The one-degree-of-freedom(DOF) mechanism has a simple structure, convenient control, and high stiffness, and it has been applied in many micro jumping robots. Meanwhile, the six-and eight-bar mechanisms can satisfy mo...
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The one-degree-of-freedom(DOF) mechanism has a simple structure, convenient control, and high stiffness, and it has been applied in many micro jumping robots. Meanwhile, the six-and eight-bar mechanisms can satisfy more complex motion requirements than the four-bar jumping leg mechanism and they have good application prospects. However, the lack of effective design methods limits the application range of these mechanisms. In this work, a type and dimensional integration synthesis method was proposed with the one-DOF six-bar leg mechanism as the research object. The initial tibia and femur were determined based on the kinematic chain atlas, and configuration design was implemented through the superposition of *** a closed chain was formed in the superposition process, the feasible range of the link length was analyzed by considering the constraint conditions. The proposed method innovatively establishes the relationship between the kinematic chain atlas and the configuration, and the feasible length ranges of the links can be quickly obtained simultaneously. Several examples were provided to prove the feasibility of the kinematic synthesis method. This method provides a useful reference for the design of one-DOF mechanisms.
With the development of modern animal husbandry, especially pig farming, large-scale, intensive, and automated farming has become the trend in the industry. The realization of accurate recognition and warning of dange...
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Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM, with applications in inv...
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This paper describes novel and fast, simple and robust algorithm with O(N) expected complexity which enables to decrease run-time needed to find an exact maximum distance of two points in E2. The proposed algorithm ha...
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Traditional Reinforcement Learning (RL) suffers from replicating human-like behaviors, generalizing effectively in multi-agent scenarios, and overcoming inherent interpretability issues. These tasks are compounded whe...
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Steady states are invaluable in the study of dynamical systems. High-dimensional dynamical systems, due to a separation of time-scales, often evolve towards a lower dimensional manifold M. We introduce an approach to ...
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Polyp segmentation plays a crucial role in the prevention of colon cancer. However, the diverse shapes of polyps and their similarity to normal areas in terms of color and texture make polyp segmentation a challenging...
Polyp segmentation plays a crucial role in the prevention of colon cancer. However, the diverse shapes of polyps and their similarity to normal areas in terms of color and texture make polyp segmentation a challenging task. Currently, most polyp segmentation methods solely focus on spatial domain features, ignoring the valuable features in the frequency domain. Consequently, many polyp segmentation algorithms struggle with the camouflage of polyps. To tackle this issue, we propose the Frequency Aware and Graph Fusion Network (FAGF-Net). Specifically, it begins with a Frequency-based Global Extraction Module (FGEM), which provides an initial estimation of the polyp regions to guide subsequent modules. Next, we design a Frequency-based Feature Attention Module (FFAM) that leverages amplitude and phase information to amplify appearance differences and enhance semantic representations. Moreover, we present a Graph-based Fusion Module (GFM), which infers the geometric characteristic of polyps through aggregating and interacting with enhanced features. Extensive experiments show that our method outperforms state-of-the-art methods with better quantitative and qualitative evaluations.
While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter,...
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Chalcogenide phase-change materials (PCMs) offer a promising approach to programmable photonics thanks to their nonvolatile, reversible phase transitions and high refractive index contrast. However, conventional desig...
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Enforcing security and reliability in the cloud is a challenging but vital task because of the multitude of heterogeneous applications utilising the same infrastructure. Integrating a security analysis system to ident...
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Enforcing security and reliability in the cloud is a challenging but vital task because of the multitude of heterogeneous applications utilising the same infrastructure. Integrating a security analysis system to identify and mitigate potential threats, such as malicious software (Malware), is crucial for the effective functioning of cloud infrastructures. Over the past few decades, new malware analysis and detection approaches have emerged because of various malware strategies that evade host- and network-based security measures. This study established a federated learning-based malware detection model for interconnected cloud infrastructures. This method protects users’ privacy as numerous devices can collaborate to build machine learning models without sharing data. Three distinct deep-learning algorithms were chosen for the models’ training, validation, and testing phases. With the training of eight clients and twenty-five federation rounds, the FeedForward Neural Networks (FFNN) model performed best. It had accuracy, an F1-score, and a precision of 84%, while the Multi-Layer Perceptron (MLP) model performed with 83% accuracy, 83% F1-score, and 83% precision, and the Long Short-Term Memory (LSTM) model performed with 80% accuracy, 80% F1-score, and 80% precision.
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