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.
As an important branch of the enterprise management, human resources management in the development of today's social and economic innovation, with the rising number of enterprise employees, the connection between ...
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Tracing, a technique essential for unraveling the complexities of computersystems' behavior, involves the organized collection of low-level events, enabling anomaly identification, performance debugging, and root...
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ISBN:
(纸本)9798400705007
Tracing, a technique essential for unraveling the complexities of computersystems' behavior, involves the organized collection of low-level events, enabling anomaly identification, performance debugging, and root cause analysis. However, the significant overhead it imposes on large-scale systems, particularly in terms of performance and storage, has made it a less favorable tool for system maintenance. Previous efforts to mitigate tracing's burden have mostly centered around automating trace analysis but have primarily neglected the duration of events, a significant aspect of the information provided by tracers. To address these challenges, we propose an Adaptive Tracing method that leverages Language Models and kernel trace for precise systemmodeling. This novel approach minimizes overhead by recording detailed traces only during significant behavioral shifts and focusing on subsystems related to the root cause. Using a multi-task model, incorporating system call sequences and durations, we propose a root cause analysis method, enhancing model transparency and enabling targeted system tracing. Evaluation using a dataset of normal and noisy traces from an Apache server reveals that our Adaptive Tracer captures events related to abrupt changes with only 5.8% loss, reducing the collected trace by 77.1%, and accurately determining the respective noise set with 91.3% accuracy, outperforming previous state-of-the-art trace models by 20.9%.
For emergency repair, meter installation and connection and other electricity marketing meter exchange operation sites, this paper designs a safety management system for small-scale operation of electricity marketing ...
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Intelligent system have a significant impact in the realm of sports and fitness, but there exists an issue with incomplete applications. Standard deep learning methods are insufficient to tackle these application chal...
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Digital microfluidic biochips provide a controlled and miniaturized environment to carry out biochemical protocols in an automated fashion. Software-based simulators are essential tools that aid the design of such pro...
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ISBN:
(数字)9783031783807
ISBN:
(纸本)9783031783791;9783031783807
Digital microfluidic biochips provide a controlled and miniaturized environment to carry out biochemical protocols in an automated fashion. Software-based simulators are essential tools that aid the design of such protocols by enabling users to verify correct execution before targeting the physical biochip. To produce a simulation that is faithful to reality, the fluidic behavior of the droplets and their interaction with the driving electrodes must be taken into account. This paper presents a framework for simulating DMF biochips in a resource-constrained web-based environment. The framework is based on a novel droplet model that uses logic-based calculations to capture fluidic behavior. Thus, enabling to faithfully simulate the movement, merging, and splitting of arbitrary-shaped droplets with a low-computational footprint. The simulation framework also includes modular component models to capture the behavior of sensors and actuators, an event-driven simulation engine, and a graphical user interface. The framework is implemented as a client-side web application and runs in a browser. The evaluation carried out using artificial and real-life test cases shows that the framework can deliver real-time simulations with a high level of fidelity.
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