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Consideration on evolution of services by rapid development of information communication technology. July 23th, 2014 Keiji YAMADA NEC Laboratories Singapore. 100 G. 10 G. 1 G. 100 M. 10 M. 1 M. 100 K. 10 K. 1 K. 100. 10. Rapid development of infoComm technologies.
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Consideration on evolution of services by rapid development of information communication technology July 23th, 2014 Keiji YAMADA NEC Laboratories Singapore
100G 10G 1G 100M 10M 1M 100K 10K 1K 100 10 Rapid development of infoComm technologies • Much faster than train technology: 29km/h in 1872 ⇒ 552km/h(Linear motor car) in 1999 (Only 19 times) Super computer(FLOPS) 10PFLOPS 1Tbit/in2 1PFLOPS 100Gbit/in2 Hard Disk(bit/in2)bit surface density Number of transistorsin a microcomputer 1GTr. 10Gbit/in2 1TFLOPS 1Gbps 100Mbit/in2 1GFLOPS 24Mbps 10MTr. 1Mbit/in2 Operation Frequency(Hz) 1MTr. 1GHz 1MFLOPS 100KTr. 1.5Mbps 100MHz 10Kbit/in2 10MHz Access bandwidth(bps) 1000Tr. 1000Hz 128kbps 14.4kbps 1970 1980 1990 2000 2010
Technology trend for serviceevolution • Convergence of real and cyber worlds (From Cloud to Crowd) • Integration of heterogeneous systems (Control chaotic huge system) Technology development Change of social structures Integration of heterogeneous systems Fusion of artificial systems and social systems Symbiotic technology Social dynamics Change of social values Non-schematic DB Heterogeneousartificial systems Change of social behaviors Intellectual productivity Distributedsystem Intention communication Intelligence, mind(Co-Creation) Environments(Internet of things) Terminal devices(Fixed and mobile) Convergence of real and cyber worlds
Community service model • Entanglement of 4 layered networks • Individual services were designed on one of networks. • Macro view: Individual services will be designed and provided to people on the combination of the four layers. • Micro view: They will be customized to individual people.
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Recommendation generation using fast simulator • Real-time micro-simulation of multi-modal transit at passenger level • Creation of recommendation to increase user’s satisfaction • Based on user choice models and stress models • Cost preference, travel time preference, and fatigue Walk, car, bus, train, taxi, …. stress, fatigue, and satisfaction effected by congestion, delay, and cumulative travel time Demo: Multimodal travel in Sydney (Australia) using open travel data
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