• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.1
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• pp.92-102
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• 2004

Performance assessment was introduced to improve self-directed learning and method of assessment for differenced learning when the seventh educational curriculum was enforced. Written examinations often fail to properly assess students higher thinking abilities ad problem solving abilities. Performance assessment addresses this drawback and also allows normalization of class and school quality. However, performance assessment also has drawbacks that could lead to faulty assessment due to lack of fairness, reliability and validity of grading, ambiguity of grading standard etc. This study proposes a fuzzy performance assessment system to address the drawbacks of the conventional performance assessment. This paper presents in objective and reliable performance assesment method through fuzzy reasoning, design of fuzzy membership function. We define a fuzzy rule analyzing factor that influences in each sacred ground of performance assessment and accounts for the principle subject The proposed performance assessment method divides into three categories, namely, formation estimation subject estimation and design of membership function. Performance assessment result that is worked through fuzzy performance assessment system can reduce the burden of appraisal's fault and provide. We fair and reliable assessment results through grading that have correct standard mid consistency to students.

• Korean Journal of Heritage: History & Science
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• v.52 no.4
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• pp.56-77
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• 2019

Bi-Seok is a treasure trove of funeral rites and an important cultural asset that can shed light on the historical and social history of calligraphy, but research of the topic is still insignificant. In particular, research on the production method of Bi-Seok remains an unproven field. The production of Bi-Seok can be roughly divided into ma-jeong (refining stone), sculpture, and the Buk-chil (process of engraving letters) process. This article reveals some facts: First, performing ma-jeong to the Sang-Seok, Honyu-Seok, Bi-seok, which are known to be God's things. This process is needed because of the change in the perception of the Honyu-Seok due to the settlement and propagation of Confucian ceremonial rituals in the times of hardship in 1592 and 1636. As the crafting process of ma-jeong did not remain concrete, it was only possible to examine the manufacturing process of Bi-Seok through its materials and tools. Second, the rapid proliferation of Oh-Seok and Sa-jeo-chwi-yong (purchase of things made by private citizens) in the Yeongjo era has great importance in social and cultural history. When the Gang-Hwa-Seok of the commodity were exhausted, the Oh-Seok that was used by Sadebu (upper civil class) were used in the tomb of Jangneung, which made Oh-Seok popular among people. In particular, the use of Oh-Seok and the Ma-Jeong process could minimize chemical and physical damage. Third, the writing method of the Bi-seok is Buk-chil. After Buk-Chil of Song Si-Yeol was used on King Hyojong's tomb, the Buk-Chil process ( printing the letters on the back of the stone and rubbing them to make letters) became the most popular method in Korea and among other East Asian countries, and the fact that it was institutionalized to this scale was quite impressive. Buk-Chil became more sophisticated by using red ink rather than black ink due to the black color that results from Oh-Seok. Fourth, the writing method changes in the late Joseon Dynasty. Until the time of Yeongjo's regime, when inscribing, the depth of the angle was based on the thickness of the stroke, thus representing the shade. This technique, of course, did not occur at every Pyo-Seok or Shindobi, but was maintained by outstanding artisans belonging to government agencies. Therefore, in order to manufacture Bi-Seok, Suk-seok, YeonJeong, Ma-jeong, Jeong-Gan, ChodoSeoIp, Jung-Cho, Ip-gak, Gyo-Jeong, and Jang-Hwang, a process was needed to make one final product. Although all of these methods serve the same purpose of paying respects and propagandizing the great work of deceased persons, through this analysis, it was possible to see the whole process of Pyo-Seok based upon the division of techniques and the collaboration of the craftsmen.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.