Machine Learning
As an artificial structure, tailings ponds exhibit regular geometric shapes and relatively straight dams in HRRSIs. Because the typical tailings dam is composed of an initial dam and successive accumulation dams, the tailings dam structure presents obvious linear stripe characteristics. The initial dam, constructed using sand, gravel, or concrete, has a bright color, while the color of the accumulation dam varies based on factors such as particle size, soil coverage, and vegetation restoration.
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79 Views
Public safety is seriously threatened by road accidents, which are a major global concern in urban settings. The capital of Bangladesh, Dhaka City, stands out among these locations as a perfect illustration of the complicated difficulties confronted by highly populated cities in ensuring road safety. In this paper, we have used time-series analysis to model the temporal patterns and trends in accident occurrence and machine learning algorithms to identify accident hotspots and comprehend the causes of traffic accidents.
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746 Views
The uploaded project is the code and dataset for Charging Efficiency Optimization Based on Swarm Reinforcement Learning under Dynamic Energy Consumption for WRSN. The details of each document in the uploaded project are as follows. Document data: The data file contains network data and simulation data. Document iostream: The iostream file contains the program for reading data and writing data. Document main: The main file contains the main program that executes the simulation. Document network: The network file
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81 Views
The Colour-Rendered Bosphorus Projections (CRBP) Face Dataset represents an innovative advancement in facial recognition and computer vision technologies. This dataset uniquely combines the precision of 3D face modelling with the detailed visual cues of 2D imagery, creating a multifaceted resource for various research activities. Derived from the acclaimed Bosphorus 3D Face Database, the CRBP dataset introduces colour-rendered projections to enrich the original dataset.
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281 Views
This work presents a new labeled dataset of videos with native and professional interpreters articulating words and expressions in Libras (Brazilian Sign Language). We used a methodology based on related studies, the support of the team of articulators, and the existing datasets in the literature.
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636 Views
This dataset is designed for the purpose of curve fitting, a key process in the reconstruction of implicit curves. It encompasses a collection of point cloud data that has been sampled directly from curves, as well as the code necessary to generate point cloud data from these curves.
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120 Views
X-CANIDS Dataset (In-Vehicle Signal Dataset)
In March 2024, one of our recent research "X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Network" was published in IEEE Transactions on Vehicular Technology. Here we publish the dataset used in the article. We hope our dataset facilitates further research using deserialized signals as well as raw CAN messages.
Real-world data collection. Our benign driving dataset is unique in that it has been collected from real-world environments.
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1407 Views
Inertial sensors are widely used in a variety of applications. A common task is orientation estimation. To tackle such a task, attitude and heading reference system algorithms are applied. Relying on the gyroscope readings, the accelerometer measurements are used to update the attitude angles, and magnetometer measurements are utilized to update the heading angle. In indoor environments, magnetometers suffer from interference that degrades their performance.
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55 Views
This dataset comprises audio recordings of ultra-high-frequency ambient noise stored in the lossless waveform format (WAW). The recordings were sampled at a frequency sample rate of 2.048 MHz and then provided at a downsampled audio rate of 48 kHz for compatibility and practical usage. The total length of the dataset is 01:30:29, consisting of approximately 260 million data points. (2024-03-30)
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20 Views
Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). However, class imbalance in semiconductor process data poses a significant obstacle to the introduction of FDC into semiconductor equipment. Overfitting can occur in machine learning due to the diversity and imbalance of datasets for normal and abnormal.
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113 Views