• Journal of Information Processing Systems
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• v.18 no.6
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• pp.813-821
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• 2022

Intelligent monitoring, life entertainment, medical rehabilitation, and other fields are only a few examples where visual image technology is becoming increasingly sophisticated and playing a significant role. Recognizing Wushu, or martial arts, movements through the use of visual image technology helps promote and develop Wushu. In order to segment and extract the signals of Wushu movements, this study analyzes the denoising of the original data using the wavelet transform and provides a sliding window data segmentation technique. Wushu movement The Wushu movement recognition model is built based on the hidden Markov model (HMM). The HMM model is trained and taught with the help of the Baum-Welch algorithm, which is then enhanced using the frequency weighted training approach and the mean training method. To identify the dynamic Wushu movement, the Viterbi algorithm is used to determine the probability of the optimal state sequence for each Wushu movement model. In light of the foregoing, an HMM-based martial arts movements recognition model is developed. The recognition accuracy of the HMM model increases to 99.60% when the number of samples is 4,000, which is greater than the accuracy of the SVM (by 0.94%), the CNN (by 1.12%), and the BP (by 1.14%). From what has been discussed, it appears that the suggested system for detecting martial arts acts is trustworthy and effective, and that it may contribute to the growth of martial arts.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.265-274
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• 2009

This study aims at examining the actual condition of recognition of dental caries(ACRDC), presenting a scheme to improve the recognition, and providing basic data necessary to prevent oral diseases, Dental caries (DC) which one of the three most important chronic diseases in Korea. A survey was conducted on ACRDC for about 390 people twenty and over who visited dental clinics for dental treatment 336 questionnaires with exception of omitted answer were used for statistical analysis. Many of them had never heard of DC and most of them failed to recognize it. People who experienced DC had a dental clinic as a main information channel and they were significant differences in those obtaining information through other channels(p=0.008). Univariate logistic regression analysis for effects on ACRDC demonstrated that possibility of subjects in their twenties recognizing DC more accurately than those in their forties Odds ratio(95% confidence Interval) was 2.06(1.16-3.66)(p=0.000), with the possibility of professionals recognizing the disease more accurately than those with other kinds of occupation 5.49(2.52-11.93)(p=0.000), showing statistically significant relevance. As for medical security, possibility of work insurance being related to more accurate recognition of dental caries than others was 1.95 (1.03-3.71)(p=0.003), with the possibility of subjects whose dental state was very good recognizing dental caries more accurately than those whose dental state was very bad 6.40(1.57-26.03) (p=0.002), showing statistically significant relevance. For prevention of DC, an education through experts working at a dental clinic are necessary for those in their twenties and over, who were other than professionals, who were insured for medical security via other than work insurance, and whose dental state was bad.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.9-15
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• 2018

From natural disasters such as floods, heavy rains, and strong winds and social disasters such as 911 U.S. terrorism and cyber attacks that could have a fatal impact on corporate continuity, it is necessary to introduce and implement a Business Continuity Management System (BCMS) within a firm to maintain continuity of business and to change the organizational structure for an emergency state in order to operate and manage it systematically and efficiently. therefore, this study analyzed and verified the impact of introducing a Business Continuity Management System (BCMS) on the change in the recognition structure of an organization in four categories, including personal recognition, organizational culture, organizational structure, and organizational strategy, in order to analyse the impact and effect of introducing a Business Continuity Management System (BCMS) on the change in the recognition structure of each category. through this study, we believe that the introduction of a Business Continuity Management System (BCMS) within a firm could effectively change the organization's perception of an emergency state and help it maintain its continuity as well as improve its value.

• Journal of the Korean Society of Radiology
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• v.6 no.5
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• pp.321-333
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• 2012

The purpose of this study is to examine radiologists' awareness and performance of hospital infection control, providing basic information needed to improve and educate how to control hospital infection. The subjects' awareness and performance of hospital infection control were respectively 141.05 and 138.15 points in average score on a 150-point scale. In all sub-areas of the control, the higher the awareness was, the higher the performance was, but the latter was relatively lower than the former. Factors that were having statistically significant effects on that awareness included the necessity of infection control education, participation or non-participation in infection prevention education and recognition or non-recognition of patients' disease state. And factors that were having statistically significant influences on that performance included participation or non-participation in infection prevention education, recognition or non-recognition of patients' disease state and the foresaid awareness itself.

• Database Research
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• v.34 no.3
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• pp.45-57
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• 2018

The drowsiness that occurs in the driving is a very dangerous driver condition that can be directly linked to a major accident. In order to prevent drowsiness, there are traditional drowsiness detection methods to grasp the driver's condition, but there is a limit to the generalized driver's condition recognition that reflects the individual characteristics of drivers. In recent years, deep learning based state recognition studies have been proposed to recognize drivers' condition. Deep learning has the advantage of extracting features from a non-human machine and deriving a more generalized recognition model. In this study, we propose a more accurate state recognition model than the existing deep learning method by learning image and PPG at the same time to grasp driver's condition. This paper confirms the effect of driver's image and PPG data on drowsiness detection and experiment to see if it improves the performance of learning model when used together. We confirmed the accuracy improvement of around 3% when using image and PPG together than using image alone. In addition, the multimodal deep learning based model that classifies the driver's condition into three categories showed a classification accuracy of 96%.

• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.299-304
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• 2021

Existing human activity recognition systems detect activities through devices such as wearable sensors and cameras. However, these methods require additional devices and costs, especially for cameras, which cause privacy issue. Using WiFi signals that are already installed can solve this problem. In this paper, we propose a CNN-based human activity recognition system using channel state information of WiFi signals, and present results of designing and implementing accelerated hardware structures. The system defined four possible behaviors during studying in indoor environments, and classified the channel state information of WiFi using convolutional neural network (CNN), showing and average accuracy of 91.86%. In addition, for acceleration, we present the results of an accelerated hardware structure design for fully connected layer with the highest computation volume on CNN classifiers. As a result of performance evaluation on FPGA device, it showed 4.28 times faster calculation time than software-based system.

• Journal of Convergence Society for SMB
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• v.5 no.3
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• pp.9-13
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• 2015

With the advance of ICT, the necessity of user authentication to verify the identity of an opponent online not face to face is increasing. The authentication, the basis of the security, is used in various fields. Because ID-based authentication has weaknesses in terms of stability and losses, two or more than two authentication tools are used in the place in which the security is important. Recently, biometric authentication rather than ID, OTP, SMS authentication has been an issue in terms of credibility and efficiency. As the fields applied to current biometric recognition technologies are increasing, the application of the biometric recognition is being used in various fields such as mobile payment system, intelligent CCTV, immigration inspection, and access control. As the biometric recognition, finger print, iris, retina, vein, and face recognition have been studied actively. This study is to inspect the current state of domestic and foreign standardization including understanding of the face recognition and the trend of technology.

• Journal of Korea Multimedia Society
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• v.4 no.6
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• pp.524-533
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• 2001

Efficient continuous speech recognition system for practical applications requires that the processing be carried out in real time and high recognition accuracy. In this paper, we study the acoustic models by adopting the PDT-SSS algorithm and the language models by iterative learning so as to improve the speech recognition accuracy. And the adaptive pruning algorithm is applied to the continuous speech. To verify the effectiveness of proposed method, we carried out the continuous speech recognition for the Korean air flight reservation task. Experimental results show that the adopted algorithm has the average 90.9% for continuous speech recognition and the average 90.7% for word recognition accuracy including continuous speech. And in case of adopting the adaptive pruning algorithm to continuous speech, it reduces the recognition time of about 1.2 seconds(15%) without any loss of accuracy. From the result, we proved the effectiveness of the PDT-SSS algorithm and the adaptive pruning algorithm.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.1-8
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• 2009

There are a lot of influences, such as location of camera, luminosity, brightness, and direction of light, which affect the performance of 2-dimensional image recognition. This paper suggests an adaptive method for face-image recognition in noisy environments using evolvable filtering and feature extraction which uses one sample image from camera. This suggested method consists of two main parts. One is the environmental-adjustment module which determines optimum sets of filters, filter parameters, and dimensions of features by using "steady state genetic algorithm". The other another part is for face recognition module which performs recognition of face-image using the previous results. In the processing, we used Gabor wavelet for extracting features in the images and k-Nearest Neighbor method for the classification. For testing of the adaptive face recognition method, we tested the adaptive method in the brightness noise, in the impulse noise and in the composite noise and verified that the adaptive method protects face recognition-rate's rapidly decrease which can be occurred generally in the noisy environments.