• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.463-466
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• 2004

This paper describes an image processing algorithm for recognition of small-obstacles using a camera and program for a mobile robot in indoor environment. Mobile robot could meet small-obstacles such as a small plastic bottle of about 1l in quantity, a small box of 7$\times$7$\times$7 cm$^3$ in volume, and so on in its designated path, and could be disturbed by them in the locomotion of a mobile robot. So, it is necessary to research on the image processing algorithm for recognition of small-obstacles using a camera and program. In this paper, 2-D the image processing algorithm for recognition of small-obstacles using a camera and program for a mobile robot in indoor environment was developed. The characteristic test of the developed program to confirm the recognition of small-obstacles was performed. It is shown that the developed program could recognize small-obstacles accurately.

• 로봇학회논문지
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• 제3권1호
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• 한국컴퓨터정보학회논문지
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• 제20권9호
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• pp.67-72
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• 2015

Recently, studies that interact with human and things through motion recognition are increasing due to the expansion of IoT(Internet of Things). This paper proposed the system that recognizes the user's logical activity in home environment by attaching some sensors to various objects. We employ Arduino sensors and appreciate the logical activity by using the physical activitymodel that we processed in the previous researches. In this System, we can cognize the activities such as watching TV, listening music, talking, eating, cooking, sleeping and using computer. After we produce experimental data through setting virtual scenario, then the average result of recognition rate was 95% but depending on experiment sensor situation and physical activity errors the consequence could be changed. To provide the recognized results to user, we visualized diverse graphs.

• 로봇학회논문지
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• 제1권2호
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• pp.158-162
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• 2006

This paper presents a speaker recognition system intended for use in human-robot interaction. The proposed speaker recognition system can achieve significantly high performance in the Ubiquitous Robot Companion (URC) environment. The URC concept is a scenario in which a robot is connected to a server through a broadband connection allowing functions to be performed on the server side, thereby minimizing the stand-alone function significantly and reducing the robot client cost. Instead of giving a robot (client) on-board cognitive capabilities, the sensing and processing work are outsourced to a central computer (server) connected to the high-speed Internet, with only the moving capability provided by the robot. Our aim is to enhance human-robot interaction by increasing the performance of speaker recognition with multiple microphones on the robot side in adverse distant-talking environments. Our speaker recognizer provides the URC project with a basic interface for human-robot interaction.

• 디지털산업정보학회논문지
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• 제4권3호
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• pp.85-90
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• 2008

Recently, with the sensor technology, research about the electronic nose system which imitated the olfactory organ are being pushed actively. But, in case of general electronic nose system, an aroma is measured at the laboratory space where blocked external environment and is analyzed a part of measured data. In this paper, we propose the system which can measure and recognize an aroma in natural environment. We propose the Entropy algorithm which can detect the sensor reaction section among the continuous detection processing about an aroma. And we implement the aroma recognition system using the PCA(Principal Components Analysis) and K-NN(K-Nearest Neighbor) about the detected aroma. In order to evaluate the performance, we measured the aroma pattern, about 9 aroma oil, 50 times respectively. And we experimented the aroma detection and recognition using this. There was an error of 0.2s in the aroma detection and we get 84.3% recognition rate of the aroma recognition.

• 자동차안전학회지
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• 제14권4호
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• pp.6-15
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• 2022

In this study, a traffic light recognition algorithm was implemented and validated for low-speed special purpose vehicles in an urban environment. Real-time image data using a camera and YOLO algorithm were applied. Two methods were presented to increase the accuracy of the traffic light recognition algorithm, and it was confirmed that the second method had the higher accuracy according to the traffic light type. In addition, it was confirmed that the optimal YOLO algorithm was YOLO v5m, which has over 98% mAP values and higher efficiency. In the future, it is thought that the traffic light recognition algorithm can be used as a dual system to secure the platform safety in the traffic information error of C-ITS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4510-4526
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• 2017

Most available methods of facial gender recognition work well under a constrained situation, but the performances of these methods have decreased significantly when they are implemented under unconstrained environments. In this paper, a method via low-rank and collaborative representation is proposed for facial gender recognition in the wild. Firstly, the low-rank decomposition is applied to the face image to minimize the negative effect caused by various corruptions and dynamical illuminations in an unconstrained environment. And, we employ the collaborative representation to be as the classifier, which using the much weaker $l_2-norm$ sparsity constraint to achieve similar classification results but with significantly lower complexity. The proposed method combines the low-rank and collaborative representation to an organic whole to solve the task of facial gender recognition under unconstrained environments. Extensive experiments on three benchmarks including AR, CAS-PERL and YouTube are conducted to show the effectiveness of the proposed method. Compared with several state-of-the-art algorithms, our method has overwhelming superiority in the aspects of accuracy and robustness.

• 한국초등과학교육학회지:초등과학교육
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• 제29권2호
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• pp.124-133
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• 2010

The study presents preliminary research on how science activities focusing on problem recognition worked and affected students' perception of their learning environment in a sixth science classroom. The science activities were based on the Science Writing Heuristic (SWH) which was suggested by Keys, Hand, Prain & Collins (1999), where problem recognition was an important part of scientific inquiry. For developing the working sheets for the modified SWH in this study, analyses of target units of 6th grade science curriculum in the aspects of problem recognition were conducted. After consecutive 6 classes with the developed working sheets for sixth graders, the student working sheets for each lesson were collected and analyzed. In order to investigate the developed units' affect on student learning, students' perceptions of their learning environment were administered before and after the applied classes. Students working sheets and questionnaires on their perceptions of learning environment indicated that students perceived that the science activities were more student-centered classes where students had active discussion and dialogue with one another giving them more chances to actively take part in the class as well as they used more properly recognized their inquiry problem.

• 한국산업융합학회 논문집
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• 제22권2호
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• pp.137-146
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• 2019

This study proposed a new technique to control of mobile robot based on voice command for (Human-Robot Cooperative operation in manufacturing precess). High performance voice recognition and control system was designed In this paper for smart factory. robust voice recognition is essential for a robot to communicate with people. One of the main problems with voice recognition robots is that robots inevitably effects real environment including with noises. The noise is captured with strong power by the microphones, because the noise sources are closed to the microphones. The signal-to-noise ratio of input voice becomes quite low. However, it is possible to estimate the noise by using information on the robot's own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. In this paper, we describe an robust voice recognition system which can robustly recognize voice by adults and students in noisy environments. It is illustrated by experiments the voice recognition performance of mobile robot placed in a real noisy environment.

• 한국음향학회지
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• 제19권3호
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• pp.51-56
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• 2000

본 논문은 음성인식을 이용한 실시간 윈도우 자동 제어 시스템에 관한 연구이다. 사용된 음성 모델은 수행 속도를 높이기 위해 제안된 가변 DMS 모델을 이용하였으며, 인식 알고리즘으로 이를 이용한 One-Stage DP 알고리즘을 사용한다. 인식 대상단어는 윈도우에서 자주 사용되는 66개의 윈도우 제어 명령어들로 구성한다. 본 연구에서 온라인으로 음성을 처리하기 위해 음성 검출 알고리즘을 구현하였으며, 기존 DMS(Dynamic Multi Section)모델 생성시 고정적으로 적용하던 섹션의 수를 입력 신호의 지속 시간을 고려하여 가변적으로 적용한 가변 DMS 모델을 제안하였다. 또한 윈도우에서 사용자 작업에 의해 현재 상태에 인식 대상으로 불필요한 인식 대상단어가 발생하게 되는데 이를 효율적으로 처리하기 위해 사용 모델을 재구성하여 사용하도록 제안하였으며, 인간의 청각적 특성을 고려하여 음성신호에서 개인의 특성은 제외하고 음성 자체의 특징만을 추출하여 특징 벡터를 생성하는 인지 선형 예측(Perceptual Linear Predictive)분석 방법을 이용하였다. 시스템 성능 평가 결과 가변 동적 다중 섹션 모델(Variable DMS model)과 기존의 DMS 모델은 인식률 면에서는 거의 동일하지만 인식 수행 속도는 제안된 모델의 계산량이 기존 모델보다 작기 때문에 향상되었고, 다중 화자 독립 인식률은 99.08%, 다중 화자 종속 인식률은 99.39%의 인식률을 나타내었으며, 실제 노이즈가 있는 환경에서 화자독립실험의 경우 96.25%의 인식률을 보여 주었다.