• The Korean Journal of Pain
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• v.20 no.2
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• pp.181-185
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• 2007

Background: The first pain clinic opened in korea in 1973 at Yonsei University Hospital, however, since then the number of pain clinics has gradually increased, as has the number of patients visiting them. This increase in patient has caused concerns about the way in which pain is managed, therefore, we conducted a retrospective review of data according to the sex, age and disease in an attept to aid us in planning for the future of our pain clinic. Methods: We analyzed 1,282 new patients who had visited our pain clinic and 828 inpatients who were admitted to our pain clinic between March 2006 and February 2007. Results: The most frequent age group was in the sixties in outpatient and in the seventies in inpatient. In addition, the incidence of disease in new patients and inpatients was as follows: in new patients, lumbar herniated intervertebral disc 16.5%, hyperhidrosis 12.3%, cervical disc disorder 10.5%, acute herpes zoster 8.2%, postherpetic neuralgia 7.9%, and trigeminal neuralgia 7.0%; in admitted patients, acute herpes zoster 17.6%, trigeminal neuralgia 15.6%, lumbar herniated intervertebral disc 13.0%, postherpetic neuralgia 11.2%, hyperhidrosis 9.8%, and complex regional pain syndrome 7.0%. Conclusions: The patients visiting our pain clinic have presented with a wide variety of diseases. This improved care reflects an effort to expand our fields not only to the management of outpatients but also inpatients, as well as to the treatment of new fields of disease. In the future, We need to manage various pain patients not only in outpatients but also in inpatients to expand our field even through pain clinic is rapidly growing in Korea.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.1
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• pp.149-155
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• 2006

To prevent human body injury from oxidative stress, antioxidants are very important and many research about antioxidants are generally being conducted. Hydrogen peroxide($H_2O_2$) that is one of vitality oxygen species has been seen that cause various diseases, DNA damage and gene change. The purpose of this study was to examine the inhibition effect of Zizania latifolia Rhizoma on apoptosis induced by $H_2O_2$ in Neuro2A cell. Neuro2A cells were cultivated in RPMI(GibcoBRL) with 5% FBS and treated with $H_2O_2$ and Zizania latifolia Rhizoma. We measured the cell viability and analyzed DNA fragmentation. Activity of PARP, Cytochrome C, caspase-9, caspase-3, p53, p21, Bax and Bcl-2 in the cell was examined dy using western blot. The results obtained were as Follows: The cell viability in Zizania latifolia Rhizoma treatment (60ug/ml<) decreased significantly compared with that of none treatment. (P<0.001) Zizania latifolia Rhizoma increased cell viability about twice as much as that being injury by $H_2O_2$. (Zizania Latifolia Rhizoma 20ug/ml, $H_2O_2$ 200uM, P<0.001) DNA fragmentation developed by $H_2O_2$, but was not developed in Zizania latifolia Rhizoma treatment. PARP, Cytochrome C, caspase-9 and caspase-3 activated all by $H_2O_2$ but were not activated in Zizania latifolia Rhizoma treatment. P53, P2l and Bax activated dy $H_2O_2$, and Bcl-2 got into inactivation. But the opposite results appeared in Zizania latifolia Rhizoma treatment. In conclusion, these results suggest that Zizania latifolia Rhizoma inhibit the development of DNA fragmentation and apoptosis by $H_2O_2$ and the antioxidant action of Zizania latifolia Rhizoma is effective. More researches about effect of Zizania latifolia Rhizoma are considered to need.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.259-262
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian menbership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.387-390
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• 1998

In this paper, we suggested a neuro-fuzzy learning algorithm for tuning fuzzy rules, in which a fuzzy system model is of additive-type. Using the method, it is possible to reduce the computation size, since performing the fuzzy inference and tuning the fuzzy rules of each fuzzy subsystem model are independent. Moreover, the efficiency of suggested method is shown by means of a numerical example.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.444-450
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• 1996

A digital hardware architecture for artificial neural network with learning capability is described in this paper. It is a modified hardware architecture known as HANNIBAL(Hardware Architecture for Neural Networks Implementing Back propagation Algorithm Learning). For implementing an efficient neural network hardware, we analyzed various type of multiplier which is major function block of neuro-processor cell. With this result, we design a efficient digital neural network hardware using serial/parallel multiplier, and test the operation. We also analyze the hardware efficiency with logic level simulation. (author). refs., figs., tabs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.845-848
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• 1993

This paper proposes a design method of fuzzy phase-lead compensator and its self-learning by neural network. The main feature of the fuzzy phase-lead compensator is to have parameters for effectively compensating phase characteristics of control systems. An important theorem which is related to phase-lead compensation is derived by introducing concept of frequency characteristics. We propose a design procedure of fuzzy phase-lead compensators for linear controlled objects. Furthermore, we realize a neuro-fuzzy compensator for unknown or nonlinear controlled objects by using Widrow-Hoff learning rule.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.571-576
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• 2002

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.

• The Korean Journal of Pain
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• v.7 no.2
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• pp.217-221
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• 1994

Percutaneous neurolysis of upper thoracic sympathetic ganglion was performed in 40 patients by simultaneously injecting 3 ml of pure alcohol into the T2 and T3 levels after 3 ml of injection of local anesthetic agent on the same sites. Using a skin temperature probe, finger tip temperatures were measured on the index finger ipsilateral to the nerve block before block, 15 and 30 minutes after test block, and 30 minutes after alcohol block. Alcohol block was performed immediately after 30 minutes test block. Finger tip temperatures obtained at 30 minutes post alcohol block and test block and the differences in the temperatures measured before and 30 minutes after alcohol block were shown to be statistically important as potential indicators for prediciting long term outcome of therapy for palmar hyperhidrosis using this technique. These results demonstrate that the palmar temperature monitoring method is sufficiently sensitive to predict the outcome of nerve block during and after thoracic sympathetic ganglion block.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.1
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• pp.77-83
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• 2003

Autonomous mobile robots based on the Web have been already used in public places such as museums. There are many kinds of problems to be solved because of the limitation of Web and the dynamically changing environment. We present a methodology for intelligent mobile robot that demonstrates a certain degree of autonomy in navigation applications. In this paper, we focus on a mobile robot navigator equipped with neuro-fuzzy controller which perceives the environment, make decisions, and take actions. The neuro-fuzzy controller equipped with collision avoidance behavior and target trace behavior enables the mobile robot to navigate in dynamic environment from the start location to goal location. Most telerobotics system workable on the Web have used standard Internet techniques such as HTTP, CGI and Scripting languages. However, for mobile robot navigations, these tools have significant limitations. In our study, C# and ASP.NET are used for both the client and the server side programs because of their interactivity and quick responsibility. Two kinds of simulations are performed to verify our proposed method. Our approach is verified through computer simulations of collision avoidance and target trace.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2313-2321
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• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.