• Journal of Korean Academy of Nursing
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• v.40 no.5
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• pp.736-746
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• 2010

Purpose: This study was done to identify the distribution and related factors for stage of change for wearing hearing protection devices (HPDs) by workers in environments with high noise. Predictors of Use of Hearing Protection Model and Trans-theoretical Model were tested. Methods: The participants were 755 workers from 20 noisy work places in Busan and Gyeongnam. Data were collected from January to April 2008 using self-administered questionnaires, and analyzed using multiple logistic regression. Results: There were significant differences in social mode (OR=1.35, 95% CI: 1.06-1.73) between precontemplation/contemplation and preparation stage, in males (OR=2.36, 95% CI: 1.24-4.51), workers with high school education or less (OR=1.39, 95% CI: 1.28-2.78), shift workers (OR=1.50, 95% CI: 1.02-2.21), workers who previously worked in noisy places (OR=1.39, 95% CI: 1.20-2.34), and workers who had previous hearing examinations (OR=1.89, 95% CI: 1.25-2.85), in the social model (OR=1.59, 95% CI: 1.42-1.78), and self-efficacy (OR=1.05, 95% CI: 1.02-1.08) between workers in preparation and action stages, in length of time working in noisy work places (OR=2.26, 95% CI: 1.17-4.39), social model (OR=1.66, 95% CI: 1.33-2.08), and perceived benefit (OR=0.95, 95% CI: 0.93-0.97) between action and maintenance stage. Conclusion: Social model was a common factor showing differences between two adjacent stages for wearing HPDs. The results provide data for developing programs to encourage workers to wear HPDs and application of these programs in work settings.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.607-610
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• 2021

When learning a model in supervised learning, input data and the label of the data are required. However, labeling is high cost task and if automated, there is no guarantee that the label will always be correct. In the case of supervised learning in such a noisy labels environment, the accuracy of the model increases at the initial stage of learning, but decrease significantly after a certain period of time. There are various methods to solve the noisy label problem. But in most cases, the probability predicted by the model is used as the pseudo label. So, we proposed a method to predict the true label more quickly by refining the probabilities predicted by the model. Result of experiments on the same environment and dataset, it was confirmed that the performance improved and converged faster. Through this, it can be applied to methods that use the probability distribution predicted by the model among existing studies. And it is possible to reduce the time required for learning because it can converge faster in the same environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.49-59
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• 2018

Smart grid system has been deployed fast despite of legal, business and technology problems in many countries. One important problem in deploying the smart grid system is to protect private smart meter readings from the unbelievable parties while the major smart meter functions are untouched. Privacy-preserving involves some challenges such as hardware limitations, secure cryptographic schemes and secure signal processing. In this paper, we focused particularly on the smart meter reading aggregation,which is the major research field in the smart meter privacy-preserving. We suggest a noisy weighted aggregation scheme to guarantee differential privacy. The noisy weighted values are generated in such a way that their product is one and are used for making the veiled measurements. In case that a Diffie-Hellman generator is applied to obtain the noisy weighted values, the noisy values are transformed in such a way that their sum is zero. The advantage of Diffie and Hellman group is usually to use 512 bits. Thus, compared to Paillier cryptosystem series which relies on very large key sizes, a significant performance can be obtained.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.2
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• pp.18-26
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• 1992

MR(Model Reduction) is presented in order to estimate the frequency of multiple sinusoids from the finite noisy data with the white or colored noises. MR, using the reduced rank models, is designed, appling the approximation of linear system to LP(Linear Prediction). The MR method is analyzed. Monte-carlo simulations are conducted for MR and Lp. The results are compared with in terms of mean, root-mean square and relative bias. MR eliminates effectevely the extremeous and exceptional poles appearing in LP and improves the accuracy of LP. Especially, MR gives promising results in short noisy measurements, low SNR's and colored noises. Power spectral density and angular frequency position are showed by figures, for examples. Finally, the new method is utilized to the communication and biomedical systems estimating the characteristics of the signal and the system identification modelling the dynamic systems from experimental data.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.787-790
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• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.55-60
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• 2018

기계학습 기반인 관계추출 모델을 설계할 때 다량의 학습데이터를 빠르게 얻기 위해 원격지도학습 방식으로 데이터를 수집한다. 이러한 데이터는 잘못 분류되어 학습데이터로 사용되기 때문에 모델의 성능에 부정적인 영향을 끼칠 수 있다. 본 논문에서는 이러한 문제를 강화학습 접근법을 사용해 해결하고자 한다. 본 논문에서 제안하는 모델은 오 분류된 데이터로부터 좋은 품질의 데이터를 찾는 문장선택기와 선택된 문장들을 가지고 학습이 되어 관계를 추출하는 관계추출기로 구성된다. 문장선택기는 지도학습데이터 없이 관계추출기로부터 피드백을 받아 학습이 진행된다. 이러한 방식은 기존의 관계추출 모델보다 좋은 성능을 보여주었고 결과적으로 원격지도학습데이터의 단점을 해결한 방법임을 보였다.

• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.189-196
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• 2011

This paper presents a method of topological modeling using only low-cost sonar sensors. The proposed method constructs a topological model by extracting sub-regions from the local grid map. The extracted sub-regions are considered as nodes in the topological model, and the corresponding edges are generated according to the connectivity between two sub-regions. A grid confidence for each occupied grid is evaluated to obtain reliable regions in the local grid map by filtering out noisy data. Moreover, a convexity measure is used to extract sub-regions automatically. Through these processes, the topological model is constructed without predefining the number of sub-regions in advance and the proposed method guarantees the convexity of extracted sub-regions. Unlike previous topological modeling methods which are appropriate to the corridor-like environment, the proposed method can give a reliable topological modeling in a home environment even under the noisy sonar data. The performance of the proposed method is verified by experimental results in a real home environment.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.97-107
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• 1997

Image segmentation is teh important step in image infromation extraction for computer vison sytems. Fuzzy clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are derived from the fuzzy c-means (FCM) algorithm. The FCM algorithm uses th eprobabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belongingor compatibility. moreover, the FCM algorithm has considerable trouble above under noisy environments in the feature space. Recently, the possibilistic C-mean (PCM) for solving growing for color image segmentation. In the PCM, the membersip values may be interpreted as degrees of possibility of the data points belonging to the classes. So, the problems in the FCM can be solved by the PCM. The clustering results by just PCM are not smoothly bounded, and they often have holes. So, the region growing was used as a postprocessing. In our experiments, we illustrated that the proposed method is reasonable than the FCM in noisy enviironments.

• Proceedings of the KIEE Conference
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• 1979.08a
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• pp.158-159
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• 1979

Based on minimum-mean-sqare error criterion, a noise filtering algorithm for the reconstruction of an image function from noisy projection data is suggested. The filter is constructed with a few projection data. This algorithm requires less computational time compared with other noise filtering algorithm.

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.113-118
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• 2015

An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection) reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.