• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.20 no.2
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• pp.123-135
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• 2016

This article deals with the pricing of Asian options under a constant elasticity of variance (CEV) model as well as a stochastic elasticity of variance (SEV) model. The CEV and SEV models are underlying asset price models proposed to overcome shortcomings of the constant volatility model. In particular, the SEV model is attractive because it can characterize the feature of volatility in risky situation such as the global financial crisis both quantitatively and qualitatively. We use an asymptotic expansion method to approximate the no-arbitrage price of an arithmetic average Asian option under both CEV and SEV models. Subsequently, the zero and non-zero constant leverage effects as well as stochastic leverage effects are compared with each other. Lastly, we investigate the SEV correction effects to the CEV model for the price of Asian options.

• Ocean and Polar Research
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• v.40 no.4
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• pp.237-247
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• 2018

This study aims to estimate optimal harvests, fishing efforts, and stock levels of hairtail harvested by the large pair bottom trawl, the large otter trawl, the large purse seine, the offshore long line, and the offshore angling fisheries by using the surplus production models and the current value Hamiltonian method. Processes of this study are as follows. First of all, this study estimates the standardized fishing efforts regarding the harvesting of the hairtail by the above five fishing gears based on the general linear model developed by Gavaris. Secondly, this study estimates environmental carrying capacity (k), intrinsic growth rate (r), and catchability coefficient (q) by applying the Clarke Yoshimoto Pooley (CY&P) model among various surplus production models. Thirdly, this study estimates the optimal harvests, fishing efforts, and stock levels regarding the hairtail by the current value Hamiltonian method, including the average landing price, the average unit cost, and the social discount rate. Finally, this study attempts a sensitivity analysis to figure out changes in optimal harvests, fishing efforts, and stock levels due to changes in the average landing price and the average unit cost. As results induced by the current value Hamiltonian method, the optimal harvests, fishing efforts, and stock levels regarding the hairtail caught by several fishing gears were estimated as 33,133 tons, 901,080 horse power, and 79,877 tons, respectively. In addition, from the results of the sensitivity analysis, first of all, if the average landing price of the hairtail constantly increases, the optimal harvests of it increase at a decreasing rate, and then harvests finally slightly decrease as a result of decreases in stock levels. Secondly, if the average unit cost of fishing efforts continuously increases, the optimal fishing efforts decreases, but optimal stock levels increase. Optimal harvests start climbing and then decrease continuously due to increases in the average unit cost. In summary, this study suggests that the optimal harvests (33,133 tons) were larger than actual harvests (25,133 tons), but the optimal fishing efforts (901,080 horse power) were much less than estimated standardized fishing efforts (1,277,284 horse power), corresponding to the average of the recent three years (2014-2016). This result implies that the hairtail has been inefficiently harvested and recently overfished due to excessive fishing efforts. Efficient management and conservation policies on stock levels need to be urgently implemented. Some appropriate strategies would be to include the hairtail in the Korean TAC species or to extend the closed fishing season for this species.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.11a
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• pp.12-27
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• 2000

During the recent decades, he problem of climate variability and change has been in the forefront of scientific problems. The objective of this study was to assess the impact of climate variability on crop growth and yield. The growth duration was the main impact of climate variability on crop yield. Phyllochronterval was shortened in the global worming situations. A simple model to describe developmental traits was provided from heading data of directly seeded rice cultivars and temperature data. Daily mean development rate could be explained by the average temperature during the growth stage. Simple regression equation between daily mean development rate(x) and the average temperature(y) during the growth period as y = ax + b. It can be simply modified as x = 1/a $\ast$ (y-b). The parameters of the model could depict the thermo sensitivity of the cultivars. On the base of this model, the three doubled CO2 GCM scenarios were assessed. The average of these would suggest a decline in rice production of about 11% if we maintained the current cultivars. Future cultivar's developmental traits could be suggested by the two model parameters.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.420-423
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• 2008

This present work is to find optimum design of a NACA65 axial fan blade with weighted average surrogate model. The numerical analysis by Reynolds-average Navier-Stokes equations with shear stress turbulence(SST) is discretized by finite volume approximations and solved on hexahedral grids for flow analysis. The blade aerodynamic shape is modified by six design variables for the optimization. The blade profile as well as stacking line is modified to enhance blade total efficiency. Six design variables, airfoil maximum camber, maximum camber location, leading edge radius, trailing edge radius, lean angle at 50% span and lean angle at 100% span, are selected for blade profile to enhance the total efficiency. The PBA model which is basically weighted average of the basis surrogates is used to find the optimal design in the design space from the constructed response surface model for the objective function. By the optimization, the total efficiency is increased by 1.4%.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• Journal of Power Electronics
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• v.17 no.4
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• pp.1097-1108
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• 2017

Accurate calculation of the conduction and switching losses of a power electronic converter is required to achieve the efficiency of the converter. Such calculation is also useful for computing the junction temperature of the switches. A few models have been developed in the articles for calculating the switching energy losses during switching transitions for the given values of switched voltage and switched current. In this study, these models are comprehensively reviewed and investigated for the first time for ease of comparison among them. These models are used for calculating the average amount of switching power losses. However, some points and details should be considered in utilizing these models when switched current or switched voltage presents time-variant and alternative quantity. Therefore, an applicable technique is proposed in details to use these models under the above-mentioned conditions. A proper switching loss model and the presented technique are used to establish a new and fast method for obtaining the average switching power losses in any type of power electronic converters. The accuracy of the proposed method is evaluated by comprehensive simulation studies and experimental results.

• Computers and Concrete
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• v.29 no.5
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• pp.303-321
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• 2022

The current design equations for predicting the torsional capacity of RC members underestimate the torsional strength of under-reinforced members and overestimate the torsional strength of over-reinforced members. This is because the design equations consider only the yield strength of torsional reinforcement and the cross-sectional properties of members in determining the torsional capacity. This paper presents an analytical model to predict the thickness of shear flow path in RC beams subjected to pure torsion. The analytical model assumes that torsional reinforcement resists torsional moment with a sufficient deformation capacity until concrete fails by crushing. The ACI 318 code is modified by applying analytical results from the proposed model such as the average stress of torsional reinforcement and the effective gross area enclosed by the shear flow path. Comparison of the calculated and observed torsional strengths of existing 129 test beams showed good agreement. Two design variables related to the compressive strength of concrete in the proposed model are approximated for design application. The accuracy of the ACI 318 code for the over-reinforced test beams improved somewhat with the use of the approximations for the average stresses of reinforcements and the effective gross area enclosed by the shear flow path.

• ETRI Journal
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• v.46 no.5
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• pp.915-927
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• 2024

We obtain the air quality index (AQI) for a descriptive system aimed to communicate pollution risks to the population. The AQI is calculated based on major air pollutants including O₃, CO, SO₂, NO, NO₂, benzene, and particulate matter PM2.5 that should be continuously balanced in clean air. Air pollution is a major limitation for urbanization and population growth in developing countries. Hence, automated AQI prediction by a deep learning method applied to time series may be advantageous. We use a seasonal autoregressive integrated moving average (SARIMA) model for predicting values reflecting past trends considered as seasonal patterns. In addition, a transductive long short-term memory (TLSTM) model learns dependencies through recurring memory blocks, thus learning long-term dependencies for AQI prediction. Further, the TLSTM increases the accuracy close to test points, which constitute a validation group. AQI prediction results confirm that the proposed SARIMA-TLSTM model achieves a higher accuracy (93%) than an existing convolutional neural network (87.98%), least absolute shrinkage and selection operator model (78%), and generative adversarial network (89.4%).

• 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.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.