This paper presents the ideas utilized by Team Ds at the 2023 Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge. The 2023 SHL Challenge involves classifying eight modes of locomotion and transportati...
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ISBN:
(纸本)9798400702006
This paper presents the ideas utilized by Team Ds at the 2023 Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge. The 2023 SHL Challenge involves classifying eight modes of locomotion and transportation using data acquired by smartphone motion and GPS sensors. The data were obtained from three users, User1 provided the training set, and parts of the data acquired by User2 and User3 were used as the validation and test sets. Long-period data (up to 110 s) were used to estimate the modes of locomotion and transportation for a sample in the main processing. We have created a model to estimate the modes of locomotion and transportation using User1's accelerometer, gyro sensor, magnetic sensor, and GPS location data. The neural network model comprised four sections, i.e., two types of a one-dimensional convolutional neural networks (1D-CNNs) with different filter size using raw waveform data for nine sensors, one 2D-CNN using the spectrograms of accelerometer and gyro sensor, and two fully-connected layers of 22 statistical data computed from the sensors. In a post-processing step, a model was constructed to output the labels from the SoftMax output obtained from the previous model, and this output was used as the final estimation. The developed model was evaluated experimentally, and the F1-score obtained on the validation data was 0.952.
The snake optimization algorithm is a new type of swarm intelligent bionic optimization algorithm that has good optimization effects in solving continuous optimization problems. Based on the discrete TSP problem, the ...
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One of the foundational data mining operations called 'Clustering' finds similar objects in a given dataset. The paper explores the study of density-based and grid-based clustering algorithms, as well as a com...
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