• The Journal of Korea Robotics Society
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• 제17권2호
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• pp.118-123
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• 2022

We implemented a real-time cloud robotics application by offloading robot navigation engine over to 5G Mobile Edge Computing (MEC) sever. We also ran a fleet management system (FMS) in the server and controlled the movements of multiple robots at the same time. The mobile robots under the test were connected to the server through 5G SA network. Public 5G network, which is already commercialized, has been temporarily modified to support this validation by the network operator. Robot engines are containerized based on micro-service architecture and have been deployed using Kubernetes - a container orchestration tool. We successfully demonstrated that mobile robots are able to avoid obstacles in real-time when the engines are remotely running in 5G MEC server. Test results are compared with 5G Public Cloud and 4G (LTE) Public Cloud as well.

• IEMEK Journal of Embedded Systems and Applications
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• 제16권6호
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• pp.259-266
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• 2021

Edge Computing enables real-time big data processing by performing computing close to the physical location of the user or data source. However, in an edge computing environment, various situations that affect big data processing performance may occur depending on temporary service requirements or changes of physical resources in the field. In this paper, we proposed a BigCrawler system that dynamically configures the computing module and storage module according to the big data collection status and computing resource usage status in the edge computing environment. And the feature of big data processing workload according to the arrangement of computing module and storage module were analyzed.

• The Transactions of the Korea Information Processing Society
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• 제7권4호
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• pp.1062-1072
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• 2000

The applications developing in distributed object computing enviroments are faced with the difficulties for managing various lots of distributed objects. Also, because the most multimedia service, like video, audio, and so forth, must be satisfied itself with real-time constraints, the users also are feeling with necessary to apply real-time mechanisms to distributed multimedia services. The goal of this paper is to solve the problems for managing distributed objects, and to be easy to develop complex applications that can provide real-time services. To do this, we designed and implemented a real-time object group platform that can be placed between applications and CORBA. This platform is extended the existing object group model[13,14] added to the scheduler and timer object components for supporting real-time concept. We designed the components for platform by using James Rumbaugh object modeling technology that consists of object, function, and dynamic model. And then we described the detailed interfaces of the components by IDL, and implemented our real-time object group's platform using OrbixMT 22 which is the IONA Technologies' ORB product. Finally, we showed the execution procedures of the schduler object of each components in a real-time object group platform.

• Journal of KIISE:Computing Practices and Letters
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• 제16권7호
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• pp.759-767
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• 2010

RT-eCos 3.0 is a micro-sized embedded real-time kernel that has been developed based on the open source eCos 3.0 to support the basic task model of the well-known distributed real-time object model, TMO(Time-Triggered Message-triggered Object). In this paper, the design and implementation techniques of the RMMC(Real-time Multicast & Memory replication Channel) that is a standard distributed IPC model of the TMO is described based on the RT-eCos 3.0. And the support technique of the global time for using the same time in a distributed environment using the RMMC is also described. The developed global time based RMMC supports highly abstracted distributed IPC environment in a wide area distributed computing environment with the RT-eCos 3.0.

• Journal of the Korean Institute of Telematics and Electronics C
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• 제35C권4호
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• pp.38-49
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• 1998

A fossil power plant can be modeled by a lot of algebraic equations and differential equations. When we simulate a large, complicated fossil power plant by a computer such as workstation or PC, it takes much time until overall equations are completely calculated. Therefore, new processing systems which have high computing speed is ultimately needed for real-time or high-speed(faster than real-time) simulators. This paper presents an enhanced strategy in which high computing power can be provided by parallel processing of DSP processors with communication links. DSP system is designed for general purpose. Parallel DSP system can be easily expanded by just connecting new DSP modules to the system. General urpose DSP modules and a VME interface module was developed. New model and techniques for the task allocation are also presented which take into account the special characteristics of parallel I/O and computation. As a realistic cost function of task allocation, we suggested 'simulation period' which represents the period of simulation output intervals. Based on the development of parallel DSP system and realistic task allocation techniques, we cound achieve good efficiency of parallel processing and faster simulation speed than real-time.

• Journal of Internet Computing and Services
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• 제15권3호
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Journal of the Korea Computer Graphics Society
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• 제6권3호
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• pp.35-40
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• 2000

There are two problems in H.26X compression technique. One is compressing time in encoding process and the other is degradation of the decoded video quality due to high compression rate. For transferring moving pictures in real-time, it is required to adopt massively high compression. In this case, there are a lot of losses of an original video data and that results in degradation of quality. Especially degradation called by blocking artifact may be produced. The blocking artifact effect is produced by DCT-based coding techniques because they operate without considering correlation between pixels in block boundaries. So it represents discontinuity between adjacent blocks. This paper describes methods of quality compensation for H.26x decoded data and enhancing encoding speed for real-time operation. Our goal of the quality compensation is not to make the decoded video identical to a original video but to make it perceived better through human eyes. We suggest an algorithm that reduces block artifact and clears decoded video in decoder. To enhance encoding speed, we adopt new four-step search algorithm. As shown in the experimental result, the quality compensation provides better video quality because of reducing blocking artifact. And then new four-step search algorithm with $MMX^{TM}$ implementation improves encoding speed from 2.5 fps to 17 fps.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제58권1호
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• pp.63-68
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• 2009

As the complexity of the power system becomes higher, tests of the new devices, such as exciter and PCS(Power Conversion System) of the distributed generation sources, in the real operating condition are more important. However tests of the unverified devices in the real power system may cause hazardous malfunction of the system. In order to avoid this problem, power devices may be tested with the real-time simulators instead of the real power system. This paper presents an real-time multi machine power system simulator using PCs(Personal Computer) and Fast Ethernet. Developed real-time simulator performs the electro-mechanical dynamic simulation of multi-machine power system by the network distributed computing technique. Because the simulator consists of usual PCs and Fast Ethernet, it is possible to make up a simulation system very cheaper than the conventional real-time simulator which consists of dedicated expensive hardware devices. The performance of the developed simulator is tested and verified with the scaled model excitation system. The test which adjust the control parameters of the exciter is performed with the well-known New England 10 generator 39 bus sample power system.

• Journal of Korea Multimedia Society
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• 제19권2호
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• pp.300-307
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• 2016

Cloud computing that provide a elastic computing service is more complex compared to the existing computing systems. Accordingly, it has become increasingly important to maintain the stability and reliability of the computing system. And troubleshooting and real-time monitoring to address these challenges must be performed essentially. For these goals, the handling of the log data is needed, but this task in cloud computing environment may be more difficult compared to the traditional logging system. In addition, there are another challenges in order to have the admissibility of the collected log data in court. In this paper, we design secure logging service that provides the management and reliability of log data in a cloud computing environment and then analyze the proposed system.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.