The two most common ways to activate intelligent voice assistants (IVAs) are button presses and trigger phrases. This paper describes a new way to invoke IVAs on smartwatches: simply raise your hand and speak naturally. To achieve this experience, we designed an accurate, low-power detector that works on a wide range of environments and activity scenarios with minimal impact to battery life, memory footprint, and processor utilization. The raise to speak (RTS) detector consists of four main components: an on-device gesture convolutional neural network (CNN) that uses accelerometer data to detect specific poses; an on-device speech CNN to detect proximal human speech; a policy model to combine signals from the motion and speech detector; and an off-device false trigger mitigation (FTM) system to reduce unintentional invocations trigged by the on-device detector. Majority of the components of the detector run on-device to preserve user privacy. The RTS detector was released in watchOS 5.0 and is running on millions of devices worldwide. CCS CONCEPTS • Human-centered computing → Gestural input; • Computing methodologies → Speech recognition; Neural networks; Supervised learning by classification.
This paper presents a design scheme of charging pile control system for electric vehicle based on BP neural network and PID control. The design goal of this scheme is to design a charging pile control system for electric vehicles which is suitable for public parking lot and district parking lot. It can realize fast charging of electric vehicles, human-computer interaction with the system, calculation of consumer price and other functions.
A three-DOF(degree of freedom) parallel mechanism is designed and integrated with the moveable wall which the solar photovoltaic panels are installed on it. In order to control the spatial posture of the wall, geometric analysis was used to get the kinematic inverse solution and the kinematic positive solution of the mechanism, to obtain the relationship among push distance, rotation angle and flip angle. MATLAB was used to get numerical solution and show the relationship of them intuitively. The motor control program was designed and optimized through feedback structure. The motor could be controlled according to the deviation. Result indicates the wall can be controlled more accurately according to the change of solar angle and gets the maxmal generating efficiency.
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