• 대한생식의학회:학술대회논문집
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• 대한불임학회 2001년도 제41차 추계학술대회
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• pp.109.2-109.2
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• 2001

• 대한수학회지
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• 제39권6호
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• pp.953-961
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• 2002

Generalized Ricci-recurrent trans-Sasakian manifolds are studied. Among others, it is proved that a generalized Ricci-recurrent cosymplectic manifold is always recurrent Generalized Ricci-recurrent trans-Sasakian manifolds of dimension $\geq$ 5 are locally classified. It is also proved that if M is one of Sasakian, $\alpha$-Sasakian, Kenmotsu or $\beta$-Kenmotsu manifolds, which is gener-alized Ricci-recurrent with cyclic Ricci tensor and non-zero A (ξ) everywhere; then M is an Einstein manifold.

• Journal of Communications and Networks
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• 제5권1호
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• pp.7-18
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• 2003

In this paper, a decision feedback recurrent neural network equalizer and a modified real time recurrent learning algorithm are proposed, and an adaptive adjusting of the learning step is also brought forward. Then, a complex case is considered. A decision feedback complex recurrent neural network equalizer and a modified complex real time recurrent learning algorithm are proposed. Moreover, weights of decision feedback recurrent neural network equalizer under burst-interference conditions are analyzed, and two anti-burst-interference algorithms to prevent equalizer from out of working are presented, which are applied to both real and complex cases. The performance of the recurrent neural network equalizer is analyzed based on numerical results.

• 한국산업융합학회 논문집
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• 제27권1호
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• pp.183-193
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• 2024

Recurrent Neural Network technology that learns past patterns and predicts future patterns using technology for recognizing and classifying objects is being applied to various industries, economies, and languages. And research for practical use is making a lot of progress. However, research on the application of Recurrent Neural Networks for evaluating and predicting the safety of mechanical structures is insufficient. Accurate detection of external load applied to the outside is required to evaluate the safety of mechanical structures. Learning of Recurrent Neural Networks for this requires a large amount of load data. This study applied the Gated Recurrent Unit technique to examine the possibility of load learning and investigated the possibility of applying a stacked Auto Encoder as a way to secure load data. In addition, the usefulness of learning mechanical loads was analyzed with the Gated Recurrent Unit technique, and the basic setting of related functions and parameters was proposed to secure accuracy in the recognition and prediction of loads.

• 대한교통학회지
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• 제25권6호
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• pp.99-109
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• 2007

지금까지 고속도로 상에서 반복 및 비반복정체에 대한 연구가 많이 진행되어 왔지만 이들이 독립적으로 발생했을 때 교통정체의 영향에 대한 연구는 활발히 진행되지 못하고 있는 실정이다. 가장 큰 이유는 반복 및 비반복정체시 교통상황에 자료수집이 부족했으며, 뿐만 아니라 이들의 영향을 독립적/정량적으로 추정할 수 있는 분석도구의 효과적인 사용이 미비했기 때문이다. 본 연구에서는 미국 고속도로 구간의 교통정체시 수집한 교통자료를 바탕으로 시뮬레이션을 이용한 반복 및 비반복정체의 독립적인 영향을 분석하는 방법을 제시하였다. 분석결과로서 대상구간에 따라 비반복정체가 반복정체보다 고속도로기능을 크게 악화시키는 것으로 나타났다. 더불어 교통정체시 실시간 교통정보제공을 위한 기존 ITS기술들의 현장평가결과로서 안정적인 교통정체에서는 정보의 정확성은 높으나, 정체가 시작되거나 해소되는 시간대 또는 비반복정체시 제공되는 교통정보의 정확성은 낮은 것으로 나타났다. 결론적으로 본 연구에서는 비반복정체의 중요성과 더불어 제시하고 있는 반복 및 비반복정체의 영향분석 방법론은 향후 대상 고속도로의 정체해소를 위한 개선사업의 투자우선순위를 판단할 수 있는 기초연구로서 사용될 수 있을 것으로 기대된다.

• 한국동물생명공학회지
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• 제34권1호
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• pp.30-34
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• 2019

Milk fever is a metabolic disease with manifestation of clinical signs due to hypocalcemia, which usually occurs within 48-72 h after delivery. However, even after a successful treatment of milk fever, recurrence of milk fever may occur, and studies on recurrent milk fever are still lacking. Accordingly, the present study was conducted for the purpose of identifying the characteristics of recurrent milk fever according to farm, season, parity, and dystocia that can cause physiological changes in the mother during peri- and postpartum periods. The analysis results showed that the incidence rate of initial and recurrent milk fever according to breeding farm was 5.7%-14.1% and 3.1%-7.2%, respectively, demonstrating a positive correlation between the initial and recurrent milk fever (r = 0.613, p < 0.01). With respect to season, the incidence rate of initial and recurrent milk fever during summer was 12.3% and 7.5%, respectively, which were significantly higher than that of other seasons (p < 0.05). In addition, the recurrence rate, the ratio of recurrence relative to initial milk fever, was highest during summer with 62.7%. Regarding parity, the incidence rate of initial and recurrent milk fever in 3rd parity was 11.1% and 5.8%, respectively, which was significantly higher than in 1st and 2nd parity (p < 0.05). Furthermore, the recurrence rate in 4th parity was 64.1%, showing a pattern of increase in incidence rate with increase in parity. Finally, there were no differences in the incidence rate of initial and recurrent milk fever according to eutocia and dystocia. The findings indicated that the incidence rate of initial milk fever should be reduced to effectively prevent the recurrent milk fever, while animals with 3rd parity or higher should be expected to occur high rate of recurrent milk fever, especially during summer, and the necessary preparations should be made for intensive treatment of such individuals.

• 제어로봇시스템학회논문지
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• 제9권11호
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• pp.917-924
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• 2003

Recurrent neural networks have been successfully applied to communications channel equalization. Major disadvantages of gradient-based learning algorithms commonly employed to train recurrent neural networks are slow convergence rates and long training sequences required for satisfactory performance. In a high-speed communications system, fast convergence speed and short training symbols are essential. We propose decision feedback equalizers using a recurrent neural network trained with Kalman filtering algorithms. The main features of the proposed recurrent neural equalizers, utilizing extended Kalman filter (EKF) and unscented Kalman filter (UKF), are fast convergence rates and good performance using relatively short training symbols. Experimental results for two time-varying channels are presented to evaluate the performance of the proposed approaches over a conventional recurrent neural equalizer.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.390-393
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• 2007

In this paper, applications of self recurrent neuro-fuzzy controller to stabilization of nonlinear system are considered. The architecture of self recurrent neuro-fuzzy controller is fix layer, and the hidden layer is comprised of self recurrent architecture. Also, generalized dynamic error-backpropagation algorithm is used for the learning of the self recurrent neuro-fuzzy controller. To demonstrate the efficiency of the self recurrent neuro-fuzzy control algorithm presented in this study, a self recurrent neuro-fuzzy controller was designed and then a comparative analysis was made with LQR controller through an simulation.

• 대한후두음성언어의학회지
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• 제8권2호
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• pp.232-234
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• 1997

After the first report of mitral stenosis as a cause of recurrent laryngeal nerve palsy by Ortner in 1897, many authors have described that some kinds of cardiovascular disease might contribute to the development of recurrent laryngeal nerve palsy. The estimated rate of aortic aneurysm related with recurrent laryngeal nerve palsy is about 5%. Aortic aneurysm is classified into 3 types according to the involving segment of aorta in which aneurysms develop, and the first class-aneurysm in ascending aorta and aortic arch-is known to be the only type related to recurrent laryngeal nerve palsy. Recently we experienced two cases of recurrent laryngeal nerve palsy each of which had aneurysm on aortic arch as a major contributing factor. We report these cases with brief review of the literature.

• ETRI Journal
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• 제39권6호
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• pp.832-840
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• 2017

Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception-based spatial and spectral-domain noise-reduced harmonic features are extracted from multichannel audio and used as high-resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short-term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.