• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.30-39
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• 2012

In these day, some of IT venders already were going to cloud computing market, as well they are going to expand their territory for the cloud computing market through that based on their hardware and software technology, making collaboration between hardware and software vender. Distributed file system is very mainly technology for the cloud computing that must be protect performance and safety for high levels service requests as well data store. This paper introduced distributed file system for cloud computing and how to use this theory such as memory database, Hadoop file system, high availability database system. now In the market, this paper define a very large distributed processing architect as a reference by kind of distributed file systems through using technology in cloud computing market.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5C
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• pp.382-390
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• 2005

Huffman codes are widely used for image and video data transmission. As the increase of real-time data, a lot of studies on effective decoding algorithms and architectures have been done. In this paper, we proposed a balanced binary search tree for Huffman decoding and compared the performance of the proposed architecture with that of previous works. Based on definitions of the comparison of codewords with different lengths, the proposed architecture constructs a balanced binary tree which does not include empty internal nodes, and hence it is very efficient in the memory requirement. Performance evaluation results using actual image data show that the proposed architecture requires small number of table entries, and the decoding time is 1, 5, and 2.41 memory accesses in minimum, maximum, and average, respectively.

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.376-382
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• 2011

Since the Industrial Revolution has caused global change by using of a fossil fuel, a reckless and growth-oriented development. A global mean temperature since 19th century has climbed up 0.4~$0.8^{\circ}C$. Our country, afterwards, global warming has increased the temperature every season. After The Kyoto Protocol regarding a greenhouse gas reduction goal took effect, be situations that decrease of greenhouse gas was acutely required. Therefore, interest of utilization of the new & renewable energy is increasing everyday. In advanced research, we shows that at first divided a country to nine range by natural geography, and second executed Meteorological data analysis of recent 30 years considering level of significance by nine range. The results of advanced research are that the similarities are low because there are the regions that temperature deviation of the similar climate regions is large in winter season, and there are not characteristics of clear discrimination of temperature. This study shows that at first divided a country to six range by temperature range, and second executed Meteorological data analysis of recent 30 years considering level of significance by six range. The results of this study are that in heating load calculation of building, periodic temperature data management is required because facility capacity and cost are affected greatly by outdoor temperature, and temperature by climate range needs consideration of pertinent area. Ground temperature was assumed of the weather in region, the ground and soil. Lastly, we were able to know that establishment of climate region by temperature range can be useful policy making and plans of design of the horticultural facilities and architectures.

• Journal of IKEEE
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• v.25 no.1
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• pp.180-192
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• 2021

One widely used algorithm for image recognition and pattern detection is the convolution neural network (CNN). To efficiently handle convolution operations, which account for the majority of computations in the CNN, we use hardware accelerators to improve the performance of CNN applications. In using these hardware accelerators, the CNN fetches data from the off-chip DRAM, as the massive computational volume of data makes it difficult to derive performance improvements only from memory inside the hardware accelerator. In other words, data communication between off-chip DRAM and memory inside the accelerator has a significant impact on the performance of CNN applications. In this paper, a simulator for the CNN is developed to analyze the main memory or DRAM access with respect to the size of the on-chip memory or global buffer inside the CNN accelerator. For AlexNet, one of the CNN architectures, when simulated with increasing the size of the global buffer, we found that the global buffer of size larger than 100kB has 0.8x as low a DRAM access count as the global buffer of size smaller than 100kB.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.87-97
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we proposed to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collected two new video datasets, and proposed a new deep learning architecture named Temporal and Spatial Segment Networks (TSSN) for rainfall depth recognition. Under TSSN, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. Also, the proposed TSSN architecture outperforms other architectures implemented in this paper.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.319-328
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• 2021

With popularization of services for massive content, the fundamental limitations of TCP/IP networking were discussed and a new paradigm called Information-centric networking (ICN) was presented. In ICN, content is addressed by the content identifier (content name) instead of the location identifier such as IP address, and network nodes can use the cache to store content in transit to directly service subsequent user requests. As the user request can be serviced from nearby network caches rather than from far-located content servers, advantages such as reduced service latency, efficient usage of network bandwidth, and service scalability have been introduced. However, these advantages are determined by how actively content stored in the cache can be utilized. In this paper, we 1) introduce content access schemes in Named-data networking, one of the representative ICN architectures; 2) in particular, review the schemes that allow access to cached content away from routing paths; 3) conduct comparative study on the performance of the schemes using the ndnSIM simulator.

• Smart Structures and Systems
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• v.31 no.4
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• Smart Media Journal
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• v.12 no.4
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• pp.19-26
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• 2023

IoT edge services utilizing neuromorphic hardware architectures are suitable for autonomous IoT applications as they perform intelligent processing on the device itself. However, spiking neural networks applied to neuromorphic hardware are difficult for IoT developers to comprehend due to their complex structures and various hyper-parameters. In this paper, we propose a method for generating spiking neural network (SNN) models that satisfy user performance requirements while considering the constraints of neuromorphic hardware. Our proposed method utilizes previously trained models from pre-processed data to find optimal SNN model parameters from profiling data. Comparing our method to a naive search method, both methods satisfy user requirements, but our proposed method shows better performance in terms of runtime. Additionally, even if the constraints of new hardware are not clearly known, the proposed method can provide high scalability by utilizing the profiled data of the hardware.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.129-138
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• 2012

In distributed memory architectures like clusters, each node stores a portion of data. How data is distributed across nodes influences the performance of such systems. The data distribution scheme is the strategy to distribute data across nodes and realize parallel data processing. Due to various reasons such as maintenance, scale up, upgrade, etc., the performance of nodes in a cluster can often become non-identical. In such clusters, data distribution without considering performance cannot efficiently distribute data on nodes. In this paper, we propose a new data distribution scheme based on the number of cores in nodes. We use the number of cores as the performance factor. In our data distribution scheme, each node is allocated an amount of data proportional to the number of cores in it. We implement our data distribution scheme using the Chapel language. To show our data distribution is effective in reducing the execution time of parallel applications, we implement Mandelbrot Set and ${\pi}$-Calculation programs with our data distribution scheme, and compare the execution times on a cluster. Based on experimental results on clusters of 8-core and 16-core nodes, we demonstrate that data distribution based on the number of cores can contribute to a reduction in the execution times of parallel programs on clusters.

• BMB Reports
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• v.46 no.5
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• pp.276-281
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• 2013

In this study, we aimed to compare the morphogenetic and neuronal characteristics between monolayer cells and spheroids. For this purpose, we established spheroid formation by growing SH-SY5Y cells on the hydrophobic surfaces of thermally-collapsed elastin-like polypeptide. After 4 days of culture, the relative proliferation of the cells within spheroids was approximately 92% of the values for monolayer cultures. As measured by quantitative assays for mRNA and protein expressions, the production of synaptophysin and neuronspecific enolase (NSE) as well as the contents of cell adhesion molecules (CAMs) and extracellular matrix (ECM) proteins are much higher in spheroids than in monolayer cells. Under the all-trans-retinoic acid (RA)-induced differentiation condition, spheroids extended neurites and further up-regulated the expression of synaptophysin, NSE, CAMs, and ECM proteins. Our data indicate that RA-differentiated SH-SY5Y neurospheroids are functionally matured neuronal architectures.