Korea Institute of Science and Technology

Samsung Electro-Mechanics Co., Ltd.

Yonsei University

Other

Samsung Electronics Co., Ltd.

Hyundai Motor Company

Korea Advanced Institute of Science and Technology

Humax Co., Ltd.

Kia Motors Corporation

Lg Innotek Co., Ltd.

Kia Corporation

Dongwoo Fine-Chem Co., Ltd.

Hyundai Transys Incorporated

Kyungdong Navien Co., Ltd.

Korea University Research and Business Foundation

Dong Hwan Kim

Woo Tae Jeong

Hyun Ok Kim

You Kyung Kwon

Han Su Kim

Jong Ho Lee

Kyung Joong Yoon

Sang Hyeok Lee

Seanie Lee

Jong Sup Hong

Yong Gyun Bae

Kibong Sung

Jin Su Kim

Young Ki Lee

Kee Ju Um

Young Hoon Kwak

Jong Man Kim

Young Ho Sohn

Si Joong Yang

Hyo Jin Lee

Sung Ju Hwang

Sang Hyuck Kim

Gilje Seong

Chang Hyeon Lim

SeongYeop Jeong

Seung Hyeon Lee

In Je Seong

So Young Ko

Sook Jin Roe

Da Hea Moon

Dong Bok Lee

Ki Beom Kwon

Hyuk Sung Kwon

Ji Won Son

Seok Pil Lee

Sun Young Lee

Jae Jin Lee

Sang Hyun Lee

Seok Bae

Byung Jin Choi

Sung Min Park

Seon Chae Na

Seung Hyun Kim

Min Gu Kang

Sun Ho Kim

Jun Kyu Park

Young Min Jhon

Deok Ha Woo

Bum Seok Suh

Chan Ki Cho

Won Jong Lee

Sung Man Pang

Young Tae Byun

Byung Yong Choi

Dong Ha Lee

In Chang Hwang

Won Jin Choi

Hyun Min Nah

Jong Duk Lee

Duck Sik Park

Hee Cheol Kim

Sang Bae Lee

Jin Mun Ryu

Jin Oh Song

Sung Kee Park

Hyun Seok Lim

Jang Hyun Cho

Min Ju Lee

Chul Min Choi

Seong Rae Lee

Tae Bae Kim

Seong Sik Moon

Cheon Kyu Lee

Won Jin Cho

Sang Sam Choi

Ji Yeon Oh

Jae Cheol Jo

Seong Kook Oh

Yo Han Moon

Hyeong Jin Jang

Won Kyu Park

Jong Soo Kim

Min Hyuk Jung

Young Woon Choi

Moon Koog Song

Tae Sik Shon

Keuk Kim

Seong Woo Park

Min Sun Song

Jae Yeob Jung

Ah Rim Sohn

Jun Hyeok Park

Han Soo Kim

Min Jun Oh

Hyunok Kim

Hui Je Che

Ki Bong Sung

Hyeon Dey Kim

Ha Kyeom Moon

Goon Seung Gong

An embodiment discloses a method of manufacturing a rare-earth magnet, the method including: preparing a magnetic sintered body including RE, Fe, and B as compositional components (RE is selected from one or two or more selected from rare earth elements); applying a solution containing a grain boundary diffusion material to the sintered body; and performing grain boundary diffusion by heat-treating the sintered body, wherein the grain boundary diffusion material includes a heavy rare earth element (HREE) hydride and a light rare earth element (LREE) hydride.

Method for manufacturing rare earth magnet

Provided are method and apparatus for data structuring of text. The apparatus for data structuring of text includes a processor; and a memory storing instructions executable by the processor, wherein the processor is configured to execute the instructions to: extract text and location information of the text from an image, set text units for the extracted text, assigning a first tag and a second tag to at least one of the text units, connect text units with related tags among the text units allocated the first tag and the second tag, label the connected text units as first text, second text, and third text respectively corresponding to an item name, an item value, and others based on a natural language processing model, and structure the extracted text by mapping the second text to the first text.

Method and apparatus for data structuring of text

A method of generating a question-answer learning model through adversarial learning may include: sampling a latent variable based on constraints in an input passage; generating an answer based on the latent variable; generating a question based on the answer; and machine-learning the question-answer learning model using a dataset of the generated question and answer, wherein the constraints are controlled so that the latent variable is present in a data manifold while increasing a loss of the question-answer learning model.

Method and apparatus for generating Q and A model by using adversarial learning

A method of retrieving a document according to an embodiment of the present application includes: acquiring a user retrieval query; calculating a user inquiry vector in a unit of sentence from the user retrieval query and acquiring a first document candidate group based on similarity between the calculated user inquiry vector and an embedding vector of a document stored in a retrieval database; acquiring a second document candidate group based on similarity between a text included in the user retrieval query and a text of the document stored in the retrieval database; and determining a summarization target document based on the first document candidate group and the second document candidate group.

Method of retrieving document and apparatus for retrieving document

An unstructured document analysis method according to an embodiment includes: operations of acquiring unstructured document data including font characteristic data and document structure data, extracting text included in the unstructured document data on the basis of the font characteristic data or the document structure data, classifying the extracted text into a pre-classified item using a trained neural network model, acquiring a content query related to the content included in the unstructured document data and associated with the pre-classified item, and generating an answer to the content query on the basis of the extracted text classified into the item.

Method, device, and system for analyzing unstructured document

Disclosed is an air heating apparatus including a burner configured to cause a combustion reaction, a main passage, through which water flows while circulating, a heat exchanging device configured to receive heat from combustion gas generated by the combustion reaction and heat the water flowing along the main passage, a heating heat exchanger configured to receive the water heated by the heat exchanging device and exchange heat with the air for heating, a fan configured to send the air to the heating heat exchanger, and a hot water discharge port connected to the main passage such that the water heated by the heat exchanging device is discharged to an outside of the main passage.

Air heating apparatus

Disclosed herein is a search method performed by a server, including: receiving a user question from a user terminal; generating a user question vector for the user question; selecting similar question candidates based on a similarity to the user question vector; generating an answer to the user question based on the similar question candidates; and transmitting the answer to the user question to the user terminal.

Method and apparatus for question-answering using a database consist of query vectors

The present invention relates to a context-based QA generation architecture, and an object of the present invention is to generate diverse QA pairs from a single context. To achieve the object, the present invention includes a latent variable generating network including at least one encoder and an artificial neural network (Multi-Layer Perceptron: MLP) and configured to train the artificial neural network using a first context, a first question, and a first answer, and generate a second question latent variable and a second answer latent variable by applying the trained artificial neural network to a second context, an answer generating network configured to generate a second answer by decoding the second answer latent variable, and a question generating network configured to generate a second question based on a second context and the second answer.

Architecture for generating QA pairs from contexts

Aspects of the subject disclosure may include, systems and methods, for example, including receiving a user question data in a speech format or a text format, analyzing the user question data, selecting a plurality of documents from a plurality of domains corresponding to the user question data, searching for a plurality of passages including candidates for an answer value determined as being suitable for the user question data, in the plurality of documents, obtaining candidates by inputting the user question data and the plurality of passages into a plurality of MRC question and answer units, determining the answer value based on whether a reliability value of each of the candidates exceeds a threshold value, and providing the determined answer value to a user. Other embodiments are disclosed.

Device and method for machine reading comprehension question and answer

Provided are a device and method for converting a natural language query into a structured query language (SQL) query for a database search. The method includes an operation A of labeling natural language queries included in training data, an operation B of converting the natural language queries into second SQL queries by applying the natural language queries to an SQL conversion model, an operation C of verifying the second SQL queries, and an operation D of training the SQL conversion model by comparing the second SQL queries with third SQL queries corresponding to the natural language queries of the training data.

Device and method for converting natural language query into SQL query

The invention relates to a method and a system for improving performance of text summarization and has an object of improving performance of a technique for generating a summary from a given paragraph. According to the invention to achieve the object, a method for improving performance of text summarization includes: an a step of generating an embedding vector by vectorizing a natural language-based context; a b step of generating a graph using the embedding vector and calculating a first likelihood of each of at least one node included in the graph; a c step of generating a second likelihood by assigning a weight to the first likelihood according to a result of comparing at least one node included in the graph with the context; and a d step of calculating a third likelihood for all candidate paths present in the graph based on the second likelihood, selecting a path having a highest third likelihood, and generating a summary based on the path.

Method and system for improving performance of text summarization

Provided are method and apparatus for data structuring of text. The apparatus for data structuring of text includes a data extraction unit configured to extract text and location information of the text from an image based on an optical character recognition (OCR) technique, a data processing unit configured to generate a text unit based on the text and the location information, a form classification unit configured to classify a form of the image based on the text, a labeling unit configured to label the text unit as first text, second text, and third text respectively corresponding to an item name, an item value, or others based on the classified form, a relationship identification unit configured to map and structure the second text corresponding to the first text, and a misrecognition correction unit configured to determine misrecognition of the first text and correct the first text determined to be misrecognized.

The present invention relates to a method for reinforcing a multiple-choice QA model based on adversarial learning techniques, wherein incorrect answers are further generated based on a data set used in the process of training the multiple-choice QA model to enrich data which are learnable by the multiple-choice QA model. To achieve this object, the method includes step A of an incorrect answer generation model encoding a text based on natural language text and a question, generating a second incorrect answer based on the text and the question, and transmitting the second incorrect answer to an incorrect answer test model, step B of the incorrect answer test model encoding the text, the question, a first correct answer corresponding to the text and the question, a first incorrect answer and the second incorrect answer, and selecting a second correct answer based on results of the encoding, step C of the incorrect answer test model generating a feedback by determining whether the first correct answer is identical to the second correct answer, and step D of the incorrect answer generation model and the incorrect answer test model performing self-learning based on the feedback.

Method and device for reinforcement of multiple choice QA model based on adversarial learning techniques

Disclosed are a separator for fuel cells capable of minimizing the volume of a system and the use of sealants, and a stack for fuel cells, more particularly, a stack for solid oxide fuel cells, including the same. Specifically, by adding a metal sheet having a specific shape, position and size to the separator, the stress applied to the sealant can be uniformized, and thus the oxidizing agent and fuel can be separated and electrically isolated using only a piece of sealant. Therefore, the stack for fuel cells is characterized in that there is no variation in temperature, reactant concentration, power, or the like between respective unit cells, so delamination and microcracks do not occur, the volume is minimized, and the power density per unit volume is very high.

Separator for solid oxide fuel cell (SOFC) stack capable of minimizing system volume and usage of sealant

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Seoul, South Korea

Dong Hwan Kim