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Bibliografias temáticas / Multisensor monitoring

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Literatura científica selecionada sobre o tema "Multisensor monitoring"

Autor: Grafiati

Publicado: 14 de dezembro de 2024

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Artigos de revistas sobre o assunto "Multisensor monitoring"

1

Martinez-Olmos, A., I. M. Pérez de Vargas-Sansalvador, A. J. Palma, J. Banqueri, M. D. Fernández-Ramos, and L. F. Capitán-Vallvey. "Multisensor probe for soil monitoring." Sensors and Actuators B: Chemical 160, no. 1 (2011): 52–58. http://dx.doi.org/10.1016/j.snb.2011.07.011.

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Bogomolov, Andrey. "Developing Multisensory Approach to the Optical Spectral Analysis." Sensors 21, no. 10 (2021): 3541. http://dx.doi.org/10.3390/s21103541.

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This article presents an overview of research aimed at developing a scientific approach to creating multisensor optical systems for chemical analysis. The review is mainly based on the author’s works accomplished over the recent 10 years at Samara State Technical University with broad international cooperation. It consists of an introduction and five sections that describe state of the art in the field of optical sensing, suggested development methodology of optical multisensor systems, related aspects of experimental design and process analytical technology followed by a collection of practic

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Sanjaya, Muhammad Fahyu, Ummu Kalsum, and Andi Rosman N. "PENERAPAN TEKNOLOGI CERDAS PENYIRAMAN TANAMAN HIDROPONIK BERBASIS MIKROKONTROLER DAN MULTISENSOR PADA PEMBUDIDAYA TANAMAN HIDROPONIK KABUPATEN MAJENE." Jurnal Abdi Insani 10, no. 3 (2023): 1880–89. http://dx.doi.org/10.29303/abdiinsani.v10i3.1113.

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Smart technology has provided many benefits for all human activities, including hydroponic farming systems. The consistent challenge in the process of cultivating plants using hydroponic systems is the monitoring of partner farmers in managing plant nutrition. Smart technology, utilizing microcontrollers and multisensors, can enhance hydroponic management, particularly concerning the control of nutrient availability, including saturation, water pH, and water availability in the hydroponic system. The purpose of this activity is to address partner-related issues regarding their lack of knowledg

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Zhou, Yuqing, and Wei Xue. "A Multisensor Fusion Method for Tool Condition Monitoring in Milling." Sensors 18, no. 11 (2018): 3866. http://dx.doi.org/10.3390/s18113866.

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Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not an

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LIU, QING (CHARLIE), and HSU-PIN (BEN) WANG. "A case study on multisensor data fusion for imbalance diagnosis of rotating machinery." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 3 (2001): 203–10. http://dx.doi.org/10.1017/s0890060401153011.

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Techniques for machine condition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being increasingly used to monitor machine status. In recent years, the fusion of multisensor data has been applied to diagnose machine faults. In this study, multisensors are used to collect signals of rotating imbalance vibration of a test rig. The characteristic features of each vibration signal are extract

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Gallo, D., C. Landi, and N. Pasquino. "Multisensor Network for Urban Electromagnetic Field Monitoring." IEEE Transactions on Instrumentation and Measurement 58, no. 9 (2009): 3315–22. http://dx.doi.org/10.1109/tim.2009.2022384.

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Noori-Khajavi, A., and R. Komanduri. "On Multisensor Approach to Drill Wear Monitoring." CIRP Annals 42, no. 1 (1993): 71–74. http://dx.doi.org/10.1016/s0007-8506(07)62394-4.

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Lehmann, Ulrike, and Alain Grisel. "Miniature Multisensor Probe for Soil Nutrient Monitoring." Procedia Engineering 87 (2014): 1429–32. http://dx.doi.org/10.1016/j.proeng.2014.11.713.

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Efendi, Rustam, Arjal Tando, Welly Padang, Mulhin Aries, and Herlina Herlina. "Pengembangan Alat Monitoring Suhu Multisensor Berbasis Mikrokontroler." Jurnal Teknik Mesin Indonesia 19, no. 02 (2024): 75–79. http://dx.doi.org/10.36289/jtmi.v19i02.723.

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Penelitian ini mengembangkan sebuah prototipe data logger suhu berbasis Arduino Mega 2560 yang menggunakan satu sensor Adafruit MCP9808 dan lima amplifier termokopel Adafruit MAX31856. Desain data logger ini memungkinkan pengukuran suhu dalam berbagai aplikasi, dengan fokus pada pemantauan suhu ruangan. Prototipe ini dapat digunakan dalam praktikum, penelitian, dan aplikasi industri yang memerlukan pemantauan suhu yang akurat. Data logger suhu ini memiliki potensi aplikasi yang luas dalam berbagai konteks, dengan keunggulan dalam fleksibilitas dan biaya yang terjangkau. Penelitian ini memvalid

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Manurung, Philippians, and Indra Hartarto Tambunan. "Automated Data Acquisition in Monitoring Automatic Composter with Multisensory System." PROSIDING SEMINAR NASIONAL SAINS DATA 4, no. 1 (2024): 1050–59. https://doi.org/10.33005/senada.v4i1.418.

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Artikel ini bertujuan untuk menggambarkan proses akuisisi data dalam pemantauan sistem pengompos otomatis dengan sistem multisensor. Sistem pengomposan konvensional mengandalkan proses katalis bakteri aerob dalam menguraikan bahan organik yang memakan waktu lebih lama hingga proses penguraiannya selesai. Sistem yang diusulkan menggabungkan beberapa sensor seperti aerasi, kelembaban, suhu, dan pH untuk mengendalikan lingkungan pengomposan. Lingkungan yang terkendali akan meningkatkan proses katalisis oleh bakteri mikro, sehingga waktu penguraian akan berkurang dibandingkan dengan proses pengomp

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Teses / dissertações sobre o assunto "Multisensor monitoring"

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Hilmersson, Anette. "Multisensor Stress Monitoring For Non-Stationary Subjects." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28340.

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Monitoring stress in real-time, in a non-laboratory environment can be benecial in several applications. One of these, which have been the motivation for this thesis, is to to perform this measurement during Attention decit hyperactivity disorder (ADHD) diagnosis. Monitoring several physiological responses to internal or external stimuli in a single soft-real-time system is nota solution widely used in an application like this. The thesis starts by studying several stress related responses in detail. Sensors for all of the responses are not implemented nor is it possible toimplement in to the

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Zouba, Valentin Nadia. "Multisensor fusion for monitoring elderly activities at home." Nice, 2010. http://www.theses.fr/2010NICE4001.

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Dans cette thèse, une approche combinant des données issues de capteurs hétérogènes pour la reconnaissance d'activités des personnes âgées à domicile est proposée. Cette approche consiste à combiner les données fournies par des capteurs vidéo avec des données fournies par des capteurs environnementaux pour suivre l'interaction des personnes avec l'environnement. La première contribution est un nouveau modèle de capteur capable de donner une représentation cohérente et efficace des informations fournies par différents types de capteurs physiques. Ce modèle inclue l'incertitude sur la mesure. La

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Ogaja, Clement Surveying &amp Spatial Information Systems Faculty of Engineering UNSW. "A framework in support of structural monitoring by real time kinematic GPS and multisensor data." Awarded by:University of New South Wales. School of Surveying and Spatial Information Systems, 2002. http://handle.unsw.edu.au/1959.4/18662.

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Due to structural damages from earthquakes and strong winds, engineers and scientists have focused on performance based design methods and sensors directly measuring relative displacements. Among the monitoring methods being considered include those using Global Positioning System (GPS) technology. However, as the technical feasibility of using GPS for recording relative displacements has been (and is still being) proven, the challenge for users is to determine how to make use of the relative displacements being recorded. This thesis proposes a mathematical framework that supports the use of R

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Raman, Srinivas. "Condition monitoring of industrial machines using wavelet packets and intelligent multisensor fusion." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/15224.

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Machine condition monitoring is an increasingly important area of research and plays an integral role in the economic competitiveness in many industries. Machine breakdown can lead to many adverse effects including increased operation and maintenance costs, reduced production output, decreased product quality and even human injury or death in the event of a catastrophic failure. As a way to overcome these problems, an automated machine diagnostics scheme may be implemented, which will continuously monitor machine health for the purpose of prediction, detection, and diagnosis of faults and malf

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Binsaeid, Sultan Hassan. "Multisensor Fusion for Intelligent Tool Condition Monitoring (TCM) in End Milling Through Pattern Classification and Multiclass Machine Learning." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_dissertations/7.

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In a fully automated manufacturing environment, instant detection of condition state of the cutting tool is essential to the improvement of productivity and cost effectiveness. In this paper, a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach was developed to investigate the effectiveness of multisensor fusion when machining 4340 steel with multi-layer coated and multi-flute carbide end mill cutter. Feature- and decision-level information fusion models utilizing assorted combinations of sensors were studied against selected ML algorithms and t

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Zanon, Mattia. "Non-Invasive Continuous Glucose Monitoring: Identification of Models for Multi-Sensor Systems." Doctoral thesis, Università degli studi di Padova, 2013. http://hdl.handle.net/11577/3423010.

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Diabetes is a disease that undermines the normal regulation of glucose levels in the blood. In people with diabetes, the body does not secrete insulin (Type 1 diabetes) or derangements occur in both insulin secretion and action (Type 2 diabetes). In spite of the therapy, which is mainly based on controlled regimens of insulin and drug administration, diet, and physical exercise, tuned according to self-monitoring of blood glucose (SMBG) levels 3-4 times a day, blood glucose concentration often exceeds the normal range thresholds of 70-180 mg/dL. While hyperglycaemia mostly affects long-term c

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Truax, Stuart. "A microscale chemical sensor platform for environmental monitoring." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45780.

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The objective of this research is to apply micromachined silicon-based resonant gravimetric sensors to the detection of gas-phase volatile organic compounds (VOCs). This is done in two primary tasks: 1) the optimization and application of silicon disk resonators to the detection of gas-phase VOCs, and 2) the development and application of a novel gravimetric-capacitive multisensor platform for the detection of gas-phase VOCs. In the rst task, the design and fabrication of a silicon-based disk resonator structure utilizing an in-plane resonance mode is undertaken. The resonance character

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Vahidi, Mayamey Farzad. "Improving the water-extent monitoring of Swedish wetlands with open-source satellite data and Google Earth Engine." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Wetlands are essential for controlling the global climate, sustaining the global hydrological cycle, conserving ecological variety, and ensuring human wellbeing. As wetlands are one of the most endangered environments due to land conversion, infrastructure development, and overexploitation, they require constant monitoring. In Sweden, there are 68 sites recognized as wetlands with international importance. The inundated area and the connectivity of the wetlands are affected by climate change. For this reason, we need to better delineate water bodies in these valuable environments. Advances in

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Millin-Chalabi, Gail Rebecca. "Radar multi-temporal and multi-sensor approach to characterise peat moorland burn scars and assess burn scar persistence in the landscape." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/radar-multitemporal-and-multisensor-approach-to-characterise-peat-moorland-burn-scars-and-assess-burn-scar-persistence-in-the-landscape(36288daf-4a05-46e8-9e29-f67c62584fc5).html.

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Peat moorlands represent a nationally significant carbon store. Wildfires in peat moorlands release CO2 into the atmosphere, reducing the carbon store and burn into the seed bank preventing vegetation recovery. Burned areas of bare peat remain, known as ‘burn scars’ which are eroded by freeze thaw and desiccation, then weathered by precipitation and wind to cause discolouration of the water supply. A technique for the systematic monitoring of peat moorland burn scars is essential for informing land management and moorland restoration. Satellite data enables peat moorland burn scars to be monit

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Krallmann, Jens [Verfasser]. "Einsatz eines Multisensors für ein Condition Monitoring von mobilen Arbeitsmaschinen / Jens Krallmann." Aachen : Shaker, 2005. http://d-nb.info/1186587822/34.

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Livros sobre o assunto "Multisensor monitoring"

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NATO, Advanced Study Institute on Data Fusion for Situation Monitoring Incident Detection Alert and Response Management (2003 Yerevan Armenia). Data fusion for situation monitoring, incident detection, alert and response management. IOS Press, 2005.

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2

F, Hutchinson C., and United States. National Aeronautics and Space Administration., eds. Multisensor monitoring of deforestation in the Guinea Highlands of West Africa: Final report. Arizona Remote Sensing Center, University of Arizona, 1990.

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3

International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining (2009 Wuhan, China). International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining: 13-14 October 2009, Wuhan, China. Edited by Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu, and SPIE (Society). SPIE, 2009.

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4

Ozer, Ekin. Multisensory Smartphone Applications in Vibration-Based Structural Health Monitoring. [publisher not identified], 2016.

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5

Chang, Ni-Bin, and Kaixu Bai. Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. Taylor & Francis Group, 2018.

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6

Chang, Ni-Bin, and Kaixu Bai. Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. Taylor & Francis Group, 2020.

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Chang, Ni-Bin, and Kaixu Bai. Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. Taylor & Francis Group, 2018.

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8

Chang, Ni-Bin, and Kaixu Bai. Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. Taylor & Francis Group, 2018.

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9

(Editor), E. Shahbazian, G. Rogova (Editor), and P. Valin (Editor), eds. Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management (NATO Science Series. 3: Computer and Systems Sciences). IOS Press, 2005.

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10

Chang, Ni-Bin, and Kaixu Bai. Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. Taylor & Francis Group, 2018.

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Capítulos de livros sobre o assunto "Multisensor monitoring"

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Onishchenko, I. N. "Multisensing in Chernobyl: The State and Monitoring of Object “Shelter”." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_38.

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2

Umadevi, K. S., S. Murali, P. Pandiaraja, and Thompson Stephan. "Architecture for Multisensor Fusion and Integration for Diabetes Monitoring." In Evolving Role of AI and IoMT in the Healthcare Market. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82079-4_13.

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Liu, Ying, and Zhong-xing Huang. "Recognition of Aerobics Movement Posture Based on Multisensor Movement Monitoring." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94551-0_14.

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Refice, Alberto, Annarita D’Addabbo, Francesco Paolo Lovergine, et al. "Monitoring Flood Extent and Area Through Multisensor, Multi-temporal Remote Sensing: The Strymonas (Greece) River Flood." In Flood Monitoring through Remote Sensing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63959-8_5.

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Bozzano, Francesca, Carlo Esposito, Andrea Fantini, et al. "Multisensor Landslide Monitoring as a Challenge for Early Warning: From Process Based to Statistic Based Approaches." In Advancing Culture of Living with Landslides. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53487-9_3.

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Gascueña, José Manuel, Antonio Fernández-Caballero, Elena Navarro, Juan Serrano-Cuerda, and Francisco Alfonso Cano. "Agent-Based Development of Multisensory Monitoring Systems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21344-1_47.

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Zhang, Zhifen, and Shanben Chen. "Data-Driven Feature Selection for Multisensory Quality Monitoring in Arc Welding." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18997-0_34.

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Almer, Alexander, Anna Weber, Lucas Paletta, et al. "Multisensory Wearable Vital Monitoring System for Military Training, Exercise and Deployment." In Advances in Neuroergonomics and Cognitive Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80285-1_57.

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Castillo, José Carlos, Angel Rivas-Casado, Antonio Fernández-Caballero, María T. López, and Rafael Martínez-Tomás. "A Multisensory Monitoring and Interpretation Framework Based on the Model–View–Controller Paradigm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21344-1_46.

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Sahu, Neelesh Kumar, Atul B. Andhare, and Abhay Khalatkar. "Tool Condition Monitoring in End Milling of Ti-6Al-4V Using Multisensory Approach." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3746-2_7.

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Trabalhos de conferências sobre o assunto "Multisensor monitoring"

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Chen, Yajiang, Shuwang Chen, and Yifei Ma. "Elderly monitoring system based on multisensor information fusion." In Advanced Sensor Systems and Applications XIV, edited by Minghong Yang, Xinyu Fan, Jianzhong Zhang, and Chang-Seok Kim. SPIE, 2024. http://dx.doi.org/10.1117/12.3035780.

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Nagler, Thomas, Gabriele Schwaizer, Lucia Felbauer, et al. "Monitoring Physical Snow Properties in Alpine Regions using Multisensor Data." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640633.

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Asen, Sebastian, Carola Has, Rainer Poeschl, and Stefan Kunze. "Drone-Based and 5G-Connected Multisensor Platform for Forest Monitoring." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705196.

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Terziyski, Atanas, Stoyan Tenev, and Vedrin Jeliazkov. "A Multisensor Environmental Monitoring Device." In 2020 21st International Symposium on Electrical Apparatus & Technologies (SIELA). IEEE, 2020. http://dx.doi.org/10.1109/siela49118.2020.9167052.

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Tiano, Antonio. "Ship Monitoring and Control by Multisensor Data Fusion." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59429.

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This paper discusses the applicability of ship monitoring and control by multisensor data fusion After introducing some critical issues in the area of ship motion control, an analysis is presented concerning the choice of different architecture of ship monitoring and prediction systems. A simulation study is presented concerning the design of a multisensor data fusion system for improving estimate of ship position based on low-cost onboard hardware.

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Sim, Sung-Han, Soojin Cho, Jong-Woong Park, and Hyunjun Kim. "Multisensor fusion for system identification." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Jerome P. Lynch, Kon-Well Wang, and Hoon Sohn. SPIE, 2014. http://dx.doi.org/10.1117/12.2047288.

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de Vries, Thorsten, Ralf Paetzold, Kai Jantos, Eberhard Voss, and Angelika Anders. "Mobile multisensor system for environmental monitoring." In Photonics East '99, edited by Tuan Vo-Dinh and Robert L. Spellicy. SPIE, 1999. http://dx.doi.org/10.1117/12.372860.

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Caduff, Andreas, Marc Donath, Mark Talary, et al. "Multisensor Concept for non-invasive Physiological Monitoring." In 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007. IEEE, 2007. http://dx.doi.org/10.1109/imtc.2007.379070.

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Ganti, Raghu Kiran, Soundararajan Srinivasan, and Aca Gacic. "Multisensor Fusion in Smartphones for Lifestyle Monitoring." In 2010 International Conference on Body Sensor Networks (BSN). IEEE, 2010. http://dx.doi.org/10.1109/bsn.2010.10.

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Novak, Milan, Milos Prokysek, Petr Dolezal, et al. "Multisensor UAV System for the Forest Monitoring." In 2020 10th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2020. http://dx.doi.org/10.1109/acit49673.2020.9208993.

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