• Advances in robotics research
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• v.2 no.3
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• pp.201-217
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• 2018

Agriculture production is a critical human intensive task, which takes place in all regions of the world. The process to grow and harvest crops is labor intensive in many countries due to the lack of automation and advanced technology. Much of the difficult, dangerous and dirty labor of crop production can be automated with intelligent and robotic platforms. We propose an intelligent, agent-oriented robotic team, which can enable the process of harvesting, gathering and collecting crops and fruits, of many types, from agricultural fields. This paper describes a novel robotic organization enabling humans, robots and agents to work together for automation of gathering and collection functions. The focus of the research is a model, called HARMS, which can enable Humans, software Agents, Robots, Machines and Sensors to work together indistinguishably. With this model, any capability-based human-like organization can be conceived and modeled, such as in manufacturing or agriculture. In this research, we model, design and implement a technology application of knowledge-based robot-to-robot and human-to-robot collaboration for an agricultural gathering and collection function. The gathering and collection functions were chosen as they are some of the most labor intensive and least automated processes in the process acquisition of agricultural products. The use of robotic organizations can reduce human labor and increase efficiency allowing people to focus on higher level tasks and minimizing the backbreaking tasks of agricultural production in the future. In this work, the HARMS model was applied to three different robotic instances and an integrated test was completed with satisfactory results that show the basic promise of this research.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.121-128
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• 2024

This study introduces specializer high school students , as a fusion education method using Inventor software to design a robot arm, which is then 3D printed and controlled by an Arduino microcontroller. Students gain practical experience and have the opportunity to integrate knowledge and skills from various academic fields. They start by designing in CAD software, proceed to fabricate actual robot arm components using 3D printing technology, and finally program and control the assembled robot arm. This interdisciplinary education enhances students' problem-solving abilities, fosters creativity, and increases their motivation to learn. To implement such educational endeavors in actual curricula, ongoing teacher support and appropriate resources are essential. This research serves as a foundational exploration of the applicability of fusion education in future learning contexts.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.175-178
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• 2005

스마트 홈과 같은 유비쿼터스 환경은 다양한 센서 및 제어 네트워크가 밀집되어 있는 복잡한 시스템이다. 본 논문에서는 이러한 환경하에서 복잡한 인터페이스의 사용에 대한 사용자의 인지 부담(cognitive load)를 줄이고 개인화된(personalized) 서비스를 자율적으로 제공하기 위한 사용자 행동 패턴 선호도 학습 기법을 제안한다. 이를 위해 지식 발견(Knowledge Discovery)을 위한 평생 학습(life-long learning)의 관점에서 퍼지 귀납(Fuzzy Inductive)학습 방법론을 제안하며, 이것은 수치 데이터로부터 입력 공간에 대한 효율적인 퍼지 분할(fuzzy partition)을 얻어내고 일관성있는(consisitent) 퍼지 상관 룰(fuzzy association rule)을 얻어내도록 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.1-11
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• 2015

A concept of Deep Level Situation Understanding is proposed to realize human-like natural communication (called casual communication) among multi-agent (e.g., humans and robots/machines), where the deep level situation understanding consists of surface level understanding (such as gesture/posture understanding, facial expression understanding, speech/voice understanding), emotion understanding, intention understanding, and atmosphere understanding by applying customized knowledge of each agent and by taking considerations of thoughtfulness. The proposal aims to reduce burden of humans in humans-robots interaction, so as to realize harmonious communication by excluding unnecessary troubles or misunderstandings among agents, and finally helps to create a peaceful, happy, and prosperous humans-robots society. A simulated experiment is carried out to validate the deep level situation understanding system on a scenario where meeting-room reservation is done between a human employee and a secretary-robot. The proposed deep level situation understanding system aims to be applied in service robot systems for smoothing the communication and avoiding misunderstanding among agents.

• ETRI Journal
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• v.29 no.4
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• pp.477-488
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• 2007

In recent years, numerous studies have identified and explored issues related to web-service-oriented business process specifications, such as business process execution language (BPEL). In particular, business rules are an important cross-cutting concern that should be distinguished from business process instances. In this paper, we present a rule-based aspect oriented programming (AOP) framework where business rule aspects contained in business processes can be effectively separated and executed. This is achieved by using a mechanism of the business rule itself at the business rule engine instead of using existing programming language-based AOP technologies. Through some illustrative examples, this work also introduces a method by which business rule aspects, separated through an external rule engine, can be represented and evaluated. We also demonstrate how they can be dynamically woven and executed by providing an implementation example which uses two open-source-based products, the Mandarax rules engine and Bexee BPEL engine.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.148-153
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• 1999

In this paper, it is proposed the adaptive-sliding mode control technique which is new approach to implement the robust control of industrial robot manipulator with external disturbances and parameter uncertainties. Over the past decade, the design of advanced control systems for industrial robotic manipulators has been a very active area of research and two major design categories have emerged. Sliding mode control is a well-known technique for robust control of uncertain nonlinear systems. The robustness of sliding model controllers can be shown in continuous time, but digital implementation may not preserve robustness properties because the sampling process limits the existence of a true sliding mode. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The proposed control scheme has a simple structure is computationally fast and does not require knowledge of the complex dynamic model or the parameter values of the manipulator or the payload. Simulation results how that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control. Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications of industrial robot control system.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.577-580
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• 2004

본 논문은 지식의 규칙 표현을 위한 저작도구인 규칙편집기에 대하여 기술한다. 지식표현방법은 인간의 일상언어와 컴퓨터와의 표현구조를 고려하여 결정된다[2]. 이러한 지식표현방법에는 규칙, 프레임, 의미망, 그래프 등이 있다[2]. 본 논문에서는 지식을 규칙의 형태로 표현하고자 한다. 또한, 표현하고자 하는 지식의 영역은 비즈니스 도메인으로 한정한다. 비즈니스 지식이란 기업의 업무처리에 필요한 제반 지식인 업무처리 절차, 규정 등을 의미하며, 현재 대부분의 기업이 운영하는 기존 응용 시스템은 프로그램 소스의 일부분으로 비즈니스 규칙을 포함하고 있다. 기존 응용 시스템은 경영 상황 및 업무의 변경 등에 따른 비즈니스 지식의 잦은 수정 요구로 시스템의 유지 관리에 많은 비용과 수고가 필요하다. 이러한 문제점의 해결을 위하여 응용 프로그램에서 비즈니스 지식을 분리하여 관리하는 비즈니스 지식처리기술이 기업 응용 프로그램 개발에 도입되고 있다. 코드 속에서 분리된 비즈니스 지식은 규칙의 형태로 표현되고, 이들 규칙은 독립된 지식베이스에서 관리된다. 본 논문에서는 코드에서 분리된 비즈니스 지식을 규칙의 형태로 표현하기 위한 규칙편집기 개발과 개발된 편집기의 기능 및 특징에 대하여 기술한다.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2731-2736
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• 2003

In highly informative, perception-rich environments that we call Omniscient Spaces, robots interact with physical objects which in turn afford robots the information showing how the objects should be manipulated. Object manipulation is commonly believed one of the most basic tasks in robot applications. However, no approaches including visual servoing seem satisfactory in unstructured environments such as our everyday life. Thus, in Omniscient Spaces, the features of the environments embed themselves in every entity, allowing robots to easily identify and manipulate unknown objects. To achieve this end, we propose a new paradigm of the interaction through Radio Frequency Identification (RFID). The aim of this paper is to learn about RFID and investigate how it works in object manipulation. Specifically, as an innovative trial for autonomous, real-time manipulation, a likely mobile robot equipped with an RFID system is developed. Details on the experiments are described together with some preliminary results.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.718-724
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• 2007

We extend the application of an adaptive controller previously introduced in the literature under the assumption that no actuator dynamics exists to the case when the dynamics of the brushed DC-motors used as actuators is not neglected. Convergence to the desired positions is ensured without requiring any feedback to cope with the additional electric dynamics. The proposed control scheme does not require the exact knowledge of neither robot nor actuator parameters to select controller gains.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.