• The KIPS Transactions:PartA
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• v.15A no.6
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• pp.309-316
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• 2008

The mission-critical engagement control computer for air defense has to maintain its operation without any fault for a long mission time. The mission performed by large-scale and complex embedded software is extremely critical in terms of dependability and safety of computer system, and it is very important that engagement control computer has high reliability. The engagement control computer was implemented using four processors. The distributed computer composed of four processors quarantees the dependability and safety, and ASR fault-tolerant technique applied to each processor guarantees the reliability. In this paper, the mechanism and performance of ASR fault-tolerant technique are analysed. And MTBF, reliability, availability, and cost-effectiveness for ASR, DMR and TMR techniques applied to the engagement control computer are analysed. The mission-critical engagement control computer using software-based ASR fault-tolerant technique provides high reliability and fast recovery time at a low cost. The mission reliability of the engagement control computer using ASR technique in 4 processors board is almost same the reliability of the computer using TMR technique in 6 processors board. ASR technique is most suitable to the mission-critical engagement control computer.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.148-155
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• 2002

Multithreaded models improve the efficiency of parallel systems by combining inner parallelism, asynchronous data availability and the locality of von Neumann model. This model executes thread code which is generated by compiler and of which quality is given by the method of generation. But multithreaded models have the demerit that execution model is restricted to a specific platform. On the contrary, Java has the platform independency, so if we can translate from threads code to Java bytecode, we can use the advantages of multithreaded models in many platforms. Java executes Java bytecode which is intermediate language format for Java virtual machine. Java bytecode plays a role of an intermediate language in translator and Java virtual machine work as back-end in translator. But, Java bytecode which is translated from multithreaded models have the demerit that it is not secure. This paper, multhithread code whose feature of platform independent can execute in java virtual machine. We design and implement translator which translate from thread code of multithreaded code to Java bytecode and which check secure problems from Java bytecode.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.81-90
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• 2013

During the past decade, many changes and attempts have been tried and are continued developing new technologies in the computing area. The brick wall in computing area, especially power wall, changes computing paradigm from computing hardwares including processor and system architecture to programming environment and application usage. The high performance computing (HPC) area, especially, has been experienced catastrophic changes, and it is now considered as a key to the national competitiveness. In the late 2000's, many leading countries rushed to develop Exascale supercomputing systems, and as a results tens of PetaFLOPS system are prevalent now. In Korea, ICT is well developed and Korea is considered as a one of leading countries in the world, but not for supercomputing area. In this paper, we describe architecture design of MAHA supercomputing system which is aimed to develop 300 TeraFLOPS system for bio-informatics applications like human genome analysis and protein-protein docking. MAHA supercomputing system is consists of four major parts - computing hardware, file system, system software and bio-applications. MAHA supercomputing system is designed to utilize heterogeneous computing accelerators (co-processors like GPGPUs and MICs) to get more performance/$, performance/area, and performance/power. To provide high speed data movement and large capacity, MAHA file system is designed to have asymmetric cluster architecture, and consists of metadata server, data server, and client file system on top of SSD and MAID storage servers. MAHA system softwares are designed to provide user-friendliness and easy-to-use based on integrated system management component - like Bio Workflow management, Integrated Cluster management and Heterogeneous Resource management. MAHA supercomputing system was first installed in Dec., 2011. The theoretical performance of MAHA system was 50 TeraFLOPS and measured performance of 30.3 TeraFLOPS with 32 computing nodes. MAHA system will be upgraded to have 100 TeraFLOPS performance at Jan., 2013.

• The Journal of Society for e-Business Studies
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• v.20 no.1
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• pp.183-199
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• 2015

Due to the Four Major Rivers Restoration Project, Nakdong River Estuary Barrage's designed flood quantity has been largely increased, and this has caused to construct several drainage gates at the right side of Eulsukdo island to secure the safety of downstream river area. For successful functioning of Nakdong River Estuary Barrage, such as flood control, disaster prevention, and the securing of sufficient water capacity, drainage gates at the both sides of island have to operate systematically and reliably. To manage this under restricted personnel and resources, we have implemented the IOS (Integrated Operation System) by integrating previous facilities and resources via information and communication technologies. The IOS has been designed to have higher availability and fault tolerance to function continuously even with the partial system's failure under the emergency situation like flood. Operators can use the system easily and acknowledge alarms of facilities through its IWS (Integrated Warning System) earlier. Preparing for Integrated Water Resources Management and Smart Water Grid, the architecture of IOS conformed to open system standards which will be helpful to link with the other systems easily.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.41-49
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• 2017

The 'NIST Framework and Road Map for Smart Grid Interoperability Standards' proposes an architecture framework to secure the direction of development and standard interoperability of smart grid and provides a list of identified standard, standard cyber security strategies, and certification framework. In particular, SEP 2.0 and OpenADR 2.0 are the examples. SEP 2.0 and OpenADR 2.0 can functionally link HEMS and Smart Grid, but interoperability standards between the two protocols are not planned in above document. The OpenADR Alliance also announced that work is underway to define mapping tables for interoperability between OpenADR 2.0 and SEP 2.0, but no information is yet available. Therefore, In this paper, in developing energy efficiency improvement HEMS, we propose a mapping model that supports syntactic and semantic founded interoperability between SEP 2.0 and OpenADR 2.0b for ICT grid convergence based on the standard specification document of each protocol and confirmed through an example of the semantic mapping function based on the demand response service scenario.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.