Naver Corporation

About: Naver Corporation is a company organization based out in Seongnam-si, South Korea. It is known for research contribution in the topics: Terminal (electronics) & Computer science. The organization has 4038 authors who have published 4294 publications receiving 35045 citations. The organization is also known as: NAVER Corporation & NAVER.

Heat Transfer Characteristics of a Speaker Using Nano-Sized Ferrofluid

Abstract: The purpose of this article is to study the heat transfer characteristics of a voice-coil and permanent magnet for a speaker using nano-sized ferrofluid. In order to investigate the temperature characteristics of the speaker, the speaker power ratings, ambient temperatures of the test chamber, chamber sizes and input signals were tested. As a result, the temperatures of the voice-coil and magnet for the speaker increased with time due to the thermal linearity. The temperature of the voice-coil increased with the decrease of the input signals, but with the increase of the nominal power rating. The voice-coil temperature of Speaker 1 using ferrofluid of an amount of 650 μL at an elapsed time of 10,000 s was 24.5% lower than that of general Speaker 1. In addition, the proper size selection of the enclosure is an important design factor to ensure the sound quality and effective heat transfer of the speaker.

Fast Encoding Algorithm of Low Density Codes

Abstract: In this paper, we consider the encoding scheme of low density codes. In particular, we propose the fast encoding algorithm of the low density codes using circulant permutation matrices, The fast encoding algorithm is similar to the fast Hadamard transform, and the results show that the proposed scheme is a simple and fast encoding algorithm of the low density codes.

Mediating Effects According to Family Type in the Complex Relationship between Psychological and Material Factors and Depression in the Elderly

Abstract: The aim of this study is to investigate influential factors for depression with respect to mental health in the elderly and present the groups in order to develop intervention strategies with human for the prevention of depression and suicide. For this purpose, this study held interviews using a questionnaire targeting normal elderly above the age of 65 who did not use social welfare services living in Seoul, Gyonggi and Jeonnam. The data collected was analysed with the use of a frequency test, a descriptive statistical analysis, a correlation analysis and a multi-regression analysis. The results of the study show that the influence of psychological factors in the subjects on their depression was significant. That is, Depending on your type of psychological factors of family influences the sense of difference of melancholy. Second, the influence of material factors of the subjects on their depression was significant depending on their family type. That is, the influence of material factors in the elderly on their depression differed depending on family type.

Method for searching for chinese character using tone mark and system for executing the method

Abstract: A method for searching for a Chinese character using a tone mark and system for executing the method is provided. The method includes: receiving a search term from a user; verifying a type of said received search term based on at least a portion of said received search term wherein the type includes at least one phonetic Chinese language or at least one Chinese character; and providing a search result based, at least in part, upon the verified type in response to the user's search request wherein the search result include dictionary definition of at least one Chinese character associated with the received search term.

Implicit Discourse Relation Classification with Syntax-Aware Contextualized Word Representations

Abstract: Automatically identifying implicit discourse relations requires an in-depth semantic understanding of the text fragments involved in such relations. While early work investigated the usefulness of different classes of input features, current state-of-the-art models mostly rely on standard pretrained word embeddings to model the arguments of a discourse relation. In this paper, we introduce a method to compute contextualized representations of words, leveraging information from the sentence dependency parse, to improve argument representation. The resulting token embeddings encode the structure of the sentence from a dependency point of view in their representations. Experimental results show that the proposed representations achieve state-of-the-art results when input to standard neural network architectures, surpassing complex models that use additional data and consider the interaction between arguments.