Artificial Intelligence
The demand for intelligent automation in factories has been steadily increasing. While traditional robotic arms perform simple automated tasks, deep reinforcement learning enables them to execute more complex operations. However, deep reinforcement learning in the field of robotics often encounters challenging learning tasks, especially in three-dimensional and continuous environments where obtaining rewards becomes sparse. To address this issue, this article proposes the Hindsight Proximal Policy Optimization (HPPO) method for intelligent robotic control.
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This collection includes multiple short text classification datasets designed for various natural language processing tasks. It contains several topic classification datasets, such as AG'News, Snippets, and TMNNews, which cover a wide range of topics and domains to evaluate the effectiveness of classification models. Additionally, the collection includes a binary sentiment classification dataset, such as Twitter, aimed at determining positive or negative sentiment in text.
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This collection includes multiple short text classification datasets designed for various natural language processing tasks. It contains several topic classification datasets, such as AG'News, Snippets, and TMNNews, which cover a wide range of topics and domains to evaluate the effectiveness of classification models. Additionally, the collection includes a binary sentiment classification dataset, such as Twitter, aimed at determining positive or negative sentiment in text.
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To promote the deployment of quadrupedal robots, this study proposes a novel bio-inspired control scheme. Specifically, inspired by the differentiated modalities of the animal's proximal and distal joints, a multi-model fusion scheme is constructed. First, the hip movement in joint space is obtained by a central pattern generator(CPG), whereby motion gaits, including trotting and galloping, are generated by a coupling network. Then, to generate the knee motion, a CPG-driven finite state machine is first proposed to determine the gait state.
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This dataset contains electromagnetic field (EMF) intensity measurements recorded at half-hour intervals. The dataset spans a continuous timeline, capturing variations in electric field strength in volts per meter (V/m). It serves as a valuable resource for environmental monitoring, predictive modeling, and studying the impacts of EMF exposure. Applications include urban planning, public health assessments, and advanced regression or machine learning modeling.
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This MATLAB script presents an innovative approach to 5G beamforming prediction using a sequence-based LSTM neural network. Unlike conventional methods that predict only final vectors, this solution provides time-stepped predictions across entire sequences, enabling real-time tracking of dynamic channel conditions. The framework achieves stable training convergence while maintaining physically meaningful performance metrics, including realistic path loss and SNR values.
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The data was collected by a tester holding a Xiaomi 13 smartphone while walking and collecting data in an underground parking lot covering a 16x70m area. The data includes 5G radio features and geomagnetic field information.
Collection Time: From 09:58 AM to 10:34 AM on July 13, 2024.
Total Samples: 12,800
Training Set (including validation set): 10,240
Test Set: 2,560
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This paper conducts in-depth research on three text classification tasks: sentiment analysis, offensive language identification, and news topic classification.
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In this dataset, a human detecting model using with UWB radar technology is presented. Two distinct datasets were created using the UWB radar device, leveraging its dual features. Data collection involved two main scenarios, each containing multiple sub-scenarios. These sub-scenarios varied parameters like the position, distance, angle, and orientation of the human subject relative to the radar. Unlike conventional approaches that rely on signal processing or noise/background removal, this study uniquely emphasizes analyzing raw UWB radar data directly.
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164 Views
The JU-Impact Radiomap Dataset is a comprehensive dataset designed for research and development in indoor positioning systems. It comprises 5431 instances characterized by readings from 105 static Wi-Fi Access Points (APs) and spans 152 distinct virtual grids. Each virtual grid represents a 1x1 square meter area, derived by dividing a physical floor of a university building into reference coordinate points (x, y). The dataset was collected over a period of 21 days using four mobile devices: Samsung Galaxy Tab, Moto G, Redmi Note 4, and Google Pixel.
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