• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1105-1110
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• 2017

Speech with various emotions degrades the performance of the speaker recognition system. In this paper, a speaker recognition method using emotional adaptation has been proposed to improve the performance of speaker recognition system using affective speech. For emotional adaptation, emotional speaker model was generated from speaker model without emotion using a small number of training affective speech and speaker adaptation method. Since it is not easy to obtain a sufficient affective speech for training from a speaker, it is very practical to use a small number of affective speeches in a real situation. The proposed method was evaluated using a Korean database containing four emotions. Experimental results show that the proposed method has better performance than conventional methods in speaker verification and speaker recognition.

• MALSORI
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• no.58
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• pp.45-66
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• 2006

In this paper, a rule-based (transformation-based) NE recognition system is proposed. This system uses Brill's rule inference approach. The performance of the rule-based system and IdentiFinder, one of most successful stochastic systems, are compared. In the baseline case (no punctuation and no capitalisation), both systems show almost equal performance. They also have similar performance in the case of additional information such as punctuation, capitalisation and name lists. The performances of both systems degrade linearly with the number of speech recognition errors, and their rates of degradation are almost equal. These results show that automatic rule inference is a viable alternative to the HMM-based approach to NE recognition, but it retains the advantages of a rule-based approach.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• Biomolecules & Therapeutics
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• v.16 no.3
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• pp.173-178
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• 2008

Several lines of evidence indicate that scopolamine as a nonselective muscarinic antagonist disrupts object recognition performance and spatial working memory when administered systemically. In the present study, we investigated the different effects of scopolamine on acquisition, consolidation, and retrieval phases of object recognition performance and spatial working memory using the object recognition and the Morris water maze tasks in mice. In the acquisition phase test, scopolamine decreased recognition index on object recognition task and the trial 1 to trial 2 differences on Morris water maze task. In the consolidation and retrieval phase tests, scopolamine also decreased recognition index on object recognition task, where as scopolamine did not exhibited any effects on the Morris water maze task.

• Journal of muscle and joint health
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• v.20 no.3
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• pp.247-257
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• 2013

Purpose: The purpose of this descriptive study was to investigate the knowledge of radiation protection and the recognition and performance of radiation protection behaviors among perioperative nurses. This study was intended to yield basic data for the development of nursing interventions aimed at improving the nurses' radiation protection behaviors. Methods: One hundred and thirty-seven nurses working in the operating room participated in a survey from September 1 to 30, 2011. The data was analyzed using t-test, ANOVA, and Pearson's correlation with the SPSS/WIN 19.0 program. Results: The average score of radiation protection knowledge was $7.57{\pm}3.45$ out of 16. The average score for the recognition and performance of radiation protection behaviors was $4.32{\pm}0.23$. The knowledge of radiation protection was significantly correlated with the recognition and performance of radiation protection behaviors. Conclusion: Expanding the knowledge of radiation protection could lead to the increase of the recognition and performance of radiation protection behaviors. Therefore, promoting the performance of radiation protection behaviors by improving perioperative nurses' knowledge of radiation protection through reinforcing radiation-related education hereafter could be an important part of nursing.

• Journal of Home Health Care Nursing
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• v.26 no.2
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• pp.166-179
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• 2019

Purpose: The purpose of this study was to identify the job performance and educational needs, and recognition of professionalism among care workers, and to organize educational programs according to the priorities of care workers. Methods: The participants were 119 care workers who were working in long-term care facilities. Data were collected from May 31 to June 7, 2019 using self-report questionnaires. Collected data were analyzed using t-tests, ANOVA, and Spearman's Correlation Coefficients. Results: The performance aspects of the job were as follows: care for safety and infection-related, communication and leisure support, and excretion. The most demanded educational needs were in first-aid. Care workers had more than average professional recognition. Job performance and educational needs, and recognition of professionalism differed significantly according to several general characteristics. Conclusions: The educational needs of the areas with low frequency of job performance were high. First-aid is low in frequency, but it is important to cope with emergencies, so it is necessary to continue education. Also, there is a difference in recognition of professionalism according to the career. It will be necessary to develop individualized education programs to meet the needs of care workers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.383-386
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• 2002

In this paper, we investigated the pattern recognition performance of the numeric patterns (from 0 to 9) using DNA coding method. The pattern recognition performance of the DNA coding method is compared to the that of the GA(Genetic Algorithm). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by A(Adenine), C(Cytosine), G(Guanine) and T(Thymine), The pattern recognition performance of GA and DNA coding method is evaluated by using the same genetic operators(crossover and mutation) and the crossover probability and mutation probability are set the same value to the both methods. The DNA coding method has better characteristics over genetic algorithms (GA). The reasons for this outstanding performance is multiple possible solution presentation in one string and variable solution string length.

• MALSORI
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• no.53
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• pp.119-127
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• 2005

Speech recognition is one of the user interface technologies in commanding and controlling any terminal such as a TV, PC, cellular phone etc. in a ubiquitous environment. In controlling a terminal, the mismatch between training and testing causes rapid performance degradation. That is, the mismatch decreases not only the performance of the recognition system but also the reliability of that. Therefore, the performance degradation due to the mismatch caused by the change of the environment should be necessarily compensated. Whenever the environment changes, environment adaptation is performed using the user's speech and the background noise of the changed environment and the performance is increased by employing the models appropriately transformed to the changed environment. So far, the research on the environment compensation has been done actively. However, the compensation method for the effect of distant-talking speech has not been developed yet. Thus, in this paper we apply MLLR-based environment adaptation to compensate for the effect of distant-talking speech and the performance is improved.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.156-165
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• 2005

Human being recognizes the physical world by integrating a great variety of sensory inputs, the information acquired by their own action, and their knowledge of the world using hierarchically parallel-distributed mechanism. In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile informations. The proposed system focuses on improving recognition performance of 3D objects. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse the two sensory signals. Tactual signals are obtained from the reaction force of the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of learning iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though the visual sensory signals get defects. The experimental results show that the proposed system can improve recognition rate and reduce teeming time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme for 3D objects.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.107-109
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• 2004

In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile information. The proposed system focuses on improving recognition performance of 3D object. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse these informations. Tactual signals are obtained from the reaction force by the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of teaming iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though visual information has a defect. The experimental results show that the proposed system can improve recognition rate and reduce learning time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme of 3D object.