• Advances in Traditional Medicine
• /
• 제2권2호
• /
• pp.101-105
• /
• 2002

A battery of neuropharmacological experiments showed the hot water extract of Ocimum sanctum Linn. (Labiateae) had a depressant effect on the central nervous system (CNS), but the aqueous extract showed no effect on it. The hot water extract reduced the spontaneous locomotor activity, exploratory head dipping, propulsive locomotion and exploratory ambulation as well as prolonged the pentobarbital induced sleeping time. The depressant effect starts from 60 minutes after the drug administration and continued to 180 minutes. The drug may exert central depressant effect by interfering with the function of the cortex.

• 소성∙가공
• /
• 제24권5호
• /
• pp.368-374
• /
• 2015

Hydroforming of a non-axisymmetric thin-walled tubular component with variable cross sections was analyzed. In order to solve the sealing problem which occurred due to the thin and non-axisymmetric shape, the use of a lead patch on the punch, which had been successful in hydroforming of thin tubes, was evaluated. A lead patch was attached to the punch to solve the sealing problem, which was caused by the stress gradient in the non-axisymmetric shape. FEM and experiments were also performed to analyze these sealing problems associated with the punch shape and non-axisymmetric shape. Finally, the lead patch was attached at tube surface where intensive local strain concentration would occur to enhance the hydroformability. These methods were successfully used to fabricate non-axisymmetric thin-walled tubular component with variable cross sections that had previously failed during traditional hydroforming.

• Journal of the Korean Data and Information Science Society
• /
• 제28권3호
• /
• pp.685-695
• /
• 2017

The traditional mixture of experts (ME) modeled the gate network using a certain parametric function. However, if the assumed parametric function does not properly reflect the true nature, the prediction strength of ME would become weak. For example, the parametric ME often uses logistic or multinomial logistic models for the network model. However, this could be very misleading if the true nature of the data is quite different from those models. Although, in this case, we may develop more flexible parametric models by extending the model at hand, we will never be free from such misspecification problems. In order to alleviate such weakness of the parametric ME, we propose to use the semi-parametric mixture of experts (SME) in which the gate network is estimated in a non-parametrical way. Based on this, we compared the performance of the SME with those of ME and neural networks via several simulation experiments and real data examples.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
• /
• pp.1014-1017
• /
• 1988

Direct-drive robots have excellent features including no backlash, small friction, and high mechanical stiffness. However, dynamic coupling among joints as well as nonlinear effects become more prominent than traditional robots with reducers. Another critical issue is that the robot becomes more sensitive to the change of load. In this paper, we develop a simple current feedback scheme for reducing the influence of dynamic coupling and load sensitivity on the direct-drive robots. The method is implemented on a 2 d.o.f. planar direct-drive robot. Then the validity of the method is demonstrated through experiments.

• 대한기계학회논문집A
• /
• 제26권12호
• /
• pp.2556-2564
• /
• 2002

The design of airfoil had practiced by repeat tests in its first stage, though an airfoil has as been designed based on simulations according to techniques of computational fluid dynamics. Here, using of traditional optimization is unsuitable because a state of flux is hypersensitive to the shape of airfoil. Therefore the paper optimized the shape of airfoil in transonic region using a genetic algorithm (GA). Response surfaces are based on back propagation neural network (BPN) and regression model. Training data of BPN and regression model were obtained by computational fluid dynamic analysis using CFD-ACE, and each analysis has been designed by design of experiments.

• 한국기계가공학회지
• /
• 제2권3호
• /
• pp.5-12
• /
• 2003

In order to reduce the lead-time and cost, many useful methods have been applied to rapid prototyping (RP) in recent years. But cutting process is still considered as one of the effective RP methods that have been developed and currently available in the industry. It also offers practical advantages in aspects of precision and versatility. However, traditional 3-axis NC machining has some inherent limitations such as the restriction of tool accessibility and the complex setup. In this work, a new rapid prototyping system with high speed 5-axis machining of plastics has been developed to overcome those limitations. And cutting experiments were conducted to determine the design factors of the system and the cutting conditions of plastics. The architecture of developed system is described in detail and the successful application examples are presented.

• 한국기계가공학회지
• /
• 제14권4호
• /
• pp.1-7
• /
• 2015

In the present study, copper micromolds with a microhole array were precisely manufactured by a synchrotron LIGA process. Like in the traditional LIGA process, a deep X-ray lithography based on a synchrotron radiation was employed as the first manufacturing step. Due to the excellent optical performance of the synchrotron X-ray used, cylindrical micropillar arrays with high aspect ratio could be efficiently obtained. The fabricated microfeatures were then used as a master of the subsequent copper electroforming process, thereby resulting in copper micromolds with a microhole array. Thermoplastic hot embossing experiments with the copper micromolds were carried out for imprinting cylindrical microfeatures onto a polystyrene sheet. Through the hot embossing, the effect of embossing temperature and usefulness of the present manufacturing method could be verified.

• 한국정밀공학회지
• /
• 제22권8호
• /
• pp.34-41
• /
• 2005

Generally, it is not easy to get a suitable controller for multi variable systems. If the modeling equation of the system can be found, it is possible to get LQR control as an optimal solution. This paper suggests an LQR learning method to design LQR controller without the modeling equation. The proposed algorithm uses the same cost function with error and input energy as LQR is used, and the LQR controller is trained to reduce the function. In this training process, the Jacobian matrix that informs the converging direction of the controller Is used. Jacobian means the relationship of output variations for input variations and can be approximately found by the simple experiments. In the simulations of a hydrofoil catamaran with multi variables, it can be confirmed that the training of LQR controller is possible by using the approximate Jacobian matrix instead of the modeling equation and this controller is not worse than the traditional LQR controller.

• 품질경영학회지
• /
• 제34권3호
• /
• pp.19-30
• /
• 2006

For highly reliable products, it is difficult to assess the lifetime of the products with traditional life tests. Accordingly, a recent approach is to observe the performance degradation of product during the test rather than regular failure time. This study compares performances of three methods(i.e. the approximation, analytical and numerical methods) to estimate the parameters and quantiles of the lifetime when the time-to-failure distribution follows Weibull and lognormal distributions under a random coefficient degradation rate model. Numerical experiments are also conducted to investigate the effects of model error such as measurements in a random coefficient model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권6호
• /
• pp.2310-2332
• /
• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.