• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.519-528
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• 2012

Voice activity detection (VAD) is generally conducted by extracting features from the acoustic signal and a decision rule. The performance of such VAD algorithms driven by the input acoustic signal highly depends on the acoustic noise. When video signals are available as well, the performance of VAD can be enhanced by using the visual information which is not affected by the acoustic noise. Previous visual VAD algorithms usually use single visual feature to detect the lip activity, such as active appearance models, optical flow or intensity variation. Based on the analysis of the weakness of each feature, we propose to combine intensity change measure and the optical flow in the mouth region, which can compensate for each other's weakness. In order to minimize the computational complexity, we develop simple measures that avoid statistical estimation or modeling. Specifically, the optical flow is the averaged motion vector of some grid regions and the intensity variation is detected by simple thresholding. To extract the mouth region, we propose a simple algorithm which first detects two eyes and uses the profile of intensity to detect the center of mouth. Experiments show that the proposed combination of two simple measures show higher detection rates for the given false positive rate than the methods that use a single feature.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.190-199
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• 2014

In this paper, the vestibular hair bundle feature model and integrated vestibular hair cell model were proposed. In conventional modeling studies of vestibular system, only partial mechanisms were modeled, such as the characteristics of the vestibular hair bundles without external forces or the action potential of synapse, and the study about action potential of vestibular afferent considering the characteristics of the vestibular hair bundle was not performed. The proposed integrated vestibular hair cell model reflects external forces considering negative stiffness features of each hair bundles with different regularities of hair cells and our model was compared with conventional model without external forces. As a result, irregular afferent and intermediate afferent with high ratio of firing frequency variations to the changes of external stimulation had small width of negative stiffness section, but the width of the negative stiffness of regular afferent with low ratio was similar to that of conventional negative stiffness features. And the proposed integrated vestibular hair cell model showed almost same results with conventional data with animal experiments in 11 chosen frequency bands. It is verified that our proposed hair bundle feature model is adequately modeled.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.562-567
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• 2009

As a key mechanism of the human emotion interaction, Facial Expression is a powerful tools in HRI(Human Robot Interface) such as Human Computer Interface. By using a facial expression, we can bring out various reaction correspond to emotional state of user in HCI(Human Computer Interaction). Also it can infer that suitable services to supply user from service agents such as intelligent robot. In this article, We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.

• Journal of Sasang Constitution and Immune Medicine
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• v.26 no.4
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• pp.350-364
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• 2014

Objectives This study was aimed to find the significant features of face form according to the Taeeumin and Soyangin by analyzing the three-dimensional face information data. Also, making standard face of the Taeeumin and Soyangin was an object of this study. Methods We collected three-dimensional face data of patients aged between 20~45 years old diagnosed by a specialist of Sasang constitutional medicine. The data were collected using a 3D scanner, Morpheus 3D(Morpheus Corporation, KOREA). Extracting a face feature point total of 64, was set to 332 pieces(height, angle, ratio, etc.) of each variable between feature points. ANOVA test were used to compare the characteristics of subjects according to the Taeeumin and Soyangin. Results When not to consider gender, the Taeeumin and Soyangin were different from the 18 items(3 items in the ear, 9 items in the eye, 1 item in the nose, 1 item in the mouth, 4 items in the jaw). When to consider gender, the Taeeumin and Soyangin men were different from the 6 items(1 item in the ear, 2 items in the nose, 3 items in the face). And the Taeeumin and Soyangin women were different from 17 items(1 item in the ear, 10 items in the eye, 2 items in the nose, 1 item in the mouth, 3 items in the face). Conclusions These results show Taeeumin's face(both men and women) width of the right and left is larger than the length of the top and bottom. Compared to men of Soyangin, men of Taeeumin has greater wings of the nose. Compared to women of Soyangin, women of Taeeumin has longer length of the eye. Soyangin's face(both men and women) length of the top and bottom is larger than the width of the right and left. Compared to men of Taeeumin, men of Soyangin has smaller wings of the nose. Compared to women of Taeeumin, women of Soyangin has more stereoscopic facial features at the top and bottom of the lateral face. Also, by accumulating three-dimensional face data, this study modeled the standard facial features by Taeeumin and Soyangin. These results may be helpful in the development of Sasang constitutional diagnostics utilizing the characteristics of the facial form at later.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12C
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• pp.1200-1208
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• 2003

Affective computing plays an important role in intelligent Human Computer Interactions(HCI). To detect emotional events, it is desirable to construct a computing model for extracting emotion related features from video. In this paper, we propose a statistical model based on the probabilistic distribution of low level features in video shots. The proposed method extracts low level features from video shots and then from a GMM(Gaussian Mixture Model) for them to detect emotional shots. As low level features, we use color, camera motion and sequence of shot lengths. The features can be modeled as a GMM by using EM(Expectation Maximization) algorithm and the relations between time and emotions are estimated by MLE(Maximum Likelihood Estimation). Finally, the two statistical models are combined together using Bayesian framework to detect emotional events in video.

• Wind and Structures
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• v.9 no.4
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• pp.315-330
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• 2006

Flow and scalar dispersion around Cheomseongdae are numerically investigated using a three-dimensional computational fluid dynamics (CFD) model with the renormalization group (RNG) $k-{\varepsilon}$ turbulence closure scheme. Cheomseongdae is an ancient astronomical observatory in Gyeongju, Korea, and is chosen as a model obstacle because of its unique shape, that is, a cylinder-shaped architectural structure with its radius varying with height. An interesting feature found is a mid-height saddle point behind Cheomseongdae. Different obstacle shapes and corresponding flow convergences help to explain the presence of the saddle point. The predicted size of recirculation zone formed behind Cheomseongdae increases with increasing ambient wind speed and decreases with increasing ambient turbulence intensity. The relative roles of inertial and eddy forces in producing cavity flow zones around an obstacle are conceptually presented. An increase in inertial force promotes flow separation. Consequently, cavity flow zones around the obstacle expand and flow reattachment occurs farther downwind. An increase in eddy force weakens flow separation by mixing momentum there. This results in the contraction of cavity flow zones and flow reattachment occurs less far downwind. An increase in ambient wind speed lowers predicted scalar concentration. An increase in ambient turbulence intensity lowers predicted maximum scalar concentration and acts to distribute scalars evenly.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.942-947
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• 2003

An important signal processing problem in PVDF sensor is the restoration of surface information from electric sensing signals. The objectives of this research are to design a new texture sensing system and to develop a new signal processing algorithm for signals from the sensor to be tangibly displayed by tangible interface systems. The texture sensing system is designed to get surface information with high resolution and dynamic range. First, a PVDF sensor is made of piezoelectric polymer (polyvinylidene fluoride) strips molded in a silicon rubber and attached in a rigid cylinder body. The sensor is mounted to a scanning system for dynamic sensing. Secondly, a new signal processing algorithm is developed to restore surface information. The algorithm consists of the two-dimensional modeling of the sensor using an identification method and inverse filtering from sensing signals into estimated surface information. Finally the two-dimensional surface information can be experimentally reconstructed from sensing signals using the developed signal processing algorithm.

• Korean Institute of Interior Design Journal
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• v.27 no.2
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• pp.175-185
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• 2018

The purpose of this study is to analyze the concept of 'Gradual Differentiation' in parametric design in terms of pure model logic and thus describe the distinctive feature from the previous design method. To meet the purpose, it explores external cases like gradual factor identified in natural phenomenon and artworks and define the inherent model principles into "Self-similarity', "Correlation', and 'Temporality' by examining these features in terms of algorithm. Meanwhile, it identified the principle of gradual model representation in parametric design within a single system called 'Attractor System' by applying these three concepts into specific methods of parametric design, and by interpreting the logical structure through the association among 'Attractor', 'Field', and 'Differentiation'. The creative utilization of parameter shows that gradual model process in parametric design does not mean a passive "conversion process" merely replacing natural parameter with algorithm; rather, it refers to an active "generating process" creating new meanings and value. By continuing this process of conceptual understanding and insight, creative perspective and practical ability to interpret parameter can be improved.