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조용현,이동현,김선희,조용현 대한마취통증의학회 2017 Anesthesia and pain medicine Vol.12 No.1
Osteonecrosis of the femoral head (ONFH) can cause femoral head depression and cortical discontinuity. Treatment for ONFH remains challenging. We performed botulinum toxin type A injection to psoas major muscle in five patients with radiological femoral head collapse (Association Research Circulation Osseus classification stage III) who were non-responsive after two years of conservative treatment (tramadol 200 mg/day, mefenamic acid 1,000 mg/day). At two weeks after the procedure, their mean hip pain was decreased from 88 ± 0.4/100 mm to 22 ± 0.4/100 mm based on visual analogue scale (VAS). The pain was maintained at a minimum of 20/100 mm and a maximum of 30/100 mm in VAS for at least six weeks after the procedure. These values were mean ± SD. These patients were followed-up for 6 months. There was no exacerbation of pain from repeated (three times) botulinum toxin type A injection to the psoas major muscle.
확률적 근사법과 후형질과 알고리즘을 이용한 다층 신경망의 학습성능 개선
조용현,최흥문 대한전자공학회 1994 전자공학회논문지-B Vol.b31 No.4
This paper proposes an efficient method for improving the training performance of the neural network by using a hybrid of a stochastic approximation and a backpropagation algorithm. The proposed method improves the performance of the training by appliying a global optimization method which is a hybrid of a stochastic approximation and a backpropagation algorithm. The approximate initial point for a stochastic approximation and a backpropagation algorihtm. The approximate initial point for fast global optimization is estimated first by applying the stochastic approximation, and then the backpropagation algorithm, which is the fast gradient descent method, is applied for a high speed global optimization. And further speed-up of training is made possible by adjusting the training parameters of each of the output and the hidden layer adaptively to the standard deviation of the neuron output of each layer. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to that of the backpropagation, the Baba's MROM, and the Sun's method with randomized initial point settings. The results of adaptive adjusting of the training parameters show that the proposed method further improves the convergence speed about 20% in training.