• Journal of the Korean Wood Science and Technology
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• v.43 no.2
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• pp.186-195
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• 2015

This study aims at modeling the rotary drying of wood chips in co-current mode and estimating the drying time of larch (Larix kaemferi) wood chip. Drying data were obtained in a lab. scale fixed bed dryer operating with an air velocity of 1 m/sec. and at hot air inlet temperatures of $100^{\circ}C$, $200^{\circ}C$, and $300^{\circ}C$. The lab. scale fixed-bed drying rates for small, medium and large size larch wood chips that had been dried from 40% wet-based moisture content (MC) to 10% MC at $200^{\circ}C$ drying temperature were 17.3 %/min., 10.2 %/min. and 5.5 %/min., respectively. It was predicted that larch large size wood chips could be dried from 40% MC to 10% MC in about 23.0, 34.6, and 44.7 minutes at $300^{\circ}C$, $200^{\circ}C$ and $150^{\circ}C$, respectively. Expected drying times for medium size chips were about 8.6, 11.2 and 13.2 minutes and those for small size chips were 4.3, 5.5 and 6.4 minutes, respectively.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.5
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• pp.687-696
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• 2019

To identify the driving dynamic characteristics of the Tractor-Trailer Type Transporter for mounting a large scale precision equipment, real vehicle driving tests on the 3 inch-bump-space-road were performed. And using general Dynamics Analysis Program - RecurDyn(V8R5), Dynamics M&S were carried out assuming the similar condition with real tests. Then the acceleration data obtained from real tests and M&S were analyzed and compared with each other in the part of root-mean-square-acceleration($g_{rms}$), peak-acceleration($g_{peak}$) and frequencies. In simple view of the $g_{rms}$ & $g_{peak}$, although the results of MRBD are more similar to ones of the real vehicle driving tests, but the results of RFlex have more information to get various useful dynamic characteristics.

• Journal of Platform Technology
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• v.6 no.4
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• pp.49-58
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• 2018

Complex disasters make greater damage and higher losses unexpected than the past. To prevent these disasters, it needs to prepare a plan for handling unexpected results. Especially an accident at a facility like an atomic power plant makes a big problem cause of climate change. A simulation needs to do preliminary researches based on diverse situations. In this research we define the basic component techniques to design and implement the disaster management system. Basically a hierarchical system design method is to build on the resources provided from high performance computing(HPC) and large-scale storage systems. To develop the system, it is considered middleware as well as application studies, data studies and decision making services in convergence areas.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.359-373
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• 2019

For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS) clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweencluster matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments on some artificial datasets and real datasets separately. And experimental results show that, compared with SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.2
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• pp.196-209
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• 2022

Driving dynamic characteristics of semi-trailer loaded with precise equipments are very important to protect them from vibration, impact or other disturbances. In this paper, in order to identify the driving dynamic characteristics of the large scale semi-trailer equipped with swivel axle and hydropneumatic suspension unit, Dynamics Modeling & Simulation(M&S) were performed using general Dynamics Analysis Program(RecurDyn V9R2). The semi-trailer was modeled as two types - one is Multi Rigid Body Dynamics(MRBD) model, and the other Rigid-Flexible Body Dynamics(RFlex) one. The natural vibration mode and frequencies of semi-trailer body, acceleration of dummy-weight, pitch, roll and yaw of dummy-weight, swivel axle and hydropneumatic suspension cylinder support structure, and acting force of hydropneumatic suspensions etc. were obtained from the M&S. Additionally frequency analysis were performed using the data of behavior obtained from above M&S. Generally the quantitative results of RFlex are larger than them of MRBD in view of magnitude of the comparable parametric values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1777-1783
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• 2021

The use of drones is gradually increasing for the efficient maintenance of large-scale renewable energy power generation complexes. For a long time, photovoltaic panels have been photographed with drones to manage panel loss and contamination. Various approaches using artificial intelligence are being tried for efficient maintenance of large-scale photovoltaic complexes. Recently, semantic segmentation-based application techniques have been developed to solve the image classification problem. In this paper, we propose a classification model using semantic segmentation to determine the presence or absence of failures such as arcs, disconnections, and cracks in solar panel images obtained using a drone equipped with a thermal imaging camera. In addition, an efficient classification model was implemented by tuning several factors such as data size and type and loss function customization in U-Net, which shows robust classification performance even with a small dataset.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.75.3-75.3
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• 2019

The filament is an interesting structure in the Universe because clusters form at the nodes of filaments and grow through the continuous accretion of individual galaxies and groups from the surrounding filaments. We study the chemical properties of star-forming (SF) galaxies in the five large-scale filamentary structures (Leo II A, Leo II B, Leo Minor, Canes Venatici, and Virgo III) related with the Virgo cluster, with the spectroscopic data taken with the SDSS DR12, and compare them with those of the Virgo cluster and field galaxies. In mass-metallicity relation, most of the SF galaxies in Virgo-related filaments (except Virgo III filament) show lower metallicity on average than the Virgo cluster SF galaxies, but similar to field counterparts. These chemically less evolved feature of SF galaxies in the filaments and field are more pronounced for lower mass galaxies. This is probably because low mass galaxies have low potential wells and are therefore likely to be sensitive to cluster environmental effects. Interestingly, we find that the metallicity enhancement of SF galaxies in the Virgo III filament. In chemical and morphological perspectives, SF galaxies in the Virgo III thought to be transitional objects possibly transformed from SF late-type galaxies and are on the way to red early-type galaxies in the filament environment. This is the first discovery of systematic 'chemical pre-processing' signature for filament galaxies in Local Universe before they fall into the cluster.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.59-61
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• 2023

본 논문에서는 감정 분류 성능 향상을 위한 초거대 언어모델로부터의 추론 데이터셋 활용 방안을 제안한다. 이 방안은 Google Research의 'Chain of Thought'에서 영감을 받아 이를 적용하였으며, 추론 데이터는 ChatGPT와 같은 초거대 언어 모델로 생성하였다. 본 논문의 목표는 머신러닝 모델이 추론 데이터를 이해하고 적용하는 능력을 활용하여, 감정 분류 작업의 성능을 향상시키는 것이다. 초거대 언어 모델(ChatGPT)로부터 추출한 추론 데이터셋을 활용하여 감정 분류 모델을 훈련하였으며, 이 모델은 감정 분류 작업에서 향상된 성능을 보였다. 이를 통해 추론 데이터셋이 감정 분류에 있어서 큰 가치를 가질 수 있음을 증명하였다. 또한, 이 연구는 기존에 감정 분류 작업에 사용되던 데이터셋만을 활용한 모델과 비교하였을 때, 추론 데이터를 활용한 모델이 더 높은 성능을 보였음을 증명한다. 이 연구를 통해, 적은 비용으로 초거대 언어모델로부터 생성된 추론 데이터셋의 활용 가능성을 보여주고, 감정 분류 작업 성능을 향상시키는 새로운 방법을 제시한다. 제시한 방안은 감정 분류뿐만 아니라 다른 자연어처리 분야에서도 활용될 수 있으며, 더욱 정교한 자연어 이해와 처리가 가능함을 시사한다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.127-136
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• 2024

This study explores the correlation between system anomalies and large-scale logs within the Spark cluster environment. While research on anomaly detection using logs is growing, there remains a limitation in adequately leveraging logs from various components of the cluster and considering the relationship between anomalies and the system. Therefore, this paper analyzes the distribution of normal and abnormal logs and explores the potential for anomaly detection based on the occurrence of log templates. By employing Hadoop and Spark, normal and abnormal log data are generated, and through t-SNE and K-means clustering, templates of abnormal logs in anomalous situations are identified to comprehend anomalies. Ultimately, unique log templates occurring only during abnormal situations are identified, thereby presenting the potential for anomaly detection.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.167-176
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• 2024

Lip-reading is the task of inferring the speaker's utterance from silent video based on learning of lip movements. It is very challenging due to the inherent ambiguities present in the lip movement such as different characters that produce the same lip appearances. Recent advances in deep learning models such as Transformer and Temporal Convolutional Network have led to improve the performance of lip-reading. However, most previous works deal with English lip-reading which has limitations in directly applying to Korean lip-reading, and moreover, there is no a large scale Korean lip-reading dataset. In this paper, we introduce the first large-scale Korean lip-reading dataset with more than 120 k utterances collected from TV broadcasts containing news, documentary and drama. We also present a preprocessing method which uniformly extracts a facial region of interest and propose a transformer-based model based on grapheme unit for sentence-level Korean lip-reading. We demonstrate that our dataset and model are appropriate for Korean lip-reading through statistics of the dataset and experimental results.