• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.261-268
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• 2014

Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method's classification rate as well as detection rate under equal false positive rate are higher than conventional one.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1564-1572
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• 2010

In this study, the falls detection and direction classification system was implemented using 3-axial acceleration signal. The acceleration signals were acquired from the 3-axial accelerometer(MMA7260Q, Freescale, USA), and then transmitted to the computer through USB interface. The implemented system can detect falls using the newly proposed algorithm, and also classify the direction of falls using fuzzy classifier. The 6 subjects was selected for experiment and the accelerometer was attached on each subject's chest. Each subject walked in normal pace for 5 seconds, and then the fall down according to the four direction(front_fall, back_fall, left_fall and right_fall) during at least 2 second. The falls was easily detect using the newly proposed algorithm in this study. The acquired signals were analyzed after 1 second from generating falls. The fuzzy classifier was used to classify the direction of falls. The mean value of the falls detection rate was 94.79%. The classifier rate according to falls direction were 95.83% in case of front falls, 100% incase of back falls, 87.5% in case of left falls, and 95.83% in case of right falls.

• ETRI Journal
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• v.46 no.2
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• pp.250-262
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• 2024

Air-writing recognition is relevant in areas such as natural human-computer interaction, augmented reality, and virtual reality. A trajectory is the most natural way to represent air writing. We analyze the recognition accuracy of words written in air considering five features, namely, writing direction, curvature, trajectory, orthocenter, and ellipsoid, as well as different parameters of a hidden Markov model classifier. Experiments were performed on two representative datasets, whose sample trajectories were collected using a Leap Motion Controller from a fingertip performing air writing. Dataset D₁ contains 840 English words from 21 classes, and dataset D₂ contains 1600 English words from 40 classes. A genetic algorithm was combined with a hidden Markov model classifier to obtain the best subset of features. Combination ftrajectory, orthocenter, writing direction, curvatureg provided the best feature set, achieving recognition accuracies on datasets D₁ and D₂ of 98.81% and 83.58%, respectively.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.91-107
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• 2023

With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.307-322
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• 2010

Traffic classification is an essential task for network management. Many researchers have paid attention to initial sub-flow features based classifiers for traffic classification. However, the existing classifiers cannot classify traffic effectively in mobile IP networks. The classifiers depend on initial sub-flows, but they cannot always capture the sub-flows at a point of attachment for a variety of elements because of seamless mobility. Thus the ideal classifier should be capable of traffic classification based on not only initial sub-flows but also various types of sub-flows. In this paper, we propose a classifiable sub-flow selection method to realize the ideal classifier. The experimental results are so far promising for this research direction, even though they are derived from a reduced set of general applications and under relatively simplifying assumptions. Altogether, the significant contribution is indicating the feasibility of the ideal classifier by selecting not only initial sub-flows but also transition sub-flows.

• Design & Manufacturing
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• v.16 no.4
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• pp.34-39
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• 2022

Sustained economic development around the world is accelerating resource depletion. Research and development of secondary batteries that can replace them is also being actively conducted. Secondary batteries are emerging as a key technology for carbon neutrality. The core of an electric vehicle is the battery (secondary battery). Therefore, in this study, the temperature change by the heat source of the hammer and the rotational speed (rpm) of the abrasive disc of the Classifier Separator Mill (CSM) was repeatedly calculated and analyzed using the heat flow simulation STAR-CCM+. As the rotational speed (rpm) of the abrasive disk increases, the convergence condition of the iteration increases. Under the condition that the inlet speed of the Classifier Separator Mill (CSM) and the heat source value of the disc hammer are the same, the disc rotation speed (rpm) and the hammer temperature are inversely proportional. As the rotational speed (rpm) of the disc increases, the hammer temperature decreases. However, since the wear rate of the secondary battery material increases due to the strong impact of the crushing rotational force, it is determined that an appropriate rpm setting is necessary. In CSM (Classifier Separator Mill), it is judged that the flow rate difference is not significantly different in the direction of the pressure outlet (Outlet 1) right above the classifier wheel with the fastest flow rate. Because the disc and hammer attachment technology is adhesive, the attachment point may deform when the temperature of the hammer rises. Therefore, it is considered necessary to develop high-performance adhesives and other adhesive technologies.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.29-32
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• 2001

Visual speech information improves the performance of speech recognition, especially in noisy environment. We have tested the various spatial-temporal features for the Korean lipreading and evaluated the performance by using a hidden Markov model based classifier. The results have shown that the direction as well as the magnitude of the movement of the lip contour over time is useful features for the lipreading.

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.107-113
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• 1997

In this paper, an interfacing method to control rehabilitation assitance system with bio-signal is proposed. Controlling with EMG signals method has certain advantage on signal-collecting, but has some drawbacks in the function resolution of EMG signals because data-processing process is not efficient. To improve function-resolution and to increase the efficiency of EMG signal interfacing with rehabilitation assistance system, Multi-layer Perception which is highly effective with static signal and hidden-Markov model for dynamic signal resolving are fused together. In proposed method. The direction and average speed of the rehabilitation assitance system are controlled by the trajectory control and estimation of the moving direction result from the fused model. From the experiment, proposed GMM and 2-level MLP hybrid-classifier yielded 8.6% perception-error rate, improving function resolution. New acceleration control method constructed with 3 nested linear filter produced continuous acceleration paths without the information of destination point. Thus, the mass output caused by non- continuous acceleration-deceleration was eliminated. In the simulation, the necessary calculation, in the case of multiplication, was reduced by 11.54%.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.621-625
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• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.