• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.316-317
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• 2013

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.254-254
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• 2014

에어컨 실내기는 소비자와 근접해 있는 실내에 설치되며, 필요성에 의해 가동되므로 소비자들의 소음에 대한 반응이 민감하다. 실내기 소음에는 Fan Noise, Cricking Noise, Refringerant Noise 등이 존재한다. 이중 Refringerant Noise 의 경우 Overall Level 은 낮지만 거슬리는 소음이 발행하여 소비자의 가장 큰 불만을 초래한다. Refringerant Noise 에 대해 발생원인 및 해결 방안 측면으로의 연구는 활발히 진행 중이다. 그러나, 소음에 대한 평가 방법에 대한 연구가 이루어지지 않고 있다. 이에 본 연구에서는 에어컨 실내기 Refringerant Noise 을 대상으로 각 소음 별 특성 분석을 수행하였다. 이를 바탕으로 각 소음 별 영향인자를 도출하였다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.70-78
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• 1992

In this paper, an approach for 3-D object segmentation and classification, which is based on eigen-values of polynomial function as their surface features, using neural network is proposed. The range images of 3-D objects are classified into surface primitives which are homogeneous in their intrinsic eigenvalue properties. The misclassified regions due to noise effect are merged into correct regions satisfying homogeneous constraints of Hopfield neural network. The proposed method has advantage of processing both segmentation and classification simultaneously.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.100-105
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• 1992

Texture is one of the important characteristics that has been used to identify objects or regions of interest in an image. Tire tread patterns can be considered as a kind of texture, and these are classified with a texture analysis method. In this sense, this paper proposes a new algorithm for the classification of tire tread pattern. For the classification, cooccurrence matrix for the binary image is used. The performances are tested by experimentally 8 different tire tread pattern and the robustness is examined by including some kinds on noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.273-277
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• 2007

In case of the Hydraulic Turbine Dynamo operating for Waterpower Generation, it makes very huge and loud noises, and it influences bad effect physically as same as mentally to those people who are working inside of power plant, and brings the decline of an effective working efficiency. However, its evaluation method or measure about such noise reflects merely its physical attribute which is sensuous Loudness of the Noise itself, since the accumulation effect of Noise or the meaning connected with psychological response did not reflect, it is the actual state that a rational evaluation is unable to expect. Consequently, this Study has attempted to evaluate the Noise of Hydraulic Turbine Dynamo by analyzing the sound quality using Zwicker‘s Psychological Acoustic Parameter, after classification by its positions of the Noise occurring at Hydraulic Turbine Dynamo.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.123-131
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• 2011

The objective of this study is to prepare the effective management plan of environmental noise with a noise map, and the guideline on the management plan of environmental noise was suggested through the review of existing application examples. The management plan of environmental noise with a noise map includes the practical contents in the stages of subdivision of management areas, establishment of reduction measures, opinion collection, post investigation, and reformulation of management plan. First, the classification of management regions is performed considering the excess degree of noise standard and the facility type in the phase of subdivision of management areas. Second, the optimal management plan is established through the investigation of regional characteristics and various noise reduction measures in the phase of establishment of reduction measures, which includes the examination of noise reduction effects with a noise map and the budget planning with the costing of noise reduction measures. Third, the opinion survey with a local resident and a expert is carried out in order to prove the validity of the management plan in the phase of opinion collection, and the management plan is modified with gathered opinions. Fourth, the post examination plan with noise measurement is performed in order to verify the real effect of noise reduction measures according to the management plan in the phase of post investigation. Finally, the amendment of the management plan as well as the improvement of a noise map is carried out at a regular cycle in the phase of reformulation of management plan.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.39-46
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• 2019

In this paper, combining features is proposed as a way to enhance the classification accuracy of sounds under noisy environments using the CNN (Convolutional Neural Network) structure. A robust log Mel-filter bank using Wiener filter and PNCCs (Power Normalized Cepstral Coefficients) are extracted to form a 2-dimensional feature that is used as input to the CNN structure. An ebird database is used to classify 43 types of bird species in their natural environment. To evaluate the performance of the combined features under noisy environments, the database is augmented with 3 types of noise under 4 different SNRs (Signal to Noise Ratios) (20 dB, 10 dB, 5 dB, 0 dB). The combined feature is compared to the log Mel-filter bank with and without incorporating the Wiener filter and the PNCCs. The combined feature is shown to outperform the other mentioned features under clean environments with a 1.34 % increase in overall average accuracy. Additionally, the accuracy under noisy environments at the 4 SNR levels is increased by 1.06 % and 0.65 % for shop and schoolyard noise backgrounds, respectively.

• Proceedings of the Acoustical Society of Korea Conference
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• 1997.06a
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• pp.68-74
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• 1997

Ship radiated noise is an infortant parameter which dtermines Anti Submarine Warfare(ASW) countermeansure or passive Sonar detection and classification performance. Its measurement should be performed under controlled ocean acoustic environment. In data reduction of the measured data from hydrophone array, theeffect fo ambient noise, surface reflection and bottom reflection etc. should be compensated to obtain the source level of the ship radiated noise. This study describes the measurement hydrophone array design criteria based on the analysis of transimission anomaly due to the surface reflection.

• Convergence Security Journal
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• v.17 no.3
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• pp.21-29
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• 2017

Accurate encoding of local patterns is a very important factor in texture classification. However, LBP based methods w idely studied have fundamental problems that are vulnerable to noise. Recently, LDP method using edge response and dire ction information was proposed in facial expression recognition. LDP is more robust to noise than LBP and can accommod ate more information in it's pattern code, but it has drawbacks that it is sensitive to rotation transforms that are critical to texture classification. In this paper, we propose a new local pattern coding method called Rotation Invariant Local Direc tional Pattern, which combines rotation-invariant transform to LDP. To prove the texture classification performance of the proposed method in this paper, texture classification was performed on the widely used UIUC and CUReT datasets. As a result, the proposed RILDP method showed better performance than the existing methods.