Journal articles: 'Segmentation multivariée'

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Klose, J. "Binary Segmentation for Multivariate Polynomials." Journal of Complexity 11, no. 3 (September 1995): 330–43. http://dx.doi.org/10.1006/jcom.1995.1015.

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Neubauer, Jakob, Konrad Wilhelm, Christian Gratzke, Fabian Bamberg, Marco Reisert, and Elias Kellner. "Effect of surface-partial-volume correction and adaptive threshold on segmentation of uroliths in computed tomography." PLOS ONE 18, no. 6 (June 23, 2023): e0286016. http://dx.doi.org/10.1371/journal.pone.0286016.

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Computed tomography (CT) is used to diagnose urolithiasis, a prevalent condition. In order to establish the strongest foundation for the quantifiability of urolithiasis, this study aims to develop semi-automated urolithiasis segmentation methods for CT images that differ in terms of surface-partial-volume correction and adaptive thresholding. It also examines the diagnostic accuracy of these methods in terms of volume and maximum stone diameter. One hundred and one uroliths were positioned in an anthropomorphic phantom and prospectively examined in CT. Four different segmentation methods were developed and used to segment the uroliths semi-automatically based on CT images. Volume and maximum diameter were calculated from the segmentations. Volume and maximum diameter of the uroliths were measured independently by three urologists by means of electronic calipers. The average value of the urologists´ measurements was used as a reference standard. Statistical analysis was performed with multivariate Bartlett’s test. Volume and maximum diameter were in very good agreement with the reference measurements (r>0.99) and the diagnostic accuracy of all segmentation methods used was very high. Regarding the diagnostic accuracy no difference could be detected between the different segmentation methods tested (p>0.55). All four segmentation methods allow for accurate characterization of urolithiasis in CT with respect to volume and maximum diameter of uroliths. Thus, a simple thresholding approach with an absolute value may suffice for robust determination of volume and maximum diameter in urolithiasis.

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Esteban, Oscar, Gert Wollny, Subrahmanyam Gorthi, María-J. Ledesma-Carbayo, Jean-Philippe Thiran, Andrés Santos, and Meritxell Bach-Cuadra. "MBIS: Multivariate Bayesian Image Segmentation tool." Computer Methods and Programs in Biomedicine 115, no. 2 (July 2014): 76–94. http://dx.doi.org/10.1016/j.cmpb.2014.03.003.

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Portillo-García, J., I. Trueba-Santander, G. de Miguel-Vela, and C. Alberola-López. "Efficient multispectral texture segmentation using multivariate statistics." IEE Proceedings - Vision, Image, and Signal Processing 145, no. 5 (1998): 357. http://dx.doi.org/10.1049/ip-vis:19982315.

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Lim, Hyunki, Heeseung Choi, Yeji Choi, and Ig-Jae Kim. "Memetic algorithm for multivariate time-series segmentation." Pattern Recognition Letters 138 (October 2020): 60–67. http://dx.doi.org/10.1016/j.patrec.2020.06.022.

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Hallac, David, Peter Nystrup, and Stephen Boyd. "Greedy Gaussian segmentation of multivariate time series." Advances in Data Analysis and Classification 13, no. 3 (August 22, 2018): 727–51. http://dx.doi.org/10.1007/s11634-018-0335-0.

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Sudbury, Lynn, and Peter Simcock. "A multivariate segmentation model of senior consumers." Journal of Consumer Marketing 26, no. 4 (June 26, 2009): 251–62. http://dx.doi.org/10.1108/07363760910965855.

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Laksono, Bagaskoro Cahyo, and Ika Yuni Wulansari. "Estimating Customer Lifetime Value in the E-Commerce Industry Using Multivariate Analysis." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (January 4, 2022): 507–18. http://dx.doi.org/10.34123/icdsos.v2021i1.161.

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Companies can develop their business using big data to support decision-making. Big data in the e-commerce industry that includes size and speed of high transactions can be used to analyze customer behaviour and predict customer value. Nowadays, companies are starting to develop customer-oriented rather than product-oriented business interests. One way that can be used to determine customer value is by calculating Customer Lifetime Value (CLV). By knowing CLV at the individual level, it will be useful to help decision-makers to develop customer segmentation and resource allocation. It is important to do segmentation or customer grouping that describes customer loyalty groups. Therefore, this research aims to calculate CLV and customer segmentation using the RFM analysis method. The dimensions of forming CLV include the values of Recency, Frequency, and Monetary. In this study, concept of multivariate statistical analysis will be applied, namely K-Means Clustering and factor analysis. Segmentation is done to determine the level of customers. The higher the CLV value, more valuable customer is to maintain. In the end, the customer segmentation method built by author can be used to optimize company's strategy to get maximum profit. This method can be applied to various cases and other companies.

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Omranian, Nooshin, Sebastian Klie, Bernd Mueller-Roeber, and Zoran Nikoloski. "Network-Based Segmentation of Biological Multivariate Time Series." PLoS ONE 8, no. 5 (May 7, 2013): e62974. http://dx.doi.org/10.1371/journal.pone.0062974.

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Ip, Barry, and Gabriel Jacobs. "Segmentation of the games market using multivariate analysis." Journal of Targeting, Measurement and Analysis for Marketing 13, no. 3 (April 2005): 275–87. http://dx.doi.org/10.1057/palgrave.jt.5740154.

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Noordam, J. C., W. H. A. M. van den Broek, and L. M. C. Buydens. "Unsupervised segmentation of predefined shapes in multivariate images." Journal of Chemometrics 17, no. 4 (2003): 216–24. http://dx.doi.org/10.1002/cem.794.

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Guo, Hongyue, Xiaodong Liu, and Lixin Song. "Dynamic programming approach for segmentation of multivariate time series." Stochastic Environmental Research and Risk Assessment 29, no. 1 (May 21, 2014): 265–73. http://dx.doi.org/10.1007/s00477-014-0897-0.

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Wang, Ling, Kang Li, Qian Ma, and YanRong Lu. "Hybrid dynamic learning mechanism for multivariate time series segmentation." Statistical Analysis and Data Mining: The ASA Data Science Journal 13, no. 2 (January 25, 2020): 165–77. http://dx.doi.org/10.1002/sam.11448.

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K. Naveen Kumar,. "Multivariate Approach for Texture Segmentation using Probabilistic Statistical Model." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 2381–85. http://dx.doi.org/10.52783/jes.2003.

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The analysis of the regions of the image is of the prerogatives in the fields of medical and global systems meant for location identification. This analysis is strongly associated with partitions of regions of interest such as segmentation. For an effective strategy of analyzing the regions of interest, texture of the image plays a major concern. The texture is generally characterized using signal processing methods namely Discrete Cosine Transformation coefficients and their specific insights leading to feature vector selection. Further, to identify regions, a statistical model needs to be identified for the feature matrix vector and thus make use of Gaussian mixture model with extensions. The Expectation Maximization approach is used, and performance is assessed by experimenting with random images from the Brodatz data store domain. Performance measurements for texture segmentation that can be attributed are Global Cons. Error (GC), Prob. Rand. Index (PR) and Variation of data (VA). These are determined alongside the confusion matrix. To assess the improvement, a comparison was made with other existing models and showed better. The algorithms will be exceptionally helpful for clinical analysis and in radio navigation map systems.

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Cao, Haoyin, Andrea Morotti, Federico Mazzacane, Dmitriy Desser, Frieder Schlunk, Christopher Güttler, Helge Kniep, et al. "External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage." Journal of Clinical Medicine 12, no. 12 (June 12, 2023): 4005. http://dx.doi.org/10.3390/jcm12124005.

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Background: The objective of this study was to assess the performance of the first publicly available automated 3D segmentation for spontaneous intracerebral hemorrhage (ICH) based on a 3D neural network before and after retraining. Methods: We performed an independent validation of this model using a multicenter retrospective cohort. Performance metrics were evaluated using the dice score (DSC), sensitivity, and positive predictive values (PPV). We retrained the original model (OM) and assessed the performance via an external validation design. A multivariate linear regression model was used to identify independent variables associated with the model’s performance. Agreements in volumetric measurements and segmentation were evaluated using Pearson’s correlation coefficients (r) and intraclass correlation coefficients (ICC), respectively. With 1040 patients, the OM had a median DSC, sensitivity, and PPV of 0.84, 0.79, and 0.93, compared to thoseo f 0.83, 0.80, and 0.91 in the retrained model (RM). However, the median DSC for infratentorial ICH was relatively low and improved significantly after retraining, at p < 0.001. ICH volume and location were significantly associated with the DSC, at p < 0.05. The agreement between volumetric measurements (r > 0.90, p > 0.05) and segmentations (ICC ≥ 0.9, p < 0.001) was excellent. Conclusion: The model demonstrated good generalization in an external validation cohort. Location-specific variances improved significantly after retraining. External validation and retraining are important steps to consider before applying deep learning models in new clinical settings.

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Lei, Tao, Yi Wang, and Weiwei Luo. "Multivariate Self-Dual Morphological Operators Based on Extremum Constraint." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/596348.

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Self-dual morphological operators (SDMO) do not rely on whether one starts the sequence with erosion or dilation; they treat the image foreground and background identically. However, it is difficult to extend SDMO to multichannel images. Based on the self-duality property of traditional morphological operators and the theory of extremum constraint, this paper gives a complete characterization for the construction of multivariate SDMO. We introduce a pair of symmetric vector orderings (SVO) to construct multivariate dual morphological operators. Furthermore, utilizing extremum constraint to optimize multivariate morphological operators, we construct multivariate SDMO. Finally, we illustrate the importance and effectiveness of the multivariate SDMO by applications of noise removal and segmentation performance. The experimental results show that the proposed multivariate SDMO achieves better results, and they suppress noises more efficiently without losing image details compared with other filtering methods. Moreover, the proposed multivariate SDMO is also shown to have the best segmentation performance after the filtered images via watershed transformation.

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Castro-López, Claudio, Purificación Vicente-Galindo, Purificación Galindo-Villardón, and Oscar Borrego-Hernández. "TAID-LCA: Segmentation Algorithm Based on Ternary Trees." Mathematics 10, no. 4 (February 11, 2022): 560. http://dx.doi.org/10.3390/math10040560.

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In this work, a statistical method for the segmentation of samples and/or populations is presented, which is based on a ternary tree structure. This approach overcomes known limitations of other segmentation methods such as CHAID, concerning the multivariate response and the non-symmetric relationship between explanatory and response variables. The multivariate response segmentation problem is handled through latent class models, while the factorial decomposition of the explanatory capability of variables is based on the Non-Symmetrical Correspondence Analysis. Stop criteria based on the CATANOVA index and impurity measures are proposed. A Simulated Annealing based post-pruning strategy is considered to avoid over-fitting relative to the training set and guarantee a better generalization capability for the method.

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Noyel, Guillaume, Jesus Angulo, Dominique Jeulin, Daniel Balvay, and Charles-André Cuenod. "MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES." Image Analysis & Stereology 34, no. 1 (May 30, 2014): 1. http://dx.doi.org/10.5566/ias.1109.

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We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way to select factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.

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ANJUM, ARFA, SEEMA JAGGI, SHWETANK LALL, ELDHO VARGHESE, ANIL RAI, ARPAN BHOWMIK, and DWIJESH CHANDRA MISHRA. "Segmentation of genomic data through multivariate statistical approaches: comparative analysis." Indian Journal of Agricultural Sciences 92, no. 7 (March 30, 2022): 892–96. http://dx.doi.org/10.56093/ijas.v92i7.118040.

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Segmenting a series of measurements along a genome into regions with distinct characteristics is widely used toidentify functional components of a genome. The majority of the research on biological data segmentation focuses on the statistical problem of identifying break or change-points in a simulated scenario using a single variable. Despite the fact that various strategies for finding change-points in a multivariate setup through simulation are available, work on segmenting actual multivariate genomic data is limited. This is due to the fact that genomic data is huge in size and contains a lot of variation within it. Therefore, a study was carried out at the ICAR-Indian Agricultural Statistics Research Institute, New Delhi during 2021 to know the best multivariate statistical method to segment the sequences which may influence the properties or function of a sequence into homogeneous segments. This will reduce the volume of data and ease the analysis of these segments further to know the actual properties of these segments. The genomic data of Rice (Oryza sativa L.) was considered for the comparative analysis of several multivariate approaches and was found that agglomerative sequential clustering was the most acceptable due to its low computational cost and feasibility.

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Li, Min, and Yu-Mei Huang. "An $L_0$-Norm Regularized Method for Multivariate Time Series Segmentation." East Asian Journal on Applied Mathematics 12, no. 2 (June 2022): 353–66. http://dx.doi.org/10.4208/eajam.180921.050122.

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Somasekhar, G., K. Srinivasa Krishna, Ashok Kumar Reddy, T. Kishore Kumar, and G. Somasekhar. "Shopper Segmentation Using Multivariate Risk Analysis for Innovative Marketing Strategies." International Journal of Asian Business and Information Management 12, no. 1 (January 2021): 60–74. http://dx.doi.org/10.4018/ijabim.20210101.oa4.

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Shopper buying behaviour is essential for the retailers to segment the shoppers in accordance to their disruptive attitude and perception for better innovative strategies which may lead to higher profits. The major purpose of this study to categorize the shoppers into distinct groups based on their risk-based perception for the organized retail outlets in Bangladesh. Seven hundred eighty-five respondents were responding on 21 variables related to store which influence their buying behaviour. In the present study, the shoppers were classified into three segments such as value seekers and disruptive to please shoppers, quality and style-driven shoppers, sensory-driven, and not interested shoppers by using innovative k-means cluster analysis. The results of the study help to retailers in understanding the various disruptive segments of shoppers in relation to their importance for store attributes affected by their demographic characteristics and guide the retailers to take necessary actions regard redesign of retail mix to provide innovative value to the shoppers.

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Hosseini, Jamshid C., Robert R. Harmon, and Martin Zwick. "An Information Theoretic Framework for Exploratory Multivariate Market Segmentation Research." Decision Sciences 22, no. 3 (July 1991): 663–77. http://dx.doi.org/10.1111/j.1540-5915.1991.tb01289.x.

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Piccolboni, Antonio. "Multivariate Segmentation in the Analysis of Transcription Tiling Array Data." Journal of Computational Biology 15, no. 7 (September 2008): 845–56. http://dx.doi.org/10.1089/cmb.2007.0141.

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Zhuang, Xiahai. "Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (December 1, 2019): 2933–46. http://dx.doi.org/10.1109/tpami.2018.2869576.

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Willse, Alan, and Bonnie Tyler. "Poisson and Multinomial Mixture Models for Multivariate SIMS Image Segmentation." Analytical Chemistry 74, no. 24 (December 2002): 6314–22. http://dx.doi.org/10.1021/ac025561i.

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Noordam, J. C., W. H. A. M. van den Broek, and L. M. C. Buydens. "Multivariate image segmentation with cluster size insensitive Fuzzy C-means." Chemometrics and Intelligent Laboratory Systems 64, no. 1 (October 2002): 65–78. http://dx.doi.org/10.1016/s0169-7439(02)00052-7.

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Lambert, Christian, Antoine Lutti, Gunther Helms, Richard Frackowiak, and John Ashburner. "Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians." NeuroImage: Clinical 2 (2013): 684–94. http://dx.doi.org/10.1016/j.nicl.2013.04.017.

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Hazel, G. G. "Multivariate Gaussian MRF for multispectral scene segmentation and anomaly detection." IEEE Transactions on Geoscience and Remote Sensing 38, no. 3 (May 2000): 1199–211. http://dx.doi.org/10.1109/36.843012.

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Li, Min, Yumei Huang null, and Youwei Wen. "A Total Variation Based Method for Multivariate Time Series Segmentation." Advances in Applied Mathematics and Mechanics 15, no. 2 (June 2023): 300–321. http://dx.doi.org/10.4208/aamm.oa-2021-0209.

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Picard, F., E. Lebarbier, E. Budinskà, and S. Robin. "Joint segmentation of multivariate Gaussian processes using mixed linear models." Computational Statistics & Data Analysis 55, no. 2 (February 2011): 1160–70. http://dx.doi.org/10.1016/j.csda.2010.09.015.

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Lu, Chang, Jiyou Fei, Xing Zhao, and Xiongfei Shao. "Laser Cutting Thermal Error Prediction Method Based on Multivariate Segmentation." Journal of Physics: Conference Series 2541, no. 1 (July 1, 2023): 012027. http://dx.doi.org/10.1088/1742-6596/2541/1/012027.

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Abstract With the increasing accuracy of manufacturing processing, laser cutting has become one of the best ways to cut thin-walled parts. A multivariate segmented thermal error prediction method is proposed for thermal errors in laser cutting. Based on actual processing data, we analyzed the thermal expansion of the material during the cutting process. Modeling simulation and heat deformation prediction are performed. W18Cr4V material was used as the study object for computational verification. The results show that multiple-segmented regression can achieve data accuracy of more than 90.4%. For the maximum deformation predicted value error rate is less than 10.35%, and the average deformation error rate is less than 10.74%.

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UR, Orth, and J. Turečková. "Segmenting the tourism market using perceptual and attitudinal mapping." Agricultural Economics (Zemědělská ekonomika) 48, No. 1 (February 29, 2012): 36–48. http://dx.doi.org/10.17221/5286-agricecon.

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Decreasing numbers of tourists to the Czech Republic point at a weakening competitive position of Czech destinations during the most recent years. For many communities, tourism may be a short-lived economic dream when understanding of tourists´ perceptions and travel motives is lacking. The two objectives pursued in this study are 1) an identification of the positions of competing destinations and 2) an a-posteriori segmentation with psychographic variables. Market segmentation becomes the crucial factor in the strategic design process of target marketing. Like many other markets, tourism markets do not respond homogeneously to marketing activities. Subdividing visitors into useful groups may provide a basis for competitive advantage. Our study establishes taxonomy of visitors to Southern Moravia. The study tries to overcome well-known insufficiencies of single segmentation approaches by exploiting the advantage of the multivariate nature of combined push factors, pull factors, and other factors of more restrictive nature (i.e. time and money). The segmentation task employs multivariate data analysis techniques such as factor analysis, cluster analysis and multi-dimensional scaling. Recent research on the European Vacation Style Typology is incorporated.

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Mishulina, O. A., and I. N. Sukonkin. "Multivariate time series segmentation for generalized description of dynamic systems operation." Optical Memory and Neural Networks 21, no. 2 (April 2012): 94–104. http://dx.doi.org/10.3103/s1060992x12020038.

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Ma, Yi, Harm Derksen, Wei Hong, and John Wright. "Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 9 (September 2007): 1546–62. http://dx.doi.org/10.1109/tpami.2007.1085.

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Schenone, A., F. Firenze, F. Acquarone, M. Gambaro, F. Masulli, and L. Andreucci. "Segmentation of multivariate medical images via unsupervised clustering with “adaptive resolution”." Computerized Medical Imaging and Graphics 20, no. 3 (May 1996): 119–29. http://dx.doi.org/10.1016/0895-6111(96)00008-0.

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Liu, Zhe, Yu-Qing Song, Jian-Mei Chen, Cong-Hua Xie, and Feng Zhu. "Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials." Neural Computing and Applications 21, no. 4 (February 16, 2011): 801–11. http://dx.doi.org/10.1007/s00521-011-0538-1.

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Abonyi, Janos, Balazs Feil, Sandor Nemeth, and Peter Arva. "Modified Gath–Geva clustering for fuzzy segmentation of multivariate time-series." Fuzzy Sets and Systems 149, no. 1 (January 2005): 39–56. http://dx.doi.org/10.1016/j.fss.2004.07.008.

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Wang, Ling, Hui Zhu, and Gaofeng Jia. "Adaptive G–G clustering for fuzzy segmentation of multivariate time series." Stochastic Environmental Research and Risk Assessment 34, no. 9 (June 2, 2020): 1353–67. http://dx.doi.org/10.1007/s00477-020-01817-w.

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Tanatavikorn, Harakhun, and Yoshiyuki Yamashita. "Batch Process Monitoring Based on Fuzzy Segmentation of Multivariate Time-Series." Journal of Chemical Engineering of Japan 50, no. 1 (2017): 53–63. http://dx.doi.org/10.1252/jcej.16we193.

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Han, Minyeon, and F. C. Park. "DTI Segmentation and Fiber Tracking Using Metrics on Multivariate Normal Distributions." Journal of Mathematical Imaging and Vision 49, no. 2 (December 6, 2013): 317–34. http://dx.doi.org/10.1007/s10851-013-0466-z.

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Qin, A. K., and David A. Clausi. "Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty." IEEE Transactions on Image Processing 19, no. 8 (August 2010): 2157–70. http://dx.doi.org/10.1109/tip.2010.2045708.

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Lazar, Cosmin, Andrei Doncescu, and Nabil Kabbaj. "Non Negative Matrix Factorisation clustering capabilities; application on multivariate image segmentation." International Journal of Business Intelligence and Data Mining 5, no. 3 (2010): 285. http://dx.doi.org/10.1504/ijbidm.2010.033363.

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Ding, Mingtao, Lihan He, David Dunson, and Lawrence Carin. "Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process." Bayesian Analysis 7, no. 4 (December 2012): 813–40. http://dx.doi.org/10.1214/12-ba727.

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Noordam, J. C., and W. H. A. M. van den Broek. "Multivariate image segmentation based on geometrically guided fuzzy C-means clustering." Journal of Chemometrics 16, no. 1 (January 2002): 1–11. http://dx.doi.org/10.1002/cem.656.

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Wang, Ling, and Peng Shen. "Memetic segmentation based on variable lag aware for multivariate time series." Information Sciences 657 (February 2024): 120003. http://dx.doi.org/10.1016/j.ins.2023.120003.

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Zhang, Changrui, and Jia Wang. "PIS-Net: Efficient Medical Image Segmentation Network with Multivariate Downsampling for Point-of-Care." Entropy 26, no. 4 (March 26, 2024): 284. http://dx.doi.org/10.3390/e26040284.

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Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional “bed imaging”. Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in PoC diagnosis. However, the image distortion caused by image preprocessing and the low resolution of medical images extracted by PoC devices are urgent problems that need to be solved. Moreover, more efficient feature representation is necessary in the design of instant image segmentation. In this paper, a new feature representation considering the relationships among local features with minimal parameters and a lower computational complexity is proposed. Since a feature window sliding along a diagonal can capture more pluralistic features, a Diagonal-Axial Multi-Layer Perceptron is designed to obtain the global correlation among local features for a more comprehensive feature representation. Additionally, a new multi-scale feature fusion is proposed to integrate nonlinear features with linear ones to obtain a more precise feature representation. Richer features are figured out. In order to improve the generalization of the models, a dynamic residual spatial pyramid pooling based on various receptive fields is constructed according to different sizes of images, which alleviates the influence of image distortion. The experimental results show that the proposed strategy has better performance on instant image segmentation. Notably, it yields an average improvement of 1.31% in Dice than existing strategies on the BUSI, ISIC2018 and MoNuSeg datasets.

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Dai, Bing, Yingjie Peng, Ning Lin, and Peng Wang. "Bearing Fault Diagnosis Based on Prime Mean Spectral Segmentation Kurtogram." Journal of Physics: Conference Series 2419, no. 1 (January 1, 2023): 012080. http://dx.doi.org/10.1088/1742-6596/2419/1/012080.

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Abstract The spectrum-based fault feature extraction method for industrial equipment can avoid the problems of modal aliasing and end effects caused by mode decomposition in the time domain. This paper proposes a kurtogram constructed based on prime mean spectral segmentation. The preset framework realizes fast spectral segmentation, and the multivariate segmentation mode provides a more reasonable distribution of center frequency and bandwidth. The precise location and diagnosis of faults can be achieved by scanning shocks in each frequency band by spectral negentropy. Simulation signals and bearing fault experimental signals verify the performance of the algorithm.

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Gaugel, Stefan, and Manfred Reichert. "Industrial Transfer Learning for Multivariate Time Series Segmentation: A Case Study on Hydraulic Pump Testing Cycles." Sensors 23, no. 7 (March 31, 2023): 3636. http://dx.doi.org/10.3390/s23073636.

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Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and has received much attention in recent industrial research. This paper focuses on the problem of time series segmentation and presents the first in-depth research on transfer learning for deep learning-based time series segmentation on the industrial use case of end-of-line pump testing. In particular, we investigate whether the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models with respect to accuracy and training speed. The benefit can be most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative transfer learning were observed as well. Thus, the research emphasizes the potential, but also the challenges, of industrial transfer learning.

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Banas, Krzysztof, Agnieszka Banas, Mariusz Gajda, Bohdan Pawlicki, Wojciech M. Kwiatek, and Mark B. H. Breese. "Pre-processing of Fourier transform infrared spectra by means of multivariate analysis implemented in the R environment." Analyst 140, no. 8 (2015): 2810–14. http://dx.doi.org/10.1039/c5an00002e.

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Lim, Jong Gwan, Mi-hye Kim, and Sahngwoon Lee. "Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/280140.

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Abstract:

Recent change in evaluation criteria from accuracy alone to trade-off with time delay has inspired multivariate energy-based approaches in motion segmentation using acceleration. The essence of multivariate approaches lies in the construction of highly dimensional energy and requires feature subset selection in machine learning. Due to fast process, filter methods are preferred; however, their poorer estimate is of the main concerns. This paper aims at empirical validation of three objective functions for filter approaches, Fisher discriminant ratio, multiple correlation (MC), and mutual information (MI), through two subsequent experiments. With respect to 63 possible subsets out of 6 variables for acceleration motion segmentation, three functions in addition to a theoretical measure are compared with two wrappers,k-nearest neighbor and Bayes classifiers in general statistics and strongly relevant variable identification by social network analysis. Then four kinds of new proposed multivariate energy are compared with a conventional univariate approach in terms of accuracy and time delay. Finally it appears that MC and MI are acceptable enough to match the estimate of two wrappers, and multivariate approaches are justified with our analytic procedures.

Journal articles on the topic 'Segmentation multivariée'

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