• International conference on construction engineering and project management
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• 2017.10a
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• pp.252-259
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• 2017

Nowadays, the existing manual method for recording actual progress of the construction site has some drawbacks, such as great reliance on the experience of professional engineers, work-intensive, time consuming and error prone. A method integrating computer vision and BIM(Building Information Modeling) is presented for indoor automatic progress monitoring. The developed method can accurately calculate the engineering quantity of target component in the time-lapse images. Firstly, sample images of on-site target are collected for training the classifier. After the construction images are identified by edge detection and classifier, a voting algorithm based on mathematical geometry and vector operation will divide the target contour. Then, according to the camera calibration principle, the image pixel coordinates are conversed into the real world Coordinate and the real coordinates would be corrected with the help of the geometric information in BIM model. Finally, the actual engineering quantity is calculated.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.498-512
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• 2010

Intra coding is an indispensable coding tool since it can provide random accessibility as well as error resiliency. However, it is the problem that intra coding has relatively low coding efficiency compared with inter coding in the area of video coding. Even though H.264/AVC has significantly improved the intra coding performance compared with previous video standards, H.264/AVC encoder complexity is significantly increased, which is not suitable for low bit rate interactive services. In this paper, a Voting-based Intra Mode Bit Skip (V-IMBS) scheme is proposed to improve coding efficiency as well as to reduce encoding time complexity using decoder-side prediction. In case that the decoder can determine the same prediction mode as what is chosen by the encoder, the encoder does not send that intra prediction mode; otherwise, the conventional H.264/AVC intra coding is performed. Simulation results reveal a performance increase up to 4.44% overall rate savings and 0.24 dB in peak signal-to-noise ratio while the frame encoding speed of proposed method is about 42.8% better than that of H.264/AVC.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.337-345
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• 2009

The dependence of numerous systems on electronic devices is causing rapidly increasing concern over fault tolerance because of safety issues of safety critical system. As an example, a vehicle with electronics-controlled system such as x-by-wire systems, which are replacing rigid mechanical components with dynamically configurable electronic elements, should be fault￢tolerant because a devastating failure could arise without warning. Fault-tolerant systems have been studied in detail, mainly in the field of aeronautics. As an alternative to solve these problems, this paper presents the fuzzy hybrid redundancy system that can remove most erroneous faults with fuzzy fault detection algorithm. In addition, several numerical simulation results are given where the fuzzy hybrid redundancy outperforms with general voting method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1347-1357
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• 1995

In this paper, we describe a modularized neuroclassifier for enhancing the recognition accuracy of mixed printed and handwritten numerals. This classifier combines four modularized subclassifiers using multi-layer perceptron module. The input of each subclassifier is comprised of a group of specialized feature sets. On applying this method to combining several subclassifiers for unconstrained handwritten numerals, the experimental result shows that the performance of individual subclassifier can be improved. In winner-take-all voting method, the result of subclassifier having the highest RF value is selected as the output. The generality of this classifier is tested with 1,080 printed and 3,000 handwritten numerals that was not shown in training the neural networks. Experimental results show 98.2% recognition rate. The typical recognition test with a threshold value(RF=1.5) has shown 97% recognition, 1% substitution and 2% rejection rates.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.561-567
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• 2010

Multiclassification is typically performed using voting scheme methods based on combining a set of binary classifications. In this paper we use multiclassification method with a hat matrix of least squares support vector machine (LS-SVM), which can be regarded as the revised one-against-all method. To tackle multiclass problems for large data, we use the $Nystr\ddot{o}m$ approximation and the quadratic Renyi entropy with estimation in the primal space such as used in xed size LS-SVM. For the selection of hyperparameters, generalized cross validation techniques are employed. Experimental results are then presented to indicate the performance of the proposed procedure.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.163-166
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• 2003

In conventional patchwork, the difference of the mean values of two groups is compared for watermark detection. We propose two modified patchwork schemes based on order statistics, which achieves significant improvements over conventional patchwork. First, we propose that the mean comparison is replaced by the median comparison, to get PSNR improvement due to informed watermarking. Second, we propose a majority voting scheme of a sequential comparison of pixel pairs in a sorted order, which produces significantly lower BER. The performance improvements are mathematically analyzed and tested. In experimental results, PSNR is about 5㏈～10㏈ higher in the first method and BER is about 1/5～l/2 times lower in the second method than conventional patchwork.

• Journal of the Korea Convergence Society
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• v.7 no.1
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• pp.49-55
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• 2016

In a delegate authentication, a user can lend his/her own authentication data to the third parties to let them be authenticated instead of himself/herself. The user authentication schemes based on the memory of unique data such as password, are vulnerable to this type of attack. Biometric authentication could minimize the risk of delegate authentication since it uses the biometric data unique by each person. Group authentication scheme is used to prove that each group member belongs to the corresponding group. For applications such as an electronic voting or a mobile meeting where the number of group members is changing dynamically, a new group authentication method is needed to reflect the status of group in real time. In this paper, we propose biometric authentication based dynamic group signature scheme. The proposed scheme is composed of biometric key generation, group public key creation, group signature generation, group signature verification and member update protocols. The proposed member update protocol is secure against colluding attacks of existing members and could reflect group status in real time.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1113-1125
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• 2013

Corporate bankruptcy prediction has been an important topic in the accounting and finance field for a long time. Several data mining techniques have been used for bankruptcy prediction. However, there are many limits for application to real classification problem with a single model. This study proposes ensemble SVM (support vector machine) model which assembles different SVM models with each different kernel functions. Our ensemble model is made and evaluated by v-fold cross-validation approach. The k top performing models are recruited into the ensemble. The classification is then carried out using the majority voting opinion of the ensemble. In this paper, we investigate the performance of ensemble SVM classifier in terms of accuracy, error rate, sensitivity, specificity, ROC curve, and AUC to compare with single SVM classifiers based on financial ratios dataset and simulation dataset. The results confirmed the advantages of our method: It is robust while providing good performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.27-32
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• 2019

Research that implements the classification process through Deep Learning algorithm, one of the outstanding human abilities, includes a unimodal model, a multi-modal model, and a multi-modal method using music videos. In this study, the results were better by suggesting a system to analyze each song's spectrum into short samples and vote for the results. Among Deep Learning algorithms, CNN showed superior performance in the category of music genre compared to RNN, and improved performance when CNN and RNN were applied together. The system of voting for each CNN result by Deep Learning a short sample of music showed better results than the previous model and the model with Softmax layer added to the model performed best. The need for the explosive growth of digital media and the automatic classification of music genres in numerous streaming services is increasing. Future research will need to reduce the proportion of undifferentiated songs and develop algorithms for the last category classification of undivided songs.

• Korean Journal of Legislative Studies
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• v.27 no.1
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• pp.117-155
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• 2021

The purpose of this study is to identify the relationship between people's ideological orientation and the populist attitude in terms of demand of populism. The influence of subjective ideology evaluation and political party support on anti-elitism (AE), people centrism (PC) and anti-pluralism (AP) are analyzed in detail. To research this, the socioeconomic factors, democracy recognition and the method of political participation are set as control variables, and the ideologies are classified into extreme conservative, conservative, moderate, progress, and extreme progress. The data are collected through nationwide online survey. The results of the analysis are as follows: First, the powerful affinity between ideological orientation and populist attitude are confirmed. The support for conservative ideology (especially extreme conservative) and the conservative party are affecting the AE and AP, and the ideology of extreme progress and support for the progressive party are influencing the PC and AP. When putting together 3 types of attitude, the conservative (especially extreme conservative) and extreme progressive ideology are the factors that determine the populism attitude. Second, There was no impact of socioeconomic variables except gender (female) and age. Third, populist attitude have a multidimensional nature determined by democratic satisfaction, government trust, external efficacy, voting and non-voting activities.