• The Research Journal of the Costume Culture
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• v.20 no.4
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• pp.594-603
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• 2012

This study aimed to investigate a bias pattern making method with geometrical approach. The bias-cut dress has soft silhouette of drape in the garment. However, the bias cut dress has problem of satisfying the intended garment size spec. This problem occurs from various sources. The main reason is that the bias-cut fabric tends to stretch on longitudinal direction and to shrink horizontal direction when it was hung on the body. The goal of this study was to develop a bias-cut dress pattern making method satisfying the intended garment size spec. The researchers developed the geometrical method of measuring dimensional change by calculating the compensation ratio of the fabric in true bias direction. The compensation ratio was calculated by applying draping ratio of the fabric. Three types of fabrics were used in the experiment. The warp and weft crossing angle of fabric was ranged from $78^{\circ}$ to $82^{\circ}$. The fabrics stretched longitudinally 6.9~9.9% and shrank horizontally 7.2~11.0%. The compensation ratio of the bias-cut pattern for sample dress was calculated for each fabric type. Two types of experimental bias-cut dress patterns were developed for each fabric. One pattern was made with applying full compensation ratio and the other one made with applying partial ratio of the fabric. Experimental dresses were made with these patterns. The results of the evaluation showed that the bias-cut dress pattern applying the partial compensation ratio was more appropriate than the pattern applying the full compensation ratio.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.46-53
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• 2019

When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.69-72
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• 2001

In this paper, we propose a new design method for bidirectional associative memories model with high error correction ratio. We extend the conventional GBAM model using bias terms and formulate a design procedure in the form of a constrained optimization problem. The constrained optimization problem is then transformed into a GEVP(generalized eigenvalue problem), which can be efficiently solved by recently developed interior point methods. The effectiveness of the proposed approach is illustrated by a example.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.353-356
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• 2000

Multi-bias parameter extraction technique for HEMT small signa] equivalent circuits is presented in this paper. The technique in this paper uses S-parameters measured at various bias points in the active region to construct one optimization problem, of which the vector of unknowns contains only a set of bias-independent elements. Tests are peformed on measured S-parameters of a pHEMT at 30 bias points. Results indicate that the calculated S-parameters is similar to the measured data.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.455-457
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• 2020

Recent studies demonstrate that deep learning model is easily biased by trained with unbalanced datasets. For example, the deep network can be trained to make a prediction by background feature instead the real target's feature. For those problem, a measurement called leakage was introduced to digitize this tendency. In this paper, we propose augmentation strategy which are used generally in computer vision problem to remedy this bias problem and we showed a simple augmentation methods have a effect to this task with experiments.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.641-651
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• 2023

Malicious hate speech and gender bias comments are common in online communities, causing social problems in our society. Gender bias and hate speech detection has been investigated. However, it is difficult because there are diverse ways to express them in words. To solve this problem, we attempted to detect malicious comments in a Korean hate speech dataset constructed in 2020. We explored bidirectional encoder representations from transformers (BERT)-based deep learning models utilizing hyperparameter tuning, data sampling, and logits ensembles with a label distribution. We evaluated our model in Kaggle competitions for gender bias, general bias, and hate speech detection. For gender bias detection, an F1-score of 0.7711 was achieved using an ensemble of the Soongsil-BERT and KcELECTRA models. The general bias task included the gender bias task, and the ensemble model achieved the best F1-score of 0.7166.

• Korean journal of communication and information
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• v.71
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• pp.274-298
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• 2015

Doctoral Candidate, Department of Communication, Pusan National University This study analyzed the political and social significance of the disaster accident news with the frame and bias concept. In particular, this study confirmed theoretically how domestic media biased frame when it presents problem definition, causing interpretation, moral evaluation, and post-prescription on the ferry Sewol accident, In addition, the bias of the frame was analyzed comparing what is the difference between the conservative newspapers and liberal newspapers. Findings are as follows. First, in diagnosis of ferry Sewol accident, news slanted fragmentation frame>personalization frame>authority-disorder frame. The Chosun Ilbo focus on fragmentation bias, meanwhile Hankyoreh focus on the authority disorder relatively. Second, in accident evaluation, responsibility frame> moral frame> problem-solution frame. The Chosun Ilbo focus on responsibility frame and moral frame. But Hankyoreh focus on responsibility frame and problem-solution frame. Third, in the matter of responsibility, government frame>personal frame>organizational frame. Chosun Ilbo biased responsibility of the government and individuals, while the Hankyoreh is relatively more emphasis on government responsibility and the responsibility of the organization also showed. Fourth, in problem solving, thematic frame and episodic frame bias appeared as rough and level. Chosun Ilbo showed episodic frame, Hankyoreh showed thematic frame. News frame and bias as well as ideological differences of media on ferry Sewol accident was discussed in the context of the social dimension.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.7
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• pp.923-929
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• 2004

A magnetostrictive sensor is a sensor measuring elastic waves. Because of its unique non-contact measurement feature, the sensor receives more attentions in recent years. These sensors have been mainly used to measure longitudinal and torsional waves in ferromagnetic waveguides, but there increases an interest in using the sensor for flexural wave measurement. Since the performance of the sensor is strongly influenced by the applied bias magnetic field distribution, the design of the bias magnetic system providing the desired magnetic field is critical. The motivation of this investigation is to design a bias magnetic system consisting of electromagnets and yokes and the specific objective is to formulate the design problem as a bias yoke topology optimization. For the formulation, we employ linear magnetic behavior and examine the optimized results for electromagnets located at various locations. After completing the design optimization, we fabricate the prototype of the proposed bias magnetic system, and test its performance through flexural wave measurements.

• Knowledge Management Research
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• v.23 no.2
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• pp.229-251
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• 2022

Online reviews are useful for other consumers to make reasonable purchase decisions by providing previous buyers' experiences. However, when online reviewers are biased, online reviews do not accurately reflect the true quality of the product. Therefore, we investigated the characteristics of reviewers with underreporting bias to cope with the problem of declining reliability of online reviews. In this context, this study attempted to examine the characteristics of reviewers with underreporting bias using 14,165 reviews of Steam, an online game platform. As a result of the analysis, reviewers with underreporting bias mainly write reviews positively, write reviews within a short period from the game release date, but tend to write reviews after playing games for longer time, and write reviews when purchasing high-priced games. Since this study has explored the characteristics of reviewers showing underreporting bias, it will be meaningful as a basic study to cope with the problem caused by underreporting bias.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.63-68
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• 2003

One of the widely accepted assumptions in many statistical problem is that the underlying distribution is symmetric. Though a large number of nonparametric test are available in the literature for this problem, very few procedures focuses on the distributional structure when the symmetry assumption is rejected. Yanagimoto and Sibuya (1972) provided the various types of asymmetric distributional structure, positive biasedness, namely. In this paper we consider the test of symmetry against several new positive biasedness restrictions which are stronger than Yanagimoto and Sibuya's type II bias but weaker than type IV (III) bias.