This topic focuses on the implementation effect of lidar technology in the early warning system of substation cranes and its potential value. While deeply exploring the operating mechanism, technical attributes and ap...
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Aiming at solving the problems of high latency, data transmission bandwidth limitation and privacy security faced by traditional object detection methods in the Internet of Things, a cloud-edge collaborative system fo...
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ISBN:
(纸本)9798350349184;9798350349191
Aiming at solving the problems of high latency, data transmission bandwidth limitation and privacy security faced by traditional object detection methods in the Internet of Things, a cloud-edge collaborative system for object detection based on KubeEdge is proposed. It takes advantage of cloud-edge collaboration technology and edge computing platform to perform tasks on edge devices to achieve faster response times. Firstly, through the deployment of KubeEdge edge computing platform, the cloud edge collaboration function is realized. Then, the object detection model is trained on the cloud server, and the trained model is deployed on the edge device to perform the model inference task. Finally, the edge device transmits the inference results to a cloud server, which stores the results for further analysis. The system has significant advantages in realizing low delay calculation, collaborative assurance, and privacy protection, etc. Taking mask detection as an example, it validated the practicality and reliability of the system, which provides strong support for the application of cloud-edge collaborative technology in the field of object detection and holds significant importance in meeting the growing demands of edge computing.
Image processing technology is an important branch in the digital age, and its application in art design has penetrated into various fields. Whether it is graphic design, three-dimensional modeling, or film and televi...
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This manuscript introduces a new English customized distance education teaching method based on computer network technology. This manuscript first discusses the theory of distance teaching in English classroom based o...
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Expressing basic needs may seem like a simple task, but not for those with deteriorated or lost ability to speak and move. This paper presents the design, application, and testing of an alternative communication syste...
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ISBN:
(纸本)9798350330656;9798350330649
Expressing basic needs may seem like a simple task, but not for those with deteriorated or lost ability to speak and move. This paper presents the design, application, and testing of an alternative communication system based on Brain computer Interface (BCI). The prototype includes six LEDs flickering at different frequencies, and each LED corresponds to one command. Depending on the direction of the gaze of the subject, the neuronal activity pattern in their occipital lobe will be consistent with the targeted LED flickering rate. By recording the electroencephalogram (EEG), and determining the neuronal firing frequency, the system uses Steady State Visual Evoked Potentials (SSVEPs) to convey one of six commands to caregivers. The SSVEP-based system uses an OpenBCI Ganglion 4-channel biosensing board to acquire brain signals and Arduino Uno for system control. Based on preliminary testing on eleven subjects, the overall accuracy of the system was 89%. Accuracy is the percentage at which the system correctly recognized and sent the selected command.
Talent development is a crucial factor for the development of enterprises and units. This article uses data analysis models and data mining techniques to help enterprises and institutions analyze talent data, discover...
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Afra is an Eclipse-based tool for the modeling and model checking of Rebeca family models. Together with the standard enriched editor, easy to trace counter-example viewer, modular temporal property definition, export...
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With the continuous advancement of human-computer interaction (HCI) technology, traditional interaction methods are increasingly unable to meet the growing complexity of application requirements. Human gesture recogni...
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ISBN:
(纸本)9798350377040;9798350377033
With the continuous advancement of human-computer interaction (HCI) technology, traditional interaction methods are increasingly unable to meet the growing complexity of application requirements. Human gesture recognition, as a natural and intuitive interaction method, has been widely applied in fields such as smart devices and virtual reality due to its convenience and flexibility. In recent years, the emergence of deep learning technology has provided new solutions for improving the performance of gesture recognition systems. This study designs a human gesture recognition and interaction system based on deep learning methods, aiming to enhance recognition accuracy and real-time responsiveness. First, the paper reviews the development of gesture recognition technology and provides a detailed analysis of deep learning-based gesture recognition methods. Subsequently, a gesture recognition model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is proposed, along with the overall system architecture. Through the processes of gesture image data collection, preprocessing, and model training, experimental results demonstrate that the proposed system exhibits significant advantages in recognition accuracy, robustness, and response speed. Finally, the paper discusses optimization strategies for the system and envisions the broad application prospects of deep learning technology in future HCI systems.
To optimize the dispatch of Electric Vehicle Virtual Energy Storage (EVVES) across wide-ranging networks, this research presents a highly precise virtual energy storage capacity estimation model. By clustering the typ...
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Assessing motor impairment is essential for understanding disease progression and tailoring treatment. Traditionally, this assessment relied on manual evaluations. We are exploring computer vision, utilizing monocular...
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ISBN:
(纸本)9798350386523;9798350386530
Assessing motor impairment is essential for understanding disease progression and tailoring treatment. Traditionally, this assessment relied on manual evaluations. We are exploring computer vision, utilizing monocular video captured on personal smartphones. However, machine learning-based assessment faces significant challenges due to limited labeled data and label quality, introducing uncertainties in the model. Here, we propose modeling aleatoric uncertainty with a two-head neural network, enabling uncertainty estimation alongside Gross Motor Function Classification system (GMFCS) scores. Our training method, involving data combination and a loss function consisting of consistency loss and regression loss, contributes to improved performance. Experimental results show that the network outputs uncertainty positively correlated with GMFCS Level estimation error. Setting confidence thresholds allows for filtering out incorrect estimations and achieving higher accuracy.
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