• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.9
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• pp.1562-1570
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• 2003

The prediction of the inelastic behavior of the structure is an essential part of reliability assessment procedure, because most of the failures are induced by the inelastic deformation, such as creep and plastic deformation. During decades, there has been much progress in understanding of the inelastic behavior of the materials and a lot of inelastic constitutive equations have been developed. The complexity of these constitutive equations generally requires a stable and accurate numerical method. The radial return mapping is one of the most robust integration scheme currently used. Nonlinear kinematic hardening model of Armstrong-Fredrick type has recovery term and the direction of kinematic hardening increment is not parallel to that of plastic strain increment. In this case, The conventional radial return mapping method cannot be applied directly. In this investigation, we expanded the radial return mapping method to consider the nonlinear kinematic hardening model and implemented this integration scheme into ABAQUS by means of UMAT subroutine. The solution of the non-linear system of algebraic equations arising from time discretization with the generalized midpoint rule is determined using Newton method and bisection method. Using dynamic yield condition derived from linearization of flow rule, the integration scheme for elastoplastic and viscoplastic constitutive model was unified. Several numerical examples are considered to demonstrate the efficiency and applicability of the present method.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.43-48
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• 2017

The underwater transients signals contain the characteristics of complexity, time varying, nonlinear, and short duration. So it is very hard to model for these signals with reference patterns. In this paper we separate the whole length of signals into some short duration of constant length with overlapping frame by frame. The 20th LPC(Linear Predictive Coding) coefficients are extracted from the original signals using Durbin algorithm and applied to neural network. The 65% of whole signals were learned and 35% of the signals were tested in the neural network with two hidden layers. The types of the whales for sound classification are Blue whale, Dulsae whale, Gray whale, Humpback whale, Minke whale, and Northern Right whale. Finally, we could obtain more than 83% of classification rate from the test signals.

• International Journal of Ocean System Engineering
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• v.2 no.2
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• pp.116-138
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• 2012

As in the other fields of mechanics, analytical methods represent an important analysis tool in marine hydrodynamics. The analytical approach is interesting for different reasons : it gives reference results for numerical codes verification, it gives physical insight into some complicated problems, it can be used as a simplified predesign tool, etc. This approach is of course limited to some simplified geometries (cylinders, spheres, ...), and only the case of one or more cylinders, truncated or not, will be considered here. Presented methods are basically eigenfunction expansions whose complexity depends on the boundary conditions. The hydrodynamic boundary value problem (BVP) is formulated within the usual assumptions of potential flow and is additionally simplified by the perturbation method. By using this approach, the highly nonlinear problem decomposes into its linear part and the higher order (second, third, ...) corrections. Also, periodicity is assumed so that the time dependence can be factorized i.e. the frequency domain formulation is adopted. As far as free surface flows are concerned, only cases without or with small forward speed are sufficiently simple to be solved semi-analytically. The problem of the floating body advancing in waves with arbitrary forward speed is far more complicated. These remarks are also valid for the general numerical methods where the case of arbitrary forward speed, even linearized, is still too difficult from numerical point of view, and "it is fair to say that there exists at present no general practical numerical method for the wave resistance problem" [9], and even less for the general seakeeping problem. We note also that, in the case of bluff bodies like cylinders, the assumptions of the potential flow are justified only if the forward speed is less than the product of wave amplitude with wave frequency.

• Journal of Preventive Medicine and Public Health
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• v.51 no.6
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• pp.265-274
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• 2018

Objectives: A positive association between air pollution and both the incidence and prevalence of diabetes mellitus (DM) has been reported in some epidemiologic and animal studies, but little research has evaluated the relationship between air pollution and diabetic coma. Diabetic coma is an acute complication of DM caused by diabetic ketoacidosis or hyperosmolar hyperglycemic state, which is characterized by extreme hyperglycemia accompanied by coma. We conducted a time-series study with a generalized additive model using a distributed-lag non-linear model to assess the association between ambient air pollution (particulate matter less than $10{\mu}m$ in aerodynamic diameter, nitrogen dioxide [$NO_2$], sulfur dioxide, carbon monoxide, and ozone) and emergency department (ED) visits for DM with coma in Seoul, Korea from 2005 to 2009. Methods: The ED data and medical records from the 3 years previous to each diabetic coma event were obtained from the Health Insurance Review and Assessment Service to examine the relationship with air pollutants. Results: Overall, the adjusted relative risks (RRs) for an interquartile range (IQR) increment of $NO_2$ was statistically significant at lag 1 (RR, 1.125; 95% confidence interval [CI], 1.039 to 1.219) in a single-lag model and both lag 0-1 (RR, 1.120; 95% CI, 1.028 to 1.219) and lag 0-3 (RR, 1.092; 95% CI, 1.005 to 1.186) in a cumulative-lag model. In a subgroup analysis, significant positive RRs were found for females for per-IQR increments of $NO_2$ at cumulative lag 0-3 (RR, 1.149; 95% CI, 1.022 to 1.291). Conclusions: The results of our study suggest that ambient air pollution, specifically $NO_2$, is associated with ED visits for diabetic coma.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2148-2167
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• 2021

The proliferation of the Internet of Things (IoT) technologies and applications, especially the rapid rise in the use of mobile devices, from individuals to organizations, has led to the fundamental role of secure wireless networks in all aspects of services that presented with many opportunities and challenges. To ensure the CIA (confidentiality, integrity and accessibility) security model of the networks security and high efficiency of performance results in various resource-constrained applications and environments of the IoT platform, DDO-(data-driven operation) based constructions have been introduced as a primitive design that meet the demand of high speed encryption systems. Among of them, the TMN-family ciphers which were proposed by Tuan P.M., Do Thi B., etc., in 2016, are entirely suitable approaches for various communication applications of wireless mobile networks (WMNs) and advanced wireless sensor networks (WSNs) with high flexibility, applicability and mobility shown in two different algorithm selections, TMN64 and TMN128. The two ciphers provide strong security against known cryptanalysis, such as linear attacks and differential attacks. In this study, we demonstrate new probability results on the security of the two TMN construction versions - TMN64 and TMN128, by proposing efficient related-key recovery attacks. The high probability characteristics (DCs) are constructed under the related-key differential properties on a full number of function rounds of TMN64 and TMN128, as 10-rounds and 12-rounds, respectively. Hence, the amplified boomerang attacks can be applied to break these two ciphers with appropriate complexity of data and time consumptions. The work is expected to be extended and improved with the latest BCT technique for better cryptanalytic results in further research.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2C
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• pp.209-216
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• 2009

The repeater for wireless broadband internet (Wibro) system using OFDM demands the short processing delay to eliminate inter-symbol interference resulted from the time delay greater than the guard time. Towards this, the total system delay of repeater is expected to be minimized as possible as it can without distorting signal quality. In general, the FIR-type of filter is commonly deployed as a channelization filter, but due to its large amount of coefficients for producing prerequisite filter response the excessive large time delay occurs. To withstand this problem, the paper proposes the method for designing IIR filter whose response almost identical to that of the original filter. Moreover, in order to linearize the phase response of the designed IIR filter, this paper also introduce the way of designing the all-pass filter to be cascaded works for linearizing phase response of the channelization as well as the de-channelization filter. To achieve the further improvement in linearization of the phase response and reduction of the overall complexity, this paper tries to transform the integrated IIR filter group into the structure in polyphase style. The computer simulation verifies that the integrated IIR filter group designed in this paper reveals the relatively short processing delay without harming the acceptible signal quality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.37-42
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• 2024

This paper suggests polynomial time algorithm for cutwidth minimization problem that classified as NP-complete because the polynomial time algorithm to find the optimal solution has been unknown yet. To find the minimum cutwidth CW_f(G)=max_𝜈∈_VCW_f(𝜈)for given graph G=(V,E),m=｜V｜, n=｜E｜, the proposed algorithm divides neighborhood N_G[𝜈_i] of the maximum degree vertex 𝜈_i in graph G into left and right and decides the vertical cut plane with minimum number of edges pass through the vertex 𝜈_i firstly. Then, we split the left and right N_G[𝜈_i] into horizontal sections with minimum pass through edges. Secondly, the inner-section vertices are connected into line graph and the inter-section lines are connected by one line layout. Finally, we perform the optimization process in order to obtain the minimum cutwidth using vertex moving method. Though the proposed algorithm requires O(n²) time complexity, that can be obtains the optimal solutions for all of various experimental data

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Acupuncture Research
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• v.29 no.5
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• pp.1-16
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• 2012

Objectives : The purpose of this study was to examine the effects of manual acupuncture at the $LI_4$, $ST_{36}$ and $LR_3$ on Electroencephalogram(EEG) of patients with stroke. Methods : 32 channel EEG measurement was carried out in 35 Stroke patients(23 males and 12 females). EEG was measured for 21 minutes(made up of 7 sessions, 1 session means 3 minutes time interval) including 15 minutes(5 sessions) of acupuncture time. Power spectrum analysis was used as a measure of complexity. Statistical analysis was performed using Linear mixed model and DUNNETT's multiple comparison. Results : The results were as follows; 1. EEG amplitude was reduced during acupuncture except electrodes PG1 and PG2. 2. There was a notable change during 6~9 minutes after needling in ${\delta}{\cdot}{\beta}{\cdot}{\gamma}$ wave, and during 6~9 minutes after needling in ${\Theta}{\cdot}{\alpha}$ wave. Overall, during 6~9 minutes after needling. 3. TP8 is a common significant electrode among five wave forms. Conclusions : These results suggest that TP8 could be typical electrodes and change of EEG compared to baseline happens most often during 6~9 minutes after manipulated acupuncture at the $LI_4$, $ST_{36}$ and $LR_3$ of patients with stroke.