Abstract: An anode active material for a secondary battery includes a carbon-based active material, and silicon-based active material particles doped with magnesium. At least some of the silicon-based active material particles include pores, and a volume ratio of pores having a diameter of 50 nm or less among the pores is 2% or less based on a total volume of the silicon-based active material particles.

Abstract: The technology and implementations disclosed in this patent document generally relate to a lithium secondary battery including: a first unit cell including a first anode including a 1-1 anode mixture layer and a 1-2 anode mixture layer on the 1-1 anode mixture layer, and a second unit cell including a second anode including a 2-1 anode mixture layer and a 2-2 anode mixture layer on the 2-1 anode mixture layer, wherein a weight ratio of the silicon-based active material in the 1-2 anode mixture layer is greater than a weight ratio of the silicon-based active material in the 1-1 anode mixture layer, and a weight ratio of the silicon-based active material in the 2-2 anode mixture layer is less than or equal to a weight ratio of the silicon-based active material in the 2-1 anode mixture layer.

Abstract: An anode for a lithium secondary battery includes an anode current collector, and an anode active material layer formed on at least one surface of the anode current collector. The anode active material layer includes a carbon-based active material, a first silicon-based active material doped with magnesium and a second silicon-based active material not doped with magnesium. A content of the first silicon-based active material is in a range from 2 wt % to 20 wt % based on a total weight of the anode active material layer.

Abstract: In some implementations, the anode includes a current collector, a first anode mixture layer formed on at least one surface of the current collector, and a second anode mixture layer formed on the first anode mixture layer. The first anode mixture layer and the second anode mixture layer include a carbon-based active material, respectively. The first anode mixture layer includes a first binder, a first silicon-based active material, and a first conductive material. The second anode mixture layer includes a second binder, a second silicon-based active material, and a second conductive material. Contents of the first conductive material and the second conductive material are different from each other with respect to the total combined weight of the first anode mixture layer and the second anode mixture layer. Types of the first silicon-based active material and the second silicon-based active material are different from each other.

Abstract: A system of automatic optimization of image quality of an image sensor includes an image learning data generation unit generating an image tuning knowledge database, which includes pairs of a plurality of sets of values of a plurality of parameters and a plurality of sets of image quality evaluation scores for a plurality of image quality evaluation items for evaluating a quality of each of a plurality of images generated by the image sensor, using an image tuning database sampling module, an image signal processor modeling unit generating a machine learning model, for each image, for automatically optimizing the quality of each image, and an image sensor image quality optimization unit automatically controlling values of some of the plurality of parameters based on a user's image quality selection and the machine learning model. The image quality evaluation scores are produced by a distributed camera simulation system including servers.

Abstract: The present invention relates to a composition for preparing a decellularized scaffold, including nano graphene oxide. The present inventors crosslinked nano graphene oxide to a decellularized liver scaffold to strengthen the properties of the scaffold and suppress protease activity of the scaffold, and thus have established an optimum crosslinking condition exhibiting the effects of anti-inflammation and polarization to M2 macrophages. That is, since it is confirmed that nano graphene oxide strengthens the durability of the scaffold so that biodegradation is suppressed and, simultaneously, inflammatory responses that may occur after transplantation are minimized, it is expected that nano graphene oxide can be effectively used in the production and transplantation of clinically applicable artificial organs.

Abstract: Provided is a method for producing highly functional artificial organs, including blood vessels capable of carrying perfusate, by using aptamers. The aptamers, according to the presently claimed subject matter, enhance blood vessel adhesion ability, viability, angiogenesis potential, and the like when used as a coating agent of a decellularized scaffold, and thus can more efficiently reconstruct vasculature than existing antibodies. Therefore, a vascularized artificial liver produced using the aptamers exhibits the effects of reducing thrombogenesis in vivo as well as enhancing liver function, and thus is expected to be applied as a method for treating diseases by using artificial organs produced using the aptamers according to the presently claimed subject matter.

Abstract: A method for generating a target network by performing neural architecture search using optimized search space is provided. The method includes steps of: a computing device (a) if a target data is inputted into the target network, allowing the target network to apply neural network operation to the target data, to generate an estimated search vector; and (b) allowing a loss layer to calculate architecture parameter losses by referring to the estimated search vector and a ground truth search vector, and to perform backpropagation by referring to the architecture parameter losses to update architecture parameter vectors for determining final layer operations among candidate layer operations included in an optimized layer type set corresponding to the optimized search space and wherein the final layer operations are to be performed by neural blocks, within cells of the target network, arranged according to an optimized cell template corresponding to the optimized search space.

Abstract: A method for generating a target network by performing neural architecture search using optimized search space is provided. The method includes steps of: a computing device (a) if a target data is inputted into the target network, allowing the target network to apply neural network operation to the target data, to generate an estimated search vector; and (b) allowing a loss layer to calculate architecture parameter losses by referring to the estimated search vector and a ground truth search vector, and to perform backpropagation by referring to the architecture parameter losses to update architecture parameter vectors for determining final layer operations among candidate layer operations included in an optimized layer type set corresponding to the optimized search space and wherein the final layer operations are to be performed by neural blocks, within cells of the target network, arranged according to an optimized cell template corresponding to the optimized search space.