• 제목/요약/키워드: feature interaction

검색결과 381건 처리시간 0.023초

상호작용을 고려한 최적의 제품휘처형상 도출 방법 (A Method for Deriving an Optimal Product Feature Configuration Considering Feature Interaction)

  • 이관우
    • 한국인터넷방송통신학회논문지
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    • 제14권2호
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    • pp.115-120
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    • 2014
  • 많은 소프트웨어 프로덕트 라인 공학 방법들은 휘처모델을 사용하여 제품들 간의 공통성과 가변성을 휘처 단위로 구조화시키고, 특정 제품 개발을 위해 필요한 휘처 집합인 제품휘처형상을 도출한다. 제품 생산 시에 선택될 휘처는 주로 제품의 요구되는 품질 속성에 의해서 결정된다. 지금까지 발표된 대부분의 방법들은 휘처와 품질속성 간의 선형적 상관관계를 통해 최적의 품질 속성을 만족시킬 수 있는 제품휘처형상을 도출하였다. 하지만, 휘처 간의 상호작용을 고려한다면 휘처와 품질 속성 간의 관계는 비선형식으로 정의될 수 있다. 본 논문에서는 휘처 간의 상호작용을 고려하여 요구되는 품질 속성을 최적으로 만족시킬 수 있는 제품휘처형상 도출 방법을 제안한다. 제안된 방법을 평가하기 위해 네 가지 프로덕트 라인 사례에 대해 실험한다.

역공학에서 측정경로생성을 위한 특징형상 인식 (Feature Recognition for Digitizing Path Generation in Reverse Engineering)

  • 김승현;김재현;박정환;고태조
    • 한국정밀공학회지
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    • 제21권12호
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    • pp.100-108
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    • 2004
  • In reverse engineering, data acquisition methodology can generally be categorized into contacting and non-contacting types. Recently, researches on hybrid or sensor fusion of the two types have been increasing. In addition, efficient construction of a geometric model from the measurement data is required, where considerable amount of user interaction to classify and localize regions of interest is inevitable. Our research focuses on the classification of each bounded region into a pre-defined feature shape fer a hybrid measuring scheme, where the overall procedures are described as fellows. Firstly, the physical model is digitized by a non-contacting laser scanner which rapidly provides cloud-of-points data. Secondly, the overall digitized data are approximated to a z-map model. Each bounding curve of a region of interest (featured area) can be 1.aced out based on our previous research. Then each confined area is systematically classified into one of the pre-defined feature types such as floor, wall, strip or volume, followed by a more accurate measuring step via a contacting probe. Assigned to each feature is a specific digitizing path topology which may reflect its own geometric character. The research can play an important role in minimizing user interaction at the stage of digitizing path planning.

목표물의 거리 및 특징점 불확실성 추정을 통한 매니퓰레이터의 영상기반 비주얼 서보잉 (Image-based Visual Servoing Through Range and Feature Point Uncertainty Estimation of a Target for a Manipulator)

  • 이상협;정성찬;홍영대;좌동경
    • 제어로봇시스템학회논문지
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    • 제22권6호
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    • pp.403-410
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    • 2016
  • This paper proposes a robust image-based visual servoing scheme using a nonlinear observer for a monocular eye-in-hand manipulator. The proposed control method is divided into a range estimation phase and a target-tracking phase. In the range estimation phase, the range from the camera to the target is estimated under the non-moving target condition to solve the uncertainty of an interaction matrix. Then, in the target-tracking phase, the feature point uncertainty caused by the unknown motion of the target is estimated and feature point errors converge sufficiently near to zero through compensation for the feature point uncertainty.

유사 아이템 정보를 이용한 콜드 아이템 추천성능 개선 (Addressing the Item Cold-Start in Recommendation Using Similar Warm Items)

  • 한정규;천세진
    • 한국멀티미디어학회논문지
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    • 제24권12호
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    • pp.1673-1681
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    • 2021
  • Item cold start is a well studied problem in the research field of recommender systems. Still, many existing collaborative filters cannot recommend items accurately when only a few user-item interaction data are available for newly introduced items (Cold items). We propose a interaction feature prediction method to mitigate item cold start problem. The proposed method predicts the interaction features that collaborative filters can calculate for the cold items. For prediction, in addition to content features of the cold-items used by state-of-the-art methods, our method exploits the interaction features of k-nearest content neighbors of the cold-items. An attention network is adopted to extract appropriate information from the interaction features of the neighbors by examining the contents feature similarity between the cold-item and its neighbors. Our evaluation on a real dataset CiteULike shows that the proposed method outperforms state-of-the-art methods 0.027 in Recall@20 metric and 0.023 in NDCG@20 metric.

컨볼루셔널 신경망과 케스케이드 안면 특징점 검출기를 이용한 얼굴의 특징점 분류 (Facial Point Classifier using Convolution Neural Network and Cascade Facial Point Detector)

  • 유제훈;고광은;심귀보
    • 제어로봇시스템학회논문지
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    • 제22권3호
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    • pp.241-246
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    • 2016
  • Nowadays many people have an interest in facial expression and the behavior of people. These are human-robot interaction (HRI) researchers utilize digital image processing, pattern recognition and machine learning for their studies. Facial feature point detector algorithms are very important for face recognition, gaze tracking, expression, and emotion recognition. In this paper, a cascade facial feature point detector is used for finding facial feature points such as the eyes, nose and mouth. However, the detector has difficulty extracting the feature points from several images, because images have different conditions such as size, color, brightness, etc. Therefore, in this paper, we propose an algorithm using a modified cascade facial feature point detector using a convolutional neural network. The structure of the convolution neural network is based on LeNet-5 of Yann LeCun. For input data of the convolutional neural network, outputs from a cascade facial feature point detector that have color and gray images were used. The images were resized to $32{\times}32$. In addition, the gray images were made into the YUV format. The gray and color images are the basis for the convolution neural network. Then, we classified about 1,200 testing images that show subjects. This research found that the proposed method is more accurate than a cascade facial feature point detector, because the algorithm provides modified results from the cascade facial feature point detector.

로봇과 인간의 상호작용을 위한 얼굴 표정 인식 및 얼굴 표정 생성 기법 (Recognition and Generation of Facial Expression for Human-Robot Interaction)

  • 정성욱;김도윤;정명진;김도형
    • 제어로봇시스템학회논문지
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    • 제12권3호
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    • pp.255-263
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    • 2006
  • In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

휴먼-로봇 인터액션을 위한 하이브리드 스켈레톤 특징점 추출 (Feature Extraction Based on Hybrid Skeleton for Human-Robot Interaction)

  • 주영훈;소제윤
    • 제어로봇시스템학회논문지
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    • 제14권2호
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    • pp.178-183
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    • 2008
  • Human motion analysis is researched as a new method for human-robot interaction (HRI) because it concerns with the key techniques of HRI such as motion tracking and pose recognition. To analysis human motion, extracting features of human body from sequential images plays an important role. After finding the silhouette of human body from the sequential images obtained by CCD color camera, the skeleton model is frequently used in order to represent the human motion. In this paper, using the silhouette of human body, we propose the feature extraction method based on hybrid skeleton for detecting human motion. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

동적 가우시안 함수를 이용한 Kohonen 네트워크 수렴속도 개선 (Improved Rate of Convergence in Kohonen Network using Dynamic Gaussian Function)

  • 길민욱;이극
    • 한국컴퓨터정보학회논문지
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    • 제7권4호
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    • pp.204-210
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    • 2002
  • 자기조직화 지도(self-organizing feature map)는 학습시 수렴하기 위하여 많은 입력패턴을 필요로 하는 단점이 있다. 본 논문에서는 자기조직화 지도 학습시 학습률이 일정한 이웃 상호작용 집합을 동적 가우시안 함수로 변환하여 수렴속도와 수렴도를 개선할 수 있는 방법을 제안한다. 제안한 방법은 이웃 상호작용 함수로 사용된 가우시안 함수의 편차와 폭을 학습 회수에 따라 감소하는 동적 성질과 승자 뉴런으로부터의 위상학적 위치에 따라 각기 다른 학습률을 갖도록 하였다. 따라서 본 논문에서는 자기조직화 지도의 수렴속도와 수렴도를 향상시켰다.

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IP 기반 융합서비스를 위한 서비스 충돌 감지 및 해결에 대한 연구 (A Mechanism for Conflict Detection and Resolution for Service Interaction : Toward IP-based Network Services)

  • 오요셉;신동민
    • 산업공학
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    • 제23권1호
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    • pp.24-34
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    • 2010
  • In the telecommunication system which is based on the existing PSTN(public switched telephone network), feature interaction has been an important research issue in order to provide seamless services to users. Recently, rapid proliferation of IP-based network and the various types of IP media supply services, the feature interaction from the perspective of application services has become a significant aspect. This paper presents conflict detection and resolution algorithms for designing and operating a variety of services that are provided through IP-based network. The algorithms use explicit service interactions to detect conflicts between a new service and registered services. They then apply various rules to reduce search space in resolving conflicts. The algorithms are applied to a wide range of realistic service provision scenarios to validate that it can detect conflicts between services and resolve in accordance with different rule sets. By applying the algorithms to various scenarios, it is observed that the proposed algorithms can be effectively used in operating an IP-based services network.

음성의 특정 주파수 범위를 이용한 잡음환경에서의 감정인식 (Noise Robust Emotion Recognition Feature : Frequency Range of Meaningful Signal)

  • 김은호;현경학;곽윤근
    • 한국정밀공학회지
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    • 제23권5호
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    • pp.68-76
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    • 2006
  • The ability to recognize human emotion is one of the hallmarks of human-robot interaction. Hence this paper describes the realization of emotion recognition. For emotion recognition from voice, we propose a new feature called frequency range of meaningful signal. With this feature, we reached average recognition rate of 76% in speaker-dependent. From the experimental results, we confirm the usefulness of the proposed feature. We also define the noise environment and conduct the noise-environment test. In contrast to other features, the proposed feature is robust in a noise-environment.