• Journal of the Korean Mathematical Society
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• v.37 no.5
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• pp.687-705
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• 2000

The main purpose of this paper is to study differentiable one-parameter groups and cosine families of operators acting on white noise functions and their associated infinitesimal generators. In particular, we prove the heredity of differentiable one-parameter group and cosine family of operators under the second quantization of the Cuchy problems for the first and second or der differential equations.

• Journal of Oral Medicine and Pain
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• v.18 no.1
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• pp.31-43
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• 1993

To evaluate the correlationship between sign/symptoms of craniomandibular disorder(CMD) and possible contributing occlusal factors, the author analyzed craniomandibular index(CMI), clinical dysfunction index(CDI) and occlusal index(Oi) obtained from 88 subjects (32 males, 56 females, mean age 28.7) consisted of 49 CMD patients(15 males, 34 females, mean age 28.7) and 39 non-CMD patients (17 males, 22 females, mean age 24.5). The obtained results were as follows : 1. There was very high significant correlation bebween CMI and CDI in total subjects (r=0.83, p<0.01) 2. There was very low correlation between working-side interference and TMJ noise which was observed as a statistically significant value(p<0.05). And also there ws very low correlation between nonworking side interference and muscle tenderness, CDI, dysfunction index(DI), palpation index(PI) which were observed as statistically significant value (p<0.01) 3. There was very low correlation between as ymmetry of tetruded contact position(RCP)-intercuspal position(ICP) slide and muscle tenderness, TMJ tenderness, TMJ noise, CMI, DI, PI which were observed as statistically significant values(p<0.05) 4. In general there was low correlation between Oi and muscle tenderness, CDI, CMI, DI, PI which were observed as statistically significant values (p,0.05). But Oi had a relatively higher correlation with muscle tenderness, PI than whith CDI, DI.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.456-464
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• 1997

We describe a random prcess model that prvides sets of patterms whth prcisely contrlolled within-class varia-bility and between-class distinctions.We used these pattems in a simulation study wity the back-propagation netwoek to chracterize its perfotmance as we varied the process-controlling parameters,the statistical differences between the processes,and the random noise on the patterns.Our results indicated that grneralized statistical difference between the processes genrating the patterns provided a good predictor of the difficulty of the clssi-fication problem. Also we analyzed the performance of the Bayes classifier whith the maximum-likeihood cri-terion and we compared the performance of the neural network to that of the Bayes classifier.We found that the performance of neural network was intermediate between that of the simulated and theoretical Bayes classifier.