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Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical measures (mean, standard deviation, maximum value, and minimum value) of hundreds of wind turbine magnitudes, usually in a 5-min or 10-min interval. Several studies have analysed the loss of information associated with the reduction of information when using five minutes instead of four seconds as a sampling frequency, or when compressing a time series recorded at 5 min to 10 min, concluding that some, but not all, of these magnitudes are seriously affected. However, to our knowledge, there are no studies on increasing the time interval beyond 10 min to take these four statistical values, and how this aggregation affects prognosis models. Our work shows that, despite the irreversible loss of information that occurs in the first 5 min, increasing the time considered to take the four representative statistical values improves the performance of the predicted targets in normality models.
Pere Marti-Puig; Alejandro Bennásar-Sevillá; Alejandro Blanco-M.; Jordi Solé-Casals. Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model. Applied Sciences 2021, 11, 6405 .
AMA StylePere Marti-Puig, Alejandro Bennásar-Sevillá, Alejandro Blanco-M., Jordi Solé-Casals. Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model. Applied Sciences. 2021; 11 (14):6405.
Chicago/Turabian StylePere Marti-Puig; Alejandro Bennásar-Sevillá; Alejandro Blanco-M.; Jordi Solé-Casals. 2021. "Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model." Applied Sciences 11, no. 14: 6405.
In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.
Cesar Caiafa; Zhe Sun; Toshihisa Tanaka; Pere Marti-Puig; Jordi Solé-Casals. Machine Learning Methods with Noisy, Incomplete or Small Datasets. Applied Sciences 2021, 11, 4132 .
AMA StyleCesar Caiafa, Zhe Sun, Toshihisa Tanaka, Pere Marti-Puig, Jordi Solé-Casals. Machine Learning Methods with Noisy, Incomplete or Small Datasets. Applied Sciences. 2021; 11 (9):4132.
Chicago/Turabian StyleCesar Caiafa; Zhe Sun; Toshihisa Tanaka; Pere Marti-Puig; Jordi Solé-Casals. 2021. "Machine Learning Methods with Noisy, Incomplete or Small Datasets." Applied Sciences 11, no. 9: 4132.
In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, easily available in modern wind turbines. The ELM technique requires very low computational costs for the training of the models, and thus allows for the integration of a grid-search approach with parallelized instances to find out the optimal model parameters. These models can be built both individually, considering the turbines separately, or as an aggregate for the whole wind plant. The followed strategy consists in predicting a target variable using the rest of the variables of the system/subsystem, computing the error deviation from the real target variable and finally comparing high error values with a selection of alarm events for that system, therefore validating the performance of the model. The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system’s failure, thus making it possible to schedule the maintenance operation before a critical failure occurs. The simplicity of the ELM systems and the ease with which the parameters can be adjusted make it a realistic option to be implemented in wind turbine models to work in real time.
Pere Marti-Puig; Alejandro Blanco-M.; Moisès Serra-Serra; Jordi Solé-Casals. Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines. Applied Sciences 2021, 11, 590 .
AMA StylePere Marti-Puig, Alejandro Blanco-M., Moisès Serra-Serra, Jordi Solé-Casals. Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines. Applied Sciences. 2021; 11 (2):590.
Chicago/Turabian StylePere Marti-Puig; Alejandro Blanco-M.; Moisès Serra-Serra; Jordi Solé-Casals. 2021. "Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines." Applied Sciences 11, no. 2: 590.
In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.
Cesar Federico Caiafa; Jordi Solé-Casals; Pere Marti-Puig; Sun Zhe; Toshihisa Tanaka. Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets. Applied Sciences 2020, 10, 8481 .
AMA StyleCesar Federico Caiafa, Jordi Solé-Casals, Pere Marti-Puig, Sun Zhe, Toshihisa Tanaka. Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets. Applied Sciences. 2020; 10 (23):8481.
Chicago/Turabian StyleCesar Federico Caiafa; Jordi Solé-Casals; Pere Marti-Puig; Sun Zhe; Toshihisa Tanaka. 2020. "Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets." Applied Sciences 10, no. 23: 8481.
This work deals with the task of distinguishing between different Mediterranean demersal species of fish that share a remarkably similar form and that are also used for the evaluation of marine resources. The experts who are currently able to classify these types of species do so by considering only a segment of the contour of the fish, specifically its head, instead of using the entire silhouette of the animal. Based on this knowledge, a set of features to classify contour segments is presented to address both a binary and a multi-class classification problem. In addition to the difficulty present in successfully discriminating between very similar forms, we have the limitation of having small, unreliably labeled image data sets. The results obtained were comparable to those obtained by trained experts.
Pere Marti-Puig; Amalia Manjabacas; Antoni Lombarte. Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours. Applied Sciences 2020, 10, 3408 .
AMA StylePere Marti-Puig, Amalia Manjabacas, Antoni Lombarte. Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours. Applied Sciences. 2020; 10 (10):3408.
Chicago/Turabian StylePere Marti-Puig; Amalia Manjabacas; Antoni Lombarte. 2020. "Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours." Applied Sciences 10, no. 10: 3408.
The otolith digital catalogue AFORO allows unknown otoliths to be classified automatically by using a comparison with its classified records. To do this, the otolith’s contour, which is extracted from an image, is used. In AFORO, otolith images follow a strict positional normalization. Only the left sagitta is considered, and the images must show the internal side of the whole otolith, with the sulcus acusticus visible, the dorsal side (D) placed in the dorsal position and the rostral side (R) placed on the right. The otolith in the incoming image to be classified must also follow the same positional normalization. Variations from the reference position worsen the classification results. In this article, robust contour descriptors are proposed to extend this functionality of AFORO to the images of otoliths that are poorly normalized, contain rotations, are entirely inverted or came from the right rather than the left sagitta. These descriptors are based on the discrete Fourier transform and could extend the classification functionality to incoming images that are taken and sent, for instance, from smartphones in a wide range of working conditions.
Pere Marti-Puig; Amalia Manjabacas; Antoni Lombarte. Fourier-based contour descriptors to relax positional standardization of the otolith images in AFORO queries. Scientia Marina 2020, 84, 27 .
AMA StylePere Marti-Puig, Amalia Manjabacas, Antoni Lombarte. Fourier-based contour descriptors to relax positional standardization of the otolith images in AFORO queries. Scientia Marina. 2020; 84 (1):27.
Chicago/Turabian StylePere Marti-Puig; Amalia Manjabacas; Antoni Lombarte. 2020. "Fourier-based contour descriptors to relax positional standardization of the otolith images in AFORO queries." Scientia Marina 84, no. 1: 27.
Supervisory Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. Ideally, they must run without interruption, but in practice, some data may be lost due to a sensor failure or a communication breakdown. When it happens, given the nature of these failures, information is lost in bursts, that is, sets of consecutive samples. When this occurs, it is necessary to fill out the gaps of the historical data with a reliable data completion method. This paper presents an ad hoc method to complete the data lost by a SCADA system in case of long bursts. The data correspond to levels of drinking water tanks of a Water Network company which present fluctuation patterns on a daily and a weekly scale. In this work, a new tensorization process and a novel completion algorithm mainly based on two tensor decompositions are presented. Statistical tests are realised, which consist of applying the data reconstruction algorithms, by deliberately removing bursts of data in verified historical databases, to be able to evaluate the real effectiveness of the tested methods. For this application, the presented approach outperforms the other techniques found in the literature.
Pere Marti-Puig; Arnau Martí-Sarri; Moisès Serra-Serra. Double Tensor-Decomposition for SCADA Data Completion in Water Networks. Water 2019, 12, 80 .
AMA StylePere Marti-Puig, Arnau Martí-Sarri, Moisès Serra-Serra. Double Tensor-Decomposition for SCADA Data Completion in Water Networks. Water. 2019; 12 (1):80.
Chicago/Turabian StylePere Marti-Puig; Arnau Martí-Sarri; Moisès Serra-Serra. 2019. "Double Tensor-Decomposition for SCADA Data Completion in Water Networks." Water 12, no. 1: 80.
Face recognition or verification remains a real challenge in the area of pattern recognition and image processing. The image acquisition process is a crucial step in which noise will inevitably be introduced, and in most cases this noise drastically decreases the accuracy of the classification rate of recognition systems, making them ineffective. This paper presents a novel approach to face recognition or verification, which increases the recognition rate in noisy environmental conditions. The latter is achieved by using the intrinsic face mode functions that result from applying a bi-dimensional empirical mode decomposition with Green’s functions in tension to noisy images. Each image is individually decomposed, and noisy modes are discarded or filtered during reconstruction. Then, the extracted modes are used for classification purposes with canonical classifiers such as vector support machines or k-nearest neighbor classifiers. Experimental results show that this method achieves very stable results, almost independently of the amount of noise added to the image, due to the ability of decomposition to capture the noise in the first mode. Classification results using noisy images are at the same level as other algorithms proposed for the same databases but working on clean images and therefore are better than those obtained using classic image filters in noisy images. Moreover, unlike most of the available algorithms, the algorithm proposed in this paper is based on the input data (without the need to adjust parameters), making it transparent to the user. Finally, the proposed new approach achieves good results independently of the type of noise, the level of noise and the type of the database, which is not possible with other classical methods requiring parameter adjustment.
Saad Al-Baddai; Pere Marti-Puig; Esteve Gallego-Jutglà; Karema Al-Subari; Ana Maria Tomé; Bernd Ludwig; Elmar Wolfgang Lang; Jordi Solé-Casals. A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions. Soft Computing 2019, 24, 3809 -3827.
AMA StyleSaad Al-Baddai, Pere Marti-Puig, Esteve Gallego-Jutglà, Karema Al-Subari, Ana Maria Tomé, Bernd Ludwig, Elmar Wolfgang Lang, Jordi Solé-Casals. A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions. Soft Computing. 2019; 24 (5):3809-3827.
Chicago/Turabian StyleSaad Al-Baddai; Pere Marti-Puig; Esteve Gallego-Jutglà; Karema Al-Subari; Ana Maria Tomé; Bernd Ludwig; Elmar Wolfgang Lang; Jordi Solé-Casals. 2019. "A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions." Soft Computing 24, no. 5: 3809-3827.
Detecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.
Alejandro Blanco-M.; Pere Marti-Puig; Karina Gibert; Jordi Cusidó; Jordi Solé-Casals. A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History. Energies 2019, 12, 1982 .
AMA StyleAlejandro Blanco-M., Pere Marti-Puig, Karina Gibert, Jordi Cusidó, Jordi Solé-Casals. A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History. Energies. 2019; 12 (10):1982.
Chicago/Turabian StyleAlejandro Blanco-M.; Pere Marti-Puig; Karina Gibert; Jordi Cusidó; Jordi Solé-Casals. 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History." Energies 12, no. 10: 1982.
This work contributes to the techniques used for SCADA (Supervisory Control and Data Acquisition) system data completion in databases containing historical water sensor signals from a water supplier company. Our approach addresses the data restoration problem in two stages. In the first stage, we treat one-dimensional signals by estimating missing data through the combination of two linear predictor filters, one working forwards and one backwards. In the second stage, the data are tensorized to take advantage of the underlying structures at five minute, one day, and one week intervals. Subsequently, a low-range approximation of the tensor is constructed to correct the first stage of the data restoration. This technique requires an offset compensation to guarantee the continuity of the signal at the two ends of the burst. To check the effectiveness of the proposed method, we performed statistical tests by deleting bursts of known sizes in a complete tensor and contrasting different strategies in terms of their performance. For the type of data used, the results show that the proposed data completion approach outperforms other methods, the difference becoming more evident as the size of the bursts of missing data grows.
Pere Marti-Puig; Arnau Martí-Sarri; Moisès Serra-Serra. Different Approaches to SCADA Data Completion in Water Networks. Water 2019, 11, 1023 .
AMA StylePere Marti-Puig, Arnau Martí-Sarri, Moisès Serra-Serra. Different Approaches to SCADA Data Completion in Water Networks. Water. 2019; 11 (5):1023.
Chicago/Turabian StylePere Marti-Puig; Arnau Martí-Sarri; Moisès Serra-Serra. 2019. "Different Approaches to SCADA Data Completion in Water Networks." Water 11, no. 5: 1023.
The thickness of the subcutaneous fat (SFT) is a very important parameter in the ham, since determines the process the ham will be submitted. This study compares two methods to predict the SFT in slaughter line: an automatic system using an SVM model (Support Vector Machine) and a manual measurement of the fat carried out by an experienced operator, in terms of accuracy and economic benefit. These two methods were compared to the golden standard obtained by measuring SFT with a ruler in a sample of 400 hams equally distributed within each SFT class. The results show that the SFT prediction made by the SVM model achieves an accuracy of 75.3%, which represents an improvement of 5.5% compared to the manual measurement. Regarding economic benefits, SVM model can increase them between 12 and 17%. It can be concluded that the classification using SVM is more accurate than the one performed manually with an increase of the economic benefit for sorting.
Gerard Masferrer; Ricard Carreras; Maria Font-I-Furnols; Marina Gispert; Moises Serra; Pere Marti-Puig. Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification. Meat Science 2019, 155, 1 -7.
AMA StyleGerard Masferrer, Ricard Carreras, Maria Font-I-Furnols, Marina Gispert, Moises Serra, Pere Marti-Puig. Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification. Meat Science. 2019; 155 ():1-7.
Chicago/Turabian StyleGerard Masferrer; Ricard Carreras; Maria Font-I-Furnols; Marina Gispert; Moises Serra; Pere Marti-Puig. 2019. "Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification." Meat Science 155, no. : 1-7.
It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines.
Pere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. Feature Selection Algorithms for Wind Turbine Failure Prediction. Energies 2019, 12, 453 .
AMA StylePere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó, Jordi Solé-Casals. Feature Selection Algorithms for Wind Turbine Failure Prediction. Energies. 2019; 12 (3):453.
Chicago/Turabian StylePere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction." Energies 12, no. 3: 453.
Essential tremor (ET) is the most common movement disorder. In fact, its prevalence is about 20 times higher than that of Parkinson's disease. In addition, studies have shown that a high percentage of cases, between 50 and 70%, are estimated to be of genetic origin. The gold standard test for diagnosis, monitoring and to differentiate between both pathologies is based on the drawing of the Archimedes' spiral. Our major challenge is to develop the simplest system able to correctly classify Archimedes' spirals, therefore we will exclusively use the information of the x and y coordinates. This is the minimum information provided by any digitizing device. We explore the use of features from drawings related to the Discrete Cosine Transform as part of a wider cross-study for the diagnosis of essential tremor held at Biodonostia. We compare the performance of these features against other classic and already analyzed ones. We outperform previous results using a very simple system and a reduced set of features. Because the system is simple, it will be possible to implement it in a portable device (microcontroller), which will receive the x and y coordinates and will issue the classification result. This can be done in real time, and therefore without needing any extra job from the medical team. In future works these new drawing-biomarkers will be integrated with the ones obtained in the previous Biodonostia study. Undoubtedly, the use of this technology and user-friendly tools based on indirect measures could provide remarkable social and economic benefits.
Jordi Solé-Casals; Iker Anchustegui-Echearte; Pere Marti-Puig; Pilar M. Calvo; Alberto Bergareche; José Ignacio Sánchez-Méndez; Karmele López-De-Ipiña. Discrete Cosine Transform for the Analysis of Essential Tremor. Frontiers in Physiology 2019, 9, 1947 .
AMA StyleJordi Solé-Casals, Iker Anchustegui-Echearte, Pere Marti-Puig, Pilar M. Calvo, Alberto Bergareche, José Ignacio Sánchez-Méndez, Karmele López-De-Ipiña. Discrete Cosine Transform for the Analysis of Essential Tremor. Frontiers in Physiology. 2019; 9 ():1947.
Chicago/Turabian StyleJordi Solé-Casals; Iker Anchustegui-Echearte; Pere Marti-Puig; Pilar M. Calvo; Alberto Bergareche; José Ignacio Sánchez-Méndez; Karmele López-De-Ipiña. 2019. "Discrete Cosine Transform for the Analysis of Essential Tremor." Frontiers in Physiology 9, no. : 1947.
The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy.
Pere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. Effects of the pre-processing algorithms in fault diagnosis of wind turbines. Environmental Modelling & Software 2018, 110, 119 -128.
AMA StylePere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó, Jordi Solé-Casals. Effects of the pre-processing algorithms in fault diagnosis of wind turbines. Environmental Modelling & Software. 2018; 110 ():119-128.
Chicago/Turabian StylePere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. 2018. "Effects of the pre-processing algorithms in fault diagnosis of wind turbines." Environmental Modelling & Software 110, no. : 119-128.
The thickness of the subcutaneous fat in hams is one of the most important factors for the dry-curing process and largely determines its final quality. This parameter is usually measured in slaughterhouses by a manual metrical measure to classify hams. The aim of the present study was to propose an automatic classification method based on data obtained from a carcass automatic classification equipment (AutoFom) and intrinsic data of the pigs (sex, breed, and weight) to simulate the manual classification system. The evaluated classification algorithms were decision tree, support vector machines (SVM), k-nearest neighbour and discriminant analysis. A total of 4000 hams selected by breed and sex were classified as thin (0–10 mm), standard (11–15 mm), semi-fat (16–20 mm) and fat (>20 mm). The most reliable model, with a percentage of success of 73%, was SVM with Gaussian kernel, including all data available. These results suggest that the proposed classification method can be a useful online tool in slaughterhouses to classify hams.
Gerard Masferrer; Ricard Carreras; Maria Font-I-Furnols; Marina Gispert; Pere Marti-Puig; Moises Serra Serra. On-line Ham Grading using pattern recognition models based on available data in commercial pig slaughterhouses. Meat Science 2018, 143, 39 -45.
AMA StyleGerard Masferrer, Ricard Carreras, Maria Font-I-Furnols, Marina Gispert, Pere Marti-Puig, Moises Serra Serra. On-line Ham Grading using pattern recognition models based on available data in commercial pig slaughterhouses. Meat Science. 2018; 143 ():39-45.
Chicago/Turabian StyleGerard Masferrer; Ricard Carreras; Maria Font-I-Furnols; Marina Gispert; Pere Marti-Puig; Moises Serra Serra. 2018. "On-line Ham Grading using pattern recognition models based on available data in commercial pig slaughterhouses." Meat Science 143, no. : 39-45.
Scoring animal behavior is increasingly needed for better understanding ecological processes. For example, behavior shapes harvesting likelihood, thus management of harvested resources should improve after accounting for behavior-driven processes. Automatic video-recording at controlled arenas is the most widespread method for scoring behavior. However, long term tracking animals while keeping identity is still an opened challenge. Here, we develop an ad-hoc algorithm for multi-tracking objects during days or even weeks, to fulfill the particular needs for a behavioral assay concerning a fish species targeted by recreational fishing. Specifically, we overcome the challenge of keeping fish identity in a context where they often disappeared from the camera when entering a shelter, the pixel size was low compared to the size of the arena and the lighting was constrained by the wellbeing of the fish. This work may contribute to better assess the behavioral features of fish in long-lasting lab conditions.
P. Marti-Puig; M. Serra-Serra; A. Campos-Candela; R. Reig-Bolaño; A. Manjabacas; M. Palmer. Quantitatively scoring behavior from video-recorded, long-lasting fish trajectories. Environmental Modelling & Software 2018, 106, 68 -76.
AMA StyleP. Marti-Puig, M. Serra-Serra, A. Campos-Candela, R. Reig-Bolaño, A. Manjabacas, M. Palmer. Quantitatively scoring behavior from video-recorded, long-lasting fish trajectories. Environmental Modelling & Software. 2018; 106 ():68-76.
Chicago/Turabian StyleP. Marti-Puig; M. Serra-Serra; A. Campos-Candela; R. Reig-Bolaño; A. Manjabacas; M. Palmer. 2018. "Quantitatively scoring behavior from video-recorded, long-lasting fish trajectories." Environmental Modelling & Software 106, no. : 68-76.
Background: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.
Alejandro Blanco-M.; Karina Gibert; Pere Marti-Puig; Jordi Cusidó; Jordi Solé-Casals. Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools. Energies 2018, 11, 723 .
AMA StyleAlejandro Blanco-M., Karina Gibert, Pere Marti-Puig, Jordi Cusidó, Jordi Solé-Casals. Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools. Energies. 2018; 11 (4):723.
Chicago/Turabian StyleAlejandro Blanco-M.; Karina Gibert; Pere Marti-Puig; Jordi Cusidó; Jordi Solé-Casals. 2018. "Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools." Energies 11, no. 4: 723.
The Wind sector has roughly 2200M euros of profit losses due to wind turbine failures and these failures do not contribute to the goal of reducing greenhouse gas emissions of many states. The 25-35% of the generation costs are operation and maintenance services. To lower this ratio, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. Signal trending analysis supported on linear regression models presents the problem of how to carefully choose the right target variable, which reproduces as close as possible the behavior of a failure from a component. This document evaluates the impact of that choice by comparing as target different variables with discrete-non normal distribution, commonly selected by feature selection methods, versus variables that are continuous over time with a near normal distribution. Experimental results on real data show the use of continuous target variables selected by human expert on the field give better results than the use of targets obtained through feature selection algorithm.
Alejandro Blanco-M.; Jordi Sole-Casals; Pere Marti-Puig; Juan Jose Cardenas Isaac Justicia; Jordi Cusido. Impact of target variable distribution type over the regression analysis in wind turbine data. 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI) 2017, 1 -7.
AMA StyleAlejandro Blanco-M., Jordi Sole-Casals, Pere Marti-Puig, Juan Jose Cardenas Isaac Justicia, Jordi Cusido. Impact of target variable distribution type over the regression analysis in wind turbine data. 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI). 2017; ():1-7.
Chicago/Turabian StyleAlejandro Blanco-M.; Jordi Sole-Casals; Pere Marti-Puig; Juan Jose Cardenas Isaac Justicia; Jordi Cusido. 2017. "Impact of target variable distribution type over the regression analysis in wind turbine data." 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI) , no. : 1-7.
Seagrasses provide an important ecological value as nursery habitats, hosting higher juvenile densities than their surrounding less-structured habitats by offering shelter and food to early stages of fish. However, the quantitative evaluations of this nursery function remain elusive. Surveys assessing abundances of juvenile fish over seagrass meadows have been largely conducted by diving visual censuses, where typically only a shallow depth range is adequately covered. Within this study, we developed a low-cost stereo-baited video camera (SBRUV) with high precision and accuracy able to deliver length frequency distributions, including the smallest juvenile fraction. The capacity of the SBRUV system increased the synopticity with respect to typical surveys. We tested the system over seagrass meadows of Posidonia oceanica in a Mediterranean bay, investigating the relationship of juvenile abundance and size of the sparid Diplodus annularis with depth, time of day and protection status (i.e. inside and outside of a Marine Protected Area, MPA). We found significant effects of the depth on the length of the older size classes fraction (larger sizes at deeper stations) and an effect of time of the day on the abundance (less abundant during the evenings), and an opposite pattern was observed for early juvenile’s abundances. The MPA protection had no effect in D. annularis population structure. Interestingly, the relative abundance of D. annularis early juveniles was comparable at all depths, from 2 to 20 m, which suggests a potentially higher nursery value of P. oceanica meadows than earlier thought.
Carlos Díaz-Gil; Sarah Louise Smee; Lucy Cotgrove; Guillermo Follana-Berná; Hilmar Hinz; Pere Marti-Puig; Amalia Grau; Miquel Palmer; Ignacio A. Catalán. Using stereoscopic video cameras to evaluate seagrass meadows nursery function in the Mediterranean. Marine Biology 2017, 164, 1 .
AMA StyleCarlos Díaz-Gil, Sarah Louise Smee, Lucy Cotgrove, Guillermo Follana-Berná, Hilmar Hinz, Pere Marti-Puig, Amalia Grau, Miquel Palmer, Ignacio A. Catalán. Using stereoscopic video cameras to evaluate seagrass meadows nursery function in the Mediterranean. Marine Biology. 2017; 164 (6):1.
Chicago/Turabian StyleCarlos Díaz-Gil; Sarah Louise Smee; Lucy Cotgrove; Guillermo Follana-Berná; Hilmar Hinz; Pere Marti-Puig; Amalia Grau; Miquel Palmer; Ignacio A. Catalán. 2017. "Using stereoscopic video cameras to evaluate seagrass meadows nursery function in the Mediterranean." Marine Biology 164, no. 6: 1.
Pere Marti-Puig; Ramon Reig-Bolano. A rotation-invariant feature space according to environmental applications needs in a data mining system using fish otoliths. AI Communications 2016, 29, 687 -699.
AMA StylePere Marti-Puig, Ramon Reig-Bolano. A rotation-invariant feature space according to environmental applications needs in a data mining system using fish otoliths. AI Communications. 2016; 29 (6):687-699.
Chicago/Turabian StylePere Marti-Puig; Ramon Reig-Bolano. 2016. "A rotation-invariant feature space according to environmental applications needs in a data mining system using fish otoliths." AI Communications 29, no. 6: 687-699.