• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1373-1382
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• 2011

In this study, we propose an automated smile analysis system for self smile training. The proposed system detects the face area from the input image with the AdaBoost algorithm, followed by identifying facial features based on the face shape model generated by using an ASM(active shpae model). Once facial features are identified, the lip line and teeth area necessary for smile analysis are detected. It is necessary to judge the relationship between the lip line and teeth for smiling degree analysis, and to this end, the second differentiation of the teeth image is carried out, and then individual the teeth areas are identified by means of histogram projection on the vertical axis and horizontal axis. An analysis of the lip line and individual the teeth areas allows for an automated analysis of smiling degree of users, enabling users to check their smiling degree on a real time basis. The developed system in this study exhibited an error of 8.6% or below, compared to previous smile analysis results released by dental clinics for smile training, and it is expected to be used directly by users for smile training.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.47-55
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• 2010

In this paper, we proposed a recognition system of smile facial expression for smile treatment training. The proposed system detects face candidate regions by using Haar-like features from camera images. After that, it verifies if the detected face candidate region is a face or non-face by using SVM(Support Vector Machine) classification. For the detected face image, it applies illumination normalization based on histogram matching in order to minimize the effect of illumination change. In the facial expression recognition step, it computes facial feature vector by using PCA(Principal Component Analysis) and recognizes smile expression by using a multilayer perceptron artificial network. The proposed system let the user train smile expression by recognizing the user's smile expression in real-time and displaying the amount of smile expression. Experimental result show that the proposed system improve the correct recognition rate by using face region verification based on SVM and using illumination normalization based on histogram matching.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1423-1431
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• 2022

Volatility is one of the variables that the Black-Scholes model requires for option pricing. It is an unknown variable at the present time, however, since the option price can be observed in the market, implied volatility can be derived from the price of an option at any given point in time and can represent the market's expectation of future volatility. Although volatility in the Black-Scholes model is constant, when calculating implied volatility, it is common to observe a volatility smile which shows that the implied volatility is different depending on the strike prices. We implement supervised learning to target implied volatility by adding V-KOSPI to ease volatility smile. We examine the estimation performance of KOSPI200 index options' implied volatility using various Machine Learning algorithms such as Linear Regression, Tree, Support Vector Machine, KNN and Deep Neural Network. The training accuracy was the highest(99.9%) in Decision Tree model and test accuracy was the highest(96.9%) in Random Forest model.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.369-376
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• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.11-18
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• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.

• Journal of Korean society of Dental Hygiene
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• v.8 no.4
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• pp.43-53
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• 2008

Some female college students have to recognize and understand the actual conditions of the tooth bleaching, and effective consultation and training to provide basic data for the purpose of investigation. this study made a survey about recognize and understand the actual conditions of the tooth bleaching and oral health knowledge and generalization methods for 649 female college student in Daejeon and Jeonbuk areas during the period between March. 15 and April. 10,2008. The results were as follows: 1. Tooth Status was found low, self-discontent respondents Status 44.2% and average 31.1%, self-contentment were 14.8%. Tooth color was average respondents were 69.8%, yellow 29.0%, White 1.7% (p=0.001, p=0.030). 2. Tooth bleaching experience has not experienced the most the military was 86.4%, to the desired Tooth bleaching for the external beauty 44.2%, confidence of smile time 37.5%. self-discontent 10.7%(p=0.000, p=0.000). 3. Tooth health status satisfaction was dental hygiene students higher than non dental hygiene, and scaling knowledge of the Tooth bleaching effect was non dental hygiene higher than non dental hygiene(p=0.039, p=0.000). 4. General knowledge for Tooth bleaching was found high 96.1%, as for the recognition route, 55.6% were through broadcast medium(p=0.025, p=0.000). 5. Medical institution chosen for Tooth bleaching treatment method appears the most preferred by 79.9% to the dental hospital dental clinic. 6. Important to consider that the choice of Tooth bleaching was Tooth bleaching duration of 37.1% cost 33%, And when Tooth bleaching hoped to be long-lasting. In this research the high recognized of Tooth bleaching treatment, but very low Tooth bleaching experience female college students for the Tooth bleaching had the wrong information. Therefore, Tooth bleaching treatment and counseling that can be used to development and education were required to provide the correct information.

• The Korean Journal of Franchise Management
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• v.9 no.3
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• pp.45-55
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• 2018

Purpose - Non-verbal Communication with customers in restaurant business can play an important role because it affects customer behavior and attitudes as a means to develop and maintain long-term relationships with customers. The purpose of this study is to analyze the effect of non-verbal communication with customers and the effect of the influence on customer satisfaction, trust, and revisit intention. Research design, data, methodology - In order to verify the research models and hypotheses of this study, questions were prepared for each variable and data were collected through questionnaires. The questionnaire survey was conducted from March 27, 2018 to April 17, 2018, for those who agreed with the citizens of the Jeju area who visited the restaurant recently. 50 out of 100 were conducted by internet survey and 50 were surveyed. Thus, a total of 100 responses were used using structural equation modeling with Smartpls 3.0. Results - The results of the study are as follows. First, non-verbal communication has a significant impact on customer emotion. Second customer emotion have a significant impact on customer trust and satisfaction. Third, Customer satisfaction had positive a significant effect on revisit intention. Fourth, Customer trust had positive a significant effect on revisit intention. Conclusions - The implications of this study are following as: The food service company should continuously provide non-verbal communication training to employees so that they can respond to customers with the right attitude and bright smile. In particular, in the case of restaurant franchises, customer response manuals should be created and distributed to the franchisees, and a regular training program for the franchisees should be implemented to provide the same service to the customer. Second, CEOs should have to worry about what kind of experience he or she has left since leaving the store. It is also necessary to constantly look at what customers experience in their stores or in their brands, and what emotions they form through their experiences. Third, the more satisfied or trusted customers are formed through the service of the employee, the more loyal the restaurant business will be, and the more likely it is to make continuous revisit and positive word-of-mouth activities..

• Journal of Dental Rehabilitation and Applied Science
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• v.39 no.4
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• pp.237-249
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• 2023

Orofacial dystonia is a neuromotor disorder that causes irregular or repetitive movements of the face, lips, tongue, and jaw involuntarily, also called tic disorder. Edentulous patients with these symptoms experience functional and aesthetic problems, including difficulty using complete dentures, speech and swallowing difficulties, and orofacial pain. In this case, for a patient with orofacial dystonia who experienced complete edentulism at a relatively young age, restorative treatment was performed with a maxillary complete denture with bilateral posterior zirconia occlusal surfaces and a mandibular implant-supported fixed prosthesis, and continuous smile training was performed. The aim was to improve the aesthetics of facial muscles. As a result of the treatment, the patient was very satisfied with not only improved chewing function and aesthetics, but also regained psychological stability and was able to lead a normal daily life, so we would like to report this.