• Journal of Computing Science and Engineering
• /
• 제8권1호
• /
• pp.17-24
• /
• 2014

In this paper, we propose a new implementation of a failure detector. The implementation uses a dual model of heartbeat and interaction. First, the heartbeat model is adopted to shorten the detection time, if the detection process does not receive the heartbeat message in the expected time. The interaction model is then used to check the process further. The expected time is calculated using the exponential smoothing method. Exponential smoothing can be used to estimate the next arrival time not only in the random data, but also in the data of linear trends. It is proven that the new detector in the paper can eventually be a perfect detector.

• 한국정보처리학회논문지
• /
• 제6권3호
• /
• pp.603-614
• /
• 1999

As multimedia application services on high-speed information network have been rapidly developed, the need for the video information management system that provides an efficient way for users to retrieve video data is growing. In this paper, we propose a video data model that integrates free annotations, image features, and spatial-temporal features for video purpose of improving content-based retrieval of video data. The proposed video data model can act as a generic video data model for multimedia applications, and support free annotations, image features, spatial-temporal features, and structure information of video data within the same framework. We also propose the video query language for efficiently providing query specification to access video clips in the video data. It can formalize various kinds of queries based on the video contents. Finally we design and implement the query processing engine for efficient video data retrieval on the proposed metadata model and the proposed video query language.

• 정보처리학회지
• /
• 제15권2호
• /
• pp.5-14
• /
• 2008

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2005년도 춘계학술대회 논문집
• /
• pp.1893-1896
• /
• 2005

Cutting force and face roughness have the largest influence on precision of a structure or processing efficiency in cutting processing. Thus cutting force model and face roughness model are necessary for this interpretation. In this paper, tool path model and face roughness model which consider the blade number of a tool and a revolution speed of tool and workpiece in the worm processing using side milling cutter are presented. This model was used to forcast the cusp. Experimental results show that the predicted cusp coincides with experimental one.

• 한국분말재료학회지
• /
• 제16권1호
• /
• pp.50-55
• /
• 2009

Numerical simulations of the powder extrusion need an appropriate pressure-dependent constitutive model for densification modeling of the magnesium powders. The present research investigated the effect of representative powder yield function of the critical relative density model. We could obtain reasonable physical properties of pure magnesium powders using cold isostatic pressing. The proposed densification model was implemented into the finite element code. The finite element analysis was applied to simulation of powder extrusion of pure magnesium powder in order to investigate the densification and processing load at room temperature.

• ETRI Journal
• /
• 제29권4호
• /
• pp.489-496
• /
• 2007

As pervasive computing is widely adopted and reliable networks are becoming more easily accessible, there is a rapidly growing need to develop a mechanism to analyze and evaluate the performance of ubiquitous environments. This paper presents an information processing model which characterizes a ubiquitous environment where a variety of pieces of information need to be exchanged among devices within a system. The proposed model not only provides analytical tools to evaluate the performance of devices, but also makes it possible to identify key factors in designing a ubiquitous environment. For illustrative purposes, a test bed is constructed and the performance of the system is assessed based on the proposed model.

• 전자공학회논문지T
• /
• 제36T권2호
• /
• pp.42-47
• /
• 1999

본 논문은 시각정보를 공장자동화에 적용하기 위한 모델을 제안하였다. 제안된 모델에서 클라이언트-서버 모델과 RPC(Remote Procedure Calling)를 이용하여 시각정보를 획득하는 시스템, 공장자동화 전체를 총괄하는 메인서버, 그리고 시각정보만을 처리하는 처리서버로 분산처리 한다. 그 유효성을 지폐인식시스템으로 구현하였다.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2022년도 춘계학술발표대회
• /
• pp.517-519
• /
• 2022

In 5G network environment, proactive mobility management is essential as 5G mobile networks provide new services with ultra-low latency through dense deployment of small cells. The importance of a system that actively controls device handover is emerging and it is essential to predict mobile trajectory during handover. Sequence-to-sequence model is a kind of deep learning model where it converts sequences from one domain to sequences in another domain, and mainly used in natural language processing. In this paper, we developed a system for predicting mobile trajectory in a wireless network environment using sequence-to-sequence model. Handover speed can be increased by utilize our sequence-to-sequence model in actual mobile network environment.

• 반도체디스플레이기술학회지
• /
• 제22권3호
• /
• pp.84-89
• /
• 2023

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.

• 한국컴퓨터정보학회논문지
• /
• 제25권4호
• /
• pp.29-35
• /
• 2020

본 논문에서는 새로운 구조의 개체명 인식 DeNERT 모델을 제안한다. 최근 자연어처리 분야는 방대한 양의 말뭉치로 사전 학습된 언어 표현 모델을 활용하는 연구가 활발하다. 특히 자연어처리 분야 중 하나인 개체명인식은 대부분 지도학습 방식을 사용하는데, 충분히 많은 양의 학습 데이터 세트와 학습 연산량이 필요하다는 단점이 있다. 강화학습은 초기 데이터 없이 시행착오 경험을 통해 학습하는 방식으로 다른 기계학습 방법론보다 조금 더 사람이 학습하는 과정에 가까운 알고리즘으로 아직 자연어처리 분야에는 많이 적용되지 않은 분야이다. 아타리 게임이나 알파고 등 시뮬레이션 가능한 게임 환경에서 많이 사용된다. BERT는 대량의 말뭉치와 연산량으로 학습된 구글에서 개발한 범용 언어 모델이다. 최근 자연어 처리 연구 분야에서 높은 성능을 보이고 있는 언어 모델이며 많은 자연어처리 하위분야에서도 높은 정확도를 나타낸다. 본 논문에서는 이러한 DQN, BERT 두가지 딥러닝 모델을 이용한 새로운 구조의 개체명 인식 DeNERT 모델을 제안한다. 제안하는 모델은 범용 언어 모델의 장점인 언어 표현력을 기반으로 강화학습 모델의 학습 환경을 만드는 방법으로 학습된다. 이러한 방식으로 학습된 DeNERT 모델은 적은 양의 학습 데이터세트로 더욱 빠른 추론시간과 높은 성능을 갖는 모델이다. 마지막으로 제안하는 모델의 개체명 인식 성능평가를 위해 실험을 통해서 검증한다.