• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1175-1186
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• 2017

Human emotion recognition is one of the promising applications in the era of artificial super intelligence. Thus far, facial expression traits are considered to be the most widely used information cues for realizing automated emotion recognition. This paper proposes a novel facial expression recognition (FER) method that works well for recognizing emotion from image sequences. To this end, we develop the so-called weighted soft voting classification (WSVC) algorithm. In the proposed WSVC, a number of classifiers are first constructed using different and multiple feature representations. In next, multiple classifiers are used for generating the recognition result (namely, soft voting) of each face image within a face sequence, yielding multiple soft voting outputs. Finally, these soft voting outputs are combined through using a weighted combination to decide the emotion class (e.g., anger) of a given face sequence. The weights for combination are effectively determined by measuring the quality of each face image, namely "peak expression intensity" and "frontal-pose degree". To test the proposed WSVC, CK+ FER database was used to perform extensive and comparative experimentations. The feasibility of our WSVC algorithm has been successfully demonstrated by comparing recently developed FER algorithms.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.243-247
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• 2005

We propose an efficient block least-mean-square (BLMS) adaptive algorithm, in conjunction with error control coding, for direct-sequence code division multiple access (DS-CDMA) systems. The proposed adaptive receiver incorporates decision feedback detection and channel encoding in order to improve the performance of the standard LMS algorithm in convolutionally coded systems. The BLMS algorithm involves two modes of operation: (i) The training mode where an uncoded training sequence is used for initial filter tap-weights adaptation, and (ii) the decision-directed where the filter weights are adapted, using the BLMS algorithm, after decoding/encoding operation. It is shown that the proposed adaptive receiver structure is able to compensate for the signal-to­noise ratio (SNR) loss incurred due to the switching from uncoded training mode to coded decision-directed mode. Our results show that by using the proposed adaptive receiver (with decision feed­back block adaptation) one can achieve a much better performance than both the coded LMS with no decision feedback employed. The convergence behavior of the proposed BLMS receiver is simulated and compared to the standard LMS with and without channel coding. We also examine the steady-state bit-error rate (BER) performance of the proposed adaptive BLMS and standard LMS, both with convolutional coding, where we show that the former is more superior than the latter especially at large SNRs ($SNR\;\geq\;9\;dB$).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.11
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• pp.1217-1224
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• 2009

This paper newly proposes a methodology towards computing antenna element weights which are satisfying asymmetric sidelobe levels(SLLs) specified arbitrarily on both sides of the main beam pattern, in the linear phased array antenna pattern synthesis problem. Opposite to the conventional methods in which the element weights are directly optimized from the array factor, this method is based on the optimum perturbations of complex roots inherent to the Schelkunoff's polynomial form which is described for the array factor. From the proposed methodology, the capability of nulling the directions of multiple jammers is also possible by independently perturbing only the complex roots corresponding to each jamming direction, hence allowing an enhancement of the simplicity of the numerical procedure by means of a proper reduction of the dimension of the solution space. The complex weights over the array are then easily computed by substituting the optimally perturbed complex roots to the Schelkunoff's polynomial. Some examples are examined and numerically verified by substituting the extracted weights into the array factor equation.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• Journal of the military operations research society of Korea
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• v.13 no.1
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• pp.116-124
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• 1987

This paper studies to develop the procedure which is combined by the progressive goals and progressive weights generation method. This procedure minimizes the number of questions the decision maker has to make, and also satisfies the generated minimum goal of each objective function. With the procedure developed, we are able to improve the previous multiple objective linear programming techniques in two points.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.33-36
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• 2002

Multiple Description Coding(MDC) is a technique used to obtain two or more (often correlated) descriptions of a source, which are transmitted over different channels to receiver. Two descriptions of the source support two levels of reconstruction quality. When all the descriptions are received and used in the reconstruction, the source should be reconstructed with acceptable quality. In this work, we consider Multiple Description Scalar Quantizer(MDSQ) to wavelet transform domain. Conventional MDSQ schemes in wavelet domain considered description with equal weights at each sub-bands after quantization. But each sub-bands is unequal contribution to whole image quality. Therefore, we experiment the multiple description with unequal weight in each sub-bands.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1620-1634
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• 2023

This paper studies the task of embedding nodes with multiple graphs representing multiple information channels, which is useful in a large volume of network clustering tasks. By learning a node using multiple graphs, various characteristics of the node can be represented and embedded stably. Existing studies using multi-channel networks have been conducted by integrating heterogeneous graphs or limiting common nodes appearing in multiple graphs to have similar embeddings. Although these methods effectively represent nodes, it also has limitations by assuming that all networks provide the same amount of information. This paper proposes a method to overcome these limitations; The proposed method gives different weights according to the source graph when embedding nodes; the characteristics of the graph with more important information can be reflected more in the node. To this end, a novel method incorporating a multi-channel gate layer is proposed to weigh more important channels and ignore unnecessary data to embed a node with multiple graphs. Empirical experiments demonstrate the effectiveness of the proposed multi-channel-based embedding methods.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.41.5-41
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• 2001

A novel method is introduced for determining the weights of a regularization network which approximates one-to-many mapping. A conventional neural network will converges to the average value when outputs are multiple for one input. The capability of proposed network is demonstrated by an example of learning inverse mapping.

• Bulletin of the Korean Mathematical Society
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• v.59 no.5
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• pp.1105-1117
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• 2022

In this paper, we obtain a second main theorem for holomorphic curves and moving hyperplanes of Pⁿ(C) where the counting functions are truncated multiplicity and have different weights. As its application, we prove a uniqueness theorem for holomorphic curves of finite growth index sharing moving hyperplanes with different multiple values.