Machine Learning

The dataset consists of measurements of different stages of degradation in low-voltage contactors used for industrial purposes. The measurements were obtained with fiber Bragg grating (FBG) sensors that detect the dynamic deformation generated in switching under different load conditions and internal components. The posted dataset was preprocessed and separated into two different events. The signal is segmented and reduced from the original measurement (separated into opening and closing). Furthermore, two sets of measurements were obtained.
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On the basis of autonomous mobile tea picking robot, aiming at the shortcomings of traditional tea bud identification methods such as slow speed, low accuracy and poor adaptability, as well as people's demand for high-quality tea, the research and experiment of tea bud quality classification recognition based on YOLOv5 were carried out. Through the construction of the autonomous mobile tea picking robot visual recognition system, the data set was constructed, which mainly included tea image acquisition, enhancement and annotation.
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On the basis of autonomous mobile tea picking robot, aiming at the shortcomings of traditional tea bud identification methods such as slow speed, low accuracy and poor adaptability, as well as people's demand for high-quality tea, the research and experiment of tea bud quality classification recognition based on YOLOv5 were carried out. Through the construction of the autonomous mobile tea picking robot visual recognition system, the data set was constructed, which mainly included tea image acquisition, enhancement and annotation.
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Global Illumination (GI) is a strategy in computer graphics to add a certain degree of realism. Several approaches exist to achieve such a visual effect for computer-generated imagery. The most physically accurate approach is through conventional raytracing. It produces similar realistic results by trading-off time and computational-resource intensive, making them unsuitable for real-time usage. For more real-time usage scenarios, a set of faster algorithms exists that utilize post-processing on top of rasterization rather than performing ray-tracing.
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Distinguishing between coal and gangue in the production lines of mining factories based on the thermal energy and infrared radiation emission of an object is feasible. In this paper, we use an infrared camera (IC) to distinguish between coal and gangue in the industrial mining field. Additionally, this system is considered to be a binary classification system that has two classes.
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The Dravidian Spam SMS dataset has Spam and Ham messages in English, Tamil, Telugu, Kannada, and Malayalam languages. Nearly 7700 messages were collected by sending friends and other contacts a Google form. Language experts (reading and writing skills) were used to label the messages of corresponding languages carefully. The dataset also includes the Tamil verbatim messages written in English. For example, “Nee Nalama”. The Ham messages are mostly normal. Spam messages include business, annoying, and unnecessary messages an anonymous user sends.
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The FMK (Finger Major Knuckle) dataset was proposed and created to support the experiments of identity verificatio of knuckles of middle and thumb fingers modalites. The images of this dataset were captured using the rear camera of an OPPO A12 smartphone. This dataset was created from 20 different subjects between the ages of 30 and 67. For each subject there are 3 images of major knuckle for the middle finger and 3 images of major knuckle for thumb finger.. The FMK dataset was proposed and constructed for testing and evaluation.
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This dataset contains full details of the use case scenarios. Those can be used for effort elicitation using the adapted Use Case Points method. Despite the extensive use of UCP in software engineering, it has yet to be adapted for IoT systems, which is essential for project management and resource planning. Our proposed adaptation, UCP for IoT, is based on a four-layer IoT architecture and tailors the standard software UCP to the specifications of IoT systems.
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The dataset is named Chinese rose disease dataset, including healthy leaves, black spot leaves, powdery mildew leaves and downy mildew leaves. All images in this dataset were collected from Nanyang City, Henan Province, China. And all images were collected under natural conditions in order to ensure the true execution of the images. To improve the image variety, we randomly enhance the images in the dataset by flipped, changed the brightness, added Salt and pepper noise and added Gaussian noise.
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