• Title/Summary/Keyword: Denoising

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Impact identification and localization using a sample-force-dictionary - General Theory and its applications to beam structures

  • Ginsberg, Daniel;Fritzen, Claus-Peter
    • Structural Monitoring and Maintenance
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    • v.3 no.3
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    • pp.195-214
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    • 2016
  • Monitoring of impact loads is a very important technique in the field of structural health monitoring (SHM). However, in most cases it is not possible to measure impact events directly, so they need to be reconstructed. Impact load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response function are usually known. Generally this leads to a so called ill-posed inverse problem. It is reasonable to use prior knowledge of the force in order to develop more suitable reconstruction strategies and to increase accuracy. An impact event is characterized by a short time duration and a spatial concentration. Moreover the force time history of an impact has a specific shape, which also can be taken into account. In this contribution these properties of the external force are employed to create a sample-force-dictionary and thus to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The reconstruction approach shown here is capable to estimate simultaneously the magnitude of the impact and the impact location, with a minimum number of accelerometers. The possibility of reconstructing the impact based on a noisy output signal is first demonstrated with simulated measurements of a simple beam structure. Then an experimental investigation of a real beam is performed.

Early warning of hazard for pipelines by acoustic recognition using principal component analysis and one-class support vector machines

  • Wan, Chunfeng;Mita, Akira
    • Smart Structures and Systems
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    • v.6 no.4
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    • pp.405-421
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    • 2010
  • This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

Damage localization and quantification in beams from slope discontinuities in static deflections

  • Ma, Qiaoyu;Solis, Mario
    • Smart Structures and Systems
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    • v.22 no.3
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    • pp.291-302
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    • 2018
  • This paper presents a flexibility based method for damage identification from static measurements in beam-type structures. The response of the beam at the Damaged State is decomposed into the response at the Reference State plus the response at an Incremental State, which represents the effect of damage. The damage is localized by detecting slope discontinuities in the deflection of the structure at the Incremental State. A denoising filtering technique is applied to reduce the effect of experimental noise. The extent of the damage is estimated through comparing the experimental flexural stiffness of the damaged cross-sections with the corresponding values provided by analytical models of cracked beams. The paper illustrates the method by showing a numerical example with two cracks and an experimental case study of a simply supported steel beam with one artificially introduced notch type crack at three damage levels. A Digital Image Correlation system was used to accurately measure the deflections of the beam at a dense measurement grid under a set of point loads. The results indicate that the method can successfully detect and quantify a small damage from the experimental data.

Wavelet Based Non-Local Means Filtering for Speckle Noise Reduction of SAR Images (SAR 영상에서 웨이블렛 기반 Non-Local Means 필터를 이용한 스펙클 잡음 제거)

  • Lee, Dea-Gun;Park, Min-Jea;Kim, Jeong-Uk;Kim, Do-Yun;Kim, Dong-Wook;Lim, Dong-Hoon
    • The Korean Journal of Applied Statistics
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    • v.23 no.3
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    • pp.595-607
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    • 2010
  • This paper addresses the problem of reducing the speckle noise in SAR images by wavelet transformation, using a non-local means(NLM) filter originated for Gaussian noise removal. Log-transformed SAR image makes multiplicative speckle noise additive. Thus, non-local means filtering and wavelet thresholding are used to reduce the additive noise, followed by an exponential transformation. NLM filter is an image denoising method that replaces each pixel by a weighted average of all the similarly pixels in the image. But the NLM filter takes an acceptable amount of time to perform the process for all possible pairs of pixels. This paper, also proposes an alternative strategy that uses the t-test more efficiently to eliminate pixel pairs that are dissimilar. Extensive simulations showed that the proposed filter outperforms many existing filters terms of quantitative measures such as PSNR and DSSIM as well as qualitative judgments of image quality and the computational time required to restore images.

A hidden Markov model for long term drought forecasting in South Korea

  • Chen, Si;Shin, Ji-Yae;Kim, Tae-Woong
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.225-225
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    • 2015
  • Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

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A Study on Image Noise Reduction Technique for Low Light Level Environment (저조도 환경의 영상 잡음제거 기술에 관한 연구)

  • Lee, Ho-Cheol;Namgung, Jae-Chan;Lee, Seong-Won
    • Journal of the Korean Society for Railway
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    • v.13 no.3
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    • pp.283-289
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    • 2010
  • Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

Pre-processing Scheme for Indoor Precision Tracking Based on Beacon (비콘 기반 실내 정밀 트래킹을 위한 전처리 기법)

  • Hwang, Yu Min;Jung, Jun Hee;Shim, Issac;Kim, Tae Woo;Kim, Jin Young
    • Journal of Satellite, Information and Communications
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    • v.11 no.4
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    • pp.58-62
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    • 2016
  • In this paper, we propose a pre-processing scheme for improving indoor positioning accuracy in impulsive noise channel environments. The impulsive noise can be generated by multi-path fading effects by complicated indoor structures or interference environments, which causes an increase in demodulation error probability. The proposed pre-processing scheme is performed before a triangulation method to calculate user's position, and providing reliable input data demodulated from a received signal to the triangulation method. Therefore, we studied and proposed an adaptive threshold function for mitigation of the impulsive noise based on wavelet denoising. Through results of computer simulations for the proposed scheme, we confirmed that Bit Error Rate and Signal-to-Noise Ratio performance is improved compared to conventional schemes.

Microbial community analysis of commercial nuruk in Korea using pyrosequencing (파이로시퀀싱을 이용한 상업용 전통누룩의 미생물 군집분석)

  • Park, Ji-Hee;Kim, Song-Gun;Lee, Yong-Jae;Chung, Chang-Ho
    • Korean Journal of Food Science and Technology
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    • v.50 no.1
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    • pp.55-60
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    • 2018
  • Microbial communities of four commercial Korean nuruks were analyzed by the 454 pyrosequencing method to correlate different characteristics of rice wine fermentation. The total and average sequencing reads of fungi in the four nuruks were 14,800 and 3,494, respectively. At the phylum level, Ascomycota was dominant in three nuruks, namely, SH, SS, and JJ, while Zygomycota was dominant in SJ. Saccharomycopsis was dominant in nuruks subjected to longer fermentation periods, such as SH and SS. The total and average sequence reads for bacteria were 31,485 and 7,871, respectively. Bacteria belonging to the phylum Firmicutes were dominant in all samples. SH showed several genera of lactic acid bacteria, such as Lactobacillus, Leuconostoc, Pediococcus, and other minor bacteria. Staphylococcus and Bacillus were the dominant bacteria in JJ and SJ, respectively.

Hand Tracking Based Projection Mapping System and Applications (손 위치 트래킹 기반의 프로젝션 매핑 시스템 및 응용)

  • Lee, Cheongun;Park, Sanghun
    • Journal of the Korea Computer Graphics Society
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    • v.22 no.4
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    • pp.1-9
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    • 2016
  • In this paper we present a projection mapping system onto human's moving hand by a projector as information delivery media and Kinect to recognize hand motion. Most traditional projection mapping techniques project a variety of images onto stationary objects, however, our system provides new user experience by projecting images onto the center of the moving palm. We explain development process of the system, and production of content as applications on our system. We propose hardware organization and development process of open software architecture based on object oriented programming approach. For stable image projection, we describe a device calibration method between the projector and Kinect in three dimensional space, and a denoising technique to minimize artifacts from Kinect coordinates vibration and unstable hand tremor.