1.1 World energy scenarios The world energy industry looks to primary shifts in the way it sells, generates, and distributes energy. The energy industry is under enormous pressure to reduce carbon emissions and to find appropriate ways to manage power supply-demand balance across power grids. The...
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What are the key challenges associated with gas leakage detection and classification in diverse environments, such as industrial, residential, and mining scenarios? 3. How does the integration of machine learning algorithms and explainable artificial intelligence (XAI) techniques improve the interpretabilit...
For the first time, Monet validates that multichannel processing properties can be optically implemented with high-efficiency, enabling real-world intelligent multichannel-processing tasks solved via optical computing, including 3D/motion detections. Extensive experiments on different scenarios demonstrate the ...
While there are many open-source datasets, such as the ASL (American Sign Language) dataset and the Hand Gestures dataset, most of the images are too preprocessed and cleaned to represent real-world scenarios. To build this project, you must collect your own dataset and annotate it. Data ...
- Experience with multi-machine distributed training and inference. - Experience in accelerating and optimizing deep learning applications, with the ability to perform targeted optimizations based on different scenarios and hardware platforms. - Proficient in Python and C/C++ programming. ...
Even mainstream frameworks like TensorFlow have been found to have vulnerabilities in interfaces, learning algorithms, compilation, deployment, and installation. Exploiting these vulnerabilities can lead to threats like escape attacks, denial of service attacks, heap overflows, and more. To mitigate...
which reflects the real-time condition where energy prices fluctuate with demand. On the other hand, UP charges consumers based on energy use regardless of time. Utilizing these price policies in the energy cost prediction framework makes it adaptable to various market scenarios. It allows the sta...
where the four fundamental terms 𝐹𝑃FP (False Positive), 𝐹𝑁FN (False Negative), 𝑇𝑁TN (True Negative), and 𝑇𝑃TP (True Positive) represent distinct scenarios. A 𝐹𝑁FN occurs when lesion regions are mistakenly classified as non-lesion regions. Conversely, a 𝐹𝑃FP ...
However, the computational complexity of traditional HAR models presents challenges for deployment on portable devices with limited resources. Human-engineered feature characteristics are the fundamental components upon which shallow machine-learning models were constructed [5–9]. However, feature engi- nee...