• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.49-63
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• 2020

The accurate prediction of box office in the early stage is crucial for film industry to make better managerial decision. With aims to improve the prediction performance, the purpose of this paper is to evaluate the use of machine learning methods. We tested both classification and regression based methods including k-NN, SVM and Random Forest. We first evaluate input variables, which show that reputation-related information generated during the first two-week period after release is significant. Prediction test results show that regression based methods provides lower prediction error, and Random Forest particularly outperforms other machine learning methods. Regression based method has better prediction power when films have small box office earnings. On the other hand, classification based method works better for predicting large box office earnings.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.293-296
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• 2002

In this study, the object is a development on uroflowmetry system to detect a voiding symptom conveniently in home or hospital. The hardware was composed of mechanism and system circuit part, the software was divided into firmware and PC program part. The following experiment was performed to evaluate an ability of classification and fitness. First, the following parameters was calculated in each flow curve pattern. The parameters are MFR, AFR, VOL, VT, FT, and TMF. A significant difference among parameters was examined through a statistical analysis for extracted parameters between normal and abnormal group. In the next work, the following experimentation was performed to classify the voiding symptom. Analysis of congregate rate was examined to find out classification possibility about each symptom of BPH, voiding difficulty, detrusor failure and hyperreflexia, unstable bladder. The uroflow data with the above symptom was divided into normal and abnormal group using fuzzy classifier. and that was performed appending the other group again. Fuzzy classification result using MFR and AFR was superior by 89.6 % more than grouping evaluation including VOL.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.22-30
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• 2011

In the IT convergence environment changed with not only the quality but also the material abundance, it is the most crucial factor for the strategy of personalized recommendation services to investigate the context information. In this paper, we proposed the recommendation using the service ontology based context awareness modeling. The proposed method establishes a data acquisition model based on the OSGi framework and develops a context information model based on ontology in order to perform the device environment between different kinds of systems. In addition, the context information will be extracted and classified for implementing the recommendation system used for the context information model. This study develops the ontology based context awareness model using the context information and applies it to the recommendation of the collaborative filtering. The context awareness model reflects the information that selects services according to the context using the Naive Bayes classifier and provides it to users. To evaluate the performance of the proposed method, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.788-796
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• 2003

This paper proposes using dynamic momentum for squential learning method. Using The dynamic momentum improves convergence speed and performance by the variable momentum, also can identify it in the RMSE(root mean squared error). The proposed method is reflected using variable momentum according to current state. While static momentum is equally influenced on the whole, dynamic momentum algorithm can control the convergence rate and performance. According to the variable change of momentum by training. Unlike former classification and regression problems, this paper confirms both performance and regression rate of the dynamic momentum. Using RMSE(root mean square error ), which is one of the regression methods. The proposed dynamic momentum has been applied to the kernel adatron and kernel relaxation as the new sequential learning method of support vector machine presented recently. In order to show the efficiency of the proposed algorithm, SONAR data, the neural network classifier standard evaluation data, are used. The simulation result using the dynamic momentum has a better convergence rate, performance and RMSE than those using the static moment, respectively.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.533-540
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• 2014

We propose an age estimation-based smart digital signage for the next-generation airport service. The proposed system can recognize the face of the customer so that it can display the selective information. Using a webcam, the system captures the face of the customer and estimates the age of the customer by calculating the wrinkle density of the face and applying bayesian classifier. The developed age estimation method is tested with a face database for the performance evaluation. We expect the new digital signage may improve the satisfaction of customers of the airport business.

• Journal of IKEEE
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• v.24 no.3
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• pp.777-782
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• 2020

In this paper, we propose CNN-based HMI system using Doppler radar and voice sensor, and present hardware design and implementation results. To overcome the limitation of single sensor monitoring, the proposed HMI system combines data from two sensors to improve performance. The proposed system exhibits improved performance by 3.5% and 12% compared to a single radar and voice sensor-based classifier in noisy environment. In addition, hardware to accelerate the complex computational unit of CNN is implemented and verified on the FPGA test system. As a result of performance evaluation, the proposed HMI acceleration platform can be processed with 95% reduction in computation time compared to a single software-based design.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.43-50
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• 2016

This paper introduces an appearance-based facial expression recognition method using ASM landmarks which is used to acquire a detailed face region. In particular, EHMM-based algorithm and SVM classifier with histogram feature are employed to appearance-based facial expression recognition, and performance evaluation of proposed method was performed with CK and JAFFE facial expression database. In addition, performance comparison was achieved through comparison with distance-based face normalization method and a geometric feature-based facial expression approach which employed geometrical features of ASM landmarks and SVM algorithm. As a result, the proposed method using ASM-based face normalization showed performance improvements of 6.39% and 7.98% compared to previous distance-based face normalization method for CK database and JAFFE database, respectively. Also, the proposed method showed higher performance compared to geometric feature-based facial expression approach, and we confirmed an effectiveness of proposed method.

• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.1
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• pp.11-22
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• 1997

It is very essential to get the accurate classification of defects in primary pressure vessel and piping welds for the safety of nuclear power plant. Ultrasonic testing has been widely applied to inspect primary pressure vessel and piping welds of nuclear power plants during PSI / ISI. Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic Pattern recognition technique. Here, a brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on Fuzzy-UTSCS (UT signal classification system) as efficient classifiers for many practical classification problems. As an example Fuzzy-UTSCS is applied to classify flaws in ferrite pressure vessel weldments into two types such as linear and volumetric. It is shown that Fuzzy-UTSCS is able to exhibit higher performance than other classifiers in the defect classification.

• Korean Journal of Food Science and Technology
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• v.27 no.6
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• pp.1008-1012
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• 1995

Glutinous rice was microparticulated and air-classified at different air classifying wheel speeds (ACWS) of 20,000 rpm and 15,000 rpm in a Turboplex classifier. The starch was concentrated to a coarse fraction and the protein was shifted to a fine fraction. The degree of starch damage of the coarse fraction was comparable to that of traditionally soaked glutinous rice. Yoogwa(Korean cracker) made from the fractions of $ACWS\;15,000{\sim}20,000\;rpm$ and below ACWS 15,000 rpm was very comparable to that made by the traditional method in degree of puffing, hardness and internal structure. It was also confirmed by the sensory evaluation, indicating that the microparticulation/air classification technology could be applied to produce raw material of Yoogwa. The developed noble process could exclude the long soaking step in the traditional Yoogwa process and reduce the pretest time remarkably.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.811-819
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• 2014

Activity recognition is a key component in identifying the context of a user for providing services based on the application such as medical, entertainment and tactical scenarios. Instead of applying numerous sensor devices, as observed in many previous investigations, we are proposing the use of smartphone with its built-in multimodal sensors as an unobtrusive sensor device for recognition of six physical daily activities. As an improvement to previous works, accelerometer, gyroscope and magnetometer data are fused to recognize activities more reliably. The evaluation indicates that the IBK classifier using window size of 2s with 50% overlapping yields the highest accuracy (i.e., up to 99.33%). To achieve this peak accuracy, simple time-domain and frequency-domain features were extracted from raw sensor data of the smartphone.