Person: Quispe Bonilla, Max David
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Quispe Bonilla
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Max David
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Ingeniería Eléctrica, Electrónica y de Comunicación
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ISC. Institute of Smart Cities
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0000-0003-0884-8789
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TA74837
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Publication Open Access Development and validation of a smart system for medullation and diameter assessment of alpaca, llama and mohair fibres(Elsevier, 2023) Quispe Bonilla, Max David; Quispe Bonilla, Christian; Serrano Arriezu, Luis Javier; Trigo Vilaseca, Jesús Daniel; Bengoechea, J. J.; Quispe Peña, Edgar; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaMedullated fibres, due to their higher resistance to bending and pressure, constitute a problem for the textile industry. Thus, having practical instruments to identify them is essential. Therefore, the aim of this research was to develop and validate a novel, swift, automatic system (referred to as S-Fiber Med) for medullation and diameter assessment of animal fibres based on artificial intelligence. The medullation of 88 samples of alpaca, llama and mohair fibres (41, 43 and 4, respectively) was evaluated. Additionally, 269 samples of alpacas were considered for average fibre diameter (AFD) and the results were compared with the Portable Fiber Tester (PFT) and Optical Fibre Diameter Analyser (OFDA) methods (72 and 197 samples, respectively). The preparation of each sample to be analysed followed the procedure described in IWTO-8-2011. Version 5 of “You Only Look Once” and DenseNet models were used to recognise the type of medullation and diameter of the fibres, respectively. Within each image (n = 661 for alpaca), all fibres were labelled (as Non-Medullated, Fragmented Medulla, Uncontinuous Medulla, Continuous Medulla and Strongly Medullated) using the LabelImg tool. Data augmentation technique was applied to obtain 3 966 images. Such data set was divided into 3 576 and 390 images for training and test data, respectively. For mohair samples (n = 321), a similar process was carried out. The data to train the model used to infer the diameter contained 16 446 fibres labelled with his respective AFD. A complementary hardware composed of three subsystems (mechanical, electronic, and optical) was developed for evaluation purposes. T-test, Pearson and Concordance correlation, Bland-Altman plot and linear regression analyses were used to validate and compare the S-Fiber Med with other methods. Results indicate that there was no significant difference between medullation percentage obtained with the projection microscope and the S-Fiber Med. The Pearson and Concordance correlation analysis shows a strong, high and significant relationship (P-value < 0.001). The AFDs of alpaca and llama fibre samples obtained with the two methods are very similar, because no significant difference was found at the t-test (P-value > 0.172), and they have a strong, high and significant relationship between them, given the high Pearson correlation value (r ≥ 0.96 with P-value < 0.001), high Concordance coefficient and bias correction factor. Similar results were found when PFT and OFDA100 were compared with S-Fiber Med. As a conclusion, this new system provides precise, accurate measurements of medullation and AFD in an expeditious fashion (40 seconds/sample).Publication Open Access Application of artificial intelligence and digital images analysis to automatically determine the percentage of fiber medullation in alpaca fleece samples(Elsevier Science, 2022) Quispe Bonilla, Max David; Serrano Arriezu, Luis Javier; Trigo Vilaseca, Jesús Daniel; Quispe Bonilla, Christian; Poma Gutiérrez, Adolfo; Quispe Peña, Edgar; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaThe aim of this research is to develop and validate two computer programs based on artificial intelligence (AI) and digital image analysis (DIA) in order to determine the incidence of medullation in white alpaca fibers. Two data sets were analyzed: 76 samples of Huacaya alpaca fibers obtained from Huancavelica, Peru, and 200 samples of white alpacas of two genotypes (Huacaya, n =100; Suri, n = 100), obtained from Arequipa, Peru. The preparation of each sample followed the procedure described in IWTO-8-2011. The Pytorch framework was used to generate several training models based on the You Only Look at Once (YOLO) architecture. Circa 4000 pictures of fibers were taken and 661 of them were selected as representative. Using the LabelImg software, the fibers present in each representative picture (approximately 10 fibers/picture) were labeled as one of these two classes: either medullated or non-medullated. Subsequently, the data augmentation technique was applied to expand the data set to 3966 photographs. Thus, 90 of them were used as initial validation data, while the reaming 3876 pictures (containing a total of 23,964 labeled fibers) were used as training data. Matlab was used to develop the DIA-based software. More specifically, algorithms of pre-processing, segmentation, smoothing, skeletonization and Hough transform were implemented to detect medullated and non-medullated fibers. Correlation and linear regression analyses were used to evaluate the models. The medullation percentage results show that there is no statistically significant difference between the AI-based method and the projection microscope method (p-value = 0.668 and 0.672 for the t-student and Wilcoxon tests, respectively). Moreover, the correlation of each of the developed computer methods with the projection microscope method is very strong (r = 0.99 and 0.97). This confirms the software ability to perform the recognition of fibers with and without medullation. Similar results (p-value = 0.357) were obtained when comparing the projection microscope method and DIA-based software method. Finally, using the proposed framework, the average time required to analyze a sample was 19.44 s. As a result, this software allows the implementation of practical, precise, and efficient methodologies to determine the incidence of medullation of alpaca fibers.Publication Open Access Classification of south american camelid and goat fiber samples based on fourier transform infrared spectroscopy and machine learning(Taylor and Francis Group, 2024) Quispe Bonilla, Max David; Trigo Vilaseca, Jesús Daniel; Serrano Arriezu, Luis Javier; Huere, Jorge; Quispe Peña, Edgar; Beruete Díaz, Miguel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa.Some animal fibers are considerably cheaper than others. Hence the existence of counterfeit products, which are detrimental to legitimate producers and consumers alike. Fourier-Transform Infrared (FTIR) spectroscopy can extract a characteristic waveform from fibers that can be later used for classification. However, visually inspecting such waveforms is imprecise. Previous research has complemented FTIR with other mathematical or physical methods to improve accuracy. In parallel, Artificial Intelligence (AI) is an emerging field that could be helpful in this domain. The objective of this work is therefore to develop and validate two machine learning models, namely, Deep Neural Networks (DNN) and Support Vector Machine (SVM) to classify spectra of fibers by species. The spectra are acquired using an FTIR spectrometer in Attenuated Total Reflectance (ATR) mode (FTIR-ATR). Camelid (alpaca: n = 51, llama: n = 50, vicuña: n = 50) and goat (mohair: n = 35 and cashmere: n = 20) samples were evaluated, from which 1236 FTIR-ATR spectra were obtained. Some visual differences were observed between the spectra of the different species. Accuracies up to 96.75% and 95.12% were obtained when evaluating the DNN and SVM models. Furthermore, an accuracy of 97.8% was obtained when evaluating the FTIR-ATR spectra of South American Camelids (SAC) fibers with DNN, and 97.2% when evaluating them with SVM. A 100% accuracy was obtained when evaluating the FTIR-ATR spectra of vicuña fibers with both models. No significant differences were found (p-value = 0.368) by comparing the number of hits against the total number of alpaca, llama, vicuña, mohair and cashmere fibers using DNN. As per the results, it seems that DNN is more accurate than SVM. In conclusion, FTIR-ATR spectrometry techniques combined with machine learning models are a reliable alternative for the identification of SAC and goats through the spectrum of their fibers.Publication Open Access Development and validation of an automatic and intelligent system for medullation and average diameter evaluation to alpaca, llama and mohair fibers(2021) Quispe Bonilla, Max David; Quispe Peña, Edgar; Serrano Arriezu, Luis Javier; Trigo Vilaseca, Jesús Daniel; Quispe Bonilla, Christian; Institute of Smart Cities - ISCThe aim of this work was to develop and validate an automatic and intelligent system (based in artificial intelligence) capable of quantify and identify fibers, by type of medullation in 5 categories. This work was carried out in Lima, Peru. To develop the software a trained model was generated based on You Only Look at Once for medullation and DenseNet for average fiber diameter (AFD), using Python. Language C was used to develop the graphical user interface. For the hardware; mechanical, electronic and optical subsystems was design and development. Samples of white alpaca, llama and mohair fibers (2108, 1858, 901 fibers, respectively) were evaluated for identification the fibers medullation. Additionally, AFD of 197 samples of white alpacas were measured with two methods. This system identifies 5 types of medullation and measures the diameter of the fibers. Each sample is evaluated in 40 sec, considering about 1500 fibers/sample. At two-proportion z-test of different fiber medullation types obtained with direct counting and our system no significant differences were found. At t-test of AFD obtained with OFDA device and our system no significant difference were found. The relationship between these methods was very stronger (r=0.95). The use of this system is recommended for fiber evaluation for the purpose of genetic improvement of fibers in animal production; purchase-sale, and processing of fiber to verify the quality of fibers; and research on medullation to increase knowledge about alpaca, llama and mohair fibers.Publication Open Access Diseño, desarrollo y evaluación preliminar de un novedoso monitor de signos vitales llevable para vacunos(Universidad Nacional Mayor de San Marcos (Perú), 2019) Quispe Bonilla, Max David; Poma Gutiérrez, Adolfo; Serrano Arriezu, Luis Javier; Led Ramos, Santiago; Quispe Peña, Edgar; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de ComunicaciónEl monitoreo de los diferentes signos vitales en vacunos tiene importancia desde el punto de vista productivo, sanitario y de bienestar animal; sin embargo, existen pocos equipos que tengan el potencial de uso a nivel de campo y que no sean invasivos. Por tal motivo se llevó a cabo el presente trabajo con la finalidad de diseñar, construir y evaluar el uso de un pequeño MOnitor de SIgnos VItales LLevable (MOSIVILLe), que sea capaz de capturar las señales vitales en vacunos bajo condiciones de campo. El diseño y desarrollo del MOSIVILLe se realizó en Lima, Perú, entre enero y octubre de 2017 y la evaluación en campo se realizó en Chota, Perú, utilizando 11 vaquillas entre noviembre de 2017 y febrero de 2018. Con el uso del MOSIVILLe se obtuvieron las señales vitales de vacunos, reconociéndose las ondas P, Q, R, S, T, complejo QRS e intervalo RR del electrocardiograma que condujeron a obtener la frecuencia cardiaca (FC). Adicionalmente, se obtuvo la temperatura de la piel (T°P) y la señal de ventilación con los picos de inhalación, exhalación, tiempo de inspiración y tiempo de espiración que determinan la frecuencia respiratoria (FR). El MOSIVILLe usado en vacunos en condiciones de campo permitió obtener una FC de 70.83±1.47, FR de 25.24±1.64 y una T°P de 31.52±0.40 (promedio ± error estándar), valores que se encuentran en concordancia con la literatura. Se concluye que el MOSIVILLe es una alternativa importante para obtener diversos signos vitales en vacunos bajo condiciones de campo.