• Steel and Composite Structures
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• v.34 no.5
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• pp.681-698
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• 2020

The paper addresses contribution to the modeling and optimization of major machinability parameters (cutting force, surface roughness, and tool wear) in finish dry hard turning (FDHT) for machinability evaluation of hardened AISI grade die steel D₃ with PVD-TiN coated (Al₂O₃-TiCN) mixed ceramic tool insert. The turning trials are performed based on Taguchi's L₁₈ orthogonal array design of experiments for the development of regression model as well as adequate model prediction by considering tool approach angle, nose radius, cutting speed, feed rate, and depth of cut as major machining parameters. The models or correlations are developed by employing multiple regression analysis (MRA). In addition, statistical technique (response surface methodology) followed by computational approaches (genetic algorithm and particle swarm optimization) have been employed for multiple response optimization. Thereafter, the effectiveness of proposed three (RSM, GA, PSO) optimization techniques are evaluated by confirmation test and subsequently the best optimization results have been used for estimation of energy consumption which includes savings of carbon footprint towards green machining and for tool life estimation followed by cost analysis to justify the economic feasibility of PVD-TiN coated Al₂O₃+TiCN mixed ceramic tool in FDHT operation. Finally, estimation of energy savings, economic analysis, and sustainability assessment are performed by employing carbon footprint analysis, Gilbert approach, and Pugh matrix, respectively. Novelty aspects, the present work: (i) contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45-60 HRC, (ii) demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing ceramic tool in hard turning processes considering technological, economical and ecological aspects, which are helpful and efficient from industrial point of view, (iii) provides environment friendliness, cleaner production for machining of hardened steels, (iv) helps to improve the desirable machinability characteristics, and (v) serves as a knowledge for the development of a common language for sustainable manufacturing in both research field and industrial practice.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.8
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• pp.760-767
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• 2008

A watershed model was constructed using HSPF(Hydrological Simulation Program - Fortran) for predicting flow and suspended solid in the Imha dam watershed. The whole watershed was divided into 33 sub-watersheds in the watershed model, which was calibrated for flow using measured data from 2001 to 2007. The accuracy of watershed model prediction was evaluated using statistical coefficients of R$_{eff}$(Nash-Sutcliffe), R$^2$(Correlation coefficient) and graphical comparison. Then, the model was calibrated for suspended solid using field data measured during 3 major rainfall events in July 2006, and then validated against data obtained in 2 rainfall events from July to August in 2007. Overall, the model showed good agreements with the field measurements for flow and suspended solid. The watershed model constructed in this study can provide flow and suspended solid entering the Imha reservoir and will be utilized for turbid water management in linkage with reservoir water quality models.

• Journal of the Korea Organic Resources Recycling Association
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• v.23 no.2
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• pp.53-60
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• 2015

Newly, nutrients recovery by bioconversion in the swine waste which caused serious problems due to its high organic fraction and content of nutrients such as phosphorus and nitrogen is viewed as a considerable approach since it produces valuable product as well as recycling of resources. Consequently, it is necessary to find new methods to treat swine waste. One possible solution to this problem is to use this potential pollutant as a growth substrate for economically valuable products. The study for the fundamental improvement of bioconversion efficiency by finding optimum growth conditions using statistical models and biotechnology was performed. A novel approach to utilize swine waste by cultivating mycelia of the mushroom Phellinus linteus are described. A central composite face-centered design (CCF) for the experiments was used to develop empirical model providing a quantitative interpretation of the relationships among the three variables, which were substrate concentration, pH, and temperature. The maximal radial extension rate (2.78mm/d) of P.linteus was determined under the condition of 5.0 g COD/L, pH 5.0, and temperature $29.7^{\circ}C$. The results of this study suggest that swine waste could be utilized as a growth substrate for the cultivation of mushroom mycelia enhancing an efficiency of utilizing this by-product of the livestock industry.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.18 no.2
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• pp.166-179
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• 1985

The seasonal distribution and migration of flying squid, Ommastrephes bartrami (LeSueur), in the North Pacific were studied by means of mantle length, surface temperature, and catch and effort data of the Korean drift gillnet fishery from 1980 to 1983. The water temperature for the best fishing ranged from $15^{\circ}\;to\;16^{\circ}C$ in May through July and from $13^{\circ}\;to\;18^{\circ}C$ in August through January. High densities of flying squid were found in the thermal fronts with $18^{\circ}C$ isotherm in August and with $15^{\circ}C$ isotherm in September. The densities of flying squid were higher in the western region than in the eastern region in the North Pacific. The high densities of flying squid in the northwestern Pacific were attributed to the high gradients of oceanographic properties in the region. Migration models for flying squid were hypothesized based on the monthly distributions of catch per unit net, mantle length compositions by statistical blocks, and the hydrographic features of the North Pacific. The large flying squid moved to the northern region and to the central Pacific region earlier than the small sized group in the northward migration period (from June to August). Flying squid begin the reverse southward migration from the Subarctic Frontal Zone in autumn with onset of cooling and the development of Oyashio Current. The large sized group starts their southward return migration from more northern waters than the small sized group but the former moves past the later ana reaches the spawing ground first.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.289-307
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• 2010

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence-termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carry out model selection and recovery of sparse signals irrespective of the phases of the nonzero entries even if the number of nonzero entries scales almost linearly with the number of rows of the Alltop Gabor frame.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.497-510
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• 2010

In medical practice and research, the problem of assessing reliability between two or more quantitative measures is quite common. Intraclass correlation coefficient(ICC) is commonly used to scale of reliability. Some methods were developed to calculate the required number of subjects, raters or replicates in one-way or two-way random ANOVA models. This paper, studies and compares the performance of four methods such as Walter et al. (1998), Giraudeau and Mary (2001), Saito et al. (2006) and Bonett (2002). In order to compare the efficiency of methods we compare the number of subjects, replicates and the width of confidence interval of ICC needed for some specific ICC values. In the case of subject size, Giraudeau's method is the best. In case of the number of replicates, Saito's method was superior to others. The width of confidence interval of ICC was narrower for Giraudeau's method than any others.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1229-1237
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• 2009

In the long-term care insurance(LTCI) system, the question of how to provide the most appropriate care has become a major issue for the elderly, their family, and for policy makers. To help beneficiaries use LTC services appropriately to their needs of care, National Health Insurance Corporation(NHIC) provide them with the individualized care plan, named the Long-term Care User Guide. It includes recommendations for beneficiaries' most appropriate type of care. The purpose of this study is to develop a recommending system for care plan(Res-CP) in LTCI system. We used data set for Long-term Care User Guide in the 3rd long-term care insurance pilot programs. To develop the model, we tested four models, including a decision-tree model in data-mining, a logistic regression model, and a boosting and boosting techniques in an ensemble model. A decision-tree model was selected to describe the Res-CP, because it may be easy to explain the algorithm of Res-CP to the working groups. Res-CP might be useful in an evidence-based care planning in LTCI system and may contribute to support use of LTC services efficiently.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9453-9458
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• 2014

Background: Breast cancer is the second leading cause of cancer death for women in the United States. Differences in survival of breast cancer have been noted among racial and ethnic groups, but the reasons for these disparities remain unclear. This study presents the characteristics and the survival curve of two racial and ethnic groups and evaluates the effects of race on survival times by measuring the lifetime data-based half-normal model. Materials and Methods: The distributions among racial and ethnic groups are compared using female breast cancer patients from nine states in the country all taken from the National Cancer Institute's Surveillance, Epidemiology, and End Results cancer registry. The main end points observed are: age at diagnosis, survival time in months, and marital status. The right skewed half-normal statistical probability model is used to show the differences in the survival times between black Hispanic (BH) and black non-Hispanic (BNH) female breast cancer patients. The Kaplan-Meier and Cox proportional hazard ratio are used to estimate and compare the relative risk of death in two minority groups, BH and BNH. Results: A probability random sample method was used to select representative samples from BNH and BH female breast cancer patients, who were diagnosed during the years of 1973-2009 in the United States. The sample contained 1,000 BNH and 298 BH female breast cancer patients. The median age at diagnosis was 57.75 years among BNH and 54.11 years among BH. The results of the half-normal model showed that the survival times formed positive skewed models with higher variability in BNH compared with BH. The Kaplan-Meir estimate was used to plot the survival curves for cancer patients; this test was positively skewed. The Kaplan-Meier and Cox proportional hazard ratio for survival analysis showed that BNH had a significantly longer survival time as compared to BH which is consistent with the results of the half-normal model. Conclusions: The findings with the proposed model strategy will assist in the healthcare field to measure future outcomes for BH and BNH, given their past history and conditions. These findings may provide an enhanced and improved outlook for the diagnosis and treatment of breast cancer patients in the United States.