KOREA UNIVERSITY OF TECHNOLOGY AND EDUCATION INDUSTRY-UNIVERSITY COOPERATION FOUNDATION

Disclosed are an anodeless lithium secondary battery, a lithium metal secondary battery, a lithium secondary battery, and an all-solid-state secondary battery comprising a Zr-based amorphous metal alloy and Ti-based amorphous metal alloy coating layer capable of suppressing the generation of a solid-electrolyte interface and dead lithium that are formed on an interface between a Cu current collector and an electrolyte and reduce the coulombic efficiency of an anodeless lithium battery.

actuator device according to the present invention includes a first electrode unit having a voltage polarity due to a power supply voltage applied from a power supply unit; a second electrode unit arranged apart from the first electrode unit and having a voltage polarity different from that of the first electrode unit due to the power supply voltage applied from the power supply unit; and an electroactive gel arranged to be in contact with the first electrode unit and the second electrode unit and electrically connecting the first electrode unit and the second electrode unit.

A vehicle, a computing system therefor, and a method thereof are provided. The computing system: calculates a longitudinal speed of the vehicle and lateral acceleration of the vehicle; calculates a target yaw rate based on the longitudinal speed of the vehicle and the lateral acceleration of the vehicle; calculates a target yaw moment based on the calculated target yaw rate; and applies the target yaw moment to a torque vectoring motor of the vehicle.

A wireless signal spectral analysis method using a multi-task learning-based spectral analysis learning model, the wireless signal spectral analysis method may be provided. The analysis method according to an embodiment of the present disclosure may include: receiving a target signal of a target band, obtaining a training dataset through pre-processing of the target signal, performing wireless signal spectral analysis learning using the training dataset, and analyzing the target signal using a trained spectral analysis learning model, wherein the performing of wireless signal spectral analysis learning comprises: configuring task specific layers for respectively performing individual learning for a plurality of tasks to be analyzed and a shared layer for performing shared learning; learning, in the shared layer, correlation data that meets a predefined criterion in the training dataset; and individually learning, in each of the plurality of task specific layers, using an individual dataset required for each task in the training dataset and a result of learning the correlation data.

The present disclosure relates to an electro-opto-acoustic composition and an electro-opto-acoustic device, wherein, by adding a non-ionic surfactant, it is possible to improve a dielectric constant while simultaneously improving electroluminescence performance, and the electro-opto-acoustic composition according to an embodiment of the present disclosure includes a stretchable silicone rubber, a non-ionic surfactant, and copper-doped zinc sulfide (ZnS:Cu).

A method of generating a dynamic curriculum control model according to an embodiment may include generating a basic curriculum based on a curriculum generation model built based on semi-supervised learning; generating reconstructed curricula based on the basic curriculum; pre-training a learning tendency estimation model that predicts an agent learning tendency pattern of a reinforcement learning model based on the reconstructed curricula; obtaining agent learning tendency information of the reinforcement learning model; and generating a dynamic curriculum control model that reflects the agent learning tendency information by fine-tuning the learning tendency estimation model using a transfer training technique.

A deep reinforcement learning framework according to an embodiment may include a reinforcement learning environment that provides an environment with which an agent may interact; a policy network that learns an optimal policy through trial and error in which the agent selects an action based on a given state in the reinforcement learning environment and obtains a reward as a result of the action; a memory for reproduction that stores information about a state, action, reward, and next state generated by the agent interacting with the reinforcement learning environment; an extrinsic uncertainty recognition unit that determines extrinsic uncertainty based on the agent's metacognitive ability and detects a new state to provide an additional exploration reward; an intrinsic uncertainty recognition unit that evaluates intrinsic uncertainty of transactions generated by the policy network; an uncertainty data filtering unit that selects a transaction with high uncertainty based on an evaluation result of the intrinsic uncertainty recognition unit; and a memory for reproduction reconstruction unit that reconstructs the memory for reproduction based on the selected transaction to optimize repeated learning of the agent.

Disclosed herein is a method for estimating a depth of an image. The method may include acquiring a first depth image based on left and right images collected from a stereo camera, estimating a second depth image based on any one of the left and right images, generating a depth estimation model based on first depth values within a depth range recognizable by the stereo camera in the first depth image and on second depth values of the second depth image matching the first depth values, and inputting third depth values of a range intended to be estimated in the second depth image to the depth estimation model, thereby estimating fourth depth values of the first depth image matching the third depth values.

The present invention relates to a poly(butyl acrylate)-based composition for an electrochromic device having transparency and elasticity and a manufacturing method of an electrochromic member, wherein the composition for an electrochromic device according to an embodiment of the present invention includes a light-transmitting polymer resin, a cross-linking agent, an initiator, an ionic liquid, and an electrochromic material, wherein the light-transmitting polymer resin is poly(butyl acrylate).

A vehicle control device includes a sensor that obtains information about a driving environment of a vehicle, a driving device that controls behavior of the vehicle, and a processor that determines a required control quantity for controlling a velocity or acceleration of the vehicle based on the information obtained by the sensor, determines a longitudinal velocity of the vehicle to minimize a tracking error representing a degree to which the vehicle follows the required control quantity while minimizing a total acceleration at a target location within the vehicle, and generates a control signal for controlling the driving device based on the longitudinal velocity of the vehicle.