• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.667-680
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• 2007

The bio-data processing is used for a suitable purpose with bio-signals, which are obtained from human individuals. Recently, there is increasing demand that the bio-data has been widely applied to various applications. However, it is often that the number of data within each class is limited and the number of classes is large due to the property of problem domain. Therefore, the conventional pattern recognition systems and classification methods are suffering form low generalization performance because the system using the lack of data is influenced by noises of that. To solve this problem, we propose a modified additive factor model for bio-data generation, with two factors; the class factor which affects properties of each individuals and the environment factor such as noises which affects all classes. We then develop a classification system through defining a new similarity function using the proposed model. The proposed method maximizes to use an information of the class classification. So, we can expect to obtain good generalization performances with robust noises from small number of datas for bio-data. Experimental results show that proposed method outperforms significantly conventional method with real bio-data.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.1001-1010
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• 2007

HMM tends to output high probability for not only the proper class data but confusable class data, since the modeling power increases as the number of parameters increases. Thus it may not be helpful for discrimination to simply increase the number of parameters of HMM. We proposed two methods in this paper. One is a CMC(Confusion Likelihood Model Selection Criterion) using confusion class data probability, the other is a new recognition method, RCM(Recognition Using Confusion Models). In the proposed recognition method, confusion models are constructed using confusable class data, then confusion models are used to depress misrecognition by confusion likelihood is subtracted from the corresponding standard model probability. We found that CMC showed better results using fewer number of parameters compared with ML, ALC2, and BIC. RCM recorded 93.08% recognition rate, which is 1.5% higher result by reducing 17.4% of errors than using standard model only.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.133-138
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• 2002

In this paper, we model a two-server queueing system with priority, to which we put a restriction of the number of servers for each customer class. A group of customers is divided into two different classes. The class 1 customers has non -preemptive priority over class 2 customers. We use the method of PGF depending on the state of server We find the PGF of the number of customers in queue, server utilization, mean queue length and mean waiting time for each class of customers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.5
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• pp.31-38
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• 2002

In this paper, we model a two-server queueing system with priority, to which we put a restriction on the number of servers for each customer class. customers are divided into two different classes. Class 1 customers have non-preemptive priority over class 2 customers. They are served by both servers when available but class 2 customers are served only by a designated server. We use a method of generating function depending on the state of servers. We find the generating function of the number of customers in queue, server utilization, mean queue length and mean waiting time for each class of customers.

• Journal of the korean academy of Pediatric Dentistry
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• v.5 no.1
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• pp.19-26
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• 1978

The author studied occlusion in the primary dentition of 3~5 year old children and the materials for the present study comprised plaster model of 266 children in Seoul. The results were as followings; 1) In sagittal canine relationship, 63.9%(170 children) showed class 1 pattern, 2.3%(6 children) showed class 2 pattern, 21%(56 children) showed class 3 pattern and 12.8%(34 children) showed a different canine relationship in each side. 2) In sagittal molar relationship, 44.3% 118 children) showed class 1 pattern, 6.1%(16 children) showed class 2 pattern, 32.3%(86 children) showed class 3 pattern and 17.3%(46 children) showed a different molar relationship in each side. 3) In overjet, 87.8%(234 children) showed under 2mm. 4) 5.3%(14 children) showed crossbite and 4.6%(12 children) showed scissors-bite. 5) 21.8%(58 children) showed midline deviation. 6) Primate space was coincided with more common position of interdental space.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.

• MALSORI
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• no.60
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• pp.145-164
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• 2006

This paper proposes class histogram equalization (CHEQ) to compensate noisy acoustic features for robust speech recognition. CHEQ aims to compensate for the acoustic mismatch between training and test speech recognition environments as well as to reduce the limitations of the conventional histogram equalization (HEQ). In contrast to HEQ, CHEQ adopts multiple class-specific distribution functions for training and test environments and equalizes the features by using their class-specific training and test distributions. According to the class-information extraction methods, CHEQ is further classified into two forms such as hard-CHEQ based on vector quantization and soft-CHEQ using the Gaussian mixture model. Experiments on the Aurora 2 database confirmed the effectiveness of CHEQ by producing a relative word error reduction of 61.17% over the baseline met-cepstral features and that of 19.62% over the conventional HEQ.

• Journal of Practical Engineering Education
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• v.9 no.2
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• pp.125-132
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• 2017

This paper is a study on the application of flipped learning, which is known as a teaching method that provides effective learning, to signal processing subjects. The teaching - learning model used for the class and the implementation examples for three years are described. In-class can be judged to be a relatively successful class, but organization of the video data provided in the pre-class and evaluation of whether or not to study pre-class video have to be improved.

• Restorative Dentistry and Endodontics
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• v.20 no.2
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• pp.432-461
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• 1995

The basic principles in the design of Class II amalgam cavity preparations have been modified but not changed in essence over the last 90 years. The early essential principle was "extension for prevention". Most of the modifications have served to reduce the extent of preparation and, thus, increase the conservation of sound tooth structure. A more recent concept relating to conservative Class II cavity preparations involves elimination of occlusal preparation if no carious lesion exists in this area. To evaluate the ideal ClassII cavity preparation design, if carious lesion exists only in the interproximal area, three cavity design conditions were studied: Rodda's conventional cavity, simple proximal box cavity and proximal box cavity with retention grooves. In this study, MO amalgam cavity was prepared on maxillary first premolar. Three dimensional finite element models were made by serial photographic method. Linear, eight and six-nodal, isoparametric brick elements were used for the three dimensional finite element model. The periodontal ligament and alveolar bone surrounding the tooth were excluded in these models. Three types model(B option, Gap option and R option model) were developed. B option model was assumed perfect bonding between the restoration and cavty wall. Gap option model(Gap distance: $2{\mu}m$) was assumed the possibility of play at the interface simulated the lack of real bonding between the amalgam and cavity wall (enamel and dentin). R option model was assumed non-connection between the restoration and cavty wall. A load of 500N was applied vertically at the first node from the lingual slope of the buccal cusp tip. This study analysed the displacement, 1 and 2 direction normal stress and strain with FEM software ABAQUS Version 5.2 and hardware IRIS 4D/310 VGX Work-station. The results were as followed. 1. Rodda's cavity form model showed greater amount of displacement with other two models. 2. The stress and strain were increased on the distal marginal ridge and buccopulpal line angle in Rodda's cavity form model. 3. The stress and strain were increased on the central groove and a part of distal marginal ridge in simple proximal box model and proximal box model with retention grooves. 4. With Gap option, Rodda's cavity form model showed the greatest amount of the stress on distal marginal ridge followed by proximal box model with retention grooves and simple proximal box model in descending order. 5. With Gap option, simple proximal box model showed greater amount of stress on the central groove with proximal box model with retention grooves. 6. Retention grooves in the proximal box played the role of supporting the restorations opposing to loads.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.4
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• pp.666-683
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• 2023

This research aims to develop based clothing design process tailored to the industry-university collaborative setting and apply it in a fashion design class. into three distinct phases: designing and organizing our fashion design class, conducting our class at a university, and gathering student feedback. First, we conducted a literature review on employing new technologies in traditional clothing design processes. We consulted with industry professionals from the Samsung C&T Fashion Group to develop an AI-based clothing design process. We then developed in-class learning activities that leveraged fashion brand product databases, a supervised learning AI model, and operating an AI-based Creativity Support Tool (CST). Next, we setup an industry-university fashion design class at a university in South Korea. Finally, we obtained feedback from undergraduate students who participated in the class. The survey results showed a satisfaction level of 4.7 out of 5. The evaluations confirmed that the instructional methods, communication, faculty, and student interactions within the class were both adequate and appropriate. These research findings highlighted that our AI-based clothing design process applied within the fashion design class led to valuable data-driven convergent thinking and technical experience beyond that of traditional clothing design processes.