Didi Xu;Weihua Yu;Yufeng Wang;Mengjun Chen;Yaze Cui
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引用次数: 0
Abstract
The classification and identification of human activities have been increasingly focused on in the fields of human-computer interaction, search and rescue, health detection, and so on. Deep learning methods have been widely employed in target classification recognition. To enhance the classification performance, an attention-mechanism-based two-channel network (AB-TCN) is proposed. In this architecture, the attention module is embedded into the convolutional neural network (CNN) to achieve feature enhancement and redundancy suppression in the spatial and channel domains. Furthermore, the short-window time-frequency image and long-window time-frequency image are separately input into two symmetrical channels for feature extraction and fusion to enhance the differential feature weight of target behavior. The method is simple and easy to implement, with low computational complexity. The experimental results show that the proposed method has higher detection accuracy, and the classification accuracy is increased by more than 5% compared with the traditional neural network architecture.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
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-Sensor Materials, Processing, and Fabrication
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-Optical Sensors
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-Sensor Systems: Signals, Processing, and Interfaces
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-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice