• The korean journal of orthodontics
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• v.50 no.6
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• pp.401-406
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• 2020

Objective: To investigate and compare the slot sizes and parallelism of metal injection molding (MIM) and computerized numerical control (CNC) brackets. Methods: The following four MIM bracket series with 0.022-inch (in) slots were selected for investigation: Di MIM mini Twin (Ortho Organizers), Mini Diamond Roth (Ormco), Gemini MBT (3M Unitek), and Formula R Roth (Tomy). The following four CNC bracket series with 0.022-in slots were selected for investigation: Econoline MBT (Adenta), Legend mini MBT (GC Orthodontics), Crown mini MBT (Adenta), and Evolve MBT (DB Orthodontics). The slot dimensions were measured using an optical microscope (XTCam-D310M; Mitutoyo) with a resolution of 1 ㎛. The results were statistically analyzed using one-way analysis of variance and the Tukey post-hoc test with a significance level of 0.05. Results: The results indicated that all the investigated slot sizes were oversized with respect to the manufacturers' specifications (0.022 in). Among the eight bracket series, the Di MIM bracket (MIM) was the most oversized by 10.4%, whereas the Evolve bracket (CNC) was the least oversized by 2.6%. The slots in seven of the bracket series had divergent walls instead of parallel ones. The Evolve bracket alone had parallel slot walls. Conclusions: Regardless of the manufacturing method, all the slot sizes of the brackets investigated in this study were significantly oversized; most of the slot walls were nonparallel, except for those of the Evolve bracket. This study could not establish that the CNC method was more accurate than the MIM method in manufacturing bracket slots.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.123-133
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• 1994

This paper presents an output arbitrator for input buffering ATM (Asynchronous Transfer Mode) switches using neural networks. To avoid blocking in ATM switches with blocking characteristics, it is required to buffer ATM cells in input buffer and to schedule them. The N$\times$N request matrix is divided into N/16 submatrices in order to get rid of internal blocking systematically in scheduling phase. The submatrices are grouped into N/4 groups, and the cells in each group are switched alternatively. As the window size of input buffer is increases, the number of input cells switched in a time slot approaches to N. The selection of nonblocking cells to be switched is done by neural network modules. N/4 neural network modules are operated simultaneously. Fast selection can be achieved by massive parallelism of neural networks. The neural networks have 4N neurons and 14N connection. The proposed method is implemented in C language, and the simulation result confirms the feasibility of this method.