• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.217-218
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• 2009

URC 로봇은 고성능 서버를 통해 로봇이 수행할 서비스의 일부를 외부 디바이스에 분담시킴으로써 저렴한 하드웨어로 로봇을 구현하고자 하는 목적을 가지는 지능형 서비스 로봇이다. SOMAR는 유비쿼터스 환경에서 사용자에게 다양한 원격 로봇 서비스를 제공하기 위한 URC 로봇 미들웨어로, URC 로봇 클라이언트와 이를 등록, 제어, 관리하는 서버로 구성된다. SOMAR 서버를 통해 로봇 서비스를 수행하기 위해서는 로봇 클라이언트의 상태정보를 모니터링하여 수행가능 여부를 판단해야 한다. 본 논문은 이러한 상태정보 모니터링을 위해 R-OSGi를 이용한 분산 Observer 패턴을 구현한다. Observer 패턴은 객체간에 느슨한 결합구조를 가져 URC 로봇 클라이언트 모니터링 프로그램의 개발과 수정이 용이하고, 플러그인 방식의 개발을 통해 다양한 프로그램 설치와 응용이 가능하다.

• The KIPS Transactions:PartA
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• v.19A no.1
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• pp.1-8
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• 2012

Network-based intelligent robot is connected with network system, provides interactions with humans, and carries out its own roles on ubiquitous computing environments. URC (Ubiquitous Robot Companion) robot has been proposed to develop network-based robot by applying distributed computing techniques. On URC robot, it is possible to save the computing power of robot client by environments, has been proposed to develop robot software using service-oriented architecture on server-client computing environments. The SOMAR client robot consists of two layers - device service layer and robot service layer. The device service controls physical devices, and the robot service abstracts robot's services, which are newly defined and generated by combining many device services. RSEL (Robot Service Executing Language) is defined in this paper to represent relations and connections between device services and robot services. A RESL document, including robot services by combining several device services, is translated to a programming language for robot client system using RSEL translator, then the translated source program is compiled and uploaded to robot client system with RPC (Remote Procedure Call) command. A SOMAR client system is easy to be applied to embedded systems of host/target architecture. Moreover it is possible to produce a light-weight URC client robot by reducing workload of RSEL processing engine.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.240-250
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• 2010

A URC, which is a Ubiquitous Robot Companion, provides services to users in ubiquitous computing environments and has advantage of simplifying robot's hardware and software by distributing the complicated functionality of robots to other system. In this paper, we propose SOWL, which is a software architecture for URC robots and a mixed word of SOMAR and CAWL. SOWL keeps the advantages of URC and it also has the loosely-coupled characteristics. Moreover it makes it easy to develop of URC robot software. The proposed architecture is composed of 4 layers: device software, robot software, robot application, and end user layer. Developers of the each layer is able to build software suitable for their requirements by combining software modules in the lower layer. SOWL consists of SOMAR and CAWL engine. SOMAR, which is a middleware for the execution of device software and robot software, is based on service-oriented architecture(SOA) for robot software. CAWL engine is a system to process CAWL which is a context-aware workflow language. SOWL is able to provide a layered architecture for the execution of a robot software. It also makes it possible for developers of the each layer to build module-based robot software.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.87-92
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• 1996

Transient signals in underwater show several characterisrics, that is, short duration, strong nonstationarity, various types of transient sources, which make it difficult to analyze and classify transient signals. In this paper, the feature vector extraction method for transient SOMAR signals is discussed by applying digital signal processing methods to the analysis of transient signals. A feature vector extraction methods using wavelet transform, which enable us to obtain better recognition rate than automatic classification using the classical method, are proposed. It is confirmed by simulation that the proposed method using wavelet transform performs better than the classical method even with smaller number of feature vectors. Especially, the feature vector extraction method using PR-QMF wavelet transform with the Daubechies coefficients is shown to perform well in noisy environment with easy implementation.