• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.372-375
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• 2008

Drivers need to process dynamic stimulus in real - time with full attention from Telematics environment. However, as the information technology revolution brings more and more data into vehicles, all of it competing for the drivers' attention, the development of automated assistance for driver information processing becomes increasingly import ant. There for e, drivers' workload is very essential factor for safety driving in Telematics environment. In this paper, we have discussed driver distraction caused by driver conversation while driving and proposed voice activity detection algorithm for measuring driver workload. Finally, we show how voice activity detection system works for measuring driver workload.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.3
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• pp.147-160
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• 2010

The system virtualization shows interest in the consolidation of servers for the efficient utilization of system resources. There are many various researches to utilize a server machine more efficiently through the system virtualization technique, and improve performance of the virtualization software. These researches have studied with the activity to control the resource allocation of virtual machines dynamically focused on CPU, or to manage resources in the cross-machine using the migration. However, the researches of the memory management have been wholly lacking. In this respect, the use of memory is limited to allocate the memory statically to virtual machine in server consolidation. Unfortunately, the static allocation of the memory causes a great quantity of the idle memory and decreases the memory utilization. The underutilization of the memory makes other side effects such as the load of other system resources or the performance degradation of services in virtual machines. In this paper, we suggest the dynamic allocation of the memory in Xen to control the memory allocation of virtual machines for the utilization without the performance degradation. Using AR model for the prediction of the memory usage and ACO (Ant Colony Optimization) algorithm for optimizing the memory utilization, the system operates more virtual machines without the performance degradation of servers. Accordingly, we have obtained 1.4 times better utilization than the static allocation.