• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2964-2985
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• 2019

Global data center IP traffic is expected to reach 20.6 zettabytes (ZB) by the end of 2021. Intra-data center networks (Intra-DCN) will account for 71.5% of the data center traffic flow and will be the largest portion of the traffic. The understanding of traffic distribution in IntraDCN is still sketchy. It causes significant amount of bandwidth to go unutilized, and creates avoidable choke points. Conventional transport protocols such as Optical Packet Switching (OPS) and Optical Burst Switching (OBS) allow a one-sided view of the traffic flow in the network. This therefore causes disjointed and uncoordinated decision-making at each node. For effective resource planning, there is the need to consider joining the distributed with centralized management which anticipates the system's needs and regulates the entire network. Methods derived from Kalman filters have proved effective in planning road networks. Considering the network available bandwidth as data transport highways, we propose an intelligent enhanced SDN concept applied to OBS architecture. A management plane (MP) is added to conventional control (CP) and data planes (DP). The MP assembles the traffic spatio-temporal parameters from ingress nodes, uses Kalman filtering prediction-based algorithm to estimate traffic demand. Prior to packets arrival at edges nodes, it regularly forwards updates of resources allocation to CPs. Simulations were done on a hybrid scheme (1+1) and on the centralized OBS. The results demonstrated that the proposition decreases the packet loss ratio. It also improves network latency and throughput-up to 84 and 51%, respectively, versus the traditional scheme.

• Journal of Practical Engineering Education
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• v.11 no.1
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• pp.17-23
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• 2019

In applying SW education for non-major undergraduates, we applied the physical computing lesson using Arduino. There is a case in which the basic problem-solving process teaching method based on the computational thinking was proposed in the physical computing class using Arduino. However, in educating computational thinking process, it is necessary to evaluate and educate understanding of SW structures. After understanding SW structures, it is correct SW education flow to make creative outputs by applying computational thinking process. However, there is a lack of examples of how to evaluate understanding of SW structures in the class using Arduino. In this paper, we proposed a one - semester curriculum for lectures on SW education using Arduino for non-majors. In addition, we proposed and analyzed the evaluation method of the understanding of SW structures and the evaluation problems developed in this course.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.165-171
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• 2019

The study of the computer architecture has shown that major improvements in price-to-energy performance stems from domain-specific hardware development. This paper analyzes the tensor processing unit (TPU) ASIC which can accelerate the reasoning of the artificial neural network (NN). The core device of the TPU is a MAC matrix multiplier capable of high-speed operation and software-managed on-chip memory. The execution model of the TPU can meet the reaction time requirements of the artificial neural network better than the existing CPU and the GPU execution models, with the small area and the low power consumption even though it has many MAC and large memory. Utilizing the TPU for the tensor flow benchmark framework, it can achieve higher performance and better power efficiency than the CPU or CPU. In this paper, we analyze TPU, simulate the Python modeled OpenTPU, and synthesize the matrix multiplication unit, which is the key hardware.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.397-400
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• 2016

In this paper, we propose an efficient algorithm for computing architecture for high-performance Inter Prediction SAD HEVC encoder. HEVC Motion Estimation (ME) of the Inter Prediction is a process for searching for the currently high prediction block PU and the correlation in the interpolated reference picture in order to remove temporal redundancy. ME algorithm uses full search(FS) or fast search algorithm. Full search technique has the guaranteed optimal results but has many disadvantages which include high calculation and operational time due to the motion prediction with respect to all candidate blocks in a given search area. Therefore, this paper proposes a new algorithm which reduces the computational complexity by reusing the SAD operation in full search to reduce the amount of calculation and computational time of the Inter Prediction. The proposed algorithm is applied to an HEVC standard software HM16.12. There was an improved operational time of 61% compared to the traditional full search algorithm, BDBitrate was decreased by 11.81% and BDPSNR increased by about 0.5%.

• Journal of IKEEE
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• v.23 no.2
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• pp.388-394
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• 2019

This paper describes a design of security system-on-chip (SoC) that integrates a Cortex-M0 CPU with an AAW (ARIA-AES- Whirlpool) crypto-core which implements two block cipher algorithms of ARIA and AES and a hash function Whirlpool into an unified hardware architecture. The AAW crypto-core was implemented in a small area through hardware sharing based on algorithmic characteristics of ARIA, AES and Whirlpool, and it supports key sizes of 128-bit and 256-bit. The designed security SoC was implemented on FPGA device and verified by hardware-software co-operation. The AAW crypto-core occupied 5,911 slices, and the AHB_Slave including the AAW crypto-core was implemented with 6,366 slices. The maximum clock frequency of the AHB_Slave was estimated at 36 MHz, the estimated throughputs of the ARIA-128 and the AES-128 was 83 Mbps and 78 Mbps respectively, and the throughput of the Whirlpool hash function of 512-bit block was 156 Mbps.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.396-408
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• 2019

The tubes which are applied in jacket platforms as the supporting structure might be collided by supply vessels. Such kind of impact will lead to plastic deformation on tube members. As a result, the ultimate strength of tubes will decrease compared to that of intact ones. In order to make a decision on whether to repair or replace the members, it is crucial to know the residual strength of the tubes. After being damaged by lateral impact, the simply supported tubes will definitely loss a certain extent of load carrying capacity under uniform axial compression. Therefore, in this paper, the relationship between the residual ultimate strength of the damaged circular tube by collision and the energy dissipation due to lateral impact is investigated. The influences of several parameters, such as the length, diameter and thickness of the tube and the impact energy, on the reduction of ultimate strength are investigated. A series of numerical simulations are performed using nonlinear FEA software LS-DYNA. Based on simulation results, a non-dimensional parameter is introduced to represent the degree of damage of various size of tubes after collision impact. By applying this non-dimensional parameter, a simplified formula has been derived to describe the relationship between axial compressive residual ultimate and lateral impact energy and tube parameters. Finally, by comparing with the allowable compressive stress proposed in API rules (RP2A-WSD A P I, 2000), the critical damage of tube due to collision impact to be repaired is proposed.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.5
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• pp.607-613
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• 2019

Objective: Intramuscular fat (IMF) content plays an important role in meat quality. Identification of single nucleotide polymorphisms (SNPs) and genes related to pig IMF, especially using pig populations with high IMF content variation, can help to establish novel molecular breeding tools for optimizing IMF in pork and unveil the mechanisms that underlie fat metabolism. Methods: We collected muscle samples of 453 Chinese Lulai black pigs, measured IMF content by Soxhlet petroleum-ether extraction method, and genotyped genome-wide SNPs using GeneSeek Genomic Profiler Porcine HD BeadChip. Then a genome-wide association study was performed using a linear mixed model implemented in the GEMMA software. Results: A total of 43 SNPs were identified to be significantly associated with IMF content by the cutoff p<0.001. Among these significant SNPs, the greatest number of SNPs (n = 19) were detected on Chr.9, and two linkage disequilibrium blocks were formed among them. Additionally, 17 significant SNPs are mapped to previously reported quantitative trait loci (QTLs) of IMF and confirmed previous QTLs studies. Forty-two annotated genes centering these significant SNPs were obtained from Ensembl database. Overrepresentation test of pathways and gene ontology (GO) terms revealed some enriched reactome pathways and GO terms, which mainly involved regulation of basic material transport, energy metabolic process and signaling pathway. Conclusion: These findings improve our understanding of the genetic architecture of IMF content in pork and facilitate the follow-up study of fine-mapping genes that influence fat deposition in muscle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.264-284
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• 2021

As the next-generation network architecture, software-defined networking (SDN) has great potential. But how to forward data packets safely is a big challenge today. In SDN, packets are transferred according to flow rules which are made and delivered by the controller. Once flow rules are modified, the packets might be redirected or dropped. According to related research, we believe that the key to forward data flows safely is keeping the consistency of flow rules. However, existing solutions place little emphasis on the safety of flow rules. After summarizing the shortcomings of the existing solutions, we propose FRChain to ensure the security of SDN data forwarding. FRChain is a novel scheme that uses blockchain to secure flow rules in SDN and to detect compromised nodes in the network when the proportion of malicious nodes is less than one-third. The scheme places the flow strategies into blockchain in form of transactions. Once an unmatched flow rule is detected, the system will issue the problem by initiating a vote and possible attacks will be deduced based on the results. To simulate the scheme, we utilize BigchainDB, which has good performance in data processing, to handle transactions. The experimental results show that the scheme is feasible, and the additional overhead for network performance and system performance is less than similar solutions. Overall, FRChain can detect suspicious behaviors and deduce malicious nodes to keep the consistency of flow rules in SDN.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.75-94
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• 2021

The 4th industrial revolution needs a fusion of artificial intelligence, robotics, the Internet of Things (IoT), edge computing, and other technologies. For the fusion of technologies, cloud computing technology can provide flexible and high-performance computing resources so that cloud computing can be the foundation technology of new emerging services. The emerging services become a global-scale, and require much higher performance, availability, and reliability. Public cloud providers already provide global-scale services. However, their services, costs, performance, and policies are different. Enterprises/ developers to come out with a new inter-operable service are experiencing vendor lock-in problems. Therefore, multi-cloud technology that federatively resolves the limitations of single cloud providers is required. We propose a software platform, denoted as Cloud-Barista. Cloud-Barista is a multi-cloud service common platform for federating multiple clouds. It makes multiple cloud services as a single service. We explain the functional architecture of the proposed platform that consists of several frameworks, and then discuss the main design and implementation issues of each framework. To verify the feasibility of our proposal, we show a demonstration which is to create 18 virtual machines on several cloud providers, combine them as a single resource, and manage it.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.63-69
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• 2022

In this paper, we propose a new GPU-based framework for quickly calculating adaptive and continuous SDF(Signed distance fields), and examine cases related to rendering/collision processing using them. The quadtree constructed from the triangle mesh is transferred to the GPU memory, and the Euclidean distance to the triangle is processed in parallel for each thread by using it to find the shortest continuous distance without discontinuity in the adaptive grid space. In this process, it is shown through experiments that the cut-off view of the adaptive distance field, the distance value inquiry at a specific location, real-time raytracing, and collision handling can be performed quickly and efficiently. Using the proposed method, the adaptive sign distance field can be calculated quickly in about 1 second even on a high polygon mesh, so it is a method that can be fully utilized not only for rigid bodies but also for deformable bodies. It shows the stability of the algorithm through various experimental results whether it can accurately sample and represent distance values in various models.