• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.473-479
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• 2012

In this paper, blind equalization technique using Gaussian two-cluster model is proposed. The proposed approach, by modeling the received M-QAM signals as Gaussian distributed two-cluster, minimizes the computational complexity and enhances the reliability of the signal estimates. In addition, by using a nonlinear estimator with variable parameters to estimate the transmitted signal, and by selectively applying the reduced constellation and the original constellation when estimating the signals, the reliability of the signal estimation was further improved. As a result, the proposed approach has improved the performance while reducing the complexity of the equalizer. Through computer simulations for blind equalization of higher-order signals of 64-QAM, it was confirmed that the proposed method showed better performance than traditional approaches.

• Computers and Concrete
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• v.11 no.4
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

• ETRI Journal
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• v.43 no.2
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• pp.260-271
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• 2021

This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies. The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N2).

• Bulletin of the Korean Chemical Society
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• v.28 no.10
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• pp.1705-1708
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• 2007

Geometric manipulation of molecules is an essential elementary component in computational modeling programs for molecular structure, stability, dynamics, and design. The computational complexity of transformation of internal coordinates to Cartesian coordinates was discussed before.1 The use of rotation matrices was found to be slightly more efficient than that of quaternion although quaternion operators have been widely advertised for rotational operations, especially in molecular dynamics simulations of liquids where the orientation is a dynamical variable.2 The discussion on computational efficiency is extended here to a more general case in which bond angles and sidechain torsion angles are allowed to vary. The algorithm of Thompson3 is derived again in terms of quaternions as well as rotation matrices, and an algorithm with optimal efficiency is described. The algorithm based on rotation matrices is again found to be slightly more efficient than that based on quaternions.

• Journal of Distribution Science
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• v.18 no.11
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• pp.69-77
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• 2020

Purpose: This research aims to verify the role of mobile shopping attributes, trustworthiness, and cultural dimensions on mobile shopping intentions in Saudi Arabia. The originality of the model stems from the verification of the moderating impact of cultural variables, namely collectivism and masculinity, and from the integration of trustworthiness as a variable depending on mobile shopping attributes. Research design, data and methodology: A survey was distributed to 233 consumers with different nationalities living in the Kingdom of Saudi Arabia. Structural equation modeling and multi-group analysis were carried out to verify the conceptual model and the moderating variables. Results: The findings support the influence of several innovation attributes, namely complexity and trialability on behavioral intentions, while relative advantage has a direct impact on trustworthiness. A few paths are moderated by masculinity and collectivism. Conclusions: Culture and mobile commerce attributes need to be thought out by managers as factors influencing mobile commerce segmentation for expatriates and locals. Trustworthiness is also a key factor of mobile shopping adoption. Limitations and future research ideas are presented to enrich the proposed model and improve its predictive validity.

• Asia pacific journal of information systems
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• v.30 no.3
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• pp.547-567
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• 2020

As the complexity of managing online personal information is increasing and data breach incidents frequently occur, online users feel a loss of control over their privacy. Such a situation leads to their cynical attitudes towards privacy protection, called privacy cynicism. This study aims to examine the role of privacy cynicism in online users' privacy behaviors. Data were gathered from a survey that 281 people participated in and were analyzed with covariance-based structural equation modeling. The findings of this study reveal that privacy cynicism has not only a direct influence on disclosure intention but also moderates an effect of privacy concerns on the intention. The analytical results also indicate that there is a nonlinear effect of privacy cynicism on the outcome variable. This study developed the concept of privacy cynicism—a phenomenon that significantly affects online privacy behavior but has been rarely examined. The study is an initial research into the nature and implications of privacy cynicism and furthermore clarified its role by the nonlinear relationship between privacy cynicism and the willingness to disclose personal information.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.2
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• pp.113-119
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• 1998

Spatial patterns of soil temperature on sloping lands are related to the amount of solar irradiance at the surface. Since soil temperature is a critical determinant of many biological processes occurring in the soil, an accurate prediction of soil temperature distribution could be beneficial to agricultural and environmental management. However, at least two problems are identified in soil temperature prediction over natural sloped surfaces. One is the complexity of converting solar irradiances to corresponding soil temperatures, and the other, if the first problem could be solved, is the difficulty in handling large volumes of geo-spatial data. Recent developments in geographic information systems (GIS) provide the opportunity and tools to spatially organize and effectively manage data for modeling. In this paper, a simple model for conversion of solar irradiance to soil temperature is developed within a GIS environment. The irradiance-temperature conversion model is based on a geophysical variable consisting of daily short- and long-wave radiation components calculated for any slope. The short-wave component is scaled to accommodate a simplified surface energy balance expression. Linear regression equations are derived for 10 and 50 cm soil temperatures by using this variable as a single determinant and based on a long term observation data set from a horizontal location. Extendability of these equations to sloped surfaces is tested by comparing the calculated data with the monthly mean soil temperature data observed in Iowa and at 12 locations near the Tennessee - Kentucky border with various slope and aspect factors. Calculated soil temperature variations agreed well with the observed data. Finally, this method is applied to a simulation study of daily mean soil temperatures over sloped corn fields on a 30 m by 30 m resolution. The outputs reveal potential effects of topography including shading by neighboring terrain as well as the slope and aspect of the land itself on the soil temperature.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2954-2972
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• 1998

It is expected that the transport of compressed video will become a significant part of total network traffic because of the widespread introduction of multimedial services such as VOD(video on demand). Accordingly, VBR(variable bit-rate) encoded video will be widely used, due to its advantages in statistical multiplexing gain and consistent vido quality. Since the transport of video traffic requires larger bandwidth than that of voice and data, the characterization of video source and traffic modeling is very important for the design of proper resource allocation scheme in ATM networks. Suitable statistical source models are also required to analyze performance metrics such as packet loss, delay and jitter. In this paper, we analyzed and described on the characterization and traffic modeling of MPEG video sources. The models are broadly classified into two categories; i.e., statistical models and deterministic models. In statistical models, the models are categorized into five groups: AR(autoregressive), Markov, composite Marko and AR, TES, and selfsimilar models. In deterministic models, the models are categorized into $({\sigma},\;{\rho}$, parameterized model, D-BIND, and Empirical Envelopes models. Each model was analyzed for its characteristics along with corresponding advantages and shortcomings, and we made comparisons on the complexity of each model.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.4
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• pp.187-194
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• 2015

This paper describes the PI controller design based on a practical transfer function model for centrifugal water chillers. The rotational speed of a compressor and the opening angle of an electronic expansion valve were simultaneously regulated as manipulated variables to maintain temperature reference and to ensure high efficiency of the chiller. The COP according to the change in each variable was investigated by performing some static experiments, and it was reflected in the PI controller design to accomplish the high efficiency control. Especially, the practical transfer function model of the chiller was built based on the dynamic experimental data considering the strong inherent non-linearity and complexity of the chiller system. The validity of the designed PI controller was proven by some experimental results using the test facility and the results were also compared to the conventional evaporating pressure control results.