• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.122-122
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• 2001

In this research, an Electronic White Board Control System is developed that stores and prints the data written on the white board. This control system is constructed using an embedded single board PC, whose function is the acquisition(from CCD image sensor system), processing and storing of the data on the screen. The properties of this system are as follows: 1. The system is able to move the screen on the right and left. 2. The system is able to output the data written on the screen to a connected printer. 3. The system is controlled by an external remote PC connected to the system by serial line as well as by the keys on the control panel. The data written on the screen can be sent to the external PC and then can be modified and stored...

• 대한임베디드공학회논문지
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• 제11권6호
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• pp.353-359
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• 2016

In this paper, we propose a high performance L1 cache structure for the high clock CPU. The proposed cache memory consists of three parts, i.e., a direct-mapped cache to support fast access time, a two-way set associative buffer to reduce miss ratio, and a way-select table. The most recently accessed data is stored in the direct-mapped cache. If a data has a high probability of a repeated reference, when the data is replaced from the direct-mapped cache, the data is stored into the two-way set associative buffer. For the high performance and fast access time, we propose an one way among two ways set associative buffer is selectively accessed based on the way-select table (WST). According to simulation results, access time can be reduced by about 7% and 40% comparing with a direct cache and Intel i7-6700 with two times more space respectively.

• 대한임베디드공학회논문지
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• 제5권2호
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• pp.57-66
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• 2010

In this paper, we propose an enhanced voice recognition algorithm using adaptive MFCC(Mel Frequency Cepstral Coefficients) and neural network. Though it is very important to extract voice data from the raw data to enhance the voice recognition ratio, conventional algorithms are subject to deteriorating voice data when they eliminate noise within special frequency band. Differently from the conventional MFCC, the proposed algorithm imposed bigger weights to some specified frequency regions and unoverlapped filterbank to enhance the recognition ratio without deteriorating voice data. In simulation results, the proposed algorithm shows better performance comparing with MFCC since it is robust to variation of the environment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.401-403
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• 2007

This paper present the Traffic information system that based on embedded WinCE board which has GPSand HSDPA. This system is able to overcome the limit of area using the Internet service other system can't provide. When the embedded board receives the data which has geometric and vehicle speed information, it transmits the data to server via HSDPN/the Internet. The server receives and processes the data for the path services. By an algorithm the data that road information is applied is provided to user. The users will be able to arrive there destination faster.

• Journal of Information Processing Systems
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• 제10권3호
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• pp.412-428
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• 2014

Request oriented sensor networks have stricter requirements than conventional event-driven or periodic report models. Therefore, in this paper we propose a minimum energy data aggregation (MEDA), which meets the requirements for request oriented sensor networks by exploiting a low power real-time scheduler, on-demand time synchronization, variable response frame structure, and adaptive retransmission. In addition we introduce a test bed consisting of a number of MEDA prototypes, which support near real-time bidirectional sensor networks. The experimental results also demonstrate that the MEDA guarantees deterministic aggregation time, enables minimum energy operation, and provides a reliable data aggregation service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5669-5684
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• 2018

The new mobile edge network architecture has been required for an increasing amount of traffic, quality requirements, advanced driver assistance system for autonomous driving and new cloud computing demands on highway. This article proposes a hierarchical cloud computing architecture to enhance performance by using adaptive data load distribution for buses that play the role of edge computing server. A vehicular dynamic cloud is based on wireless architecture including Wireless Local Area Network and Long Term Evolution Advanced communication is used for data transmission between moving buses and cars. The main advantages of the proposed architecture include both a reduction of data loading for top layer cloud server and effective data distribution on traffic jam highway where moving vehicles require video on demand (VOD) services from server. Through the description of real environment based on NS-2 network simulation, we conducted experiments to validate the proposed new architecture. Moreover, we show the feasibility and effectiveness for the connected car media service on highway.

• 대한임베디드공학회논문지
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• 제16권3호
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• pp.113-117
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• 2021

The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

• 한국정보통신학회논문지
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• 제11권1호
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• pp.14-19
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• 2007

XML이 다양한 분야에서 정보의 표현 및 교환을 위한 표준 포맷으로 사용하게 되면서 많은 회사가 SVG를 무선 인터넷 기반의 모바일 폰과 같은 임베디드 시스템의 사용자 인터페이스나 표현 도구로 사용하게 되었다. SVG 파일의 실제 사용되는 데이터에 비하여 문서의 구조를 이루기 위한 많은 부가적인 정보를 유지해야 하는 문제가 있기 때문에 실제 전송되는 데이터에 비하여 SVG 파일의 전송 시간이 많이 소요되는 문제가 있다. 이런 문제를 해결하기 위하여 압축 기법을 이용한 많은 연구들이 진행되어 왔다. 본 논문에서는 압축 기법을 이용한 기존의 연구와 동시에 사용이 가능한 SMETA를 제안한다. SMETA는 SVG 파일을 의미 부여가 가능한 최소 단위로 분할한 후, SVG 파일을 구성하는 구조를 변경하지 않으면서 각각의 분할된 부분에 의미가 부여된 메타 데이터를 할당한다. SVG 파일 전체를 전송하지 않고 임베디드 시스템과 서버 시스템 간의 SVG 파일과 관련된 메타 정보들을 분석하여 임베디드 시스템에 해당 메타데이터가 없거나 일치하지 않는 부분만을 전송하게 함으로써 실제 전송되는 SVG 파일의 사이즈를 줄일 수 있다. 또한 실제 전송되는 SVG 파일의 크기가 줄어듬으로써 전송 시간을 단축시킬 수 있는 장점이 있다.

• Journal of Platform Technology
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• 제8권4호
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• pp.11-19
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• 2020

자율주행 환경은 실시간으로 상황이 급변하기 때문에 동적 객체인식 알고리즘이 반드시 필요하다. 또한, 자율주행자동차에 내장된 센서와 제어모듈이 증가하면서 중앙제어장치의 부하가 급격히 증가하고 있다. 중앙제어장치의 부하를 줄이기 위해서 단일 센서에서 출력되는 데이터의 최적화가 필요하다. 본 연구는 라이다에 탑재된 임베디드 프로세서를 기반으로 한 동적 객체인식 알고리즘을 제안한다. 라이다에서 출력되는 포인트클라우드 기반 객체인식을 위한 오픈소스들이 존재하지만, 대부분 고성능 프로세서를 요구한다. 라이다에 탑재된 임베디드 프로세서는 리소스 제약 때문에 기능 구현을 위한 최적화 된 아케텍처가 반드시 필요하다. 본 연구에서는 자율주행자동차를 위한 라이다 임베디드 프로세서 기반 동적 객체인식 아키텍처를 설계하고, 포인트클라우드 크기와 객체인식 처리 지연시간의 상관관계를 분석하였다. 제안하는 객체인식 아키텍처는 포인트클라우드 크기가 증가함에 따라 객체인식 처리 지연시간이 증가하였고, 특정한 지점에서 프로세서의 과부하가 발생하여 포인트를 처리하지 못하는 현상이 발생하였다.

• PNF and Movement
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• 제22권1호
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.