27 Papers
24 Citations
Kan Wang is an academic researcher from Xi'an University of Science and Technology. The author has contributed to research in topics: Computer science & Scheduling (computing). The author has an hindex of 6, co-authored 22 publications.
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Papers
Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques
Huaijun Wang,Jing Zhao,Junhuai Li,Ling Tian,Pengjia Tu,Ting Cao,Yang An,Kan Wang,Shancang Li +8 more
TL;DR: This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications and builds a deep convolutional neural network for extracting features from the data collected by sensors.
Segmentation and Recognition of Basic and Transitional Activities for Continuous Physical Human Activity
TL;DR: A systematic human activity recognition method to recognize basic activities (BA) and transitional activities (TA) in a continuous sensor data stream and can deliver high accuracy with all activities considered is proposed and implemented.
A Road Quality Detection Method Based on the Mahalanobis-Taguchi System
TL;DR: This paper proposes a novel road detection approach based on Mahalanobis–Taguchi system (MTS), leveraging smartphones for data collection and involving the correlation between characteristics in road quality, and develops an application to collect and process the data, and then classify road quality conditions.
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A correlation-based binary particle swarm optimization method for feature selection in human activity recognition:
TL;DR: A correlation-based binary particle swarm optimization method for feature selection in human activity recognition, which can work well with six classifiers, and can improve the classification accuracy in the OPPORTUNITY Activity Recognition dataset.
21
Non-intrusive load monitoring method with inception structured CNN
TL;DR: A method based on multiple overlapping sliding windows combined with the inception structure of CNN to disaggregate highly mixed loads of multiple appliances, which can stack each layer disorderly and run each process in parallel, without deepening the depth is proposed.
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