• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.193-197
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• 2012

This paper focuses on identifying which appliance is currently operating by analyzing electrical load signature for home energy monitoring system. The identification framework is comprised of three steps. Firstly, specific appliance features, or signatures, were chosen, which are DC (Duty Cycle), SO (Slope of On-state), VO (Variance of On-state), and ZC (Zero Crossing) by reviewing observations of appliances from 13 houses for 3 days. Five appliances of electrical rice cooker, kimchi-refrigerator, PC, refrigerator, and TV were chosen for the identification with high penetration rate and total operation-time in Korea. Secondly, K-NN and Naive Bayesian classifiers, which are commonly used in many applications, are employed to estimate from which appliance the signatures are obtained. Lastly, one of candidates is selected as final identification result by majority voting. The proposed identification frame showed identification success rate of 94.23%.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.67-90
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• 2019

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a state-of-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.25-36
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• 2020

Motivation: Fibers as the extracellular filamentous structures determine the shape of the cytoskeletal structures. Their characterization and reconstruction from a 3D cellular image represent very useful quantitative information at the cellular level. In this paper, we presented a novel automatic method to extract fiber diameter distribution through a pipeline to reconstruct fibers from 3D fluorescence confocal images. The pipeline is composed of four steps: segmentation, skeletonization, template fitting and fiber tracking. Segmentation of fiber is achieved by defining an energy based on tensor voting framework. After skeletonizing segmented fibers, we fit a template for each seed point. Then, the fiber tracking step reconstructs fibers by finding the best match of the next fiber segment from the previous template. Thus, we define a fiber as a set of templates, based on which we calculate a diameter distribution of fibers.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• Journal of Nutrition and Health
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• v.36 no.5
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• pp.515-527
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• 2003

Objectives： A meta-analysis of the literatures was conducted to evaluate the cost-effectiveness of medical nutrition therapy by dietitians. Methods : The 30 studies were identified from a computerized search of published research on MEDLINE, Science-Direct and the PQD database until May, 2002 and a review of reference lists. The main search terms were“dietitian”,“dietary intervention”,“nutrition intervention”, “cost”,“cost-effectiveness”and“cost-benefit analysis”. The subgroup analysis was performed by publication year, study design, intervention provider, type of patient (in/out-patient) and type of cost (total cost/direct cost). Two reviewers independently selected trials for inclusion, assessed the quality and extracted the data. Results : The 30 studies were identified using the electric database search and bibliographies. The 17 trials were eligible for inclusion criteria, then the systematic review and a meta-analysis were conducted on effectiveness and cost-effectiveness of medical nutrition therapy. The quality of the studies was evaluated using the quality assessment tool for observational studies. The quality score was 0.515 $\pm$ 0.121 (range : 0.279-0.711, median : 0.466). The meta-analysis of 17 studies based on the random effect model showed that medical nutrition therapy was highly effective in treating the diseases (effect size 0.3092 : 95% confidence interval 0.2282-0.3303). The vote-counting method, one of meta-analysis methods, was applied to evaluate the cost-effectiveness of medical nutrition therapy conducted by dietitians. Two criteria (method 1, method 2) for voting were used. The calculated p-values for method 1 (more conservative method) and method 2 (less conservative method) were 0.1250 and 0.0106, respectively. Medical nutrition therapy by dietitians was significantly cost-effective in the method 2. Conclusion. This meta-analysis showed that the effectiveness of medical nutrition therapy was statistically significant in treating disease (effect size 0.3092), and that the cost-effectiveness of medical nutrition therapy was statistically significant in the method 2 (less conservative method) of vote counting. (Korean J Nutrition 36(5): 515~527, 2003)

• Journal of the Korea Computer Graphics Society
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• v.28 no.1
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• pp.1-9
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• 2022

Non-small cell lung cancer (NSCLC) accounts for a high proportion of 85% among all lung cancer and has a significantly higher mortality rate (22.7%) compared to other cancers. Therefore, it is very important to predict the prognosis after surgery in patients with non-small cell lung cancer. In this study, the types of preoperative chest CT image patches for non-small cell lung cancer patients with tumor as a region of interest are diversified into five types according to tumor-related information, and performance of single classifier model, ensemble classifier model with soft-voting method, and ensemble classifier model using 3 input channels for combination of three different patches using pre-trained ResNet and EfficientNet CNN networks are analyzed through misclassification cases and Grad-CAM visualization. As a result of the experiment, the ResNet152 single model and the EfficientNet-b7 single model trained on the peritumoral patch showed accuracy of 87.93% and 81.03%, respectively. In addition, ResNet152 ensemble model using the image, peritumoral, and shape-focused intratumoral patches which were placed in each input channels showed stable performance with an accuracy of 87.93%. Also, EfficientNet-b7 ensemble classifier model with soft-voting method using the image and peritumoral patches showed accuracy of 84.48%.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2E
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• pp.17-24
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• 2001

In this paper, we propose the bootstrap and aggregating (bagging) vector quantization (VQ) classifier to improve the performance of the text-independent speaker recognition system. This method generates multiple training data sets by resampling the original training data set, constructs the corresponding VQ classifiers, and then integrates the multiple VQ classifiers into a single classifier by voting. The bagging method has been proven to greatly improve the performance of unstable classifiers. Through two different experiments, this paper shows that the VQ classifier is unstable. In one of these experiments, the bias and variance of a VQ classifier are computed with a waveform database. The variance of the VQ classifier is compared with that of the classification and regression tree (CART) classifier[1]. The variance of the VQ classifier is shown to be as large as that of the CART classifier. The other experiment involves speaker recognition. The speaker recognition rates vary significantly by the minor changes in the training data set. The speaker recognition experiments involving a closed set, text-independent and speaker identification are performed with the TIMIT database to compare the performance of the bagging VQ classifier with that of the conventional VQ classifier. The bagging VQ classifier yields improved performance over the conventional VQ classifier. It also outperforms the conventional VQ classifier in small training data set problems.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.659-669
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• 2012

This paper proposes the method to extract a binary audio fingerprint by combining several base binary fingerprints. Based on majority voting of base fingerprints, which are designed by mimicking the fingerprint used in Philips fingerprinting system, the proposed fingerprint is determined. In the matching part, the base fingerprints are extracted from the query, and distance is computed using the sum of them. In the experiments, the proposed fingerprint outperforms the base binary fingerprints. The method can be used for enhancing the existing binary fingerprint or for designing a new fingerprint.

• Survey Research
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• v.3 no.2
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• pp.77-90
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• 2002

This study reports the results of a random sampling telephone survey conducted in the case of the Ulsan mayoral election 2002. We interview at least five times to a respondent who is randomly selected by means of the birthday method from a randomly sampled telephone number list of 1,233 households, We analyze the result of interviewing, such as absence and promise. And we compare the demographic variables of the surveyed sample and those of the population and we also compare the randomly selected sample's voting preference with outcome of the election in various ways. Finally, we discuss difficulty of random sampling with the birthday method and suggest some technical tips to conduct random sampling telephone survey.