| 1. |
- Alzghoul, Ahmad, et al.
(författare)
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Screening paper runnability in a web-offset pressroom by data mining
- 2009
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Ingår i: Proceedings of the 9th Industrial Conference on Advances in Data Mining. - Berlin : Springer Berlin/Heidelberg. - 9783642030666 ; , s. 161-175
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Konferensbidrag (refereegranskat)abstract
- This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom.Two approaches are explored. The first one treats the problem as a task of data classification into “break” and “non break” classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.
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| 2. |
- Bacauskiene, Marija, et al.
(författare)
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A feature selection technique for generation of classification committees and its application to categorization of laryngeal images
- 2009
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Ingår i: Pattern Recognition. - New York : Pergamon Press. - 0031-3203 .- 1873-5142. ; 42:5, s. 645-654
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with a two phase procedure to select salient features (variables) for classification committees. Both filter and wrapper approaches to feature selection are combined in this work. In the first phase, definitely redundant features are eliminated based on the paired t-test. The test compares the saliency of the candidate and the noise features. In the second phase, the genetic search is employed. The search integrates the steps of training, aggregation of committee members, selection of hyper-parameters, and selection of salient features into the same learning process. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real-world problems have shown that significant improvements in Classification accuracy can be obtained in a small number of iterations if compared to the case of using all the features available.
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| 3. |
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| 4. |
- Bacauskiene, Marija, et al.
(författare)
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Random forests based monitoring of human larynx using questionnaire data
- 2012
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Ingår i: Expert systems with applications. - Amsterdam : Elsevier. - 0957-4174 .- 1873-6793. ; 39:5, s. 5506-5512
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF data proximity matrix. When testing the developed tools on a set of data collected from 109 subjects, the 100% classification accuracy was obtained on unseen data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of data and facilitate data exploration aimed at identifying subjects belonging to a “risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.
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| 5. |
- Brorsson, Sofia, 1973-, et al.
(författare)
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Differences in the muscle activities in the forearm muscles in healthy men and women
- 2012
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Ingår i: Proceedings of the XIXth Congress of the International Society of Electrophysiology & Kinesiology. - Brisbane, Australia. - 9780646582283 ; , s. 437-437
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Konferensbidrag (refereegranskat)abstract
- Balance between flexor and extensor muscle activity is essential for optimal function. This has been demonstrated previously for the lower extremity, trunk and shoulder function, but information on the relationship in hand function is lacking. AIM: Was to evaluate whether there are qualitative differences in finger extension force(fef), grip force, force duration, force balance and the muscle activities in the forearm flexor and extensor muscles in healthy men and women in different ages.
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| 6. |
- Ejnarsson, Marcus
(författare)
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Data Mining and Analysis for Characterizing Paper from On-line Multisensor Measurements
- 2008
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The objective of this thesis is to develop a multi-resolution tool for screening paper formation variations, aiming to detect abnormalities in various frequency regions ranging from millimeters to several meters. The abnormalities detected in different frequency regions give an indication for the paper maker about specific disturbances in the paper production process. A paper web, running at a speed of 30 m/s, is illuminated by two red diode lasers and the reflected light are recorded as two time series of high resolution measurements constitutes the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as a kernel based novelty detection applied to a multi-resolution time series representation obtained from the frequency bands of the Fourier power spectra of the blocks. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The experimental investigations performed have shown that the presented paper formation deficiencies monitoring technique and the system can be used for on-line monitoring of paper deficiencies manifesting themselves in a broad frequency range. A software, implementing the technique, was developed and used for online paper formation monitoring at a Swedish paper mill.
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| 7. |
- Gelzinis, Adas, et al.
(författare)
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A novel technique to extract accurate cell contours applied to analysis of phytoplankton images
- 2015
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Ingår i: Machine Vision and Applications. - Heidelberg : Springer Berlin/Heidelberg. - 0932-8092 .- 1432-1769. ; 26:2-3, s. 305-315
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Tidskriftsartikel (refereegranskat)abstract
- Active contour model (ACM) is an image segmentation technique widely applied for object detection. Most of the research in ACM area is dedicated to the development of various energy functions based on physical intuition. Here, instead of constructing a new energy function, we manipulate values of ACM parameters to generate a multitude of potential contours, score them using a machine-learned ranking technique, and select the best contour for each object in question. Several learning-to-rank (L2R) methods are evaluated with a goal to choose the most accurate in assessing the quality of generated contours. Superiority of the proposed segmentation approach over the original boosted edge-based ACM and three ACM implementations using the level-set framework is demonstrated for the task of Prorocentrum minimum cells’ detection in phytoplankton images. Experiments show that diverse set of contour features with grading learned by a variant of multiple additive regression trees (λ-MART) helped to extract precise contour for 87.6 % of cells tested.
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| 8. |
- Gelzinis, Adas, et al.
(författare)
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Automatic detection and morphological delineation of bacteriophages in electron microscopy images
- 2015
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Ingår i: Computers in Biology and Medicine. - Kidlington : Pergamon Press. - 0010-4825 .- 1879-0534. ; 64, s. 101-116
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Tidskriftsartikel (refereegranskat)abstract
- Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host. © 2015 Elsevier Ltd.
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| 9. |
- Gelzinis, Adas, et al.
(författare)
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Boosting performance of the edge-based active contour model applied to phytoplankton images
- 2012
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Ingår i: Proceedings of the 13th IEEE International Symposium on Computational Intelligence and Informatics. - Piscataway, NJ : IEEE Press. - 9781467352062 - 9781467352055 - 9781467352109 ; , s. 273-277
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Konferensbidrag (refereegranskat)abstract
- Automated contour detection for objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the core goal of this study. The speciesis known to cause harmful blooms in many estuarine and coastal environments. Active contour model (ACM)-based image segmentation is the approach adopted here as a potential solution. Currently, the main research in ACM area is highly focused ondevelopment of various energy functions having some physical intuition. This work, by contrast, advocates the idea of rich and diverse image preprocessing before segmentation. Advantage of the proposed preprocessing is demonstrated experimentally by comparing it to the six well known active contour techniques applied to the cell segmentation in microscopy imagery task. © 2012 IEEE.
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| 10. |
- Gelzinis, Adas, et al.
(författare)
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Categorizing cells in phytoplankton images
- 2011
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Ingår i: Recent Advances in Signal Processing, Computational Geometry and Systems Theory. - Athens : World Scientific and Engineering Academy and Society. - 9781618040275 - 1618040278 ; , s. 82-87
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Konferensbidrag (refereegranskat)abstract
- This article is concerned with detection of invasive species---Prorocentrum minimum (P. minimum)---in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization, image segmentation, and SVM and random forest-based classification of objects was developed to solve the task. The developed algorithms were tested using 114 images of 1280 x 960 pixels. There were 2088 P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classify 94.9% of all objects. The results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species.
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