automated *** stages involved include image gathering, preprocessing, selection and design of CNN models such as GoogLeNet and MobileNet, intensive training, evaluation, and deployment. The aim of this system is to help healthcare professionals make reliable diagnoses especially in areas where dermatologists are not accessible, thus allowing for more timely treatments. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Performance Prediction for Twisted String Actuator under Variable Load 6

Improved Performance Prediction for Twisted String Actuator ...

引用

6th international conference on robotics, Control and Automation engineering, RCAE 2023

作者： Zhan, Jingwei Xu, Chanchan Tong, Lianghuai Ma, Yifan Wang, Xiaojie Anhui University Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China Hefei Institutes of Physical Science University of Science and Technology of China Chinese Academy of Sciences Hefei China Quzhou Academy of Metrology and Quality Inspection Quzhou China University of Science and Technology of China Hefei China

ISBN: (纸本)9798350315301

Twisted String Actuator (TSA) is an innovative artificial muscle that converts the rotary motion of an electric motor into linear motion of a load. TSA exhibits great potential for soft robots and robotic exoskeletons due to its high transmission ratio, simple structure, intrinsic compliance, and low cost. However, the actual dynamic behavior of TSA is greatly influenced by varying load conditions, making it challenging to accurately predict its behavior under different loads. This study addresses this issue by proposing a variable load model for TSA based on the backpropagation (BP) neural network. By training the neural network using extensive experimental data, we establish and validate the model against various load conditions. The proposed model outperforms conventional variable load mathematical models, offering smaller prediction errors and more accurate descriptions of TSA behavior under different load conditions. This finding has important implications for system design and control of TSA. © 2023 IEEE.

关键词： Actuators

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Forecasting Electricity Consumption in Five Regions of Dhaka City: A Comparative Assessment among various ML-Boosting Models 6

Forecasting Electricity Consumption in Five Regions of Dhaka...

引用

6th international conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Hasan Muaz, Md. Mehedi Akhtaruzzaman, M. Rahman, Md. Mahbubur Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350385779

Predicting electricity usage is vital to maintain a consistent power supply, making it a top concern for all nations. However, in Bangladesh, research in this area is inadequate. So, this research focuses on predicting power consumption in some regions of Dhaka City using XGBoost, LightGBM, and CatBoost algorithms. Monthly consumption, sanctioned load, meter type, and tariff are the key factors employed in the prediction process where the grid search technique is utilized to discover the most effective hyper-parameters. Model performance is evaluated by MAE, RMSE, and R2 scores. Experimental results indicate that CatBoost outperforms other algorithms (RMSE 2.0% to 42.0%), although LightGBM offers faster training. Implementing these techniques holds significant promise for enhancing power infrastructure planning and energy forecasting, ultimately minimizing system losses and equipment damages. © 2024 IEEE.

关键词： Electric power utilization

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Hate Speech Detection And Classification Using NLP 2

Hate Speech Detection And Classification Using NLP

引用

2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Ilhan, Syed Shahidh Sivakumar, Soubraylu Nagaraj, J. Ramesh, S. Sreeram, N. Rajalakshmi, Ratnavel School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Madanapalle Institute of Technology& Science Andhra Pradesh Madanapalle India Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350383867

Detected hate speech instances are appropriately classified, rebuilding the intricate landscape of disparaging language in an increasingly digitized society. We present an exhaustive analysis of the techniques, gravitating towards their efficiency, precision, and capability to adapt to the evolving dynamics of language manifestation online. The study uncovers fascinating insights into the relative strengths, weaknesses, limitations, and potential areas of improvement of Bi-LSTM and the Bi-LSTM with GRU in the realm of hate speech detection and classification. The results of Bi-LSTM with GRU has obtained an accuracy of 95% with an improvement of 1% over Bi-LSTM model. Serving as a cornerstone in the burgeoning NLP research, our findings provide promising direction and robust groundwork for future improvements and attempts in the continuous drive towards safer, more respectful digital communication platforms. © 2024 IEEE.

关键词： Tweets

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RAONet: CNNs with Residuals and Adaptive Occlusion for Optical Flow Estimation 5

RAONet: CNNs with Residuals and Adaptive Occlusion for Optic...

引用

5th international conference on advances in Electrical engineering and Computer Applications, AEECA 2024

作者： Shi, Yuehong Zhao, Yanan Zhou, Wenjiang Zhang, Rufei Guan, Hongli Xu, Haitao University of Science and Technology TravelSky Mobile Technology Limited Beijing China China Academy of Information and Communications Technology Beiing China University of Science and Technology Beijing Beijing China TravelSky Mobile Technology Limited Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350355253

PWC-Net is a convolutional neural network for optical flow estimation that is superior in both inference speed and accuracy. However, the detection accuracy of this network for small moving objects is poor, and there is a problem of overlapping blur caused by occluded areas during the warping process. This paper proposes an improved PWC-Net network, this method can meet the real-time requirements, and the accuracy has been greatly improved compared with PWC-Net. Compared with PWC-Net, RAONet has a 22.1% lower error on Flying Chairs validation dataset, 12.3% lower error on MPI Sintel Clean dataset, and 12.3% lower error on MPI Sintel Final dataset and KITTI dataset by about 9%. Under the hardware environment of a 12- core CPU and RTX 2080 Ti GPU, RAONet can run at about 50fps on 1024 × 436 resolution images. And through the visual analysis of optical flow, it is concluded that RAONet can improve the detection accuracy of small moving objects, and can effectively solve the overlapping blur problem caused by the warping process. © 2024 IEEE.

关键词： Convolutional neural networks

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Development of an Autonomous Ground Robot Using a Real-Time Appearance Based (RTAB) Algorithm for Enhanced Spatial Mapping: Development of a Ground Robot with Autonomous Navigation Capability 23

Development of an Autonomous Ground Robot Using a Real-Time ...

引用

6th international conference on advances in robotics, AIR 2023

作者： Peter, Jerin Thomas, Mervin Joe Mohan, Santhakumar Department of Electronics and Communication Engineering Rajagiri School of Engineering and Technology India Department of Mechanical Engineering Indian Institute of Technology Palakkad India

ISBN: (纸本)9781450399807

This paper describes the preliminary results of developing an autonomous ground robot that can navigate dynamic indoor/outdoor and GPS-restricted environments. This study outlines the demerit of applying conventional LIDAR sensors alone for mapping, as they cannot detect obstacles beyond their range and compromise reliability in real-time applications. This paper showcases the application of a Real-Time Appearance Based (RTAB) algorithm by combining LIDAR sensors with 3D cameras for three-dimensional mapping. This algorithm generates a realistic view of the spatial environment enabling superior clarity of the robot's surroundings. The simulations have been conducted on the open-source Robotic Operating System (ROS) NOETIC platform. Additionally, the real-time mapping of outdoor environments using an in-house miniature-sized prototype designed for proof of concept is also described in this paper. The developed robot can find suitable applications as delivery bots, surveillance robots, rescue operations, and teleoperation. © 2023 ACM.

关键词： Mapping

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A Multi-feature Co-distillation Algorithm Based on Human Skeletal Point Behavior Recognition 24

A Multi-feature Co-distillation Algorithm Based on Human Ske...

引用

2nd international conference on advances in Artificial Intelligence and Applications, AAIA 2024

作者： Pan, Xuefeng Tian, Zhiqiang School of Computer Wuhan Qingchuan University Wuhan China School of Information Wuhan Software Engineering Vocational College Wuhan Open University Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400712883

Depth model-based behavior estimation of human skeletal points is widely used in the field of behavior recognition. In order to improve the accuracy of behavior recognition, the complexity and computation of the designed network model are increasing, while the mobile platforms have limited arithmetic power and memory, and thus cannot be deployed. To solve the above problems, the AGCN network, a skeletal point-based behavior recognition model, is clipped to compress the network size so that it can support common mobile platforms;and a multi-feature co-distillation method is proposed to efficiently migrate the performance of the original network to the target network, so that the performance of the clipped lightweight network tends to be close to that of the original network. The model training, testing, visualization, and ablation experiments are completed on the NTU-RGB+D60 data set. The experimental results show that the algorithm after lightweighting (clipping network and multi-feature co-distillation), can significantly reduce the computational amount of behavior recognition. © 2024 Copyright held by the owner/author(s).

关键词： Distillation

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Leveraging Frequency and Spatial Domain Information for Underwater Image Restoration

Leveraging Frequency and Spatial Domain Information for Unde...

引用

2024 international conference on Intelligent Systems and robotics, CISR 2024

作者： Zhang, Haopeng Xu, Hongli Yu, Xiaosheng Zhang, Xiangyue Wu, Chengdong Faculty of Robot Science and Engineering Northeastern University Shenyang China

Underwater image restoration is pivotal for enhancing the visual perception of underwater robots by improving image visibility and quality. Typically, underwater images are compromised by color distortion, low contrast, and noise, which degrade the robots' environmental perception. This paper introduces FS-Net, an innovative approach that synergistically utilizes information from both the frequency and spatial domains to overcome these challenges. By strategically separating and processing different frequency components, FS-Net effectively reduces noise and preserves intricate details. Concurrently, advanced spatial domain filtering and enhancement techniques are employed to augment contrast and rectify color distortions. This dual-domain integration ensures a comprehensive and efficient restoration process. We rigorously evaluate FS-Net using a well-established underwater image dataset, where it demonstrates substantial enhancements in both visual quality and quantitative performance metrics over existing methods. This work significantly advances underwater imaging technology, offering a robust solution that markedly improves image clarity and utility for underwater robotic applications. © Published under licence by IOP Publishing Ltd.

关键词： Underwater photography

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