IT technology and traditional industries have been combined recently, resulting in IT convergence technology in various fields. Through convergence with the automobile, pedestrian detection technology, in particular, is used in the autonomous navigation control service of autonomous vehicles and also applied in various fields such as intelligent CCTV and robot recognition technology. For pedestrian detection, hierarchical classification and feature vector were used in early stage, and deep learning is under active progress. However, since deep learning for pedestrian detection is timeconsuming for processing a large volume of image data, it requires a lot of computing resources, and hence building such a system is very expensive. Therefore, in this paper we shall present a distributed deep learning platform which can easily build a cluster, and execute deep learning process in the distributed cloud environment, while achieving performance improvement in various ways. Our platform provides a convenient interface for easily and efficiently executing the deep learning process in a distributed environment by providing a multilayered system architecture. Our system builds and utilizes computing power in easy and efficient way by leveraging container technique, so-called OS-level virtualization, rather than traditional hypervisor-based virtualization. In our system, we improve the whole performance by exploiting both of data and parameter parallelisms at once and reduce the synchronization overhead by exploiting asynchronous communication for parameter updates. Also, we propose an efficient resource allocation scheme for parameter servers and slaves which can improve the performance from the experiment.
Wireless and mobile sensor network technologies in M2M (machine to machine) are rapidly applied to our real life. Thus, in near future, advanced wireless and mobile sensor network in M2M application will be major key factor of the future generation convergence network which is based on the state of the application. It is expected that smart machines will appear as new business service model with other machines. Most numerous researches within M2M sectors are carried out in intelligent vehicle sector integrated with IT technology. Intelligent vehicle section shows severe changes in position between vehicles and has numerous large scales of networks in its components; therefore, it is required to provide safety by exchanging information between vehicles equipped with wireless communication function via VANET (vehicular ad hoc network) and fixed apparatus at roadside regarding the status of road. In this paper, we proposed cluster authentication scheme that mutually authenticates between vehicles by composing vehicle movement as cluster configuration architecture. We have successfully included the establishment of secure channels, the detection of replay attacks, mutual cluster authentication, prevention of vehicle identity fabrication, and secure distribution of provisional session key.
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