Abstract:
A photoelectric conversion device according to an exemplary embodiment includes a first substrate, a photoelectric conversion layer disposed above the first substrate, a second substrate which is different from the first substrate and disposed on the photoelectric conversion layer, and a nano pillar layer disposed above the second substrate in which the nano pillar layer includes a plurality of nano pillars which is spaced apart from each other, so as to easily absorb the light.
Abstract:
In an apparatus and method for training artificial intelligence model for self-sensing actuator, the method includes acquiring a dataset from a shape memory alloy system by changing control parameters for controlling the shape memory alloy system, classifying the dataset into input data and ground truth data and labeling the dataset based on the ground truth data, and performing supervised training on the artificial intelligence model using the labeled dataset so that the artificial intelligence model outputs a generated force of a shape memory alloy with a specific shape or a length change of the shape memory alloy with the specific shape from the input data.
Abstract:
An autocorrelator and a pulse amplifying device including the same are provided. The autocorrelator includes a parabolic mirror configured to transmit and reflect pulse laser beam, a prism on one side of the parabolic mirror and configured to split the pulse laser beam, a first lower retro-reflector under the prism and provided at a first distance from the prism to reflect a portion of the pulse laser beam, a first upper retro-reflector on the prism, provided at a second distance different from the first distance from the prism, and configured to reflect another portion of the pulse laser beam to generate a first time difference between pulses of the pulse laser beam upper retro-reflector, and a first sensor under the parabolic mirror and configured to receive the pulse laser beam to detect a pulse width of the pulse laser beam.
Abstract:
An apparatus for analyzing microbiome according to an embodiment of the inventive concept includes a light source unit configured to excite first light, a sample unit on which a sample to which the first light is incident is disposed, and a data analysis unit configured to receive second light emitted from the sample unit and analyze microbiome in the sample from the second light. Here, the sample unit includes a conductive polymer structure that surrounds the sample.
Abstract:
Disclosed is an integral label-free biosensor capable of analyzing a biomolecule with high sensitivity by integrating a light source, a photodetector, an optical waveguide, and a microcantilever on a substrate, and a method of detecting a bio-antigen by using the same. The integral label-free biosensor according to the present invention may be manufactured with low cost, be easily integrated with a silicon electron device, and detect a biomolecule with high sensitivity by using a label-free method.
Abstract:
Provided are a pulse compressor and a two-photon excited fluorescence microscope. The microscope includes a light source which generates a laser beam having a pulse, a pulse compressor which compresses the pulse of the laser beam, an objective lens which provides the laser beam to a specimen, and image sensors which receive the laser beam and obtain images of the specimen. The pulse compressor may include a grating plate, a corner cube provided on one side of the grating plate, and a retroreflector provided on the other side of the grating plate.
Abstract:
A blood glucose prediction method comprising: acquiring a PAS signal by irradiating light to skin of the body, obtaining a photoacoustic image of the skin from the PAS signal, selecting at least one measurement location based on the photoacoustic image; and predicting the blood glucose based on a photoacoustic spectrum of a PAS signal corresponding to the at least one measurement location among the PAS signals, a blood glucose sensor, and a blood glucose prediction system are provided.
Abstract:
An apparatus for controlling emotion of a driver includes an emotion sensor unit configured to collect a biomedical signal from the driver, and generate biomedical information data based on the collected biomedical signal, a user memory unit configured to store driver information that includes biomedical signals for respective emotional states of the driver and a plurality of correspondence contents, and deliver the driver information and the correspondence content in response to a received request, and an emotion management unit configured to determine the emotional state of the driver from the driver information received from the user memory unit and the biomedical information data received from the emotion sensor unit, request a correspondence content corresponding to the determined emotional state of the driver from the user memory unit, and provide the driver with the content received from the user memory unit.