• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.353-362
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• 2005

Recently, study of Bimodal recognition has become very active. In this paper we propose a Bimodal user recognition system that uses face information and audio information. Face recognition consists of face detection step and face recognition step. Face detection uses AdaBoost to find face candidate area. After finding face candidates, PCA feature extraction is applied to decrease the dimension of feature vector. And then, SVM classifiers are used to detect and recognize face. Audio recognition uses MFCC for audio feature extraction and HMM is used for audio recognition. Experimental results show that the Bimodal recognition can improve the user recognition rate much more than audio only recognition, especially in the Presence of noise.

• Journal of Power Electronics
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• v.15 no.1
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• pp.246-255
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• 2015

This paper presents experimental results and its assessment of a variable-speed wind power generation system (VSWPGS) using permanent magnet synchronous generator (PMSG) and boost chopper circuit (BCC). Experimental results are obtained by a test bench with a wind turbine emulator (WTE). WTE reproduces the behaviors of a windmill by using servo motor drives. The mechanical torque references to drive the servo motor are calculated from the windmill wing profile, wind velocity, and windmill rotational speed. VSWPGS using PMSG and BCC has three speed control modes for the level of wind velocity to control the rotational speed of the wind turbine. The control mode for low wind velocity regulates an armature current of generator with BCC. The control mode for middle wind velocity regulates a DC link voltage with a vector-controlled inverter. The control mode for high wind velocity regulates a pitch angle of the wind turbine with a pitch angle control system. The hybrid of three control modes extends the variable-speed range. BCC simplifies the maintenance of VSWPGS while improving reliability. In addition, VSWPGS using PMSG and BCC saves cost compared with VSWPGS using a PWM converter.

• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.141-148
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• 2011

Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.

• Journal of Microbiology and Biotechnology
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• v.18 no.7
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• pp.1326-1334
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• 2008

The prime-boost vaccination with DNA vaccine and recombinant viral vector has emerged as an effective prophylactic strategy to control infectious diseases. Here, we compared the protective immunities induced by multiple alternating immunizations with DNA vaccine (pCIgB) and replication-incompetent adenovirus (Ad-gB) expressing glycoprotein gB of pseudorabies virus (PrV). The platform of pCIgB-prime and Ad-gB-boost induced the most effective immune responses and provided protection against virulent PrV infection. However, priming with pCIgB prior to vaccinating animals by the DNA vaccine-prime and Ad-boost protocol provided neither effective immune responses nor protection against PrV. Similarly, boosting with Ad-gB following immunization with DNA vaccine-prime and Ad-boost showed no significant responses. Moreover, whereas the administration of Ad-gB for primary immunization induced Th2-type-biased immunity, priming with pCIgB induced Th1-type-biased immunity, as judged by the production of PrV-specific IgG isotypes and cytokine IFN-$\gamma$. These results indicate that the order and injection frequency of vaccine vehicles used for heterologous prime-boost vaccination affect the magnitude and nature of the immunity. Therefore, our demonstration implies that the prime-boost protocol should be carefully considered and selected to induce the desired immune responses.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.999-1012
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• 2005

We compare two popular algorithms in current machine learning and statistical learning areas, boosting method represented by AdaBoost and kernel based SVM (Support Vector Machine) using 13 real data sets. This comparative study shows that boosting method has smaller prediction error in data with heavy noise, whereas SVM has smaller prediction error in the data with little noise.

• ETRI Journal
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• v.36 no.5
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• pp.696-703
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• 2014

In this paper, we present a walking guidance system for the visually impaired for use at subway stations. This system, which is based on environmental knowledge, automatically detects and recognizes both exit numbers and arrow signs from natural outdoor scenes. The visually impaired can, therefore, utilize the system to find their own way (for example, using exit numbers and the directions provided) through a subway station. The proposed walking guidance system consists mainly of three stages: (a) sign detection using the MCT-based AdaBoost technique, (b) sign recognition using support vector machines and hidden Markov models, and (c) three verification techniques to discriminate between signs and non-signs. The experimental results indicate that our sign recognition system has a high performance with a detection rate of 98%, a recognition rate of 99.5%, and a false-positive error rate of 0.152.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.301-304
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• 1989

This paper desoribes a method which can make easily the calculations of current and instantaneous output torque by using the representation techniques of Inverter output voltage space vector. And also, a technique for determinations the optical PWM switching patterns which can boost a output voltage and can minimize higher order harmonic components through appropriate movements of output voltage space vector is introduced.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.102-108
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• 2011

Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.