• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.93-98
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• 2023

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the parameters of micro-scaled flow channels electrochemical machining and the channel shape was established by training the samples. The depth and width of micro-scaled flow channels electrochemical machining on stainless steel surface were predicted, and the flow channels experiment was carried out with pulse power supply in NaNO3 solution to verify the established network model. The results show that the depth and width of the channel predicted by the simulated annealing artificial neural network with "4-7-2" structure are very close to the experimental values, and the error is less than 5.3%. The predicted and experimental data show that the etching degree in the process of channels electrochemical machining is closely related to voltage and current density. When the voltage is less than 5V, a "small island" is formed in the channel; When the voltage is greater than 40V, the lateral etching of the channel is relatively large, and the "dam" between the channels disappears. When the voltage is 25V, the machining morphology of the channel is the best.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.171-190
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• 2024

This paper conducts a comparative analysis of citation patterns between journals and conferences using bibliometric and social network analysis on references from the 'Journal of the Korea Institute of Information Security and Cryptology (JKIISC)'. The results indicate that conference references slightly exceed journal references, with around 80% being international publications, highlighting Korean researchers' high dependency on overseas publications. Analysis of citation age shows trends of increasing immediacy citation rate, lengthening citing half-life, and shortening peak time, with domestic publications having higher immediacy citation rate and international publications having slower citing half-life. Mapping SCOPUS journals and ICORE conferences revealed that journal citations mainly come from 'Computer science' (32.3%), 'Engineering' (23.5%), 'Mathematics' (16.7%), and 'Social Cciences' (12.8%), along with other research fields (25.6%), while conference citations are predominantly in 'Cybersecurity and Privacy' with recent increases in 'Computer Vision and Multimedia Computation' and 'Machine Learning'. Co-citation network analysis shows higher degree centrality for conference groups and international publications. The co-citation frequency between different types of literature was highest between journals and conferences (36.9%), compared to within journals (34.3%) or within conferences (28.8%). Lastly, network visualization maps are presented to explore the structural connections among co-cited publications and their research fields. The results of this study suggest that the field of information security research in Korea effectively balances the use of journal and conference literature, indicating that the field is developing through a complementary relationship between these sources.

• ETRI Journal
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• v.35 no.1
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• pp.131-141
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• 2013

Integral cryptanalysis, which is based on the existence of (higher-order) integral distinguishers, is a powerful cryptographic method that can be used to evaluate the security of modern block ciphers. In this paper, we focus on substitution-permutation network (SPN) ciphers and propose a criterion to characterize how an r-round integral distinguisher can be extended to an (r+1)-round higher-order integral distinguisher. This criterion, which builds a link between integrals and higher-order integrals of SPN ciphers, is in fact based on the theory of direct decomposition of a linear space defined by the linear mapping of the cipher. It can be directly utilized to unify the procedure for finding 4-round higher-order integral distinguishers of AES and ARIA and can be further extended to analyze higher-order integral distinguishers of various block cipher structures. We hope that the criterion presented in this paper will benefit the cryptanalysts and may thus lead to better cryptanalytic results.

• Journal of Contemporary Eastern Asia
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• v.13 no.1
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• pp.19-37
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• 2014

In an attempt to develop comprehensive evidence-based methods for evaluation of the R&D performance of cross-disciplinary projects, a joint bibliometric analysis of patents and publications was performed for two industry-university-government collaborative projects aimed at commercialization: Hokkaido University Research & Business Park Project (2003-2007; 63 inventors; 176 patents; 853 papers), and Matching Program for Innovations in Future Drug Discovery and Medical Care - phase I (2006-2010; 46 inventors; 235 patents; 733 papers). Besides the simple output indicators (for five years period), and citations (from the publication date to the end of 2012), science maps based on the network analysis of words and co-authorship relations were generated to identify the prominent research themes and teams. Our joint analysis of publications and patents yields objective and mutually complementing information, which provides better insights on research and commercialization performance of the large-scale projects. Hence, such analysis has potential for use in the industry-university project's performance evaluation.

• Environmental Engineering Research
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• v.23 no.1
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• pp.84-91
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• 2018

The present study deals with the management of groundwater resources of an important agriculture track of north-western part of Saudi Arabia. Due to strategic importance of the area efforts have been made to estimate aquifer proneness to attenuate contamination. This includes determining hydrodynamic behavior of the groundwater system. The important parameters of any vulnerability model are geological formations in the region, depth to water levels, soil, rainfall, topography, vadose zone, the drainage network and hydraulic conductivity, land use, hydrochemical data, water discharge, etc. All these parameters have greater control and helps determining response of groundwater system to a possible contaminant threat. A widely used DRASTIC model helps integrate these data layers to estimate vulnerability indices using GIS environment. DRASTIC parameters were assigned appropriate ratings depending upon existing data range and a constant weight factor. Further, land-use pattern map of study area was integrated with vulnerability map to produce pollution risk map. A comparison of DRASTIC model was done with GOD and AVI vulnerability models. Model validation was done with $NO_3$, $SO_4$ and Cl concentrations. These maps help to assess the zones of potential risk of contamination to the groundwater resources.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.10
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• pp.51-58
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• 2003

In this paper, we propose a group scheduling scheme to efficiently utilize network resources for core nodes in optical burst switching networks. This scheme schedules multiple bursts utilizing an interval graph to obtain the maximum stable set using the information such as arrival times and burst lengths from the collected header packets. Simultaneous scheduling of multiple bursts in a scheduling window results in lower burst loss probability and increased channel utilization than those proposed previously using one-to-one mapping. Simulation results for both cases of variable and fixed burst sizes show that the group scheduling scheme is better than the immediate scheduling, so called Latest Available Unused Channel with Void Filling, scheme in both performance metrics above mentioned.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.345-355
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• 1998

The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data are employed. In this paper two types of robust backpropagation algorithms are discussed both from a theoretical point of view and in the case studies of nonlinear regression function estimation and handwritten Korean character recognition. For future research we suggest Bayesian learning approach to neural networks and compare it with two robust backpropagation algorithms.

• Journal of Environmental Health Sciences
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• v.38 no.5
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• pp.369-379
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• 2012

Objectives: Spatial analysis is useful for understanding complicated causal relationships. This paper focuses trends and appling methods for spatial analysis associated with environmental asbestos exposure. Methods: Literature review and reflection of experience of authors were conducted to know academic background of spatial analysis, appling methods on epidemiology and asbestos exposure. Results: Spatial analysis based on spatial autocorrelation provides a variety of methods through which to conduct mapping, cluster analysis, diffusion, interpolation, and identification. Cause of disease occurrence can be investigated through spatial analysis. Appropriate methods can be applied according to contagiousness and continuity. Spatial analysis for asbestos exposure source is needed to study asbestos related diseases. Although a great amount of research has used spatial analysis to study exposure assessment and distribution of disease occurrence, these studies tend to focus on the construction of a thematic map without different forms of analysis. Recently, spatial analysis has been advanced by merging with web tools, mobile computing, statistical packages, social network analysis, and big data. Conclusions: Because the trend in spatial analysis has evolved from simple marking into a variety of forms of analyses, environmental researchers including asbestos exposure study are required to be aware of recent trends.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.74-97
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• 2005

IDEF0 is the IEEE standard for functional enterprise modeling and has been used for business process modeling or process mapping in US and Europe. But it does not reflect the potential benefits of modeling and simulation of the dynamic aspects of an enterprise or a system. On the other hand, simulation tools concentrate mostly on the simulation of material flows and are difficult to include information flows and control flows. Additionally, the simulation models that include elements such as queues, event generators and process nodes is a visual interactive representation for the model builder, but is inconvenient for the domain expert. In an attempt to fill that void, we provide an integration of business process and simulation models in this paper. An enhancement of the IDEF0, called parameterized IDEF0, is proposed and its conversion mechanism to network simulation model is developed. Using this methodology, business process models for alternative systems can be evaluated and compared through simulation on time, cost, and quality metrics. As an application of the proposed methodology, economic evaluation of EDI (Electronic Data Interchange) for time-based BPR (Business Process Redesign) is demonstrated. In addition to BPR, the developed methodology may be further integrated with ABC (Activity Based Costing), TQM (Total Quality Management), and economic evaluation of information systems.