Abstract: A storage device includes a nonvolatile memory device including a plurality of first memory cells coupled to a first wordline and a plurality of second memory cells coupled to a second wordline, the first wordline and the second wordline being adjacent to each other, and a storage controller configured to control the nonvolatile memory device. The storage controller is further configured to encode data to be programmed into the plurality of second memory cells, based on a program state of each of the plurality of first memory cells, and encode the data to be programmed into the second wordline such that a first portion of the data to be written into a first portion of the plurality of second memory cells satisfies a first condition, and a second portion of the data to be written into a second portion of the plurality of second memory cells satisfies a second condition.

Abstract: A homomorphic operation system according to an embodiment includes a homomorphic encryption device configured to output a first ciphertext data generated based on a first base, a homomorphic encryption server including a storage device storing base conversion table configured to convert ciphertext data based on the first base into a second ciphertext data based on a second base and the first ciphertext data received from the homomorphic encryption device, and a homomorphic encryption operation device configured to perform a predetermined operation using the base conversion table on the first ciphertext data to convert the first ciphertext data into the second ciphertext data based on the second base.

Abstract: Provided is an antibacterial composition including lemongrass oil, lavender oil, and ylang ylang oil.

Abstract: The present disclosure relates to a system for recovering valuable materials from a crucible used in the production of a positive electrode active material. The system includes: a crucible input unit provided such that used positive electrode firing crucibles containing positive electrode material residue and lithium; steam injection unit defining a chamber and configured to inject high-pressure to inside and outside of the crucibles to separately collect slurry; a solid-liquid separation unit configured to separate the slurry into solid and liquid, to discharge waste positive electrode material residue, and to separately collect a low-concentration lithium liquid phase; a lithium concentration unit configured to recover a high-concentration lithium liquid phase and to separately discharge steam; and a steam supply unit configured to collect and reheat the discharged steam to a target temperature, and to supply high-pressure steam to the steam injection unit.

Abstract: According to an embodiment of the present disclosure, a pre-training modeling system for predicting an educational element using a pre-trained AI model includes: a pre-training unit configured to pre-train an AI model, through an evaluation element that can be automatically collected every time a user solves an individual question by receiving an interaction element according to an offline user's operation from a user terminal, classifying an AI element of educational content into the interaction element and the educational element, and intersecting the interaction element and the educational element; a fine-tuning unit configured to perform an additional training operation with respect to the pre-trained AI model, according to an educational element to be predicted; and an educational element prediction unit configured to predict, on the basis of the fine-tuned AI model, one or more pieces of information among test scores, grades, qualifications, and review information indicating whether the user got a corre

Abstract: Disclosed herein method and device for personalized active safety control. The method includes: generating input data including body information of a vehicle occupant and occupancy state information of the occupant; outputting initial control data of a restraint device of a vehicle based on collision data that responds to occurrence of a collision of the vehicle; producing injury risk information for each piece of adjustment restraint information by using a safety control model based on the adjustment restraint information, in which a variable adjustment parameter is applied to the initial control data, and the input data; generating, by the safety control model, optimal restraint control information among the adjustment restraint information, based on the injury risk information; and controlling the restraint device based on the optimal restraint control information.

Abstract: A decoder including a main memory, a flag memory and a decoding logic is provided. The flag memory is configured to store flag data and the decoding logic configured to perform an iteration. Further, the decoding logic is configured to: perform an ith operation using first data, wherein i is a natural number, flag-encode second data that is results obtained by performing the ith operation on the first data, store results obtained by performing the flag encoding on the second data in the flag memory as first flag data if the flag encoding succeeds, and store predetermined second flag data that is different from the first flag data of the second data in the flag memory if the flag encoding fails.

Abstract: Provided is an operating method of a memory controller which comprises receiving first decision data of M bits from a memory device, where M is a natural number; converting the M-bit first decision data into second decision data of N bits, where N is a natural number less than M; and attempting a first decoding using the second decision data.

Abstract: Disclosed herein is a method of providing user-customized learning content in a service server, which includes a) for a specific subject, configuring a problem database including at least one of multiple-choice problems each including at least one example, providing the problem to user devices, and collecting example selection data of users for the problem from the user devices, b) estimating a probability of right answer to the problem for each of the users using the example selection data of each of the users, and assuming that any user selects an example of any problem, calculating, for each problem, a change rate of probabilities of right answer to all problems contained in the problem database for the user, and sorting the problems contained in the problem database in the order of the high change rate to recommend them to the user.

Abstract: Provided is an operating method of a memory controller which comprises receiving first decision data of M bits from a memory device, where M is a natural number; converting the M-bit first decision data into second decision data of N bits, where N is a natural number less than M; and attempting a first decoding using the second decision data.

Abstract: A memory system includes; a memory device, a memory controller including a first interface, a second interface, and a first data processor having a first error correction code (ECC) engine, and a field programmable gate array (FPGA) including a third interface connected to the first interface, a fourth interface connected to the second interface, a fifth interface connected to an external host, and a second data processor having a second ECC engine. The memory controller may configure a normal write operation path or highly reliable write operation path.

Abstract: The present disclosure relates to a method and device for providing training content. A method may comprise displaying a question card including a first area and a second area; determining a recommended question using an artificial intelligence (AI) model trained on the basis of training data of a user; displaying, on the first area of a screen of a learner terminal, a plurality of prediction values for information obtained by analyzing a correlation between the training ability of the user and the recommended question using the AI model, together with the recommended question; displaying summary information about the recommended question on the second area of the screen of the learner terminal; and displaying a question object of the recommended question on the screen of the learner terminal when the user decides to solve the recommended question on the basis of the plurality of prediction values and the summary information.

Abstract: Disclosed herein are operation methods of a memory controller which controls a memory device. The method includes storing write data in a first area of the memory device, extracting first error position information indicating a position of at least one error included in data stored in the first area, storing the first error position information in a second area of the memory device, reading read data from the first area of the memory device, reading the first error position information from the second area of the memory device, refining the read data based on the first error position information to generate refined data, performing soft decision decoding based on the refined data to generate corrected data, and outputting the corrected data.

Abstract: In the present specification, a method of performing pre-training using an automated machine learning (AutoML) model by a server includes: using a first model for performing a first task to generate a second model for a second task; inputting, to the Auto ML model, a preset feature based on 1) components of the first model and the second model and 2) an element obtainable from training of the first model and generating of the second model as a state value; and changing the first model using the AutoML model.

Abstract: The present invention relates to a user knowledge tracking method with more improved accuracy. An operation method of a user drop-out rate prediction system including a plurality of encoder neural networks and a plurality of decoder neural networks may include the steps of: inputting question information to a kth encoder neural network and inputting response information to a kth decoder neural network; generating query data, which is information about a question of which the correct answer probability is desired to be identified by the user, by reflecting a weight to the response information, and generating attention information to be used as a weight for the query data by reflecting a weight to the question information; and learning the user drop-out rate prediction system using the attention information as a weight for the query data.

Abstract: An error correction circuit includes a memory that stores at least one decoding parameter, a low density parity check (LDPC) decoder that includes a first variable node storing one bit of the data, receives the at least one decoding parameter from the memory, decides a degree of the first variable node based on the at least one decoding parameter, and decides a decoding rule necessary for decoding of the one bit based on the degree of the first variable node, and an adaptive decoding controller that outputs corrected data based on a decoding result of the LDPC decoder.

Abstract: The present disclosure relates to a system capable of increasing the accuracy of a prediction result when predicting a user drop-out rate or predicting a correct answer probability of a user in an online learning environment, and further relates to an operation method of the system. A system according to the present disclosure may include a plurality of encoder neural networks and a plurality of decoders, and may input question information to a kth encoder neural network and input response information to kth decoder neural network to learn the system, thereby predicting the user drop-out rate information and the users correct answer probability information with higher accuracy on the basis of the learned system.

Abstract: A user knowledge tracking device is provided. The user knowledge tracking device use a transformer structure-based artificial intelligence model as a knowledge tracking model which is trained by inputting exercise information to an encoder thereof and inputting response information to a decoder thereof so as to predict a user's correct answer probability based on the trained knowledge tracking model.

Abstract: Provided is an operating method of a memory controller which comprises receiving first decision data of M bits from a memory device, where M is a natural number; converting the M-bit first decision data into second decision data of N bits, where N is a natural number less than M; and attempting a first decoding using the second decision data.

Abstract: A memory system includes; a memory device, a memory controller including a first interface, a second interface, and a first data processor having a first error correction code (ECC) engine, and a field programmable gate array (FPGA) including a third interface connected to the first interface, a fourth interface connected to the second interface, a fifth interface connected to an external host, and a second data processor having a second ECC engine. The memory controller may configure a normal write operation path or highly reliable write operation path.