• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.120-126
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• 2020

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1435-1439
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• 2004

Most recently, CP(Cellular Phone) has been one of the most important technologies in the IT(Information Tech-nology) field, and it is situated in a position of great importance industrially and economically. To produce the best CP in the world, a new technological concept and its advanced implementation technique is required, due to the extreme level of competition in the world market. The RT(Robot Technology) has been developed as the next generation of a future technology. Current robots require advanced technology, such as soft computing, human-friendly interface, interaction technique, speech recognition, object recognition etc. unlike the industrial robots of the past. Therefore, this paper explains conceptual research for development of the RCP(Robotic Cellular Phone), a new technological concept, in which a synergy effect is generated by the merging of IT & RT. RCP infra consists of $RCP^{Mobility}$ $RCP^{Interaction}$, $RCP^{Integration}$ technologies. For $RCP^{Mobility}$, human-friendly motion automation and personal service with walking and arming ability are developed. $RCP^{Interaction}$ ability is achieved by modeling an emotion-generating engine and $RCP^{Integration}$ that recognizes environmental and self conditions is developed. By joining intelligent algorithms and CP communication network with the three base modules, a RCP system is constructed. Especially, the RCP mobility system is focused in this paper. $RCP^{Mobility}$ is to apply a mobility technology, which is popular robot technology, to CP and combine human-friendly motion and navigation function to CP. It develops a new technological application system of auto-charging and real-world entertainment function etc. This technology can make a CP companion pet robot. It is an automation of human-friendly motions such as opening and closing of CPs, rotation of antenna, manipulation and wheel-walking. It's target is the implementation of wheel and manipulator functions that can give service to humans with human-friendly motion. So, this paper presents the definition, the basic theory and experiment results of the RCP mobility system. We confirm a good performance of the RCP mobility system through the experiment results.

• Journal of Korea Multimedia Society
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• v.18 no.5
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• pp.583-598
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• 2015

We need many assembly lines to produce industrial product such as automobiles that require a lot of composited parts. Big portion of such assembly line are still operated by manual works of human. Such manual works sometimes cause critical error that may produce artifacts. Also, once the assembly is completed, it is really hard to verify whether of not the product has some error. In this paper, for monitoring behaviors of manual human work in an assembly line automatically, we proposes a realtime hybrid monitoring system that combines 2D vision sensor tracking technique with 3D motion recognition sensors.

• ETRI Journal
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• v.44 no.2
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• pp.327-338
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• 2022

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.4
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• pp.217-226
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• 2014

Since the world has changed to a society of 21st century high-tech industries, the modern people have become reluctant to work in a difficult and dirty environment. Therefore, unmanned technologies through robots are being demanded. Now days, effects such as voice, control, obstacle avoidance are being suggested, and especially, voice recognition technique that enables convenient interaction between human and machines is very important. In this study, in order to conduct study on the stable motion control of the robot system that has mobile-manipulator structure and is voice command-based, kinetic interpretation and dynamic modeling of two-armed manipulator and three-wheel mobile robot were conducted. In addition, autonomous driving of three-wheel mobile robot and motion control system of two-armed manipulator were designed, and combined robot control through voice command was conducted. For the performance experiment method, driving control and simulation mock experiment of manipulator that has two-armed structure was conducted, and for experiment of combined robot motion control which is voice command-based, through driving control, motion control of two-armed manipulator, and combined control based on voice command, experiment on stable motion control of voice command-based robot system that has mobile-manipulator structure was verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1904-1926
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• 2016

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.7
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• pp.717-722
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• 2007

Inverse synthetic aperture radar(ISAR) images represent two-dimensional(2-D) spatial distribution of electromagnetic scattering phenomenology against a target. Hence, they are usually used in the areas of automatic target recognition (ATR) or non-cooperative target recognition(NCTR), identifying a target using radar in a long distance. This paper makes use of Korea Miniature Synthetic Aperture Radar(KOMSAR) to generate ISAR images of a real and maneuvering aircraft. The data obtained from KOMSAR are processed to eliminate phase errors due to motion of a target, with the use of entropy-based ISAR autofocusing technique. Results show that we can successfully obtain ISAR images of a real aircraft, and the success of experiments implies that a significant step toward ATR using radar has been established.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2118-2125
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• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.4
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• pp.301-314
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• 1990

This paper is an attempt to use vision-pattern recognition technique to analyzation on a hidden rigid body motion. Specially shaped rod, rigidly connected to the hidden body is extended to the ouside of hiding object so that a camera may catch the motion data. Every motion can be described with translatio and rotation. But translation can be explanied with ratation with a infinitly far centroid. Motion analysis is to find the instantaneous centroid and ratation angle. With this theory jaw motion is analyzed in this paper.