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운전자의 차선 변경 의도 파악을 위한 머신 러닝 기법의 응용
이상형(Sang Hyoung Lee),양지현(Ji Hyun Yang),이상헌(Sang Hun Lee) (사)한국CDE학회 2013 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2013 No.8
Inference of driver’s intent is an important issue for developing advanced driver assistance systems(ADAS). To enhance the inference capability, various algorithms for pattern recognition and machine learning have been adopted widely. In order to detect the drivers" intent just before they initiate a lane change maneuver, in this paper, we introduced as the algorithm parameters not only the drivers" behavior measurements such as eye glance and head rotation but also vehicle"s motion measurements such as vehicle’s directions and accelerations. In addition, we investigated the effectiveness and adaptability of the selected algorithms for detection of drivers’ intent.
베이지안 행동유발성 모델을 이용한 행동동기 기반 행동 선택 메커니즘
이상형(Sang Hyoung Lee),서일홍(Il Hong Suh) 대한전자공학회 2009 電子工學會論文誌-SC (System and control) Vol.46 No.4
로봇이 지능적이고 합리적으로 임무를 수행하기 위해서는 다양한 솜씨(skill)가 필요하다. 우리는 솜씨를 생성하기 위해 우선 행동유발성(affordance)을 학습한다. 행동유발성은 행동을 유발하게 하는 물체 또는 환경의 성질로써 솜씨를 생성하는데 유용하게 사용될 수 있다. 로봇이 수행하는 대부분의 임무는 순차적이고 목표 지향적인 행동을 필요로 한다. 그러나 행동유발성만을 이용하여 이러한 임무를 수행하는 것은 쉽지 않다. 이를 위해 우리는 행동유발성과 목표 지향적 요소를 반영하기 위한 소프트 행동동기 스위치(soft behavioral motivation switch)를 이용하여 솜씨를 생성한다. 솜씨는 현재 인지된 정보와 목표 지향적 요소를 결합하여 행동동기를 생성한다. 여기서 행동동기는 목표 지향적인 행동을 활성화시키기 위한 내부 상태를 말한다. 또한, 로봇은 임무 수행을 위해 순차적인 행동 선택을 필요로 한다. 우리는 목표 지향적이고 순차적인 행동 선택이 가능하도록 솜씨를 이용하여 솜씨 네트워크(skill network)를 생성한다. 로봇은 솜씨 네트워크를 이용하여 목표 지향적이고 순차적인 행동을 선택할 수 있다. 본 논문에서는 베이지안 네트워크를 이용한 행동유발성 모델링 및 학습 방법, 행동유발성과 소프트 행동동기 스위치를 이용한 솜씨 및 솜씨 네트워크 생성 방법, 마지막으로 솜씨 네트워크를 이용한 목표 지향적 행동 선택 방법을 제안한다. 우리의 방법을 증명하기 위해 제니보(애완 로봇)를 이용한 교시 기반 학습 방법을 통해 "물체 찾기”, "물체에 접근하기”, "물체의 냄새 맡기”, 그리고 "물체를 발로 차기" 행동유발성들을 학습하였다. 또한, 이들을 이용하여 솜씨 및 솜씨 네트워크를 생성하여 제니보에 적용하고 실험하였다. A robot must be able to generate various skills to achieve given tasks intelligently and reasonably. The robot must first learn affordances to generate the skills. An affordance is defined as qualities of objects or environments that induce actions. Affordances can be usefully used to generate skills. Most tasks require sequential and goal-oriented behaviors. However, it is usually difficult to accomplish such tasks with affordances alone. To accomplish such tasks, a skill is constructed with an affordance and a soft behavioral motivation switch for reflecting goal-oriented elements. A skill calculates a behavioral motivation as a combination of both presently perceived information and goal-oriented elements. Here, a behavioral motivation is the internal condition that activates a goal-oriented behavior. In addition, a robot must be able to execute sequential behaviors. We construct skill networks by using generated skills that make action selection feasible to accomplish a task. A robot can select sequential and a goal-oriented behaviors using the skill network. For this, we will first propose a method for modeling and learning Bayesian networks that are used to generate affordances. To select sequential and goal-oriented behaviors, we construct skills using affordances and soft behavioral motivation switches. We also propose a method to generate the skill networks using the skills to execute given tasks. Finally, we will propose action-selection-mechanism to select sequential and goal-oriented behaviors using the skill network. To demonstrate the validity of our proposed methods, "Searching-for-a-target- object”, "Approaching-a-target-object”, "Sniffing-a-target-object”, and "Kicking-a-target-object" affordances have been learned with GENIBO(pet robot) based on the human teaching method. Some experiments have also been performed with GENIBO using the skills and the skill networks.
모듈 형태의 마이크로프로세서 훈련용 키트 개발에 관한 연구
이상형(Sang-Hyoung Lee) 산업기술교육훈련학회 2020 산업기술연구논문지 (JITR) Vol.25 No.4
The purpose of this study is to realize a training device that allows learners to learn circuit design and firmware development skills in the field of electronics and automatic control. Currently, education and hands-on training are facilitated using devices produced by the relevant educational device manufacturers. However, devices developed to be integrated are not compatible with one another because different manufacturers quote different device specifications and provide different functions. Thus, consistent education and skill inculcation in the industrial field are difficult to achieve. In addition, achieving circuit design and firmware programming capabilities through integrated configuration and manual execution is not suitable in an environment limited in terms of hardware. To solve these problems, a modular microprocessor training device has been designed, manufactured, and utilized herein, allowing learners to practice efficiently via direct creation and connection of only the parts necessary for each training section, similar to the case of building blocks. This paper presents an effective direction in the field of educational training devices, whereby learners can receive effective education and learn good practices and applications.
이상형(Sang Hyoung Lee),이건영(Keon Young Yi) 대한전기학회 2006 대한전기학회 학술대회 논문집 Vol.2006 No.7
청소 로봇의 장애물 판단은 청소 로봇이 정확하고 빠르게 장애물을 파악하여 정밀한 제어를 수행하며 청소 효율을 향상 시키는데 중요하다. 청소 로봇이 장애물을 판단하는데 여러 가지 알고리즘이 있지만 신경망 알고리즘 특히, BP(Back-Propagation) 알고리즘을 적용하여 장애물 인식에 있어 반복학습 시키면 청소 로봇은 보다 빠르고 정확하게 장애물을 스스로 판단 할 수 있다. 본 논문에서는 청소 로봇에 부착된 초음파 센서와 장애물과의 거리데이터를 얻어, 이를 BP 알고리즘에 적용하는 것을 연구하며 학습률, 반복학습, 최대 제곱 오차값를 조정한 실험결과로 특성변화를 관찰하고 해석하여 검증한다.
조윤성 ( Yun Sung Jo ),권지영 ( Ji Young Kwon ),임희순 ( Hee Sun Lim ),문영주 ( Young Joo Mun ),이상형 ( Sang Hyoung Lee ),제동성 ( Dong Sung Jae ),한구택 ( Gu Taek Han ),류기성 ( Ki Sung Ryu ) 대한산부인과학회 2006 Obstetrics & Gynecology Science Vol.49 No.12
Myoma is the most common tumor in gynecologic field. As ultrasonography because popular in antenatal care, the more cases of myoma and those adverse effects during pregnancy are more frequently detected. The management of myoma during pregnancy is conservative, but in rare circumstances, surgical intervention including myomectomy may be required. We have experienced a case of protruded subserosal myoma with the uterine cervix in midtrimester of pregnancy. The patient was managed surgically by transvaginal myomectomy and had successfully maintained pregnancy. We report a case of protruded subserosal myoma through pelvic floor in pregnancy with brief review of literatures.
지능로봇의 동기 기반 행동선택을 위한 베이지안 행동유발성 모델
손광희(Gwang Hee Son),이상형(Sang Hyoung Lee),서일홍(Il Hong Suh) 대한전기학회 2009 정보 및 제어 심포지엄 논문집 Vol.2009 No.5
A skill is defined as the special ability to do something well, especially as acquired by learning and practice. To learn a skill, a Bayesian network model for representing the skill is first learned. We will regard the Bayesian network for a skill as an affordance. We propose a soft Behavior Motivation (BM)switch as a method for ordering affordances to accomplish a task. Then, a skill is constructed as a combination of an affordance and a soft BM switch. To demonstrate the validity of our proposed method, some experiments were performed with GENIBO (Pet robot) performing a task using skills of Search-a-target-object, Approach-a-target-object, Push-up-in front of - a-target-object.