• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.51-60
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• 2015

Traceability targets provision of information to stakeholders required for analyzing impacts among artifacts due to changes. Unlike single product development, in software product line developing the family of products the complexity of maintaining and managing traceability between two life cycles, domain and application engineering is so high. Accordingly, variability traceability management approach centred on orthogonal variability model that manages variability separated from development artifacts has been conceptually proposed, but its efficiency has not verified yet. This paper verifies whether orthogonal variability model based traceability can provide required traceability through an example. As the results, the OVM-based variability tracing method supports well to narrow down artifacts affected by the changes. However, the method does not support tracing the exact artifacts or exact part of an artifact affected by the change.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.52 no.6
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• pp.719-724
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• 2019

This study is an extension of our previous model for a size-based fish stock assessment. In the previous model, we applied an allometric length-weight relationship (W=α·L^β) to convert lengths of fish to weights, and estimated those parameters α and β, using data about lengths and weights aggregated over years. In this study, we focused on whether consideration of temporal (e.g., year-to-year) variability in those estimates (i.e., ${\hat{\alpha}}$ and ${\hat{\beta}}$) would contributive. After calculating year-specific estimates (i.e., year-specific pairs of ${\hat{\alpha}}$ and ${\hat{\beta}}$) by applying data about lengths and weights separated by year, we evaluated the contribution of those year-specific pairs of ${\hat{\alpha}}$ and ${\hat{\beta}}$ to the performance of the size-based stock assessment model. The model with such year-to-year variability being considered (lower AIC) outperformed that with the variability being ignored (higher AIC). We illustrated this study using data on Korean chub mackerel Scomber japonicus from 2005-2017.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.386-395
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• 2009

Business entities with which most service components interact are kind of cross-cutting concerns in a multi-layered distributed application architecture. When business entities are modified, service components related to them should also be modified, even though they implement common functions of the application framework. This paper proposes what we call DTT (Data Type-Tolerant) component model to process the variability of business entities, or externalized data, which feature modern application architectures. The DTT component model expresses the data variability of product lines at the implementation level by means of SCDTs (Self-Contained Data Types) and variation point interfaces. The model improves the efficiency of application engineering through data type converters which support type conversion between SCDTs and business entities of particular applications. The value of this model lies in that data and functions are coupled locally in each component again by allowing service components to deal with SCDTs only instead of externalized business eutities.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.101-101
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• 2020

Traditional stochastic simulation of hydroclimatological variables often underestimates the variability and correlation structure of larger timescale due to the difficulty in preserving long-term memory. However, the Long Short-Term Memory (LSTM) model illustrates a remarkable long-term memory from the recursive hidden and cell states. The current study, therefore, employed the LSTM model in stochastic generation of hydrologic and climate variables to examine how much the LSTM model can preserve the long-term memory and overcome the drawbacks of conventional time series models such as autoregressive (AR). A trigonometric function and the Rössler system as well as real case studies for hydrological and climatological variables were tested. Results presented that the LSTM model reproduced the variability and correlation structure of the larger timescale as well as the key statistics of the original time domain better than the AR and other traditional models. The hidden and cell states of the LSTM containing the long-memory and oscillation structure following the observations allows better performance compared to the other tested conventional models. This good representation of the long-term variability can be important in water manager since future water resources planning and management is highly related with this long-term variability.

• Journal of Industrial Technology
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• v.22 no.B
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• pp.163-167
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• 2002

Autonomic nervous system play an important role of keeping our health as balancing homeostasis. But the abnormality of these abilities makes our presence be feeble. To obtain these information of body which helps for us to decide whether one is healthy or not, based on the study of Heart Rate Variability. In this paper, we presented HRV model and its processing steps to extract some information of human body. After that, some experimental results are presented in time-domain and frequency-domain.

• Journal of Ship and Ocean Technology
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• v.8 no.2
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• pp.20-28
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• 2004

A dynamic-thermodynamic sea ice model with 50-km spatial and 24-hour temporal resolution is used to investigate the spatial and long-term temporal variability of the sea ice cover the Arctic Basin. The model satisfactorily reproduces the averaged main characteristics of the sea ice and the sea ice extent in the Arctic Basin and its decrease in early 1990th. At times model allows to suppose partial recovery of sea ice cover in the last years of twenty century. The employment of explicit form for description of ridging gives opportunity to assume that the observed thinning is the result of reduction the intensity of ridging processes and to estimate long-term variability of probability the ridge free navigation in the different parts of the Arctic Ocean including the North Sea Route area.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1240-1245
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• 2015

The purpose of this paper is to investigate the autonomic nervous system activity in various ambient temperatures. To evaluate autonomic function, we use the frequency domain analysis of heart rate variability such as FFT(fast fourier transformation), AR(Auto-Regressive) model and Lomb-Scargle peridogram. HRV(heart rate variability) is calculated by using ECG recorded from 3 different temperature room which temperature is controlled in 18℃(low), 25℃(mid) and 38℃(high), respectively. Totally 22 subjects were participated in the experiment. In the results, the most significant autonomic changes caused by temperature load were found in the HF(high frequency) component of FFT and AR model. And the HF power is decreased by increasing temperature. Significance level was increased by increasing the difference of temperatures.

• Ocean Systems Engineering
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• v.7 no.4
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• Geomechanics and Engineering
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• v.23 no.5
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• pp.419-429
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• 2020

To investigate the effect of spatial variability on hydraulic properties of unsaturated soils, a numerical model is set up which can simulate seepage process in an unsaturated heterogeneous soil. The unsaturated heterogeneous soil is composed of matrix sand embedded with a small proportion of clay for simulating the heterogeneity. Soil-water characteristic curve and unsaturated hydraulic conductivity curve of the unsaturated soil are expressed by Van Genuchten model. Hydraulic parameters of the matrix sand are considered as random fields. Different autocorrelation lengths (ACLs) of hydraulic parameter of the matrix sand and different proportions of clay are assumed to investigate the influence of spatial variability on the equivalent hydraulic properties of the heterogeneous soil. Four model sizes are used in the numerical experiments to investigate the influence of scale effects and to determine the sizes of representative volume element (RVE) in the numerical simulations. Through a number of Monte Carlo simulations of unsaturated seepage analysis, the means and the coefficients of variations (COVs) of the equivalent hydraulic parameters of the heterogeneous soil are calculated. Simulations show that the ACL and model size has little influence on the means of the equivalent hydraulic parameters, but they have a large influence on the COVs of the equivalent hydraulic parameters. The size of an RVE is mainly affected by the ACL and the proportion of heterogeneity. The influence of spatial variability on the hydraulic parameters of the heterogeneous unsaturated soil can be used as a guidance for geotechnical reliability analysis and design related to unsaturated soils.

• Journal of Construction Engineering and Project Management
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• v.8 no.2
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• pp.17-33
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• 2018

This study investigates the stability of professionals' cost variability factor-rankings across different levels of cost-variability and response scenarios. Descriptive statistics are used to examine the stability of factor-ranking for 20 cost variability factors and a Multinomial Logistic (MNL) regression model was implemented to examine the stability of cost variability factors across three cost variability levels. The finding on the descriptive statistics indicated that professionals' factors-rankings are stable only for external factors. The MNL regression results on factor-stability suggested that 8 out of the 20 evaluated factors were unstable determinant of lower cost variability levels. These factors are "risk associated with the project", "personal bias and poor professionalism of the estimators", "limited time available to complete the project", "lack of skills and experience by estimator" "geographical location of projects", "incomplete & rush designs for estimate", "unforeseen or unexpected site constraints", "high class bidders for the contractors". Similarly lack of experience and large size projects were observed to be unstable as well. These observations suggest that professionals' view on pre-tender cost variability factor-ranking yields unstable factor rankings hence should not be relied upon as the only mechanisms to mitigate cost related risks in construction projects.