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Готові списки джерел за темами / Data quality and noise

Добірка наукової літератури з теми "Data quality and noise"

Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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Статті в журналах з теми "Data quality and noise":

1

Van Hulse, Jason, Taghi M. Khoshgoftaar, and Amri Napolitano. "Evaluating the Impact of Data Quality on Sampling." Journal of Information & Knowledge Management 10, no. 03 (September 2011): 225–45. http://dx.doi.org/10.1142/s021964921100295x.

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Learning from imbalanced training data can be a difficult endeavour, and the task is made even more challenging if the data is of low quality or the size of the training dataset is small. Data sampling is a commonly used method for improving learner performance when data is imbalanced. However, little effort has been put forth to investigate the performance of data sampling techniques when data is both noisy and imbalanced. In this work, we present a comprehensive empirical investigation of the impact of changes in four training dataset characteristics — dataset size, class distribution, noise level and noise distribution — on data sampling techniques. We present the performance of four common data sampling techniques using 11 learning algorithms. The results, which are based on an extensive suite of experiments for which over 15 million models were trained and evaluated, show that: (1) even for relatively clean datasets, class imbalance can still hurt learner performance, (2) data sampling, however, may not improve performance for relatively clean but imbalanced datasets, (3) data sampling can be very effective at dealing with the combined problems of noise and imbalance, (4) both the level and distribution of class noise among the classes are important, as either factor alone does not cause a significant impact, (5) when sampling does improve the learners (i.e. for noisy and imbalanced datasets), RUS and SMOTE are the most effective at improving the AUC, while SMOTE performed well relative to the F-measure, (6) there are significant differences in the empirical results depending on the performance measure used, and hence it is important to consider multiple metrics in this type of analysis, and (7) data sampling rarely hurt the AUC, but only significantly improved performance when data was at least moderately skewed or noisy, while for the F-measure, data sampling often resulted in significantly worse performance when applied to slightly skewed or noisy datasets, but did improve performance when data was either severely noisy or skewed, or contained moderate levels of both noise and imbalance.

2

Li, Benchong, and Qiong Gao. "Improving data quality with label noise correction." Intelligent Data Analysis 23, no. 4 (September 26, 2019): 737–57. http://dx.doi.org/10.3233/ida-184024.

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3

Ning, Ai Min, Cheng Li, and Zhao Liu. "Acoustic Transceiver Optimization Analysis for Downhole Sensor Data Telemetry via Drillstring." Applied Mechanics and Materials 302 (February 2013): 389–94. http://dx.doi.org/10.4028/www.scientific.net/amm.302.389.

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Downhole sensor data telemetry using acoustic waves along the drillstring helps to know the physical and chemical properties of the formation and drilling fluid in Logging While Drilling. However, complex drillstring channel characteristics and normal downhole drilling operations will often adversely affect the quality of acoustic telemetry. Based on a theoretical channel model, we analyze the effects of transceiver optimal placements on acoustic transmission through a periodic drillstring. Considering the downhole noisy conditions including the surface noise sources, the downhole noise sources and multiple reflection echoes, dual acoustic receivers and an acoustic isolator are analyzed to improve the Signal-to-Noise Ratio and the capacity of the uplink channel. By arranging two receivers spaced one-quarter wavelength apart at receiver ends, the suppression results of one-way downlink noises are evaluated with the aid of the channel transient simulation model. Then the isolating results of uplink noises from drilling bit are investigated, with regard to the isolator placed between the downhole transmitter and a noise source. These methods, in conjunction with the complex drillstring features, show that the uses of the available transceiver design and signal processing techniques can make the drillstring as a waveguide for transmitting downhole sensor information at high data rate.

4

Terbe, Dániel, László Orzó, Barbara Bicsák, and Ákos Zarándy. "Hologram Noise Model for Data Augmentation and Deep Learning." Sensors 24, no. 3 (February 1, 2024): 948. http://dx.doi.org/10.3390/s24030948.

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This paper introduces a noise augmentation technique designed to enhance the robustness of state-of-the-art (SOTA) deep learning models against degraded image quality, a common challenge in long-term recording systems. Our method, demonstrated through the classification of digital holographic images, utilizes a novel approach to synthesize and apply random colored noise, addressing the typically encountered correlated noise patterns in such images. Empirical results show that our technique not only maintains classification accuracy in high-quality images but also significantly improves it when given noisy inputs without increasing the training time. This advancement demonstrates the potential of our approach for augmenting data for deep learning models to perform effectively in production under varied and suboptimal conditions.

5

V, Malathi, and Gopinath MP. "Noise Deduction in Novel Paddy Data Repository using Filtering Techniques." Scalable Computing: Practice and Experience 21, no. 4 (December 20, 2020): 601–10. http://dx.doi.org/10.12694/scpe.v21i4.1718.

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Classification of paddy crop diseases in prior knowledge is the current challenging task to evolve the economicgrowth of the country. In image processing techniques, the initial process is to eliminate the noise present in the dataset. Removing the noise leads to improvements in the quality of the image. Noise can be removed by applying filtering techniques. In this paper, a novel data repository created from different paddy areas in Vellore, which includes the following diseases, namely Bacteria Leaf Blight, Blast, Leaf Spot, Leaf Holder, Hispa and Healthy leaves. In the initial process, three kinds of noises, namely Salt and Pepper noise, Speckle noise, and Poisson noises, were removed using noise filtering techniques, namely Median and Wiener filter. Theinterpretation made over the median and Wiener filtering techniques concerning noises, the performance of the methods measured using metrics namely PSNR (peak to signal to noise ration), MSE (mean square error), Maxerr (Maximum squared error), L2rat (ratio of squared error). It is observed that the PSNR value of the hybrid approach is 18.42dB, which produces less error rate as compared with the traditional approach. Results suggest that the methods used in this paper are suitable for processing noise.

6

Hedderich, Michael A., Dawei Zhu, and Dietrich Klakow. "Analysing the Noise Model Error for Realistic Noisy Label Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7675–84. http://dx.doi.org/10.1609/aaai.v35i9.16938.

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Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors. A popular technique to overcome the negative effects of these noisy labels is noise modelling where the underlying noise process is modelled. In this work, we study the quality of these estimated noise models from the theoretical side by deriving the expected error of the noise model. Apart from evaluating the theoretical results on commonly used synthetic noise, we also publish NoisyNER, a new noisy label dataset from the NLP domain that was obtained through a realistic distant supervision technique. It provides seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances. Parallel, clean labels are available making it possible to study scenarios where a small amount of gold-standard data can be leveraged. Our theoretical results and the corresponding experiments give insights into the factors that influence the noise model estimation like the noise distribution and the sampling technique.

7

Ataeyan, Mahdieh, and Negin Daneshpour. "Automated Noise Detection in a Database Based on a Combined Method." Statistics, Optimization & Information Computing 9, no. 3 (June 9, 2021): 665–80. http://dx.doi.org/10.19139/soic-2310-5070-879.

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Data quality has diverse dimensions, from which accuracy is the most important one. Data cleaning is one of the preprocessing steps in data mining which consists of detecting errors and repairing them. Noise is a common type of error, that occur in database. This paper proposes an automated method based on the k-means clustering for noise detection. At first, each attribute (Aj) is temporarily removed from data and the k-means clustering is applied to other attributes. Thereafter, the k-nearest neighbors is used in each cluster. After that a value is predicted for Aj in each record by the nearest neighbors. The proposed method detects noisy attributes using predicted values. Our method is able to identify several noises in a record. In addition, this method can detect noise in fields with different data types, too. Experiments show that this method can averagely detect 92% of the noises existing in the data. The proposed method is compared with a noise detection method using association rules. The results indicate that the proposed method have improved noise detection averagely by 13%.

8

Shin, Jaegwang, and Suan Lee. "Robust and Lightweight Deep Learning Model for Industrial Fault Diagnosis in Low-Quality and Noisy Data." Electronics 12, no. 2 (January 13, 2023): 409. http://dx.doi.org/10.3390/electronics12020409.

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Machines in factories are typically operated 24 h a day to support production, which may result in malfunctions. Such mechanical malfunctions may disrupt factory output, resulting in financial losses or human casualties. Therefore, we investigate a deep learning model that can detect abnormalities in machines based on the operating noise. Various data preprocessing methods, including the discrete wavelet transform, the Hilbert transform, and short-time Fourier transform, were applied to extract characteristics from machine-operating noises. To create a model that can be used in factories, the environment of real factories was simulated by introducing noise and quality degradation to the sound dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII). Thus, we proposed a lightweight model that runs reliably even in noisy and low-quality sound data environments, such as a real factory. We propose a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model using Short-Time Fourier Transforms (STFTs), and the proposed model can be very effective in terms of application because it is a lightweight model that requires only about 6.6% of the number of parameters used in the underlying CNN, and has only a performance difference within 0.5%.

9

Liu, Xiaoqiong, Guang Li, Jin Li, Xiaohui Zhou, Xianjie Gu, Cong Zhou, and Meng Gong. "Self-organizing Competitive Neural Network Based Adaptive Sparse Representation for Magnetotelluric Data Denoising." Journal of Physics: Conference Series 2651, no. 1 (December 1, 2023): 012129. http://dx.doi.org/10.1088/1742-6596/2651/1/012129.

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Abstract The existing sparse decomposition denoising methods for magnetotelluric (MT) data need to set the iterative stop condition manually, which not only has a large workload and high difficulty, but also easily causes subjective bias. To this end, we propose a new adaptive sparse representation method for MT data denoising. First, the data to be processed is divided into high-quality segments and noisy segments by machine learning algorithm. Then, the characteristic parameters of high-quality segments are calculated, and the boundary value of the characteristic parameters is taken as the threshold. The threshold has two functions, one is as a criterion for signal-to-noise identification, and the other is as an iterative stop condition for subsequent sparse decomposition. Finally, the optimized orthogonal matching pursuit algorithm is used to separate the signal and noise of the noisy segments, and the denoised segments and high-quality segments are combined to obtain the complete denoised MT data. The field data processing results show that this method is a fully automatic and intelligent MT data denoising method. It greatly improves the signal-to-noise ratio and the apparent resistivity-phase curves.

10

Kaspirzhny, Anton V., Paul Gogan, Ginette Horcholle-Bossavit, and Suzanne Tyč-Dumont. "Neuronal morphology data bases: morphological noise and assesment of data quality." Network: Computation in Neural Systems 13, no. 3 (January 2002): 357–80. http://dx.doi.org/10.1088/0954-898x_13_3_307.

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Більше джерел

Дисертації з теми "Data quality and noise":

1

Alkharboush, Nawaf Abdullah H. "A data mining approach to improve the automated quality of data." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65641/1/Nawaf%20Abdullah%20H_Alkharboush_Thesis.pdf.

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This thesis describes the development of a robust and novel prototype to address the data quality problems that relate to the dimension of outlier data. It thoroughly investigates the associated problems with regards to detecting, assessing and determining the severity of the problem of outlier data; and proposes granule-mining based alternative techniques to significantly improve the effectiveness of mining and assessing outlier data.

2

Lie, Chin Cheong Patrick. "Iterative algorithms for fast, signal-to-noise ratio insensitive image restoration." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63767.

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3

Al, Jurdi Wissam. "Towards next generation recommender systems through generic data quality." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD005.

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Les systèmes de recommandation sont essentiels pour filtrer les informations en ligne et fournir un contenu personnalisé, réduisant ainsi l’effort nécessaire pour trouver des informations pertinentes. Ils jouent un rôle crucial dans divers domaines, dont le commerce électronique, en aidant les clients à trouver des produits pertinents, améliorant l’expérience utilisateur et augmentant les ventes. Un aspect significatif de ces systèmes est le concept d’inattendu, qui implique la découverte d’éléments nouveaux et surprenants. Cependant, il est complexe et subjectif, nécessitant une compréhension approfondie des recommandations fortuites pour sa mesure et son optimisation. Le bruit naturel, une variation imprévisible des données, peut influencer la sérendipité dans les systèmes de recommandation. Il peut introduire de la diversité et de l’inattendu dans les recommandations, conduisant à des surprises agréables. Cependant, il peut également réduire la pertinence de la recommandation. Par conséquent, il est crucial de concevoir des systèmes qui équilibrent le bruit naturel et la sérendipité. Cette thèse souligne le rôle de la sérendipité dans l’amélioration des systèmes de recommandation et la prévention des bulles de filtre. Elle propose des techniques conscientes de la sérendipité pour gérer le bruit, identifie les défauts de l’algorithme, suggère une méthode d’évaluation centrée sur l’utilisateur, et propose une architecture basée sur la communauté pour une performance améliorée
Recommender systems are essential for filtering online information and delivering personalized content, thereby reducing the effort users need to find relevant information. They can be content-based, collaborative, or hybrid, each with a unique recommendation approach. These systems are crucial in various fields, including e-commerce, where they help customers find pertinent products, enhancing user experience and increasing sales. A significant aspect of these systems is the concept of unexpectedness, which involves discovering new and surprising items. This feature, while improving user engagement and experience, is complex and subjective, requiring a deep understanding of serendipitous recommendations for its measurement and optimization. Natural noise, an unpredictable data variation, can influence serendipity in recommender systems. It can introduce diversity and unexpectedness in recommendations, leading to pleasant surprises. However, it can also reduce recommendation relevance, causing user frustration. Therefore, it is crucial to design systems that balance natural noise and serendipity. Inconsistent user information due to natural noise can negatively impact recommender systems, leading to lower-quality recommendations. Current evaluation methods often overlook critical user-oriented factors, making noise detection a challenge. To provide powerful recommendations, it’s important to consider diverse user profiles, eliminate noise in datasets, and effectively present users with relevant content from vast data catalogs. This thesis emphasizes the role of serendipity in enhancing recommender systems and preventing filter bubbles. It proposes serendipity-aware techniques to manage noise, identifies algorithm flaws, suggests a user-centric evaluation method, and proposes a community-based architecture for improved performance. It highlights the need for a system that balances serendipity and considers natural noise and other performance factors. The objectives, experiments, and tests aim to refine recommender systems and offer a versatile assessment approach

4

Sorensen, Thomas J. "Inverse Scattering Image Quality with Noisy Forward Data." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2541.pdf.

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5

Demiroglu, Cenk. "Multisensor Segmentation-based Noise Suppression for Intelligibility Improvement in MELP Coders." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10455.

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This thesis investigates the use of an auxiliary sensor, the GEMS device, for improving the quality of noisy speech and designing noise preprocessors to MELP speech coders. Use of auxiliary sensors for noise-robust ASR applications is also investigated to develop speech enhancement algorithms that use acoustic-phonetic properties of the speech signal. A Bayesian risk minimization framework is developed that can incorporate the acoustic-phonetic properties of speech sounds and knowledge of human auditory perception into the speech enhancement framework. Two noise suppression systems are presented using the ideas developed in the mathematical framework. In the first system, an aharmonic comb filter is proposed for voiced speech where low-energy frequencies are severely suppressed while high-energy frequencies are suppressed mildly. The proposed system outperformed an MMSE estimator in subjective listening tests and DRT intelligibility test for MELP-coded noisy speech. The effect of aharmonic comb filtering on the linear predictive coding (LPC) parameters is analyzed using a missing data approach. Suppressing the low-energy frequencies without any modification of the high-energy frequencies is shown to improve the LPC spectrum using the Itakura-Saito distance measure. The second system combines the aharmonic comb filter with the acoustic-phonetic properties of speech to improve the intelligibility of the MELP-coded noisy speech. Noisy speech signal is segmented into broad level sound classes using a multi-sensor automatic segmentation/classification tool, and each sound class is enhanced differently based on its acoustic-phonetic properties. The proposed system is shown to outperform both the MELPe noise preprocessor and the aharmonic comb filter in intelligibility tests when used in concatenation with the MELP coder. Since the second noise suppression system uses an automatic segmentation/classification algorithm, exploiting the GEMS signal in an automatic segmentation/classification task is also addressed using an ASR approach. Current ASR engines can segment and classify speech utterances in a single pass; however, they are sensitive to ambient noise. Features that are extracted from the GEMS signal can be fused with the noisy MFCC features to improve the noise-robustness of the ASR system. In the first phase, a voicing feature is extracted from the clean speech signal and fused with the MFCC features. The actual GEMS signal could not be used in this phase because of insufficient sensor data to train the ASR system. Tests are done using the Aurora2 noisy digits database. The speech-based voicing feature is found to be effective at around 10 dB but, below 10 dB, the effectiveness rapidly drops with decreasing SNR because of the severe distortions in the speech-based features at these SNRs. Hence, a novel system is proposed that treats the MFCC features in a speech frame as missing data if the global SNR is below 10 dB and the speech frame is unvoiced. If the global SNR is above 10 dB of the speech frame is voiced, both MFCC features and voicing feature are used. The proposed system is shown to outperform some of the popular noise-robust techniques at all SNRs. In the second phase, a new isolated monosyllable database is prepared that contains both speech and GEMS data. ASR experiments conducted for clean speech showed that the GEMS-based feature, when fused with the MFCC features, decreases the performance. The reason for this unexpected result is found to be partly related to some of the GEMS data that is severely noisy. The non-acoustic sensor noise exists in all GEMS data but the severe noise happens rarely. A missing data technique is proposed to alleviate the effects of severely noisy sensor data. The GEMS-based feature is treated as missing data when it is detected to be severely noisy. The combined features are shown to outperform the MFCC features for clean speech when the missing data technique is applied.

6

Correia, Fábio Gonçalves. "Quality control of ultra high resolution seismic data acquisition in real-time." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22007.

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Mestrado em Engenharia Geológica
A aquisicção de grandes volumes de dados durante uma campanha sísmica exige, necessariamente, mais tempo para o controlo de qualidade (QC). No entanto, o tempo de QC não pode ser extendido devido a limitações do tempo de operação, tendo de ser feito mais rápido, o que pode comprometer a qualidade. A alternativa, alocar mais pessoas e recursos para QC e melhorar a eficiência, leva a aumentos de custo e à necessidade de maiores embarcações. Além disso, o QC tradicional requer tempo de análise após a aquisição, atrasando a desmobilização da embarcação, aumentando assim os custos da aquisição. A solução proposta passou pelo desenvolvimento de um QC automático em tempo real eficiente, testando a Comparação Espetral e o Atributo Razão Sinal-Ruído - ferramentas desenvolvidas no software SPW, usado para processamento de dados sísmicos. Usando este software foi testada a deteção e identificação de dados de fraca qualidade através das ferramentas de QC automáticas e os seus parâmetros ajustados para incluir pelo menos todos os maus registos encontrados manualmente. Foi também feita a deteção e identificação de vários problemas encontrados durante uma campanha de aquisição, tais como fortes ondulações e respetiva direção, o ruído de esteira provocado pelas hélices da embarcação e consequente Trouser’s Effect e mau funcionamento das fontes ou dos recetores. A deteção antecipada destes problemas pode permitir a sua resolução atempada, não comprometendo a aquisição dos dados. Foram feitos vários relatórios para descrever problemas encontrados durante os testes de versões beta do software SPW e os mesmos reportados à equipa da Parallel Geoscience, que atualizou o software de forma a preencher os requisitos necessários ao bom funcionamento do QC em tempo real. Estas atualizações permitiram o correto mapeamento dos headers dos ficheiros, otimização da velocidade de análise das ferramentas automáticas e correção de erros em processamento dos dados em multi-thread, para evitar atrasos entre o QC em tempo real e a aquisição dos dados, adaptação das ferramentas à leitura de um número variável de assinaturas das fontes, otimização dos limites de memória gráfica e correção de valores anómalos de semelhança espetral. Algumas atualizações foram feitas através da simulação da aquisição de dados na empresa, de forma a efetuar alguns ajustes e posteriormente serem feitos testes numa campanha futura. A parametrização destas ferramentas foi alcançada, assegurando-se assim a correta deteção automática dos vários problemas encontrados durante a campanha de aquisição usada para os testes, o que levará à redução do tempo gasto na fase de QC a bordo e ao aumento da sua eficácia.
The acquisition of larger volumes of seismic data during a survey requires, necessarily, more time for quality control (QC). Despite this, QC cannot be extended due operational time constraints and must be done faster, compromising its efficiency and consequently the data quality. The alternative, to allocate more people and resources for QC to improve efficiency, leads to prohibitive higher costs and larger vessel requirements. Therefore, traditional QC methods for large data require extended standby times after data acquisition, before the vessel can be demobilized, increasing the cost of survey. The solution tested here consisted on the development of an efficient Real- Time QC by testing Spectral Comparison and Signal to Noise Ratio Attribute (tools developed for the SPW seismic processing software). The detection and identification of bad data by the automatic QC tools was made and the parameters adapted to include at least all manual QC flags. Also, the detection and identification of common problems during acquisition, such strong wave motion and its direction, strong propeller’s wash, trouser’s effect and malfunction in sources or receivers were carried out. The premature detection of these problems will allow to solve them soon enough to not compromise the data acquisition. Several problem reports from beta tests of SPW were transmitted to the Parallel Geoscience team, to be used as a reference to update the software and fulfil Real-Time QC requirements. These updates brought the correct mapping of data headers in files, optimization of data analysis speed along with multi-thread processing debug, to assure it will be running fast enough to avoid delays between acquisition and Real-Time QC, software design to read a variable number of source signatures, optimization of graphic memory limits and debugging of anomalous spectral semblance values. Some updates resulted from a data acquisition simulation that was set up in the office, to make some adjustments to be later tested on an upcoming survey. The parameterization of these tools was finally achieved, assuring the correct detection of all major issues found during the survey, what will eventually lead to the reduction of time needed for QC stage on board, as also to the improvement of its efficiency.

7

Hardwick, Jonathan Robert. "Synthesis of Noise from Flyover Data." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50531.

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Flyover noise is a problem that affects citizens, primarily those that live near or around places with high air traffic such as airports or military bases. Such noise can be of great annoyance. The focus of this thesis is in determining a method to create a high fidelity sound source simulation of rotorcraft noise for the purpose of producing a complete flyover scenario to be used in psychoacoustic testing. The focus of the sound source simulation is simulating rotorcraft noise fluctuations during level flight to aid in psychoacoustic testing to determine human perception of such noise. Current methods only model the stationary or time-average components when synthesizing the sound source. The synthesis process described in this thesis determines the steady-state waveform of the noise as well as the time-varying fluctuations for each rotor individually. The process explored in this thesis uses an empirical approach to synthesize flyover noise by directly using physical flyover recordings. Four different methods of synthesis were created to determine the combination of components that produce high fidelity sound source simulation. These four methods of synthesis are: a) Unmodulated main rotor b) Modulated main rotor c) Unmodulated main rotor combined with the unmodulated tail rotor d) Modulated main rotor combined with the modulated tail rotor Since the time-varying components of the source sound are important to the creation of high fidelity sound source simulation, five different types of time-varying fluctuations, or modulations, were implemented to determine the importance of the fluctuating components on the sound source simulation. The types of modulation investigated are a) no modulation, b) randomly applied generic modulation, c) coherently applied generic modulation, d) randomly applied specific modulation, and e) coherently applied specific modulation. Generic modulation is derived from a different section of the source recording to which it is applied. For the purposes of this study, it is not clearly dominated by either thickness or loading noise characteristics, but still displays long-term modulation. Random application of the modulation implies that there is a loss of absolute modulation phase and amplitude information across the frequency spectrum. Coherent application of the modulation implies that an attempt is made to line up the absolute phase and amplitude of the modulation signal with that which is being replaced (i.e. that which was stripped from the original recording and expanding or contracting to fit the signal to which it is applied). Specific modulation is the modulation from the source recording which is being reconstructed. A psychoacoustic test was performed to rank the fidelity of each synthesis method and each type of modulation. Performing this comparison for two different emission angles provides insight as to whether the ranking will differ between the emission angles. The modulated main rotor combined with the modulated tail rotor showed the highest fidelity and had a much higher fidelity than any of the other synthesis methods. The psychoacoustic test proved that modulation is necessary to produce a high fidelity sound source simulation. However, the use of a generic modulation or a randomly applied specific modulation proved to be an inadequate substitute for the coherently applied specific modulation. The results from this research show that more research is necessary to properly simulate a full flyover scenario. Specifically, more data is needed in order to properly model the modulation for level flight.
Master of Science

8

Durand, Philippe. "Traitement des donnees radar varan et estimation de qualites en geologie, geomorphologie et occupation des sols." Paris 7, 1988. http://www.theses.fr/1988PA077183.

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Ce travail porte sur l'exploitation thematique des donnees radar varan en geologie et l'occupation des sols. Les deux premieres parties passent en revue les pretraitements subis par l'image: elimination du bruit et corrections geometriques. Ces chapitres suivants exploitent l'analyse multisources, ainsi que les methodes issus de la morphologie mathematique et de l'analyse de texture

9

Grillo, Aderibigbe. "Developing a data quality scorecard that measures data quality in a data warehouse." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17137.

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The main purpose of this thesis is to develop a data quality scorecard (DQS) that aligns the data quality needs of the Data warehouse stakeholder group with selected data quality dimensions. To comprehend the research domain, a general and systematic literature review (SLR) was carried out, after which the research scope was established. Using Design Science Research (DSR) as the methodology to structure the research, three iterations were carried out to achieve the research aim highlighted in this thesis. In the first iteration, as DSR was used as a paradigm, the artefact was build from the results of the general and systematic literature review conduct. A data quality scorecard (DQS) was conceptualised. The result of the SLR and the recommendations for designing an effective scorecard provided the input for the development of the DQS. Using a System Usability Scale (SUS), to validate the usability of the DQS, the results of the first iteration suggest that the DW stakeholders found the DQS useful. The second iteration was conducted to further evaluate the DQS through a run through in the FMCG domain and then conducting a semi-structured interview. The thematic analysis of the semi-structured interviews demonstrated that the stakeholder's participants' found the DQS to be transparent; an additional reporting tool; Integrates; easy to use; consistent; and increases confidence in the data. However, the timeliness data dimension was found to be redundant, necessitating a modification to the DQS. The third iteration was conducted with similar steps as the second iteration but with the modified DQS in the oil and gas domain. The results from the third iteration suggest that DQS is a useful tool that is easy to use on a daily basis. The research contributes to theory by demonstrating a novel approach to DQS design This was achieved by ensuring the design of the DQS aligns with the data quality concern areas of the DW stakeholders and the data quality dimensions. Further, this research lay a good foundation for the future by establishing a DQS model that can be used as a base for further development.

10

Stone, Ian. "The effect of noise on image quality." Thesis, University of Westminster, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283456.

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Книги з теми "Data quality and noise":

1

Laboratories, Wyle, and Langley Research Center, eds. Data quality analysis at the National Transonic Facility. Washington, D.C: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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2

United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., ed. Electrical noise reduction techniques contributing to improved data quality at the National Transonic Facility. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.

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3

Tarpey, Simon. Data quality. [U.K]: NHS Executive, 1996.

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4

Wang, Richard Y. Data quality. Boston: Kluwer Academic Publishers, 2001.

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5

Wang, Richard Y. Data quality. New York: Kluwer Academic Publishers, 2002.

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6

Willett, Terrence, and Aeron Zentner. Assessing Data Quality. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071858769.

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7

Otto, Boris, and Hubert Österle. Corporate Data Quality. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-46806-7.

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8

Fisher, Peter F., and Michael F. Goodchild. Spatial Data Quality. Edited by Wenzhong Shi. Abingdon, UK: Taylor & Francis, 2002. http://dx.doi.org/10.4324/9780203303245.

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9

Wang, Y. Richard. Quality data objects. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1992.

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10

O'Day, James. Accident data quality. Washington, D.C: National Academy Press, 1993.

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Частини книг з теми "Data quality and noise":

1

Kąkol, Krzysztof, Gražina Korvel, and Bożena Kostek. "Improving Objective Speech Quality Indicators in Noise Conditions." In Data Science: New Issues, Challenges and Applications, 199–218. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39250-5_11.

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2

Bertrand, Yannis, Rafaël Van Belle, Jochen De Weerdt, and Estefanía Serral. "Defining Data Quality Issues in Process Mining with IoT Data." In Lecture Notes in Business Information Processing, 422–34. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_31.

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AbstractIoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare collect data on the execution of the processes. In the last years, there has been a growing awareness of the opportunity to use the data these devices generate for process mining (PM) by deriving an event log from a sensor log via event abstraction techniques. However, IoT data are often affected by data quality issues (e.g., noise, outliers) which, if not addressed at the preprocessing stage, will be amplified by event abstraction and result in quality issues in the event log (e.g., incorrect events), greatly hampering PM results. In this paper, we review the literature on PM with IoT data to find the most frequent data quality issues mentioned in the literature. Based on this, we then derive six patterns of poor sensor data quality that cause event log quality issues and propose solutions to avoid or solve them.

3

Schott, Moritz, Adina Zell, Sven Lautenbach, Gencer Sumbul, Michael Schultz, Alexander Zipf, and Begüm Demir. "Analyzing and Improving the Quality and Fitness for Purpose of OpenStreetMap as Labels in Remote Sensing Applications." In Volunteered Geographic Information, 21–42. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35374-1_2.

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AbstractOpenStreetMap (OSM) is a well-known example of volunteered geographic information. It has evolved to one of the most used geographic databases. As data quality of OSM is heterogeneous both in space and across different thematic domains, data quality assessment is of high importance for potential users of OSM data. As use cases differ with respect to their requirements, it is not data quality per se that is of interest for the user but fitness for purpose. We investigate the fitness for purpose of OSM to derive land-use and land-cover labels for remote sensing-based classification models. Therefore, we evaluated OSM land-use and land-cover information by two approaches: (1) assessment of OSM fitness for purpose for samples in relation to intrinsic data quality indicators at the scale of individual OSM objects and (2) assessment of OSM-derived multi-labels at the scale of remote sensing patches ($$1.22 \times 1.22$$ 1.22 × 1.22 km) in combination with deep learning approaches. The first approach was applied to 1000 randomly selected relevant OSM objects. The quality score for each OSM object in the samples was combined with a large set of intrinsic quality indicators (such as the experience of the mapper, the number of mappers in a region, and the number of edits made to the object) and auxiliary information about the location of the OSM object (such as the continent or the ecozone). Intrinsic indicators were derived by a newly developed tool based on the OSHDB (OpenStreetMap History DataBase). Afterward, supervised and unsupervised shallow learning approaches were used to identify relationships between the indicators and the quality score. Overall, investigated OSM land-use objects were of high quality: both geometry and attribute information were mostly accurate. However, areas without any land-use information in OSM existed even in well-mapped areas such as Germany. The regression analysis at the level of the individual OSM objects revealed associations between intrinsic indicators, but also a strong variability. Even if more experienced mappers tend to produce higher quality and objects which underwent multiple edits tend to be of higher quality, an inexperienced mapper might map a perfect land-use polygon. This result indicates that it is hard to predict data quality of individual land-use objects purely on intrinsic data quality indicators. The second approach employed a label-noise robust deep learning method on remote sensing data with OSM labels. As the quality of the OSM labels was manually assessed beforehand, it was possible to control the amount of noise in the dataset during the experiment. The addition of artificial noise allowed for an even more fine-grained analysis on the effect of noise on prediction quality. The noise-tolerant deep learning method was capable to identify correct multi-labels even for situations with significant levels of noise added. The method was also used to identify areas where input labels were likely wrong. Thereby, it is possible to provide feedback to the OSM community as areas of concern can be flagged.

4

Lavandier, Catherine, Roalt Aalmoes, Romain Dedieu, Ferenc Marki, Stephan Großarth, Dirk Schreckenberg, Asma Gharbi, and Dimitris Kotzinos. "Towards Innovative Ways to Assess Annoyance." In Aviation Noise Impact Management, 241–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91194-2_10.

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AbstractTechnological changes have driven the developments in the field of noise annoyance research. It helped to increase knowledge on the topic substantially. It also provides opportunities to conduct novel research. The introduction of the internet, the mobile phone, and miniaturisation and improved sensor technology are at the core of the three research examples presented in this chapter. The first example is the use of a Virtual Reality simulation to evaluate aircraft flyovers in different environments, and it examines how visual perception influences noise annoyance. The second example describes the use of a mobile application applying an Experience Sampling Method to assess noise annoyance for a group of people living near an airport. The third and final example is a study over social media discussions in relation to noise annoyance and quality of life around airports. These three examples demonstrate how novel technologies help to collect and analyse data from people who live around airports, and so improve our understanding of the effect of noise on humans.

5

Zatuchny, Dmitry Alexandrovich, Ruslan Nikolaevich Akinshin, Nina Ivanovna Romancheva, Igor Viktorovich Avtin, and Yury Grigorievich Shatrakov. "Quality Enhancement of Data Transmission via Civil Aircraft Communication Systems by Proper Use of Communication Resources." In Noise Resistance Enhancement in Aircraft Navigation and Connected Systems, 109–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0630-4_4.

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6

Scherer, Andreas, Manhong Dai, and Fan Meng. "Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiments." In Methods in Molecular Biology, 155–76. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-60327-337-4_10.

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7

Jiang, Hongliang, Chaobo Lu, Chunfa Xiong, and Mengkun Ran. "Seismic Data Denoising Analysis Based on Monte Carlo Block Theory." In Lecture Notes in Civil Engineering, 339–49. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2532-2_28.

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AbstractDenoising of seismic data has always been an important focus in the field of seismic exploration, which is very important for the processing and interpretation of seismic data. With the increasing complexity of seismic exploration environment and target, seismic data containing strong noise and weak amplitude seismic in-phase axis often contain many weak feature signals. However, weak amplitude phase axis characteristics are highly susceptible to noise and useful signal often submerged by background noise, seriously affected the precision of seismic data interpretation, dictionary based on the theory of the monte carlo study seismic data denoising method, selecting expect more blocks of data, for more accurate MOD dictionary, to gain a higher quality of denoising of seismic data. Monte carlo block theory in this paper, the dictionary learning dictionary, rules, block theory and random block theory is example analysis test, the dictionary learning algorithm based on the results of three methods to deal with, and the numerical results show that the monte carlo theory has better denoising ability, the denoising results have higher SNR, and effectively keep the weak signal characteristics of the data; In terms of computational efficiency, the proposed method requires less time and has higher computational efficiency, thus verifying the feasibility and effectiveness of the proposed method.

8

Nikonorov, A., A. Kolsanov, M. Petrov, Y. Yuzifovich, E. Prilepin, S. Chaplygin, P. Zelter, and K. Bychenkov. "Vessel Segmentation for Noisy CT Data with Quality Measure Based on Single-Point Contrast-to-Noise Ratio." In E-Business and Telecommunications, 490–507. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30222-5_23.

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9

Vanlaer, Jef, Pieter Van den Kerkhof, Geert Gins, and Jan F. M. Van Impe. "The Influence of Input and Output Measurement Noise on Batch-End Quality Prediction with Partial Least Squares." In Advances in Data Mining. Applications and Theoretical Aspects, 121–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31488-9_11.

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10

Verdonck, Lieven, and Michel Dabas. "Test with ImpulseRadar Raptor GPR array at Gisacum (Vieil-Évreux, France), and comparison with MALÅ MIRA." In Advances in On- and Offshore Archaeological Prospection, 561–70. Kiel: Universitätsverlag Kiel | Kiel University Publishing, 2023. http://dx.doi.org/10.38072/978-3-928794-83-1/p57.

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The ImpulseRadar Raptor-45 GPR array was tested. The instrument achieves a high signal-to-noise ratio, also at high survey speed. Lifting the sensors off the ground introduced multiple reflections. 3-D migration can enhance these multiples in profiles and time-slices. Fast data acquisition by lifting the sensors should be balanced against data quality.

Тези доповідей конференцій з теми "Data quality and noise":

1

Brown, Clifford, Brenda Henderson, and James Bridges. "Data Quality Assurance for Supersonic Jet Noise Measurements." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22545.

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The noise created by a supersonic aircraft is a primary concern in the design of future high-speed planes. The jet noise reduction technologies required on these aircraft will be developed using scale-models mounted to experimental jet rigs designed to simulate the exhaust gases from a full-scale jet engine. The jet noise data collected in these experiments must accurately predict the noise levels produced by the full-scale hardware in order to be a useful development tool. A methodology has been adopted at the NASA Glenn Research Center’s Aero-Acoustic Propulsion Laboratory to insure the quality of the supersonic jet noise data acquired from the facility’s High Flow Jet Exit Rig so that it can be used to develop future nozzle technologies that reduce supersonic jet noise. The methodology relies on mitigating extraneous noise sources, examining the impact of measurement location on the acoustic results, and investigating the facility independence of the measurements. The methodology is documented here as a basis for validating future improvements and its limitations are noted so that they do not affect the data analysis. Maintaining a high quality jet noise laboratory is an ongoing process. By carefully examining the data produced and continually following this methodology, data quality can be maintained and improved over time.

2

Shah, Sayed Khushal, Zeenat Tariq, Jeehwan Lee, and Yugyung Lee. "Real-Time Machine Learning for Air Quality and Environmental Noise Detection." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377939.

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3

Frank, Eric C., D. J. Pickering, and Chris Raglin. "In-Vehicle Tire Sound Quality Prediction from Tire Noise Data." In SAE 2007 Noise and Vibration Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2007. http://dx.doi.org/10.4271/2007-01-2253.

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4

Viswanathan, K. "Quality of jet noise data - Issues, implications and needs." In 40th AIAA Aerospace Sciences Meeting & Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-365.

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Herr, Michaela, Roland Ewert, and J. Dierke. "Trailing-Edge Noise Data Quality Assessment for CAA Validation." In 16th AIAA/CEAS Aeroacoustics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-3877.

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6

Thom, Brian, Gabriella Cerrato, and Mark Sturgill. "Augmenting Vehicle Production Audit with Objective Data and Sound Quality Metrics to Improve Customer Experience in a Changing Automotive Landscape." In Noise and Vibration Conference & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2019. http://dx.doi.org/10.4271/2019-01-1531.

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7

Purekar, Dhanesh. "Drive by Noise System and Corresponding Facility Upgrades for Test Efficiency, Data Quality and Customer Satisfaction." In SAE 2011 Noise and Vibration Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2011. http://dx.doi.org/10.4271/2011-01-1611.

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8

Simonich, John, Satish Narayanan, and Robert Schlinker. "Data Quality and Facility Issues for Model-scale Jet Noise Testing." In 41st Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-1057.

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9

Al-Sabbagh, Khaled Walid, Miroslaw Staron, Regina Hebig, and Wilhelm Meding. "Improving Data Quality for Regression Test Selection by Reducing Annotation Noise." In 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2020. http://dx.doi.org/10.1109/seaa51224.2020.00042.

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10

Ghosh, Arindam, Prithviraj Pramanik, Kartick Das Banerjee, Ashutosh Roy, Subrata Nandi, and Sujoy Saha. "Analyzing Correlation Between Air and Noise Pollution with Influence on Air Quality Prediction." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00133.

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Звіти організацій з теми "Data quality and noise":

1

Ichinose, G. A. Source Physics Experiment Data Quality Using Background Noise. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1490946.

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2

Chandath, Him, Ing Chhay Por, Yim Raksmey, and Diane Archer. Air Pollution and Workers’ Health in Cambodia’s Garment Sector. Stockholm Environment Institute, March 2023. http://dx.doi.org/10.51414/sei2023.017.

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The findings of this study can inform and enable policymakers in improving occupational air pollution, including addressing air pollution, pollution sources and other related issues in the garment manufacturing sector in Cambodia. Such interventions will help to uphold the health of workers as a human right, ensure safe workplaces, and also be beneficial for the country’s economic growth, as a healthy workforce is more productive. While the garment sector serves as Cambodia’s economic backbone and creates much-needed jobs, it is also a highly polluting industry, alongside being regularly implicated for not upholding labour rights. The sector emits pollutants to air from intensive energy use, solid and hazardous waste emissions, noise pollution and wastewater pollution discharge. Despite this, the sector’s environmental impacts in Cambodia, particularly in relation to air pollution, are not well known, and this gap was highlighted in the development of Cambodia’s 2021 Clean Air Plan. Aiming to fill this gap, in cooperation with SEI, the Air Quality and Noise Management Department of the General Directorate of Environmental Protection of Cambodia’s Ministry of Environment conducted a research project to improve understanding of air pollutant emissions from the textile industry and the health impacts on workers in Cambodia’s garment industry. The study drew on in-depth interviews with 323 garment factory workers across 16 factories, interviews with 16 factory owners, and quantitative data to better understand all interviewees’ experiences with occupational air pollution. While the research documented any symptoms related to air pollution, it did not employ medical research to assess the workers’ health status, nor did it attempt to investigate the cost or impact of air pollution on factory production. This policy briefing draws on a longer report prepared by the Ministry of Environment (Chandath, H., Chhay Por, I., Sokyimeng, S., Dana, S., Raksmey, Y. 2023. Understanding Air Pollution in the Garment Sector and Health Impacts on Workers: A Cambodian Case Study. Ministry of Environment, Cambodia. https://epa.moe.gov.kh/pages/categories/view/document-daqnm).

3

Job, Jacob. Mesa Verde National Park: Acoustic monitoring report. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286703.

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In 2015, the Natural Sounds and Night Skies Division (NSNSD) received a request to collect baseline acoustical data at Mesa Verde National Park (MEVE). Between July and August 2015, as well as February and March 2016, three acoustical monitoring systems were deployed throughout the park, however one site (MEVE002) stopped recording after a couple days during the summer due to wildlife interference. The goal of the study was to establish a baseline soundscape inventory of backcountry and frontcountry sites within the park. This inventory will be used to establish indicators and thresholds of soundscape quality that will support the park and NSNSD in developing a comprehensive approach to protecting the acoustic environment through soundscape management planning. Additionally, results of this study will help the park identify major sources of noise within the park, as well as provide a baseline understanding of the acoustical environment as a whole for use in potential future comparative studies. In this deployment, sound pressure level (SPL) was measured continuously every second by a calibrated sound level meter. Other equipment included an anemometer to collect wind speed and a digital audio recorder collecting continuous recordings to document sound sources. In this document, “sound pressure level” refers to broadband (12.5 Hz–20 kHz), A-weighted, 1-second time averaged sound level (LAeq, 1s), and hereafter referred to as “sound level.” Sound levels are measured on a logarithmic scale relative to the reference sound pressure for atmospheric sources, 20 μPa. The logarithmic scale is a useful way to express the wide range of sound pressures perceived by the human ear. Sound levels are reported in decibels (dB). A-weighting is applied to sound levels in order to account for the response of the human ear (Harris, 1998). To approximate human hearing sensitivity, A-weighting discounts sounds below 1 kHz and above 6 kHz. Trained technicians calculated time audible metrics after monitoring was complete. See Methods section for protocol details, equipment specifications, and metric calculations. Median existing (LA50) and natural ambient (LAnat) metrics are also reported for daytime (7:00–19:00) and nighttime (19:00–7:00). Prominent noise sources at the two backcountry sites (MEVE001 and MEVE002) included vehicles and aircraft, while building and vehicle predominated at the frontcountry site (MEVE003). Table 1 displays time audible values for each of these noise sources during the monitoring period, as well as ambient sound levels. In determining the current conditions of an acoustical environment, it is informative to examine how often sound levels exceed certain values. Table 2 reports the percent of time that measured levels at the three monitoring locations were above four key values.

4

Xiong, Hui, Gaurav Pandey, Michael Steinbach, and Vipin Kumar. Enhancing Data Analysis with Noise Removal. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada439494.

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5

Slagley, Jeremy M., and Steven E. Guffey. A Better Noise Compliance Method and Validation of Mine Noise Dosimetry Data. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada434225.

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6

Mellors, R. Preliminary noise survey and data report of Saudi Arabian data. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/641096.

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7

DEFENSE LOGISTICS AGENCY ALEXANDRIA VA. Data Quality Engineering Handbook. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada315573.

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8

Ichinose, G. Waveform Data Quality Assessment. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1863669.

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9

Sasaki, Masaru, and Kazuhiro Nakashima. Sound Quality Evaluation Method in Time Domain for Diesel Engine Noise. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0026.

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10

Canavan, G. H. Example of scattering noise in radar data interpretation. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/434321.

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