• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.47-53
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• 2009

We define product line (or mix) selection problem as selecting a subset of potential product variants that can simultaneously minimize product proliferation and maintain market coverage. Selecting the most efficient product mix is a complex problem, which requires analyses of multiple criteria. This paper proposes a method based on Data Envelopment Analysis (DEA) for product line selection. Data Envelopment Analysis (DEA) is a linear programming based technique commonly used for measuring the relative performance of a group of decision making units with multiple inputs and outputs. Although DEA has been proved to be an effective evaluation tool in many fields, it has not been applied to solve the product line selection problem. In this study, we construct a five-step method that systematically adopts DEA to solve a product line selection problem. We then apply the proposed method to an existing line of staplers to provide quantitative evidence for managers to generate desirable decisions to maximize the company profits while also fulfilling market demands.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2A
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• pp.91-98
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• 2009

This paper provides new exact-closed form expressions for average SER and average BER as well as outage probability for M-PSK signaling with selection combining over independent but non-identically distributed Rayleigh fading paths. The validity of these expressions is verified by the Monte-Carlo simulations. All of numerical results are in excellent agreement with simulation results.

• The Journal of Advanced Prosthodontics
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• v.16 no.1
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• pp.38-47
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• 2024

PURPOSE. This study aims to evaluate the accuracy of different shade selection techniques and determine the matching success of crown restorations fabricated using digital shade selection techniques. MATERIALS AND METHODS. Teeth numbers 11 and 21 were prepared on a typodont model. For the #11 tooth, six different crowns were fabricated with randomly selected colors and set as the target crowns. The following four test groups were established: Group C, where the visual shade selection was performed using the Vita 3D Master Shade Guide and the group served as the control; Group Ph, where the shade selection was performed under the guidance of dental photography; Group S, where the shade selection was performed by measuring the target tooth color using a spectrophotometer; and Group I, where the shade selection was performed by scanning the test specimens and target crowns using an intraoral scanner. Based on the test groups, 24 crowns were fabricated using different shade selection techniques. The ΔE values were calculated according to the CIEDE2000 (2:1:1) formula. The collected data were analyzed by means of a one-way analysis of variance. RESULTS. For the four test groups (Groups C, Ph, S, and I), the following mean ΔE values were obtained: 2.74, 3.62, 2.13, and 3.5, respectively. No significant differences were found among the test groups. CONCLUSION. Although there was no statistically significant difference among the shade selection techniques, Group S had relatively lower ΔE values. Moreover, according to the test results, the spectrophotometer shade selection technique may provide more successful clinical results.

• IE interfaces
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• v.24 no.2
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• pp.97-104
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• 2011

Research for multi-variate classification has been studied through two kinds of procedures which are feature selection and classification. Feature Selection techniques have been applied to select important features and the other one has improved classification performances through classifier applications. In general, each technique has been independently studied, however consideration of the interaction between both procedures has not been widely explored which leads to a degraded performance. In this paper, through integrating these two procedures, classification performance can be improved. The proposed model takes advantage of KBANN (Knowledge-Based Artificial Neural Network) which uses prior knowledge to learn NN (Neural Network) as training information. Each NN learns characteristics of the Feature Selection and Classification techniques as training sets. The integrated NN can be learned again to modify features appropriately and enhance classification performance. This innovative technique is called ALBNN (Algorithm Learning-Based Neural Network). The experiments' results show improved performance in various classification problems.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.43-46
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• 1994

A new spatial localization technique using high-order surface gradient coil (SGC) is proposed. Although the Spatial Selection with High-Order gradient (SHOT) can provide a 2-D selection with only one selective RF pulse, the high-order gradient produced by cylindrical-shape coils has not been clinically useful for clinical systems due to the large minimum selection size caused by the limited radial gradient intensity. However, by using the proposed high-order SGCs located near the imaging region, the size of volume selection can be reduced to a clinically useful 1-4 cm in diameter by applying stronger radial gradient with much less gradient driving power. A 40 cm-by-40 cm $r^{2}$ SGC has been designed and constructed, and phantom and volunteer studies have been performed. Experimental results using spatially localized MRI show good agreement to the theoretically predicted behavior.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.181-188
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• 2022

The purpose of this study is to identify selection attributes, functional benefits, psychological benefits, and values according to crossover shopping patterns (showrooming and webrooming). To achieve objectives of this study, a survey was designed based on the means-end chain theory, using the in-depth laddering technique and APT laddering technique which understanding the linkage of A(attributes)-FB(functional benefits)-PB(psychological benefit)-V(value). These two laddering techniques were used to construct a hierarchical value map (HVM) by linking selection attributes, functional benefits, psychological benefits, and value levels. The selection attribute items that showrooming shoppers consider important are 'price conformity', 'product information', 'product variety', and 'delivery service'. Functional benefit items were 'free purchase', 'economic benefit', 'communication', 'safety', and 'accurate Information', and psychological benefit items were 'convenience', 'relaxation', 'pleasure', 'rational consumption', and 'stability'. Finally, the value items were 'self-satisfaction', 'abundant life', 'achievement', 'happiness', and 'reasonable life'. Next, the selection attribute items that webrooming shoppers consider important are 'price conformity', 'product information', 'product variety', 'AS', 'shopping atmosphere', and 'seller service'. Functional benefit items were 'free purchase', 'economic profit', 'expression opinion', 'safety', and 'accurate information', and psychological benefit items were 'convenience', 'relaxation', 'rational consumption', and 'stability'. Finally, the value items were 'self-satisfaction', 'abundant life', 'happiness', and 'reasonable life'.

• Korean Journal of Computational Design and Engineering
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• v.1 no.1
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• pp.20-32
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• 1996

The boundary evaluation technique for Boolean operation on non-manifold models which is regarded as the most popular and powerful method to create and modify 3-D CAD models has been developed. This technique adopted the concept of Merge and Selection in which the CSG tree for Boolean operation can be edited quickly and easily. In this method, the merged set which contains complete information about primitive models involved is created by merging primitives one by one, then the alive entities are selected following the given CSG tree. This technique can support the hybrid representation of B-rep(Boundary Representation) and CSG(Constructive Solid Geometry) tree in a unified non-manifold model data structure, and expected to be used as a basic method for many modeling problems such as data representation of form features, and the interference between them, and data representation of conceptual models in design process, etc.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.127-130
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• 2007

The conventional feature recombination technique is very effective in the band-limited noise condition, but in broad-band noise condition, the conventional feature recombination technique does not produce notable performance improvement compared with the full-band system. To cope with this drawback, we introduce a new technique of sub-band likelihood computation in the feature recombination, and propose a new feature recombination method by using this sub-band likelihood computation. Furthermore, the reliable sub-band selection based on the signal-to-noise ratio is used to improve the performance of this proposed feature recombination. Experimental results shows that the average error reduction rate in various noise condition is more than 27% compared with the conventional full-band speaker identification system.

• Asia pacific journal of information systems
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• v.9 no.4
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• pp.107-124
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• 1999

This paper describes a personalized advertisement technique as a part of intelligent customer services in Internet shopping malls. Based on customers' initial profile, purchase history, and behaviors in an Internet shopping mall, the technique displays appropriate advertisements on Internet web pages when customers' visit to the shopping mall. Customers preference scores for product groups which are main sources to select advertisements, are stored either a preference table or preference trees. Both of the two storage methods can support selection of advertisements on real time, and the preference tree method can reflect affinity among product groups. The suggested technique selects different advertisements to reflect changes of customers preferences as time goes by. An experiment has been performed to evaluate the effectiveness of the algorithm, which revealed that the algorithm selects more customer-oriented advertisements rather than random selection.