• Polymer(Korea)
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• v.36 no.4
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• pp.407-412
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• 2012

The characteristics of nylon6/ionomer semi interpenetrating networks (IPN) as a molded-in-color (MIC) compound had been studied, and comparison was made with nylon6/ionomer blends. Nylon6/ionomer semi IPN shows better homogeneity in phase morphology than nylon6/ionomer blend, and it caused better anti-scratching performance than the blend. This semi IPN structure resulted in lowered crystallization rate, increased melt viscosity and less temperature dependency of viscosity. As a result, we may expect the enhancement of melt processing characteristics in an injection molding process using nylon6/ionomer semi IPN as a MIC compound.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.77-83
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• 2021

The dependency on technology has increased with the increase in population. Technology plays a crucial role in facilitating, organizing and securing people's life nowadays. The Internet has penetrated every face of present-day lifestyles. Yet another ubiquitous use of digital technology today is evident in transferring money and speeding cross border payments that are done through digital transactions. This paper investigates transferring money and data through banks and companies by using the Blockchain concept through decentralized distributed system. The present research also peruses several contexts in which this technology has already been implemented successfully and demonstrates the advantages of replacing the paper money with digital money. Using cryptocurrency will facilitate people's life by reducing time, securing the process of money transfer, and increasing data integrity. The primary benefit of this content analysis is that it addresses an innovative subject, in a new light and using timely recent research references drawn from 2018-2020. Thus, our study is a contemporary and conclusive source for all present and future endeavours being undertaken in the domain of using blockchain for e-transactions.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.359-364
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• 2021

In recent years Hadoop usage has been increasing day by day. The need of development of the technology and its specified outcomes are eagerly waiting across globe to adopt speedy access of data. Need of computers and its dependency is increasing day by day. Big data is exponentially growing as the entire world is working in online mode. Large amount of data has been produced which is very difficult to handle and process within a short time. In present situation industries are widely using the Hadoop framework to store, process and produce at the specified time with huge amount of data that has been put on the server. Processing of this huge amount of data having small files & its storage optimization is a big problem. HDFS, Sequence files, HAR, NHAR various techniques have been already proposed. In this paper we have discussed about various existing techniques which are developed for accessing and storing small files efficiently. Out of the various techniques we have specifically tried to implement the HDFS- HAR, NHAR techniques.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.2
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• pp.72-79
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• 2022

Deep-learning-based Super-Resolution (SR) methods were evaluated to reconstruct pressure fields with a high resolution from low-resolution images taken from a coarse grid simulation. In addition to a canonical SRCNN(super-resolution convolutional neural network) model, two modified models from SRCNN, adding an activation function (ReLU or Sigmoid function) to the output layer, were considered in the present study. High resolution images obtained by three models were more vivid and reliable qualitatively, compared with a conventional super-resolution method of bicubic interpolation. A quantitative comparison of statistical similarity showed that SRCNN model with Sigmoid function achieved best performance with less dependency on original resolution of input images.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3944-3951
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• 2021

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.201-209
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• 2023

Time comparison techniques are required for generating and keeping Coordinated Universal Time (UTC) and to distribute standard clocks. These techniques play an important role in various fields, including science, finance, military, and communication. Among these techniques, Two-Way Satellite Time and Frequency Transfer (TWSTFT) ensures a relatively high accuracy, with a time comparison accuracy at a nanosecond level. However, TWSTFT systems have some limitations, such as the dependency on extra network links. In this paper, we propose an improved method for TWSTFT system operation and design a message structure for the suggestion. Additionally, we estimate the data rate and redundancy for the new TWSTFT signal with the designed message structure.

• Geomechanics and Engineering
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• v.28 no.5
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• pp.477-491
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• 2022

In the present study, the effects of thermal loading on the buckling and resonance frequency of graphene platelets (GPL) reinforced nano-composites are examined. Functionally graded (FG) material properties are considered in thickness direction for the thermal responses of the composite. The equivalent material properties are obtained using Halphin-Tsai nano-mechanical model for composite layers. Moreover, the effects of nano-scale sizes are taken into account, employing functionally modified couple stress (FMCS) parameter. In this regard, for the first time, it is demonstrated that at certain values of GPL weight fraction, thermal buckling occurs. In obtaining results of vibrational behavior, both analytical solution and deep neural network (DNN) methods are used. The DNN method needs low computational costs to predict the resonance behavior. A comprehensive parametric study is conducted to indicate the effects of several geometrical, material, and loading conditions on the vibrational and buckling behavior of cylindrical shell structures made of GPL-nanocomposites. It is shown that the effect of temperature change on the occurrence of buckling is vital while it has a negligible impact on the resonance frequency of the structure. Moreover, the size-dependency of the results is demonstrated, and it cannot be neglected in nano-scales.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.47-48
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• 2024

Heating, Ventilating, and Air Conditioning(HVAC) 시스템은 난방(Heating), 환기(Ventilating), 공기조화(Air Conditioning)를 제공하는 공조시스템으로, 실내 환경의 온도, 습도 조절 및 지속적인 순환 및 여과를 통해 실내 공기 질을 개선한다. 이러한 HVAC 시스템에 이상이 생기는 경우 공기 여과율이 낮아지며, COVID-19와 같은 법정 감염병 예방에 취약해진다. 또한 장비의 과부하를 유발하여, 시스템의 효율성 저하 및 에너지 낭비를 불러올 수 있다. 따라서 본 논문에서는 HVAC 시스템의 이상 탐지 및 조기 조치를 위한 Transformer 기반 이상 탐지 기법의 적용을 제안한다. Transformer는 기존 시계열 데이터 처리를 위한 기법인 Recurrent Neural Network(RNN)기반 모델의 구조적 한계점을 극복함에 따라 Long Term Dependency 문제를 해결하고, 병렬처리를 통해 효율적인 Feature 추출이 가능하다. Transformer 모델이 HVAC 시스템의 이상 탐지에서 RNN 기반의 비교군 모델보다 약 1.31%의 향상을 보이며, Transformer 모델을 통한 HVAC의 이상 탐지에 효율적임을 확인하였다.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.3925-3934
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• 2023

The importance of fast neutron detection for nuclear safeguards purposes has increased due to its potential advantages such as reasonable cost and higher precision for larger sample masses of nuclear materials. Pulse-shape discrimination (PSD) is inevitably used to discriminate neutron- and gamma-ray- induced signals from organic scintillators of very high gamma sensitivity. The light output (LO) threshold corresponding to several MeV of recoiled proton energy could be necessary to achieve fine PSD performance. However, this leads to neutron count losses and possible distortion of results obtained by neutron multiplicity counting (NMC)-based nuclear material accountancy (NMA). Moreover, conventional PSD techniques are not effective for counting of neutrons in a high-gamma-ray environment, even under a sufficiently high LO threshold. In the present work, PSD performance (figure-of-merit, FOM) according to LO bands was confirmed using a conventional charge comparison method (CCM) and compared with results obtained by convolution neural network (CNN)-based PSD algorithms. Also, it was attempted, for the first time ever, to reject fake neutron signals from distorted PSD regions where neutron-induced signals are normally detected. The overall results indicated that higher neutron detection efficiency with better accuracy could be achieved via CNN-based PSD algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12C
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• pp.199-207
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• 2001

We propose an adaptive shaping controller equipped with the technologies of shaping and buffering VoIP packets arriving at the receiving end by the CAM-type controller. In order to conceal interarrival packet delay variation, the conventional jitter buffers force them to be too large, thereby causing the audio quality to suffer excessive delay. However, by using our proposed method, the delay caused by shaping operation dynamically increases or decreases on the level of jitter that exists with in the IP network. This makes the delay accommodates adaptively the network jitter condition. The less jitter network has the fewer delay the shaping controller requires for jitter elimination. And the CAM-type method generally makes the shaping operation faster and leads to processing packets in as little time as can. We analyse the packet loss and delay performance dependency on the average talk ratio and the number of jitter buffer entries in shaping controller. Surprising, we show that the average delay using our shaping controller is about 70msec. This performance is much better than with the delay equalization method which forces the receiving end to delay about 60msec.