Relevant bibliographies by topics / T-stochastic neighbor embedding

Academic literature on the topic 'T-stochastic neighbor embedding'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "T-stochastic neighbor embedding"

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Chan, David M., Roshan Rao, Forrest Huang, and John F. Canny. "GPU accelerated t-distributed stochastic neighbor embedding." Journal of Parallel and Distributed Computing 131 (September 2019): 1–13. http://dx.doi.org/10.1016/j.jpdc.2019.04.008.

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Huang, Yanyong, Kejun Guo, Xiuwen Yi, Jing Yu, Zongxin Shen, and Tianrui Li. "T-copula and Wasserstein distance-based stochastic neighbor embedding." Knowledge-Based Systems 243 (May 2022): 108431. http://dx.doi.org/10.1016/j.knosys.2022.108431.

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Valente, Daria, Chiara De Gregorio, Valeria Torti, Longondraza Miaretsoa, Olivier Friard, Rose Marie Randrianarison, Cristina Giacoma, and Marco Gamba. "Finding Meanings in Low Dimensional Structures: Stochastic Neighbor Embedding Applied to the Analysis of Indri indri Vocal Repertoire." Animals 9, no. 5 (May 15, 2019): 243. http://dx.doi.org/10.3390/ani9050243.

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Although there is a growing number of researches focusing on acoustic communication, the lack of shared analytic approaches leads to inconsistency among studies. Here, we introduced a computational method used to examine 3360 calls recorded from wild indris (Indri indri) from 2005–2018. We split each sound into ten portions of equal length and, from each portion we extracted spectral coefficients, considering frequency values up to 15,000 Hz. We submitted the set of acoustic features first to a t-distributed stochastic neighbor embedding algorithm, then to a hard-clustering procedure using a k-means algorithm. The t-distributed stochastic neighbor embedding (t-SNE) mapping indicated the presence of eight different groups, consistent with the acoustic structure of the a priori identification of calls, while the cluster analysis revealed that an overlay between distinct call types might exist. Our results indicated that the t-distributed stochastic neighbor embedding (t-SNE), successfully been employed in several studies, showed a good performance also in the analysis of indris’ repertoire and may open new perspectives towards the achievement of shared methodical techniques for the comparison of animal vocal repertoires.
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Yu, Meiting, Lingjun Zhao, Siqian Zhang, Boli Xiong, and Gangyao Kuang. "SAR target recognition using parametric supervised t-stochastic neighbor embedding." Remote Sensing Letters 8, no. 9 (May 28, 2017): 849–58. http://dx.doi.org/10.1080/2150704x.2017.1332795.

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Zhang, Haili, Pu Wang, Xuejin Gao, Yongsheng Qi, and Huihui Gao. "Process Data Visualization Using Bikernel t-Distributed Stochastic Neighbor Embedding." Industrial & Engineering Chemistry Research 59, no. 44 (October 21, 2020): 19623–32. http://dx.doi.org/10.1021/acs.iecr.0c03333.

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Zhang, Qiang, Yi Yao, Dongsheng Zhou, and Rui Liu. "Motion Key-Frame Extraction by Using Optimized t-Stochastic Neighbor Embedding." Symmetry 7, no. 2 (April 21, 2015): 395–411. http://dx.doi.org/10.3390/sym7020395.

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Pitsianis, Nikos, Dimitris Floros, Alexandros-Stavros Iliopoulos, and Xiaobai Sun. "SG-t-SNE-Π: Swift Neighbor Embedding of Sparse Stochastic Graphs." Journal of Open Source Software 4, no. 39 (July 31, 2019): 1577. http://dx.doi.org/10.21105/joss.01577.

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Cieslak, Matthew C., Ann M. Castelfranco, Vittoria Roncalli, Petra H. Lenz, and Daniel K. Hartline. "t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis." Marine Genomics 51 (June 2020): 100723. http://dx.doi.org/10.1016/j.margen.2019.100723.

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Ma, Xiaobo, Yuchen Zhang, Fengshan Zhang, and Hongbin Liu. "Monitoring of papermaking wastewater treatment processes using t-distributed stochastic neighbor embedding." Journal of Environmental Chemical Engineering 9, no. 6 (December 2021): 106559. http://dx.doi.org/10.1016/j.jece.2021.106559.

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Koolstra, Kirsten, Peter Börnert, Boudewijn P. F. Lelieveldt, Andrew Webb, and Oleh Dzyubachyk. "Stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries." Magnetic Resonance Materials in Physics, Biology and Medicine 35, no. 2 (October 23, 2021): 223–34. http://dx.doi.org/10.1007/s10334-021-00963-8.

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Abstract Objective To visualize the encoding capability of magnetic resonance fingerprinting (MRF) dictionaries. Materials and methods High-dimensional MRF dictionaries were simulated and embedded into a lower-dimensional space using t-distributed stochastic neighbor embedding (t-SNE). The embeddings were visualized via colors as a surrogate for location in low-dimensional space. First, we illustrate this technique on three different MRF sequences. We then compare the resulting embeddings and the color-coded dictionary maps to these obtained with a singular value decomposition (SVD) dimensionality reduction technique. We validate the t-SNE approach with measures based on existing quantitative measures of encoding capability using the Euclidean distance. Finally, we use t-SNE to visualize MRF sequences resulting from an MRF sequence optimization algorithm. Results t-SNE was able to show clear differences between the color-coded dictionary maps of three MRF sequences. SVD showed smaller differences between different sequences. These findings were confirmed by quantitative measures of encoding. t-SNE was also able to visualize differences in encoding capability between subsequent iterations of an MRF sequence optimization algorithm. Discussion This visualization approach enables comparison of the encoding capability of different MRF sequences. This technique can be used as a confirmation tool in MRF sequence optimization.
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Dissertations / Theses on the topic "T-stochastic neighbor embedding"

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Droh, Erik. "T-Distributed Stochastic Neighbor Embedding Data Preprocessing Impact on Image Classification using Deep Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237422.

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Image classification in Machine Learning encompasses the task of identification of objects in an image. The technique has applications in various areas such as e-commerce, social media and security surveillance. In this report the author explores the impact of using t-Distributed Stochastic Neighbor Embedding (t-SNE) on data as a preprocessing step when classifying multiple classes of clothing with a state-of-the-art Deep Convolutional Neural Network (DCNN). The t-SNE algorithm uses dimensionality reduction and groups similar objects close to each other in three-dimensional space. Extracting this information in the form of a positional coordinate gives us a new parameter which could help with the classification process since the features it uses can be different from that of the DCNN. Therefore, three slightly different DCNN models receives different input and are compared. The first benchmark model only receives pixel values, the second and third receive pixel values together with the positional coordinates from the t-SNE preprocessing for each data point, but with different hyperparameter values in the preprocessing step. The Fashion-MNIST dataset used contains 10 different clothing classes which are normalized and gray-scaled for easeof-use. The dataset contains 70.000 images in total. Results show minimum change in classification accuracy in the case of using a low-density map with higher learning rate as the data size increases, while a more dense map and lower learning rate performs a significant increase in accuracy of 4.4% when using a small data set. This is evidence for the fact that the method can be used to boost results when data is limited.
Bildklassificering i maskinlärning innefattar uppgiften att identifiera objekt i en bild. Tekniken har applikationer inom olika områden så som e-handel, sociala medier och säkerhetsövervakning. I denna rapport undersöker författaren effekten av att användat-Distributed Stochastic Neighbour Embedding (t-SNE) på data som ett förbehandlingssteg vid klassificering av flera klasser av kläder med ett state-of-the-art Deep Convolutio-nal Neural Network (DCNN). t-SNE-algoritmen använder dimensioneringsreduktion och grupperar liknande objekt nära varandra i tredimensionellt utrymme. Att extrahera denna information i form av en positionskoordinat ger oss en ny parameter som kan hjälpa till med klassificeringsprocessen eftersom funktionerna som den använder kan skilja sig från DCNN-modelen. Tre olika DCNN-modeller får olika in-data och jämförs därefter. Den första referensmodellen mottar endast pixelvärden, det andra och det tredje motar pixelvärden tillsammans med positionskoordinaterna från t-SNE-förbehandlingen för varje datapunkt men med olika hyperparametervärden i förbehandlingssteget. I studien används Fashion-MNIST datasetet som innehåller 10 olika klädklasser som är normaliserade och gråskalade för enkel användning. Datasetet innehåller totalt 70.000 bilder. Resultaten visar minst förändring i klassificeringsnoggrannheten vid användning av en låg densitets karta med högre inlärningsgrad allt eftersom datastorleken ökar, medan en mer tät karta och lägre inlärningsgrad uppnår en signifikant ökad noggrannhet på 4.4% när man använder en liten datamängd. Detta är bevis på att metoden kan användas för att öka klassificeringsresultaten när datamängden är begränsad.
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Agis, Cherta David. "Desarrollo de un sistema de monitorización de la integridad estructural para aplicaciones en ingeniería mediante técnicas de reducción de la dimensionalidad." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/670561.

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This thesis describes a structural health monitoring (SHM) strategy to detect and classify changes in structures that can be equipped with sensors. SHM is an area of great interest, because its main objective is to verify the health of the structure to ensure its correct operation and, in turn, save maintenance costs. This objective is achieved by using algorithms and equipping the structure with a network of sensors that continuously monitor it. Researchers from around the world focus their efforts on the development of new forms of continuous monitoring to know the current state of the structure and to avoid possible failures or catastrophes. In this sense, in this work, a network of piezoelectric sensors (PZTs) is used for the development of the strategy of detection and classification of structural changes. This network of PZTs, attached to the surface of the structure to be diagnosed, applies vibrational excitation signals and, at the same time, collects the responses propagated through the structure. With this collected information, certain mathematical algorithms are developed. To carry out the main task of the proposed methodology, detection and classification of structural changes, the technique called t-distributed stochastic neighbor embedding (t-SNE) is essentially used. This technique is capable of representing the local structure of the high-dimensional data collected by the sensor network in two-dimensional or three-dimensional space. Furthermore, for the classification of structural changes, the detection methodology is expanded by adding the use of three strategies: (a) the smallest point-centroid distance; (b) the majority vote; and (c) the sum of the inverses of the distances. The methodology proposed in this study is tested and validated using an aluminum plate equipped with four PZT sensors and for certain predefined structural changes. The promising results obtained show the great classification capacity and the strong performance of this methodology, successfully classifying about 100% of the cases in various experimental scenarios. The main contribution of this project is the combination of the t-SNE technique with a carefully selected pre-processing of the data and with the three proposed classification strategies. This combination significantly improves the quality of the groups or clusters obtained with the damage detection and classification method, which represent the different structural states. Likewise, said combination diagnoses a structure with a low computational cost and high reliability. Regarding the applicability of the suggested strategy, there is no prescribed field of application: if a network of sensors can be installed in the structure to be diagnosed and several phases of action can be considered, the approach presented here can be, a priori, implemented.
Esta tesis describe una estrategia de monitorización de la salud estructural (SHM, por sus siglas en inglés) para detectar y clasificar fallos en estructuras que pueden ser equipadas con sensores. La SHM es un área de gran interés, ya que su objetivo principal es la verificación de la salud de la estructura para asegurar su correcto funcionamiento y, a su vez, ahorrar costes de mantenimiento. Este objetivo se consigue haciendo uso de algoritmos y equipando a la estructura con una red de sensores que la monitorizan de forma continuada. Investigadores de todo el mundo centran sus esfuerzos en el desarrollo de nuevas formas de monitorización continua para conocer el estado actual de la estructura y evitar posibles fallos o catástrofes. En este sentido, en este trabajo, se utiliza una red de sensores piezoeléctricos (PZT, por sus siglas en inglés) para el desarrollo de la estrategia de detección y clasificación de los cambios estructurales. Esta red de PZT, adherida a la superficie de la estructura a diagnosticar, aplica señales vibracionales de excitación y al mismo tiempo recoge las respuestas propagadas a través de la estructura. Con esta información recopilada se desarrollan ciertos algoritmos matemáticos. Para llevar a cabo la tarea principal de la metodología propuesta, detección y clasificación de fallos, se utiliza esencialmente la técnica denominada t-distributed stochastic neighbor embedding (t-SNE). Dicha técnica es capaz de representar la estructura local de los datos de alta dimensionalidad recopilados por la red de sensores en un espacio bidimensional o tridimensional. Además, para la clasificación de los cambios estructurales, se amplía la metodología de detección añadiendo el uso de tres estrategias: (a) la distancia punto-centroide más pequeña; (b) el voto mayoritario; y (c) la suma de las inversas de las distancias. La metodología propuesta en este estudio se prueba y valida utilizando una placa de aluminio equipada con cuatro sensores PZT y para ciertos daños predefinidos. Los prometedores resultados obtenidos ponen de manifiesto la gran capacidad de clasificación y el fuerte rendimiento de esta metodología, clasificando con éxito cerca del 100% de los casos en varios escenarios experimentales. La principal contribución de este proyecto es la combinación de la técnica t-SNE con un preprocesamiento de los datos cuidosamente seleccionado y con las tres estrategias de clasificación propuestas. Esta combinación mejora significativamente la calidad de los grupos o clústeres obtenidos con el método de detección y clasificación de daños, que representan los diferentes estados estructurales. Asimismo, dicha combinación diagnostica una estructura con un bajo coste computacional y una alta fiabilidad. En cuanto a la aplicabilidad de la estrategia sugerida, no hay un campo de aplicación prescrito: si se puede instalar una red de sensores en la estructura a diagnosticar y se pueden considerar varias fases de actuación, el enfoque aquí presentado puede implementarse a priori.
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Book chapters on the topic "T-stochastic neighbor embedding"

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Balamurali, Mehala. "t-Distributed Stochastic Neighbor Embedding." In Encyclopedia of Mathematical Geosciences, 1–9. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_446-1.

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Tripathy, B. K., S. Anveshrithaa, and Shrusti Ghela. "t-Distributed Stochastic Neighbor Embedding (t-SNE)." In Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization, 127–35. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003190554-13.

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Schubert, Erich, and Michael Gertz. "Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection." In Similarity Search and Applications, 188–203. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68474-1_13.

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Kitazono, Jun, Nistor Grozavu, Nicoleta Rogovschi, Toshiaki Omori, and Seiichi Ozawa. "t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom." In Neural Information Processing, 119–28. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46675-0_14.

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Fooladgar, E., and C. Duwig. "Identification of Combustion Trajectories Using t-Distributed Stochastic Neighbor Embedding (t-SNE)." In Direct and Large-Eddy Simulation XI, 245–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04915-7_33.

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Soni, Jayesh, Nagarajan Prabakar, and Himanshu Upadhyay. "Visualizing High-Dimensional Data Using t-Distributed Stochastic Neighbor Embedding Algorithm." In Principles of Data Science, 189–206. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43981-1_9.

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Lee, John A., and Michel Verleysen. "On the Role and Impact of the Metaparameters in t-distributed Stochastic Neighbor Embedding." In Proceedings of COMPSTAT'2010, 337–46. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_31.

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Meniailov, Ievgen, Serhii Krivtsov, and Tetyana Chumachenko. "Dimensionality Reduction of Diabetes Mellitus Patient Data Using the T-Distributed Stochastic Neighbor Embedding." In Smart Technologies in Urban Engineering, 86–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20141-7_9.

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Deng, Xing, Feipeng Da, and Haijian Shao. "Short-Term Wind Power Forecasting Based on the Deep Learning Approach Optimized by the Improved T-distributed Stochastic Neighbor Embedding." In Proceedings of the 11th International Conference on Computer Engineering and Networks, 53–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6554-7_6.

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Ceballos, Julian, Leandro Ariza-Jiménez, and Nicolás Pinel. "Standardized Approaches for Assessing Metagenomic Contig Binning Performance from Barnes-Hut t-Stochastic Neighbor Embeddings." In IFMBE Proceedings, 761–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30648-9_101.

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Conference papers on the topic "T-stochastic neighbor embedding"

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Huang, Yanyong, Kejun Guo, and Wei Deng. "T-copula-based stochastic neighbor embedding." In 14th International FLINS Conference (FLINS 2020). WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811223334_0015.

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Rogovschi, Nicoleta, Jun Kitazono, Nistor Grozavu, Toshiaki Omori, and Seiichi Ozawa. "t-Distributed stochastic neighbor embedding spectral clustering." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966046.

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Zhang, Biyin, and Xin Yu. "Hyperspectral image visualization using t-distributed stochastic neighbor embedding." In Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), edited by Jianguo Liu and Hong Sun. SPIE, 2015. http://dx.doi.org/10.1117/12.2205840.

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Yu, Meiting, Lingjun Zhao, Siqian Zhang, and Gangyao Kuang. "SAR target recognition via linear t-stochastic neighbor embedding and sparse representation." In 2017 Progress In Electromagnetics Research Symposium - Spring (PIERS). IEEE, 2017. http://dx.doi.org/10.1109/piers.2017.8262066.

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Lasisi, Ahmed, Antonio Merheb, Allan Zarembski, and Nii Attoh-Okine. "Rail Track Quality and T-Stochastic Neighbor Embedding for Hybrid Track Index." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006236.

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Du, Jing, and Qingbin Tong. "Application of t-Distribution Stochastic Neighbor Embedding (t-SNE) And VMD In Fault Feature Extraction." In 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2020. http://dx.doi.org/10.1109/aemcse50948.2020.00171.

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Yang, Zan, Yuan Sun, Dan Li, Zhihao Zhang, and Yuchen Xie. "T-Distributed Stochastic Neighbor Embedding with Gauss Initialization of Quantum Whale Optimization Algorithm." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189639.

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Qiu, Mengdie, Zan Yang, Wei Nai, Dan Li, Yidan Xing, and Kai Li. "T-Distributed Stochastic Neighbor Embedding Based on Cockroach Swarm Optimization with Student Distribution Parameters." In 2021 IEEE 12th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2021. http://dx.doi.org/10.1109/icsess52187.2021.9522161.

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Fang, Xu, Sen Zhang, Xiaoli Su, Baoyong Zhao, Wendong Xiao, Yixin Yin, and Fenhua Wang. "Blast furnace condition data clustering based on combination of T-distributed stochastic neighbor embedding and spectral clustering." In 2019 IEEE 15th International Conference on Control and Automation (ICCA). IEEE, 2019. http://dx.doi.org/10.1109/icca.2019.8899670.

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Afrasiabi, Shahabodin, Mousa Afrasiabi, Behzad Behdani, Mohammad Mohammadi, Mohammad S. Javadi, Gerardo J. Osorio, and Joao P. S. Catalao. "Photovoltaic Array Fault Detection and Classification based on T-Distributed Stochastic Neighbor Embedding and Robust Soft Learning Vector Quantization." In 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2021. http://dx.doi.org/10.1109/eeeic/icpseurope51590.2021.9584770.

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