Literatura académica sobre el tema "Short-time features"
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Artículos de revistas sobre el tema "Short-time features"
Martinez-Heath, M. R. y A. G. Deacon. "Engineering Risk Assessment in Manufacturing Products with Short Time-to-Market Windows". Journal of Engineering for Industry 117, n.º 1 (1 de febrero de 1995): 49–54. http://dx.doi.org/10.1115/1.2803277.
Texto completoBehzad, M., A. R. Bastami y D. Mba. "Rolling bearing fault detection by short-time statistical features". Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 226, n.º 3 (19 de octubre de 2011): 229–37. http://dx.doi.org/10.1177/0954408911422635.
Texto completoAkulenko, L. D., Yu G. Markov, V. V. Perepelkin y L. V. Rykhlova. "Short-time-scale features of the Earth’s polar motion". Astronomy Reports 53, n.º 11 (noviembre de 2009): 1070–77. http://dx.doi.org/10.1134/s1063772909110122.
Texto completoHeinen, Marco, Peter Holmqvist, Adolfo J. Banchio y Gerhard Nägele. "Short-time diffusion of charge-stabilized colloidal particles: generic features". Journal of Applied Crystallography 43, n.º 5 (19 de agosto de 2010): 970–80. http://dx.doi.org/10.1107/s002188981002724x.
Texto completoRiest, Jonas y Gerhard Nägele. "Short-time dynamics in dispersions with competing short-range attraction and long-range repulsion". Soft Matter 11, n.º 48 (2015): 9273–80. http://dx.doi.org/10.1039/c5sm02099a.
Texto completoSundararajan, Narasimman, A. Ebrahimi y Nannappa Vasudha. "Two Dimensional Short Time Hartley Transforms". Sultan Qaboos University Journal for Science [SQUJS] 21, n.º 1 (1 de noviembre de 2016): 41. http://dx.doi.org/10.24200/squjs.vol21iss1pp41-47.
Texto completoRamalingam, A. y S. Krishnan. "Gaussian Mixture Modeling of Short-Time Fourier Transform Features for Audio Fingerprinting". IEEE Transactions on Information Forensics and Security 1, n.º 4 (diciembre de 2006): 457–63. http://dx.doi.org/10.1109/tifs.2006.885036.
Texto completoRusnak, Yu. "SEMANTIC AND STRUCTURAL FEATURES OF TIME ADVERBS IN OLGA KOBYLYANSKA’S SHORT PROSE". International Humanitarian University Herald. Philology 2, n.º 46 (2020): 104–7. http://dx.doi.org/10.32841/2409-1154.2020.46-2.25.
Texto completoSun, Dechao, Jiali Wu, Hong Huang, Renfang Wang, Feng Liang y Hong Xinhua. "Prediction of Short-Time Rainfall Based on Deep Learning". Mathematical Problems in Engineering 2021 (30 de marzo de 2021): 1–8. http://dx.doi.org/10.1155/2021/6664413.
Texto completoQiao, Mu y Zixuan Cheng. "A Novel Long- and Short-Term Memory Network with Time Series Data Analysis Capabilities". Mathematical Problems in Engineering 2020 (13 de octubre de 2020): 1–9. http://dx.doi.org/10.1155/2020/8885625.
Texto completoTesis sobre el tema "Short-time features"
Mubarak, Omer Mohsin Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "Speech and music discrimination using short-time features". Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/31954.
Texto completoDíaz, González Fernando. "Federated Learning for Time Series Forecasting Using LSTM Networks: Exploiting Similarities Through Clustering". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254665.
Texto completoFederated Learning utgör en statistisk utmaning vid träning med starkt heterogen sekvensdata. Till exempel så uppvisar tidsseriedata inom telekomdomänen blandade variationer och mönster över längre tidsintervall. Dessa distinkta fördelningar utgör en utmaning när en nod inte bara ska bidra till skapandet av en global modell utan även ämnar applicera denna modell på sin lokala datamängd. Att i detta scenario införa en global modell som ska passa alla kan visa sig vara otillräckligt, även om vi använder oss av de mest framgångsrika modellerna inom maskininlärning för tidsserieprognoser, Long Short-Term Memory (LSTM) nätverk, vilka visat sig kunna fånga komplexa mönster och generalisera väl till nya mönster. I detta arbete visar vi att genom att klustra klienterna med hjälp av dessa mönster och selektivt aggregera deras uppdateringar i olika globala modeller kan vi uppnå förbättringar av den lokal prestandan med minimala kostnader, vilket vi demonstrerar genom experiment med riktigt tidsseriedata och en grundläggande LSTM-modell.
Dai, Shin-Hao y 戴欣浩. "Feature Selection and Feature Extraction for Emotion Recognition Based on Multiple Short-Time Physiological Signals". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/78424722899130791289.
Texto completo國立中正大學
電機工程研究所
103
In this paper, we proposed an emotion recognition system based on three short-time physiological signals. Electrocardiogram (ECG), Photoplethysmorgraphy (PPG) and Skin Impedance (SI) were used to recognize five kinds of negative emotions, including neutral (non-stimulated state), sad, stress, anger and disgust. In our study, we aimed to develop a user-independent system. This emotion recognition system was composed of data acquisition (physiological signals), feature calculation, normalization, feature selection or feature extraction, and classification. First, in the data acquisition part, 50 subjects were recruited to participate in this study, including 22 males and 28 females. By employing visual and audio stimulation, the subject emotions were induced and the signals were recorded. Second, in the feature calculation part, we calculated 7 types ECG features from wave-form and HRV sequence, 10 types PPG features from wave-form and HRV sequence and 3 types SI features from wave-form and SCR sequence. Totally, 140 features were calculated. Third, we normalized our feature set to the same level. Fourth, in the feature selection part, we performed Genetic Algorithm (GA) to select the most effective feature set to enhance accuracy. On the other hand, the feature extraction part, we compared the performance of the Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and 3 modified LDA (OLDA, SLDA and RLDA) methods in reducing the feature dimensions by mapping the original data to the better subspace. Finally, we used SVM to classify emotions. And we performed leave-one-out scheme for cross validation. According to the result, the accuracy were 70.4% when using GA feature selector, 67.6% when using OLDA feature extractor, 95.2% when using OLDA feature extractor in combination with the GA feature selector.
Libros sobre el tema "Short-time features"
Allen, Robert C. The Industrial Revolution: A Very Short Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/actrade/9780198706786.001.0001.
Texto completoCharon, Rita. A Framework for Teaching Close Reading. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199360192.003.0009.
Texto completoStokes, Lisa Odham. Food for Thought: Cannibalism in The Untold Story and Dumplings. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474424592.003.0011.
Texto completoStone, Derrick. Walks, Tracks and Trails of Victoria. CSIRO Publishing, 2009. http://dx.doi.org/10.1071/9780643097919.
Texto completoRokison, Abigail. Shakespeare’s Dramatic Verse Line. Editado por Jonathan Post. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199607747.013.0024.
Texto completoSullivan, Sean G. Impulse Control Disorders in Medical Settings. Editado por Jon E. Grant y Marc N. Potenza. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195389715.013.0123.
Texto completoMeyer, Michel. The role of pathos: from argumentative responses to feeling and emotions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199691821.003.0010.
Texto completoMoney, Jeannette. Comparative Immigration Policy. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190846626.013.380.
Texto completoThatamanil, John J. Circling the Elephant. Fordham University Press, 2020. http://dx.doi.org/10.5422/fordham/9780823288526.001.0001.
Texto completoOrtiz, Julian Arias, Raphaël Favory y Jean-Louis Vincent. Infection, sepsis, and multiorgan dysfunction syndrome. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199687039.003.0072.
Texto completoCapítulos de libros sobre el tema "Short-time features"
Gómez, P., J. M. Ferrández, V. Rodellar, L. M. Mazaira y C. Muñoz. "Modeling Short-Time Parsing of Speech Features in Neocortical Structures". En Trends in Applied Intelligent Systems, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13033-5_17.
Texto completoHerff, Christian y Dean J. Krusienski. "Extracting Features from Time Series". En Fundamentals of Clinical Data Science, 85–100. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99713-1_7.
Texto completoGómez, V., A. Álvarez, P. Herrera, G. Castellanos y A. Orozco. "Short Time EEG Connectivity Features to Support Interpretability of MI Discrimination". En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 699–706. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13469-3_81.
Texto completoSmith, Leslie S. "Extracting Features from the Short-term Time Structure of Cochlear Filtered Sound". En 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997, 113–25. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1546-5_10.
Texto completoVelasquez-Martinez, F., A. M. Alvarez-Meza y G. Castellanos-Dominguez. "Connectivity Analysis of Motor Imagery Paradigm Using Short-Time Features and Kernel Similarities". En Artificial Computation in Biology and Medicine, 439–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18914-7_46.
Texto completoMorales, Juddy Y., Juan D. Castillo, Brayan M. León, Roberto Ferro Escobar y Andrés E. Gaona. "Audio Scene Classification Based on Convolutional Neural Networks: An Evaluation of Multiple Features and Topologies in Short Time Segments". En Lecture Notes in Electrical Engineering, 414–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53021-1_42.
Texto completoWernhard, Christoph y Wolfgang Bibel. "Learning from Łukasiewicz and Meredith: Investigations into Proof Structures". En Automated Deduction – CADE 28, 58–75. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79876-5_4.
Texto completoCerone, Antonio y Graham Pluck. "A Formal Model for Emulating the Generation of Human Knowledge in Semantic Memory". En From Data to Models and Back, 104–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70650-0_7.
Texto completoZhan, Peng, Yupeng Hu, Wei Luo, Yang Xu, Qi Zhang y Xueqing Li. "Feature-based Online Segmentation Algorithm for Streaming Time Series (Short Paper)". En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 477–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12981-1_33.
Texto completoNakanishi, Tomoko M. "Real-Time Water Movement in a Plant". En Novel Plant Imaging and Analysis, 39–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4992-6_2.
Texto completoActas de conferencias sobre el tema "Short-time features"
Sepulveda-Cano, L. M., A. M. Alvarez-Meza y G. Castellanos-Dominguez. "Training using short-time features for OSA discrimination". En 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6345858.
Texto completoSyamanthika, Puppala, Tekkali Yogitha, Manche Kuruba Sai Hitha, Tiramareddy Manasa Swetha, S. S. Poorna y K. Anuraj. "Digit Identification from Speech using Short-Time Domain Features". En 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2020. http://dx.doi.org/10.1109/icirca48905.2020.9182788.
Texto completoTsiakoulis, Pirros, Alexandros Potamianos y Dimitrios Dimitriadis. "Short-time instantaneous frequency and bandwidth features for speech recognition". En Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373305.
Texto completoKim, Bobae, Beomhee Jang, Donggeon Lee y Sungbin Im. "CNN-based UAV Detection with Short Time Fourier Transformed Acoustic Features". En 2020 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2020. http://dx.doi.org/10.1109/iceic49074.2020.9051099.
Texto completoHuang, Huan, Natalie Baddour y Ming Liang. "Short-Time Kurtogram for Bearing Fault Feature Extraction Under Time-Varying Speed Conditions". En ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85165.
Texto completoRizzi, A., M. Buccino, M. Panella y A. Uncini. "Optimal Short-Time Features for Music/Speech Classification of Compressed Audio Data". En 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06). IEEE, 2006. http://dx.doi.org/10.1109/cimca.2006.160.
Texto completoKai, Ding, Zhang Shigong, Zhang Kesheng y Lei Zhen. "Short-time and Spectrum Features of Noises Made by Vehicles for Recognition". En 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2020. http://dx.doi.org/10.1109/auteee50969.2020.9315641.
Texto completoMohdiwale, Samrudhi, Tirath Prasad Sahu, Naresh Kumar Nagwani, Rahul Kumar Chaurasia y Shrish Verma. "Abnormal activity detection in forest reserve using cumulative short time fourier transform features". En 2017 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2017. http://dx.doi.org/10.1109/iss1.2017.8389259.
Texto completoPinzon, Jaime D. y D. Graciela Colome. "Data Analytics of PMU Measurement Features for Real-time Short-term Voltage Stability Prediction". En 2019 FISE-IEEE/CIGRE Conference - Living the energy Transition (FISE/CIGRE). IEEE, 2019. http://dx.doi.org/10.1109/fisecigre48012.2019.8985004.
Texto completoNuhoglu, Mustafa Atahan. "Classification of radar signal features in electronic warfare with convolutional long-short time memory". En 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404452.
Texto completoInformes sobre el tema "Short-time features"
Berkowitz, Jacob, Nathan Beane, Kevin Philley, Nia Hurst y Jacob Jung. An assessment of long-term, multipurpose ecosystem functions and engineering benefits derived from historical dredged sediment beneficial use projects. Engineer Research and Development Center (U.S.), agosto de 2021. http://dx.doi.org/10.21079/11681/41382.
Texto completoDownes, Jane, ed. Chalcolithic and Bronze Age Scotland: ScARF Panel Report. Society for Antiquaries of Scotland, septiembre de 2012. http://dx.doi.org/10.9750/scarf.09.2012.184.
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