• 분석과학
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• 제8권4호
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• pp.623-630
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• 1995

Silicone rubber-based sodium-selective membranes are developed for solid-state ion sensors. It was shown that the potetiometric performance of SR-based membranes are greatly dependent on the type of neutral carriers employed; among the three ionophores, N,N,N',N'-tetracyclohexyl-1,2-phenylenedioxydiacetamide (ETH 2120), bis[(12-crown-4)methyl]dodecylmethylmalonate (D12C4DMM) and monensin methyl ester (MME), examined, only ETH 2120 was compatible with the SR-based matrix. Addition of about 20 wt% plasticizer to the SR-based matrix provided the resulting membranes with potentiometric properties essentially equivalent to those of the corresponding PVC-based membranes. Owing to the strong adhesive strength of SR-based membranes, the CWEs coated \vith those membranes exhibited long lifetime with conventional electrode-like performance. Blending of PU into the SR matrix increased the lifetime of CWEs from two weeks to one month.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2008년도 춘계학술대회 논문집
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• pp.435-438
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• 2008

High temperature deformation of Ti-6Al-4V alloy with a lamellar colony microstructure was investigated by hot compression and torsion tests. The torsion and compression tests were carried out under a wide range of temperatures and strain rates with true strain up to 2 and 0.7, respectively. The processing maps were generated on the basis of compression and torsion test data and using the principles of dynamic materials modeling (DMM). The shapes of the strain-stress curves in alpha-beta region and processing maps obtained on the two different tests have been compared with a view to evaluate the effect of the microstructure evolution on the flow softening behavior of Ti-6Al-4V alloy with a lamellar colony microstructure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.