• Journal of Korean Academy of Nursing
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• v.45 no.4
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• pp.501-512
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• 2015

Purpose: The purpose of this study, which was guided by the Resiliency Model of Family Stress, Adjustment, and Adaptation, was twofold: (a) to explore family and parental adaptation and factors influencing family adaptation in Korean families of children with Down syndrome (DS) through a quantitative methodology and (b) to understand the life with a Korean child with DS through a qualitative method. Methods: A mixed-methods design was adopted. A total of 147 parents of children with DS completed a package of questionnaires, and 19 parents participated in the in-depth interviews. Quantitative and qualitative data were analyzed using stepwise multiple regression and content analysis respectively. Results: According to the quantitative data, the overall family adaptation scores indicated average family functioning. Financial status was an important variable in understanding both family and parental adaptation. Family adaptation was best explained by family problem solving and coping communication, condition management ability, and family hardiness. Family strains and family hardiness were the family factors with the most influence on parental adaption. Qualitative data analysis showed that family life with a child with DS encompassed both positive and negative aspects and was expressed with 5 themes, 10 categories, and 16 sub-categories. Conclusion: Results of this study expand our limited knowledge and understanding concerning families of children with DS in Korea and can be used to develop effective interventions to improve the adaptation of family as a unit as well as parental adaptation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1167-1177
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• 1989

In this paper, a generalized adaptive law is proposed which uses a rational function type operator for parameter adjustment. To satisfy the passivity condition of the adaptation block, we introduce a constant feedback gain into the adaptation block. This adaptation scheme is applied to the model reference adaptive control of a continuous-time, linear time-invariant, minimum-phase system whose relative degree is 1. We prove the asymptotic stability of the output error of this adaptive system by hyperstability method. It is shown that by digital computer simulations this law can give a better output error transient response in some cases than the conventional gradient adaptive law. And the output error responses for the several types of the proposed adaptation law are examined in the presence of a kind of unmodeled dynamics.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.21-26
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• 2001

An expectation-maximization (EM) based Bayesian adaptation method for the mean of noise is proposed for noise-robust speech recognition. In the algorithm, the on-line testing utterances are used for the unsupervised Bayesian adaptation and the prior distribution of the noise mean is estimated using the off-line training data. For the noisy speech modeling, the parallel model combination (PMC) method is employed. The proposed method has shown to be effective compared with the conventional PMC method for the speech recognition experiments in a car-noise condition.

• Proceedings of the KSPS conference
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• 2003.05a
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• pp.127-130
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• 2003

This paper describes an efficient method for unsupervised speaker adaptation. This method is based on selecting a subset of speakers who are acoustically close to a test speaker, and calculating adapted model parameters according to the previously stored sufficient HMM statistics of the selected speakers' data. In this method, only a few unsupervised test speaker's data are required for the adaptation. Also, by using the sufficient HMM statistics of the selected speakers' data, a quick adaptation can be done. Compared with a pre-clustering method, the proposed method can obtain a more optimal speaker cluster because the clustering result is determined according to test speaker's data on-line. Experiment results show that the proposed method attains better improvement than MLLR from the speaker independent model. Moreover the proposed method utilizes only one unsupervised sentence utterance, while MLLR usually utilizes more than ten supervised sentence utterances.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.290-294
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• 2021

Experts have designed popular and successful model architectures, which, however, were not the optimal option for different scenarios. Despite the remarkable performances achieved by deep neural networks, manually designed networks for classification tasks are the backbone of object detection. One major challenge is the ImageNet pre-training of the search space representation; moreover, the searched network incurs huge computational cost. Therefore, to overcome the obstacle of the pre-training process, we introduce a network adaptation technique using a pre-trained backbone model tested on ImageNet. The adaptation method can efficiently adapt the manually designed network on ImageNet to the new object-detection task. Neural architecture search (NAS) is adopted to adapt the architecture of the network. The adaptation is conducted on the MobileNetV2 network. The proposed NAS is tested using SSDLite detector. The results demonstrate increased performance compared to existing network architecture in terms of search cost, total number of adder arithmetics (Madds), and mean Average Precision(mAP). The total computational cost of the proposed NAS is much less than that of the State Of The Art (SOTA) NAS method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1047-1054
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• 2015

This paper presents an acoustic model transform method using untranscribed speech database for improved speech recognition. In the presented model transform method, an adapted GMM is obtained by employing the conventional adaptation method, and the most similar Gaussian component is selected from the adapted GMM. The bias vector between the mean vectors of the clean GMM and the adapted GMM is used for updating the mean vector of HMM. The presented GAMT combined with MAP or MLLR brings improved speech recognition performance in car noise and speech babble conditions, compared to singly-used MAP or MLLR respectively. The experimental results show that the presented model transform method effectively utilizes untranscribed speech database for acoustic model adaptation in order to increase speech recognition accuracy.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.5-11
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• 1996

The purpose of this paper is to propose a method improving the performance of a semi-continuous hidden Markov model(SCHMM) speaker adaptation system which uses Bayesian Parameter reestimation approach. The performance of Bayesian speaker adaptation could be degraded in case that the features of a new speaker are severely different from those of a reference codebook. The excessive codewords of the reference codebook still remain after adaptation proess. which cause confusion in recognition process. To solve such problems, the proposed method uses formant information which is extracted from the cepstral coefficients of the reference codebook and adaptation data. The reference codebook is adapted to represent the formant distribution of a new speaker and it is used for Bayesian speaker adaptation as an initial codebook. The proposed method provides accurate correspondence between reference codebook and adaptation data. It was observed that the excessive codewords were not selected during recognition process. The experimental results showed that the proposed method improved the recognition performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.4
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• pp.219-225
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• 2013

This paper presents a method using Maximum-Likelihood Linear Regression (MLLR) adaptation to improve recognition performance of Limabeam algorithm for speech recognition using microphone array. From our investigation on Limabeam algorithm, we can see that the performance of filtering optimization depends strongly on the supporting optimal state sequence and this sequence is created by using Viterbi algorithm trained with HMM model. So we propose an approach using MLLR adaptation for the recognition of speech uttered in a new environment to obtain better optimal state sequence that support for the filtering parameters' optimal step. Experimental results show that the system embedded with MLLR adaptation presents the word correct recognition rate 2% higher than that of original calibrate Limabeam and also present 7% higher than that of Delay and Sum algorithm. The best recognition accuracy of 89.4% is obtained when we use 4 microphones with 5 utterances for adaptation.

• Journal of Korean Public Health Nursing
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• v.30 no.3
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• pp.395-404
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• 2016

Purpose: The purpose of this study was to investigate the effects of positive thinking and subjective happiness on school adaptation in nursing students. Methods: Data were collected by questionnaires from 282 nursing students in the month of November 2013. The collected data were analyzed using descriptive statistics, independent t-test, ANOVA, Pearson's correlation coefficient, and stepwise multiple regression. Results: A positive correlation was found for school adaptation with positive thinking and subjective happiness. Positive thinking, subjective happiness, and major satisfaction were all significant predictors of school adaptation. The model explained 30.2% of the valuables. Conclusion: The results of this study suggest that positive thinking should be considered when developing strategies to increase school adaptation in nursing students.

• The Korean Nurse
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• v.33 no.3
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• pp.79-91
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• 1994

The purpose of this study was to identify the meaningful nursing diagnosis in maternity unit and to suggest formally the basal data to the nursing service with scientific approach. The subject for this paper were 64 patients who admitted to Chung Ang University Hospital, Located in Seoul, from Mar. 10, to July 21, 1993. The results were as follows: 1. The number of nursing diagnosis from 64 patients were 892 and average number of nursing diagnosis per patient was 13.9. 2. Applying the division of nursing diagnosis to Roy's Adaptation Model, determined nursing diagnosis from the 64 patients were 621 (69.6%) in physiological adaptation mode and (Comfort, altered r/t), (Injury, potential for r/t), (Infection, potential for r/t), (Bowel elimination, altered patterns r/t), (Breathing pattern, ineffective r/t), (Nutrition, altered r/t less than body requirement) in order, and 139 (15.6%) in role function mode, (Self care deficit r/t), (Knowledge deficit r/t), (Mobility, impaired physical r/t) in order, 122 (13.7%) in interdependence adaptation mode, (Anxiety r/t), (Family Process, altered r/t) in order, 10(1.1%) in self concept adaptation mode, (Powerlessness r/t), (Grieving, dysfunctional r/t) in order. 3. Nursing diagnosis in maternity unit by the medical diagnosis, the average hospital dates were 3.8 days in normal delivery and majority of used nursing diagnosis, (Comfort, altered r/t) 64.6%, (Self care deficit r/t) 13.6% in order, and the average hospital dates were 9.6 days in cesarean section delivery and majority of used nursing diagnosis, (Comfort, altered r/t) 51.6%, (Self care deficit r/t) 15.2%, (Infection, potential for r/t) 9.9%, (Injury, potential "for r/t) 8.1%, (Anxiety r/t) 5.0%, (Mobility, impaired physical r/t) 3.3% in order, and the average hospital dates were 15.8days in preterm labor and majority of used nursing diagnosis, (Comfort, altered r/ t), (Anxiety r/t), (Injury, potential for r/t) in order, and the average short-term hospital dates were 2.5days, long-term hospital dates were 11.5days in gynecologic diseases and majority of used nursing diagnosis, (Comfort, altered r/t). (Self care deficit r/t), (Injury, potential for r/t), (Infection, potential for r/t) in order.