• 한국멀티미디어학회논문지
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• 제22권5호
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• pp.558-572
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• 2019

Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.49.2-49
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• 2002

This paper describes the configuration of a multi-agent system that can recognize human intentions. This system constructs ontologies of human intentions and enables knowledge acquisition and sharing between intelligent agents operating in different environments. This is achieved by using a bi-directional associative memory network. The process of intention recognition is based on fuzzy association inferences. This paper shows the process of information sharing by using ontologies. The purpose of this research is to create human-centered systems that can provide a natural interface in their interaction with people.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.19-22
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• 2018

본 논문에서는 기존 Stack Pointer Network의 의존 파싱 모델을 확장한 Bi-Stack Pointer Network를 제안한다. Stack Pointer Network는 기존의 Pointer Network에 내부 stack을 만들어 전체 문장을 읽어 dependency tree를 구성한다. stack은 tree의 깊이 우선 탐색을 통해 선정되고 Pointer Network는 stack의 top 단어(head)의 자식(child)을 선택한다. 제안한 모델은 기존의 Stack Pointer Network가 지배소(head)정보로 의존소(child)를 예측하는 부분에 Biaffine attention을 통해 의존소(child)에서 지배소(head)를 예측하는 방향을 추가하여 양방향 예측이 가능하게 한 모델이다. 실험 결과, 제안 Bi-Stack Pointer Network모델은 UAS 91.53%, LAS 90.93%의 성능을 보여주어 기존 최고 성능을 개선시켰다.

• ETRI Journal
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• 제40권4호
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• pp.511-521
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• 2018

The ability to realize human-motion imitation using robots is closely related to developments in the field of artificial intelligence. However, it is not easy to imitate human motions entirely owing to the physical differences between the human body and robots. In this paper, we propose a work chain-based inverse kinematics to enable a robot to imitate the human motion of upper limbs in real time. Two work chains are built on each arm to ensure that there is motion similarity, such as the end effector trajectory and the joint-angle configuration. In addition, a two-phase filter is used to remove the interference and noise, together with a self-collision avoidance scheme to maintain the stability of the robot during the imitation. Experimental results verify the effectiveness of our solution on the humanoid robot Nao-H25 in terms of accuracy and real-time performance.

• 한국콘텐츠학회논문지
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• 제6권9호
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• pp.50-59
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• 2006

본 논문에서는 신경망을 이용한 동작분석 기법을 통한 자동화 영상감시시스템의 구현과 응급상황 검출에의 응용을 제안한다. 카메라로부터 입력된 영상은 통계적 배경 모델에 의한 배경 감산법에 의해 객체영역이 분리되고, 분리된 객체영역의 특징을 표현할 수 있는 특징벡터의 형태로 변형된다. 특징벡터를 이용한 동작분석을 위해 신경망을 사용하였고 간단한 연산에 의해 동작을 구분할 수 있도록 하였다. 본 논문에서는 실험을 위해 stand, faint, squat 등 3가지의 동작 상태를 분류할 수 있도록 하였고, 실험 결과 응급상황을 검출하기 위한 알고리즘으로 유용함을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2580-2582
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• 2004

In this paper, a Neural Network(NN) based approach for classification of two dimensions images. The proposed algorithm is able to apply in the actual industry. The described diagnostic algorithm is presented to defect surface failures on tiles. A way to get data for a digital image process is several kinds of it. The tiles are scanned and the digital images are preprocessed and classified using neural networks. It is important to reduce the amount of input data with problem specific preprocessing. The auto-associative neural network is used for feature generation and selection while the probabilistic neural network is used for classification. The proposed algorithm is evaluated experimentally using one hundred of the real tile images. Sample image data to preprocess have histogram. The histogram is used as input value of probabilistic neural network. Auto-associative neural network compress input data and compressed data is classified using probabilistic neural network. Classified sample images are determined by human state. So it is intervened human subjectivity. But digital image processing and neural network are better than human classification ability. Therefore it is very useful of quality control improvement.

• 산업진흥연구
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• 제8권4호
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• pp.123-129
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• 2023

본 연구는 초고령사회에서 겪을 수 있는 노인에 대한 경제적 배제를 논의하고 이를 극복하기 위해 사회관계망 확충을 통해 노인의 인간관계만족도를 향상시키는 방안을 제안하고자 하였다. 본 연구 결과, 첫째, 경제적 배제를 극복하고 인간만족도를 향상시키는 방법은 그 대상에 있어서 여성에 대한 관심이 높아야 된다는 것을 암시하고 있으며, 고연령층, 저학력층, 저소득층에 대한 경제적 배제의 극복이 시급한 것으로 나타났다. 둘째, 사회관계망이 인간관계만족도에 미치는 영향을 조사한 결과, 여성일수록, 연령이 높을수록, 주소비처가 쇼핑일수록, 자녀와의 소통이 원활할수록 인간관계 만족도가 높아지는 것으로 나타났다. 따라서 사회관계에 대한 개선책이 요구된다고 볼 수 있다. 셋째, 경제적 배제가 인간관계 만족도에 미치는 영향을 조사한 결과, 여가활동이 친지 및 친척 방문일수록, 사회관계망 이용처가 유료시설일수록 인간관계 만족도가 낮아지는 것으로 나타났다. 따라서 여가활동과 사회관계망에 대한 개선책이 요구된다고 볼 수 있다. 넷째, 사회관계망의 매개효과를 조사한 결과, 소득영역에서의 배제, 노동시장의 배제, 주거복지의 배제를 항목으로 하는 독립변수인 경제적 배제가 종속변수인 인간관계만족도에 영향을 미치는 인과관계에서 사회관계망은 완전 매개효과가 있는 것으로 나타났다. 결론적으로 경제적 배제와 사회관계망은 인간관계만족에 영향을 끼치며, 경제적 배제가 극복되어 사회관계망이 개선될 때 비로소 인간관계만족도는 향상되는 것으로 나타났다.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 1989년도 한글날기념 학술대회 발표논문집
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• pp.267-271
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• 1989

최근 Scofield는 coulomb energy network에 적용할 수 있는 learning algorithm(supervised learning algorithm)을 제안하였다. 이 learning algorithm은 multi-layer network에도 쉽게 적용이 가능하고 한 layer 에서 발생한 error가 다른 layer에 영향을 주지 않아서 system을 modular하게 구성할 수가 있으며 각 layer를 독립적으로 learning 시킬 수 있는 특징이 있다. 본 논문에서는 coulomb energy network를 이용하여 한글인식을 위한 neural network를 구현하여 인식실험을 한 결과와 구현한 network 에서 인식율을 높이기 위한 방안 (2 stage learning) 을 제시한다.

• 한국환경복원기술학회지
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• 제18권5호
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• pp.13-26
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• 2015

Urban park and green space provide various functions. Among the functions, human benefit and increase of biodiversity are known to be important. Therefore, it is important to consider human and biotic aspect in the process of selecting suitable site for park and green space. However, there is insufficient research on both aspects. In this study, we used green network to analyze human and biotic aspect to select suitable site for park and green space in Seongnam City in Korea. To analyze the green network, we used accessibility for human aspect and used dispersal distance and habitat size for biotic aspect. We conducted least-cost path modelling using movement cost. In case of biotic aspect, GFS (generic focal species) is used to estimate habitat size and dispersal distance. To find out suitable site for park and green space, we used an overlay analysis method. As the result, old residential areas are shown have insufficient green network which needs park and green space. Furthermore, the green network for biotic aspect is insufficient in old residential areas comapred to green network for human aspect. The result of this study could contribute in planning of park and green space to maximize their functions.

• KIEAE Journal
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• 제3권2호
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• pp.59-66
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• 2003

To be the design method supporting aesthetic ability of human, CAAD system should essentially recognize architectural form in the same way of human. In this study, visual perception process of human was analyzed to search proper computational method performing similar step of perception of it. Through the analysis of visual perception, vision was separated to low-level vision and high-level vision. Edge detection and neural network were selected to model after low-level vision and high-level vision. The 24 images of building, tree and landscape were processed by edge detection and trained by neural network. And 24 new images were used to test trained network. The test shows that trained network gives right perception result toward each images with low error rate. This study is on the meaning of artificial intelligence in design process rather than on the design automation strategy through artificial intelligence.