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Consider a jib crane with an external load suspended from the pulley. The dimensions of the crane members are shown in the figure. A systematic analysis of the frame structure is required to determine the reaction forces at the pin joints, assuming that the pulleys are frictionless.
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Frames: Problem Solving II

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Consider a hydraulic hoist supporting a load of 1 kN. Assuming a simplified schematic representation of this frame structure, the force acting on BD and BF members can be determined.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Related Experiment Video

Updated: Jun 13, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Bamboo: A Novel Session-Aware Framework With Equiangular Tight Frame Prototypes for Few-Shot Class-Incremental

Xuehan Lu, Zhe Wang, Zhiling Fu

    IEEE Transactions on Neural Networks and Learning Systems
    |November 10, 2025
    PubMed
    Summary

    We introduce Bamboo, a novel framework for few-shot class-incremental learning (FSCIL). Bamboo infers session-IDs without prior knowledge, enabling accurate classification of all past classes and achieving state-of-the-art results.

    Related Experiment Videos

    Last Updated: Jun 13, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Few-shot class-incremental learning (FSCIL) is challenging due to the need to classify samples from all previous sessions without knowing the session identifier.
    • Existing methods struggle with classifying samples across unknown sessions, hindering performance in dynamic learning environments.

    Purpose of the Study:

    • To propose a novel framework, Bamboo, for few-shot class-incremental learning (FSCIL) that addresses the challenge of session-ID inference.
    • To enable accurate classification of samples belonging to all previously learned classes without prior knowledge of the session identifier.

    Main Methods:

    • Bamboo utilizes a cascading inference mechanism to explicitly infer the session-ID for each incoming sample.
    • A novel session-specific equiangular tight frame prototype (ETF-P) classifier is introduced, which adaptively fuses session-agnostic and session-specific semantics.
    • The framework models incremental learning as a cascade of session classifiers, akin to bamboo growth, where each sample traverses sequentially to determine its session.

    Main Results:

    • The ETF-P classifier reliably determines the correct session for each sample within the cascading mechanism.
    • Bamboo successfully perceives session-IDs without requiring prior knowledge, a critical step for effective FSCIL.
    • The proposed framework achieved state-of-the-art performance across multiple benchmark datasets for FSCIL tasks.

    Conclusions:

    • Bamboo offers a robust solution for few-shot class-incremental learning by effectively inferring session-IDs.
    • The cascading inference mechanism and ETF-P classifier enable accurate classification in dynamic, session-aware learning scenarios.
    • This framework advances the capabilities of AI systems in handling sequential data with evolving class distributions.