Abstract:
An antenna for charging and measurement may comprise: a telescopic support installed at a lower portion of an aerial vehicle and configured to contract when the aerial vehicle lands on a wireless station and extend when the aerial vehicle takes off from the wireless station; and an antenna coil part deformed into a spiral shape when the telescopic support is contracted so that the wireless station receives wireless power and deformed into a conical shape when the telescopic support is extended to measure a radio signal.
Abstract:
An antenna device for magnetic field communication may include: a first coil; a second coil; a third coil; a first capacitor connected to a 1-1 terminal of the first coil; a second capacitor connected to a 2-1 terminal of the second coil; a third capacitor connected to a 3-1 terminal of the third coil; and an input port including a first input terminal connected to a 1-2 terminal of the first coil, a 2-2 terminal of the second coil, and a 3-2 terminal of the third coil, and a second input terminal connected to the first capacitor, the second capacitor, and the third capacitor.
Abstract:
Disclosed are a device and a method for calibrating a reference voltage. The reference voltage calibrating device includes a data signal communication unit that transmits/receives a data signal, a data strobe signal receiving unit that receives a first data strobe signal and a second data strobe signal, a voltage level of the second data strobe signal being opposite to a voltage level of the first data strobe signal, and a reference voltage generating unit that sets a reference voltage for determining a data value of the data signal, based on the first data strobe signal and the second data strobe signal, and the reference voltage generating unit adjusts the reference voltage based on the first data strobe signal and the second data strobe signal.
Abstract:
Provided is an artificial neural network device including pre-synaptic neurons configured to generate a plurality of input spike signals, and a post-synaptic neuron configured to receive the plurality of input spike signals and to generate an output spike signal during a plurality of time periods, wherein the post-synaptic neuron respectively applies different weights in the plurality of time periods according to contiguousness with a reference time period in which input spike signals, which lead generation of the output spike signal from among the plurality of input spike signals, are received.
Abstract:
Provided is a convolutional neural network system. The system includes an input buffer configured to store an input feature, a parameter buffer configured to store a learning parameter, a calculation unit configured to perform a convolution layer calculation or a fully connected layer calculation by using the input feature provided from the input buffer and the learning parameter provided from the parameter buffer, and an output buffer configured to store an output feature outputted from the calculation unit and output the stored output feature to the outside. The parameter buffer provides a real learning parameter to the calculation unit at the time of the convolution layer calculation and provides a binary learning parameter to the calculation unit at the time of the fully connected layer calculation.
Abstract:
Provided is a convolution neural network system including an image database configured to store first image data, a machine learning device configured to receive the first image data from the image database and generate synapse data of a convolution neural network including a plurality of layers for image identification based on the first image data, a synapse data compressor configured to compress the synapse data based on sparsity of the synapse data, and an image identification device configured to store the compressed synapse data and perform image identification on second image data without decompression of the compressed synapse data.
Abstract:
Provided is a convolutional neural network system including a data selector configured to output an input value corresponding to a position of a sparse weight from among input values of input data on a basis of a sparse index indicating the position of a nonzero value in a sparse weight kernel, and a multiply-accumulate (MAC) computator configured to perform a convolution computation on the input value output from the data selector by using the sparse weight kernel.
Abstract:
A coaxial resonance coil having a toroidal shape for wireless power transmission is provided. The coaxial resonance coil may include a central conductive wire used as a power feeding loop for indirectly feeding power to a resonance coil, and an outer conductive wire used as a resonance coil which is wound a plurality of turns in a toroidal shape around the central conductive wire as an axis.
Abstract:
Disclosed is a wireless power receiving apparatus, which includes: a residual power collecting unit configured to collect residual power remained after supplying an RF power signal to a load while receiving the RF power signal through a resonator; a power selecting unit configured to select one of the RF power signal and a power signal of an auxiliary battery according to a reception state of the RF power signal; a communication unit configured to receive the power signal of the auxiliary battery and perform wireless communication with the wireless power transmitting apparatus, when a supply of power to the load is required in a state in which the transmission of the RF power signal from the wireless power transmitting apparatus is stopped; and a controller configured to request the transmission of the RF power signal to be supplied to the load.