• 대한임베디드공학회논문지
• /
• 제6권6호
• /
• pp.361-367
• /
• 2011

In this paper, we propose an interactive embedded system framework for efficient training management in u-health environment. First, we analyzed various requirements of smart training systems for quality of life. We also analyzed the oversea trends and positive effects of the embedded system in terms of both technical and economical factors. Second, we proposed detailed design specification for embedded hardware implementation. Third, we developed effective OS(Operating System) specification for the embedded hardware. Finally, we developed a training scenario and embedded applications such as training control software and analysis software for the smart training systems.

• 시스템엔지니어링학술지
• /
• 제4권2호
• /
• pp.27-33
• /
• 2008

As a part of development of Korean K2 main battle tank, embedded training computer to be operated in the main equipment, which makes it possible to train without a special-purposed training simulator, was adopted for tank combat training. The category of embedded training of Korean K2 main battle tank includes driving training, gunnery training, single tank combat training, platoon level combat training, and command and platoon leaders combat training. For realization unit level tank embedded training system, the virtual reality was utilized for real time image rendering, and network based real time communication system of K2 tank was utilized for sharing status information between tanks. As a result, it is possible to train themselves on their own tank for enhancing the operational skills and harmonized task with members.

• 한국지능시스템학회논문지
• /
• 제21권5호
• /
• pp.581-587
• /
• 2011

이 논문은 임베디드 소프트웨어 설계 과목을 위한 실습 키트 하드웨어와 이 키트를 사용한 임베디드 소프트웨어 설계 교육 과정에 대해서 소개한다. 임베디드 소프트웨어 설계 과목은 그 특성상 실습과 한 학기에 걸친 프로젝트 위주로 진행되는데 이를 위해서는 소프트웨어를 실행시킬 실습 키트가 꼭 필요하다. 학생들이 하드웨어를 완벽하게 이해하고 소프트웨어 설계 및 개발을 진행할 수 있도록 학생들의 수준에 맞는 맞춤형 실습 키트 하드웨어를 설계하고 제작하였다. 학생들은 제작된 실습 키트를 사용하여 디바이스구동 소프트웨어에서부터 사용자 인터페이스까지 임베디드 소프트웨어 전 계층에 걸친 프로그램 설계하고 구현해 봄으로써 보다 수월하게 임베디드 시스템에 대한 이해를 넓히고 프로그램 개발 능력을 향상시킬 수 있었다.

• 실천공학교육논문지
• /
• 제11권2호
• /
• pp.283-289
• /
• 2019

최근 스마트제조에 따른 제어시스템은 제품 이동성, 데이터 호환성, 통신 연계성 등이 매우 중요해지고 있다. 이에 임베디드 시스템은 가전제품, 통신, 국방 등 전 산업분야에서 필수적으로 적용되고 있다. 따라서 임베디드 시스템 개발인력 수요는 더욱 더 증가하는 추세이므로 프로그래밍 능력과 하드웨어를 포함하여 산업현장 실무능력을 겸비한 교육훈련 프로그램이 필요하다. 현재 임베디드 시스템 교육은 아두이노를 활용한 다양한 교육을 수행하고 있지만 이는 비전공자와 초심자 중심이며 임베디드 시스템 전공자 교육과정으로 미약한 실정이다. 또한 임베디드 시스템 기반의 시제품 연구는 활발하지만 산업현장에서 요구하는 실무인재 양성을 위한 교육훈련 프로그램은 매우 미비하다. 따라서 본 논문은 스마트제조 적용을 위한 임베디드 시스템 실무인재 양성을 위해 NCS 기반 전문가 10인의 심층 인터뷰와 설문 분석을 바탕으로 능력단위를 선정하고 교육훈련 프로그램과 컨텐츠를 개발하였다.

• ETRI Journal
• /
• 제32권1호
• /
• pp.160-162
• /
• 2010

For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.

• 컴퓨터교육학회논문지
• /
• 제21권6호
• /
• pp.27-37
• /
• 2018

본 논문에서는 임베디드 시스템의 어플리케이션 계층 소프트웨어 학습 도구로 사용할 수 있는 엘리베이터 시뮬레이터의 설계 및 개발 방법과 이를 이용한 교수학습방법을 제안한다. 본 시뮬레이터는 학생들에게 하드웨어와 임베디드 OS 계층의 이슈를 배제한 어플리케이션 계층에서 엘리베이터 시스템의 동작 원리와 제어 방법을 소프트웨어로 구현할 수 있는 환경을 제공하여, 반응(reactive)적이며 실시간(real-time)적인 특징을 갖는 임베디드 어플리케이션 개발 경험을 가질 수 있도록 한다. 아울러 본 논문에서는 시뮬레이터를 이용하여 단계별로 난이도가 높아지는 실습이 포함된 4주간의 임베디드 어플리케이션 소프트웨어 교육 과정을 제시하고, 실제 학생들을 대상으로 교육을 진행한 결과 학습 성취도 점수 83.3점을 얻어 본 교육 과정이 임베디드 어플리케이션 학습에 유의미한 효과가 있음을 입증하였다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 ITC-CSCC -1
• /
• pp.690-693
• /
• 2002

According to the growth of communication biz, embedded market rapidly developing in domestic and overseas. Embedded system can be used in various way such as wire and wireless communication equipment or information products. There are lots of developing performance applying speech recognition to embedded system, for instance, PDA, PCS, CDMA-2000 or IMT-2000. This study implement minimum memory of speech recognition engine and DB for apply real time embedded system. The implement measure of speech recognition equipment to fit on embedded system is like following. At first, DC element is removed from Input voice and then a compensation of high frequency was achieved by pre-emphasis with coefficients value, 0.97 and constitute division data as same size as 256 sample by lapped shift method. Through by Levinson - Durbin Algorithm, these data can get linear predictive coefficient and again, using Cepstrum - Transformer attain feature vectors. During HMM training, We used Baum-Welch reestimation Algorithm for each words training and can get the recognition result from executed likelihood method on each words. The used speech data is using 40 speech command data and 10 digits extracted form each 15 of male and female speaker spoken menu control command of Embedded system. Since, in many times, ARM CPU is adopted in embedded system, it's peformed porting the speech recognition engine on ARM core evaluation board. And do the recognition test with select set 1 and set 3 parameter that has good recognition rate on commander and no digit after the several tests using by 5 proposal recognition parameter sets. The recognition engine of recognition rate shows 95%, speech commander recognizer shows 96% and digits recognizer shows 94%.

• 대한임베디드공학회논문지
• /
• 제17권3호
• /
• pp.129-138
• /
• 2022

Collecting and labeling sufficient training data, which is essential to deep learning-based visual inspection, is difficult for manufacturers to perform because it is very expensive. This paper presents a steel plate surface defect detection system with industrial-grade detection performance by training a small amount of steel plate surface images consisting of labeled and non-labeled data. To overcome the problem of lack of training data, we propose two data augmentation techniques: program-based augmentation, which generates defect images in a geometric way, and generative model-based augmentation, which learns the distribution of labeled data. We also propose a 4-step semi-supervised learning using pseudo labels and consistency training with fixed-size augmentation in order to utilize unlabeled data for training. The proposed technique obtained about 99% defect detection performance for four defect types by using 100 real images including labeled and unlabeled data.

• 대한임베디드공학회논문지
• /
• 제10권6호
• /
• pp.345-352
• /
• 2015

Recently, by developing many training systems in battle field, the demand for interconnecting and internetworking between Live, Virtual, Constructive training systems has been increased to support efficient data distribution and system control. But, there are lots of problems for them to interwork, because the existing researches only support L-L, V-V, C-C Interoperability. Therefore, we propose L-V-C gateway to provide interoperable simulation environment based on HLA and DDS between them. First, we illustrate FOM Management that parses RPR-FOM XML file to acquire Data information to be shared between them, and generates common data structure and source code used for L-V-C Gateway. L-V-C Gateway created from FOM Management supports Data Conversion and Quality of Service between HLA and DDS. HLA Federate and DDS Domainparticipant in L-V-C Gateway play a role of logical communication channel and relay data from HLA Federation to DDS Domain and vice versa.

• 대한임베디드공학회논문지
• /
• 제14권4호
• /
• pp.207-218
• /
• 2019

The purpose of this study is to develop a model that can systematically study the whole learning process of machine learning. Since the existing model describes the learning process with minimum coding, it can learn the progress of machine learning sequentially through the new model, and can visualize each process using the tensor flow. The new model used all of the existing model algorithms and confirmed the importance of the variables that affect the target variable, survival. The used to classification training data into training and verification, and to evaluate the performance of the model with test data. As a result of the final analysis, the ensemble techniques is the all tutorial model showed high performance, and the maximum performance of the model was improved by maximum 5.2% when compared with the existing model using. In future research, it is necessary to construct an environment in which machine learning can be learned regardless of the data preprocessing method and OS that can learn a model that is better than the existing performance.