• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1481-1491
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• 2021

Since most biosignals rely on contact-based measurement, there is still a problem in that it is hard to provide convenience to users by applying them to daily life. In this paper, we present a mobile application for estimating heart rate based on a deep learning model. The proposed application measures heart rate by capturing real-time face images in a non-contact manner. We trained a three-dimensional convolutional neural network to predict photoplethysmography (PPG) from face images. The face images used for training were taken in various movements and situations. To evaluate the performance of the proposed system, we used a pulse oximeter to measure a ground truth PPG. As a result, the deviation of the calculated root means square error between the heart rate from remote PPG measured by the proposed system and the heart rate from the ground truth was about 1.14, showing no significant difference. Our findings suggest that heart rate measurement by mobile applications is accurate enough to help manage health during daily life.

• Korean Security Journal
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• no.10
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• pp.79-102
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• 2005

This study aims to present implications to the basic directions according to organization's own objectives, roles, physiology and changes, etc for private security being activated. The change in organization can lead to foster the foundation for coping with sharp social environmental changes. The fundamental factors leading organizational changes should include intentional, planned and future-oriented activities. This is because organizational changes can be achieved by organizational learning. Besides, organizational learning is indeed important in terms of improvement in business methods and security services, but it should be also much more examined than in any other organization, since business particularity lies in getting in most sensitive contact with customers on the spot. Therefore, this study aims to propose the developmental directions of organizational learning by positively exploring the basic learning directions according to the influences of organizational learning, focusing the organizational efficientization strategies coping with environmental changes on the organizational learning.

• English Language & Literature Teaching
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• v.11 no.1
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• pp.17-36
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• 2005

The purpose of this study is to examine the ways in which CALL apply to enhance the motivational aspects of second language learning. Theories relevant to social, cognitive, and affective foundations of motivation are first reviewed to demonstrate the important role of motivational influences in improving learners' affect and achievements. Then, implications arising from such theories in strengthening the motivational aspects of CALL are explicated in the second part. With the spread of computer technology in language classrooms, the innovative role of CALL in the development and maintenance of intrinsic motivation can be illustrated. Specifically, CALL may provide cognitively supportive instruction geared towards improving students' performance. Also, it has been reported from the affective perspective that CALL can captivate learners' attention, promote their feelings and expectations of success, improve perceptions of control, and increase positive attributions to effort and ability. Finally, from a social learning perspective, CALL may enhance learners' self-efficacy and foster their achievement and positive affect through social interactions, proximal goal-setting, and attributional feedback. In the framework of CALL, students seem to be benefited by the immediacy and authenticity of contact with target languages and cultures made at their choices and decisions.

• Molecules and Cells
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• v.27 no.6
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• pp.629-634
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• 2009

G protein-coupled receptors (GPCRs) are part of multi-protein networks called 'receptosomes'. These GPCR interacting proteins (GIPs) in the receptosomes control the targeting, trafficking and signaling of GPCRs. PDZ domain proteins constitute the largest protein family among the GIPs, and the predominant function of the PDZ domain proteins is to assemble signaling pathway components into close proximity by recognition of the last four C-terminal amino acids of GPCRs. We present here a machine learning based approach for the identification of GPCR-binding PDZ domain proteins. In order to characterize the network of interactions between amino acid residues that contribute to the stability of the PDZ domain-ligand complex and to encode the complex into a feature vector, amino acid contact matrices and physicochemical distance matrix were constructed and adopted. This novel machine learning based method displayed high performance for the identification of PDZ domain-ligand interactions and allowed the identification of novel GPCR-PDZ domain protein interactions.

• Smart Structures and Systems
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• v.30 no.3
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.171-181
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• 2012

Outdoor mobile robots are faced with various terrain types having different characteristics. To run safely and carry out the mission, mobile robot should recognize terrain types, physical and geometric characteristics and so on. It is essential to control appropriate motion for each terrain characteristics. One way to determine the terrain types is to use non-contact sensor data such as vision and laser sensor. Another way is to use contact sensor data such as slope of body, vibration and current of motor that are reaction data from the ground to the tire. In this paper, we presented experimental results on terrain classification using contact sensor data. We made a mobile robot for collecting contact sensor data and collected data from four terrains we chose for experimental terrains. Through analysis of the collecting data, we suggested a new method of terrain feature extraction considering physical characteristics and confirmed that the proposed method can classify the four terrains that we chose for experimental terrains. We can also be confirmed that terrain feature extraction method using Fast Fourier Transform (FFT) typically used in previous studies and the proposed method have similar classification performance through back propagation learning algorithm. However, both methods differ in the amount of data including terrain feature information. So we defined an index determined by the amount of terrain feature information and classification error rate. And the index can evaluate classification efficiency. We compared the results of each method through the index. The comparison showed that our method is more efficient than the existing method.

• Convergence Security Journal
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• v.22 no.1
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• pp.29-38
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• 2022

The importance of prevention of epidemics is increasing due to the serious spread of infectious diseases. For prevention of epidemics, we need to focus on the non-contact industry. Therefore, in this paper, a face recognition door lock that controls access through non-contact is designed and developed. First very simple features are combined to find objects and face recognition is performed using Haar-based cascade algorithm. Then the texture of the image is binarized to find features using LBPH. An non-contact door lock system which composed of Raspberry PI 3B+ board, an ultrasonic sensor, a camera module, a motor, etc. are suggested. To verify actual performance and ascertain the impact of light sources, various experiment were conducted. As experimental results, the maximum value of the recognition rate was about 85.7%.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.1 no.1
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• pp.1-8
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• 2020

Recent studies revealed that many animals identify individual humans. In this account, we review previous literatures on individual human recognition by wild or domestic animals and discuss the three hypotheses: "high cognitive abilities" hypothesis, "close human contact" and "pre-exposure to stimuli" hypothesis. The three hypotheses are not mutually exclusive. Close human contact hypothesis is an ultimate explanation for adaptive benefits whereas high cognitive abilities and pre-exposure to stimuli hypothesis are proximate explanations for mechanisms to perform such discriminatory behaviour. We report a case study of two bird species in a human-free habitat. Long-tailed skuas, which are known for having high cognitive abilities, exhibited the human discriminatory abilities whereas ruddy turnstones did not display such abilities toward approaching humans. This suggests that highly intelligent species may have this type of discriminatory ability so that they could learn to identify individual humans quickly by pre-exposure to stimuli, even in a human-free habitat. Here, we discuss that human recognition is more common in species with rapid learning ability and it could develop for a short period of time between an intelligent species and human.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.1-6
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• 1992

A principle of "orthogonalization" is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric endpoint constraints. The principle realizes the hybrid control in a strict sense by letting position and velocity feedback signals be orthogonal in joint space to the contact force vector whose components are exerted at corresponding joints. This orthogonalization is executed via a projection matrix computed in real-time from a gradient of the equation of the surface in joint coordinates and hence both projected position and velocity feedback signals become perpendicular to the force vector that is normal to the surface at the contact point in joint space. To show the important role of the principle in control of robot manipulators, three basic problems are analyzed, the first is a hybrid trajectory tracking problem by means of a "modified hybrid computed torque method", the second is a model-based adaptive control problem for robot manipulators under geometric endpoint constraints, and the third is an iterative learning control problem. It is shown that the passivity of residual error dynamics of robots follows from the orthogonalization principle and it plays a crucial role in convergence properties of both positional and force error signals.force error signals.

• Journal of Music and Human Behavior
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• v.1 no.1
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• pp.47-71
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• 2004

Students with learning disability have difficulty with attention and academic achievement Music can be an effective tool to enhance level of participation. The purpose of this study is to examine how much can be structured to enhance the attention span and related academic skills needed to achieve educational goals. The study has implemented structured music therapy sessions for three middle school students with learning disability. They participated in 20 sessions which were 30 minutes in length for ten weeks. The implemented music therapy sessions were designed using songs, playing, and listening to music. In order to test their level of attention, Frankfurter Aufmerksamkeits-Inventar(FAIR) Attention Test is implemented and Conners' Comprehensive Teacher's Rating Scale(CTRS-10) are used on the week before and after music activities. Also videotaping is used so as to analyze how correctly they do their task and how the correctness is changed period by period and to evaluate how often for ten minutes they make an eye contact with their teacher. The conclusions of this study are as follows: Firstly, the organized music activities have a positive affection on improving the attention of three middle school students who have learning disabilities. Fair Attention Test shows that they can do their task with more accuracy than in the previous period. Secondly, three students of this study improved their attention and made an eye contact more often than before this study, which is revealed through the analysis of the pre and post test results evaluated by CTRS-10. The results of the study indicate that structured use of music in various level of activities can help students to enhance attention span and the related academic skills.