• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of Information Display
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• v.12 no.3
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• pp.115-119
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• 2011

Optically isotropic liquid crystal (LC) mixture such as blue-phase LC and nanostructured LC composites exhibit the advantages of fast response time, high contrast ratio and wide-viewing angle due to the induced birefringence along the horizontal electric field. Utilizing this mixture, a novel single cell gap in-plane switching-type polymer-stabilized blue-phase transflective liquid crystal display by embedding the nanowire grid polarizer as a polarization-dependent reflective polarizer in the R region is proposed. This device can be used as a normal black mode without any quarter-wave plate or patterned in-cell phase retarder. Moreover, the transmittance is identical to the reflectance so that it will be suitable for single gamma driving. Detailed electro-optic performances, such as voltage-dependent light efficiency and viewing angle of the proposed device configuration, are investigated.

• Steel and Composite Structures
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• v.28 no.5
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• pp.541-557
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• 2018

This paper presents the influence of carbon nanotubes (CNTs) waviness and aspect ratio on the vibrational behavior of functionally graded nanocomposite sandwich annular sector plates resting on two-parameter elastic foundations. The carbon nanotube-reinforced (CNTR) sandwich plate has smooth variation of CNT fraction along the thickness direction. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes' effective aspect ratio as the filler content increases. Effects of CNT distribution, volume fraction, aspect ratio and waviness, and also effects of Pasternak's elastic foundation coefficients, sandwich plate thickness, face sheets thickness and plate aspect ratio are investigated on the free vibration of the sandwich plates with wavy CNT-reinforced face sheets. The study is carried out based on three-dimensional theory of elasticity and in contrary to two-dimensional theories, such as classical, the first- and the higher-order shear deformation plate theories, this approach does not neglect transverse normal deformations. The sandwich annular sector plate is assumed to be simply supported in the radial edges while any arbitrary boundary conditions are applied to the other two circular edges including simply supported, clamped and free.

• Structural Engineering and Mechanics
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• v.25 no.2
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• pp.239-268
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• 2007

This paper deals with optimal stacking sequence design of laminate composite structures. The stacking sequence optimisation of laminate composites is formulated as a combinatorial problem and is solved using Simulated Annealing (SA), an algorithm devised based on inspiration of physical process of annealing of solids. The combinatorial constraints are handled using a correction strategy. The SA algorithm is strengthened by embedding Tabu search in order to prevent recycling of recently visited solutions and the resulting algorithm is referred to as tabu embedded simulated Annealing (TSA) algorithm. Computational performance of the proposed TSA algorithm is enhanced through cache-fetch implementation. Numerical experiments have been conducted by considering rectangular composite panels and composite cylindrical shell with different ply numbers and orientations. Numerical studies indicate that the TSA algorithm is quite effective in providing practical designs for lay-up sequence optimisation of laminate composites. The effect of various neighbourhood search algorithms on the convergence characteristics of TSA algorithm is investigated. The sensitiveness of the proposed optimisation algorithm for various parameter settings in simulated annealing is explored through parametric studies. Later, the TSA algorithm is employed for multi-criteria optimisation of hybrid composite cylinders for simultaneously optimising cost as well as weight with constraint on buckling load. The two objectives are initially considered individually and later collectively to solve as a multi-criteria optimisation problem. Finally, the computational efficiency of the TSA based stacking sequence optimisation algorithm has been compared with the genetic algorithm and found to be superior in performance.

• Computational Structural Engineering
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• v.8 no.1
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• pp.161-172
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• 1995

An Integrated processing system for finite element structural analysis has been studied. It is designed to control integratedly the preprocessing, the execution and the postprocessing of a finite element structural analysis program on Windows. It becomes a better graphic user interface(GUI) for the concurrent representation of various inputs and outputs through the dialog-type on multi-windows by the multi-tasking and the object linking and embedding(OLE). Data input can be done easily through menus, dialog boxes and automatic stepwise inputs on the multiple windows, and then output results can be seen with input data on the same screen. Efficiency and validity of the system were examined by solving several numerical examples.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.198-205
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• 2003

This paper proposes the adaptive relaying of protective devices applied in the neighboring distribution feeders for reliable and efficient operations of a wind farm interconnected with distribution networks by dedicated lines. A wind farm connected to an electric power network is one of the greatest alternative energy sources. However, the wind turbine generators are influenced by abnormal grid conditions such as disturbances occurring in the neighboring distribution feeders as well as the dedicated power. Particularly, in cases of a fault happening in the neighboring distribution feeders, a wind farm might be accelerated until protective devices clear the fault. Therefore, the delayed operation time of protective devices for satisfying the coordination might overly expose the interconnected wind turbine generators to the fault and cause damage to them. This paper describes the proper delayed operation time of protective relay satisfying the coordination of the distribution networks as well as reducing damage on the interconnected wind farm. The simulation results for the Hoenggye substation model composed of five feeders and one dedicated line using PSCAD/EMTDC showed that the proper delayed time of protective devices reflecting the fault condition and the power output of the wind farm could improve the operational reliability, efficiency, and stability of the wind farm.

• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.449-454
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• 2016

Radicals of Korean characters consist of some strokes, and complete letters are comprised of a combination of radicals. During the process of combining strokes and radicals, it requires a lot of effort as the size and the position of the components can be changed. Unlike this, METAFONT can improve the efficiency in the production process of fonts by changing its parameters. It also provides a system which can easily transform size and position of the strokes and radicals. We propose a structural Korean font generator which allows users to modify parameters of letters through METAFONT and generates a variety of fonts automatically. The suggested Korean font generator can be applied to font embedding and font editor.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.191-191
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• 2012

InP quantum dot (QD) - organosilicon nanocomposites were synthesized and their photoluminescence quenching was mainly investigated because of their applicability to white LEDs (light emitting diodes). The as-synthesized InP QDs which were capped with myristic acid (MA) were incompatible with typical silicone encapsulants. Post ligand exchange the MA with a new ligand, 3-aminopropyldimethylsilane (APDMS), resulted in soluble InP QDs bearing Si-H groups on their surface (InP-APDMS) which allow embedding the QDs into vinyl-functionalized silicones through direct chemical bonding, overcoming the phase separation problem. However, the ligand exchange from MA to APDMS caused a significant decrease in the photoluminescent efficiency which is interpreted by ligand induced surface corrosion relying on theoretical calculations. The InP-APDMS QDs were cross-linked by 1,4-divinyltetramethylsilylethane (DVMSE) molecules via hydrosilylation reaction. As the InP-organosilicon nanocomposite grew, its UV-vis absorbance was increased and at the same time, the PL spectrum was red-shifted and, very interestingly, the PL was quenched gradually. Three PL quenching mechanisms are regarded as strong candidates for the PL quenching of the QD nano-composites, namely the scattering effect, Forster resonance energy transfer (FRET) and cross-linker tension preventing the QD's surface relaxation.

• Journal of the Optical Society of Korea
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• v.16 no.3
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• pp.305-312
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• 2012

We numerically demonstrate the performance of a plasmonic lens composed of an array of nanoslits perforated on thin metallic film with slanted cuts on the output surface. Embedding Kerr nonlinear material in nanoslits is employed to modulate the output beam. A two dimensional nonlinear-dispersive finite-difference time-domain (2D N-D-FDTD) method is utilized. The performance parameters of the proposed lens such as focal length, full-width half-maximum, depth of focus and the efficiency of focusing are investigated. The structure is illuminated by a TM-polarized plane wave and a Gaussian beam. The effect of the beam waist of the Gaussian beam and the incident light intensity on the focusing effect is explored. An exact formula is proposed to derive electric field E from electric flux density D in a Kerr-Dispersive medium. Surface plasmon (SPs) modes and Fabry-Perot (F-P) resonances are used to explain the physical origin of the light focusing phenomenon. Focused ion beam milling can be implemented to fabricate the proposed lens. It can find valuable potential applications in integrated optics and for tuning purposes.