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Zeitschriftenartikel zum Thema „Multisensor monitoring“

Autor: Grafiati

Veröffentlicht am 14. Dezember 2024

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1

Martinez-Olmos, A., I. M. Pérez de Vargas-Sansalvador, A. J. Palma, J. Banqueri, M. D. Fernández-Ramos und L. F. Capitán-Vallvey. „Multisensor probe for soil monitoring“. Sensors and Actuators B: Chemical 160, Nr. 1 (Dezember 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, Nr. 10 (19.05.2021): 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 practical examples in different application fields: food and pharmaceutical production, medical diagnostics, and ecological monitoring. The conclusion summarizes trends and prospects of the multisensory approach to optical spectral analysis.

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Sanjaya, Muhammad Fahyu, Ummu Kalsum und Andi Rosman N. „PENERAPAN TEKNOLOGI CERDAS PENYIRAMAN TANAMAN HIDROPONIK BERBASIS MIKROKONTROLER DAN MULTISENSOR PADA PEMBUDIDAYA TANAMAN HIDROPONIK KABUPATEN MAJENE“. Jurnal Abdi Insani 10, Nr. 3 (13.09.2023): 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 knowledge and skills in managing businesses aided by smart technology such as microcontrollers and multisensors and to improve the economic viability of partner businesses by producing nutritious and high-quality vegetables. The method used in this community engagement activity involves initial observation at the partner's business location, theoretical instruction to enhance partner knowledge, and finally, training in the creation and operation of smart technology involving microcontrollers and multisensors in the partner's hydroponic area. Questionnaires were distributed to assess the initial conditions before the training and to measure the success of the training after its completion. The initial observation showed a lack of knowledge about smart technology using microcontrollers and multisensors. Based on this, theoretical training was conducted to improve partner knowledge about smart technology using microcontrollers and multisensors. The theoretical content included 1) technology and innovation in hydroponic plant nutrition, 2) an introduction to microcontrollers and sensors in agriculture/horticulture technology, and 3) a demonstration of monitoring physical parameters and nutrient content of hydroponic plants using the Wokwi simulator. Subsequently, a simulation of microcontroller and multisensory device use was carried out, resulting in an enhancement of partner skills in using microcontrollers and multisensors. The improvement in partner knowledge and skills after participating in this training is evident from the questionnaire results, and partner satisfaction with the conducted training is also evident. The conclusion of this community engagement activity is that partners have gained knowledge and skills after participating in the training on the application of smart technology for microcontroller-based irrigation of hydroponic plants with multisensor systems in hydroponic plant cultivation.

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Zhou, Yuqing, und Wei Xue. „A Multisensor Fusion Method for Tool Condition Monitoring in Milling“. Sensors 18, Nr. 11 (10.11.2018): 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 analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time–frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson’s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.

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LIU, QING (CHARLIE), und 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, Nr. 3 (Juni 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 extracted with an auto-regressive (AR) model. Data fusion is then implemented with a Cascade-Correlation (CC) neural network. The results clearly show that multisensor data-fusion-based diagnostics outperforms the single sensor diagnostics with statistical significance.

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Gallo, D., C. Landi und N. Pasquino. „Multisensor Network for Urban Electromagnetic Field Monitoring“. IEEE Transactions on Instrumentation and Measurement 58, Nr. 9 (September 2009): 3315–22. http://dx.doi.org/10.1109/tim.2009.2022384.

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Noori-Khajavi, A., und R. Komanduri. „On Multisensor Approach to Drill Wear Monitoring“. CIRP Annals 42, Nr. 1 (1993): 71–74. http://dx.doi.org/10.1016/s0007-8506(07)62394-4.

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Lehmann, Ulrike, und 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 und Herlina Herlina. „Pengembangan Alat Monitoring Suhu Multisensor Berbasis Mikrokontroler“. Jurnal Teknik Mesin Indonesia 19, Nr. 02 (24.09.2024): 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 memvalidasi bahwa sensor Adafruit MCP9808 dan Adafruit amplifier MAX31856 memiliki tingkat akurasi yang tinggi dalam pengukuran suhu. Hasil pengukuran suhu ruangan menunjukkan suhu sekitar 29 ℃. Desain data logger ini menjadi alternatif yang kuat bagi peneliti yang memiliki keterbatasan dalam pengadaan data logger standar hasil pabrikan. Prototipe ini memiliki potensi untuk menjadi alat yang sangat berguna dalam eksperimen dan penelitian ilmiah di berbagai aplikasi yang memerlukan pemantauan suhu yang akurat.

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Manurung, Philippians, und Indra Hartarto Tambunan. „Automated Data Acquisition in Monitoring Automatic Composter with Multisensory System“. PROSIDING SEMINAR NASIONAL SAINS DATA 4, Nr. 1 (10.10.2024): 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 pengomposan konvensional. Sistem multisensor ini dioperasikan menggunakan kit sistem mikrokontroler seperti Raspberry Pi 3 dan Arduino Uno. Sistem ini akan memanfaatkan pemantauan proses dekomposisi secara real-time melalui pembacaan sensor dan secara otomatis mengirim data ke server untuk ditampilkan. Data yang diperoleh dari semua sensor akan ditampilkan menggunakan antarmuka pengguna berbasis web dan memberi tahu pengguna untuk mengambil tindakan jika diperlukan. Hasilnya menunjukkan bahwa sistem dengan sistem multisensor dapat mengakuisisi data dengan baik dan pemantauan real-time dari proses penguraian dapat dipantau secara komprehensif.

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Tibbetts, Jake, Bethany L. Goldblum, Christopher Stewart und Arman Hashemizadeh. „Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks“. Journal of Nuclear Engineering 3, Nr. 4 (22.09.2022): 243–62. http://dx.doi.org/10.3390/jne3040014.

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Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

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Xie, Nan, Lin Chen, Beirong Zheng und Xinfang Liu. „A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System“. Advances in Mechanical Engineering 6 (01.01.2014): 634107. http://dx.doi.org/10.1155/2014/634107.

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Multisensor improves the accuracy of machine tool condition monitoring system, which provides the critical feedback information to the manufacture process controller. Multisensor monitoring system needs to collect abundant data to employ attribute extraction, election, reduction, and classification to form the decision knowledge. A machine tool condition monitoring system has been built and the method of tool condition decision knowledge discovery is also presented. Multiple sensors include vibration, force, acoustic emission, and main spindle current. The novel approach engages rough theory as a knowledge extraction tool to work on the data that are obtained from both multisensor and machining parameters and then extracts a set of minimal state identification rules encoding the preference pattern of decision making by domain experts. By means of the knowledge acquired, the tool conditions are identified. A case study is presented to illustrate that the approach produces effective and minimal rules and provides satisfactory accuracy.

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Caduff, Andreas, Mattia Zanon, Pavel Zakharov, Martin Mueller, Mark Talary, Achim Krebs, Werner A. Stahel und Marc Donath. „First Experiences With a Wearable Multisensor in an Outpatient Glucose Monitoring Study, Part I: The Users’ View“. Journal of Diabetes Science and Technology 12, Nr. 3 (14.01.2018): 562–68. http://dx.doi.org/10.1177/1932296817750932.

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Background: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. Methods: To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient. Results: Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase. Conclusions: The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.

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Chen, Lequn, Xiling Yao, Kui Liu, Chaolin Tan und Seung Ki Moon. „MULTISENSOR FUSION-BASED DIGITAL TWIN IN ADDITIVE MANUFACTURING FOR IN-SITU QUALITY MONITORING AND DEFECT CORRECTION“. Proceedings of the Design Society 3 (19.06.2023): 2755–64. http://dx.doi.org/10.1017/pds.2023.276.

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AbstractEarly detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser-directed energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defect correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.

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Rodriguez-Mendez, Maria Luz, Celia García-Hernandez, Cristina Medina-Plaza, Cristina García-Cabezón und Jose Antonio de Saja. „Multisensor systems based on phthalocyanines for monitoring the quality of grapes“. Journal of Porphyrins and Phthalocyanines 20, Nr. 08n11 (August 2016): 889–94. http://dx.doi.org/10.1142/s1088424616500796.

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Arrays of phthalocyanine-based sensors with complementary activity have been used to develop voltammetric electronic tongues. Such systems have demonstrated to be useful in enology for the evaluation of quality of wines in different production stages, from grapes to bottles. In this paper, the state of the art of multisensor systems based on phthalocyanines dedicated to the analysis of musts (juices obtained from crushed grapes) is described. Such multisensor systems cover different types of sensors from simple Carbon Paste Electrodes, to sophiticated nanostructured sensors, including Langmuir–Blodgett or Layer by Layer thin films and biomimetic biosensors where phthalocyanines play a crucial role as electron mediator between enzymes and electrodes. In all cases, multisensor systems based on phthalocyanines have been able to discriminate musts prepared from different varieties of grapes. The performance of these systems can be improved by combining non-specific sensors with biosensors containing enzymes selective to phenols. In this case, excellent relationships have been found between the responses provided by the array and the content in phenols and acids provided by traditional chemical analysis.

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Reyana, A., und P. Vijayalakshmi. „Multisensor Information Fusion for Condition Based Environment Monitoring“. Intelligent Automation & Soft Computing 36, Nr. 1 (2023): 1013–25. http://dx.doi.org/10.32604/iasc.2023.032538.

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Wägele, J. Wolfgang, Paul Bodesheim, Sarah J. Bourlat, Joachim Denzler, Michael Diepenbroek, Vera Fonseca, Karl-Heinz Frommolt et al. „Towards a multisensor station for automated biodiversity monitoring“. Basic and Applied Ecology 59 (März 2022): 105–38. http://dx.doi.org/10.1016/j.baae.2022.01.003.

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Hu, W., A. Starr und A. Leung. „A multisensor-based system for manufacturing process monitoring“. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 215, Nr. 9 (September 2001): 1165–75. http://dx.doi.org/10.1243/0954405011519204.

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Zahrah, Syifa Amira, Ade Silvia Handayani und Ali Nurdin. „Implementasi Support Vector Machine Pada Alat Monitoring Kecelakaan Dengan Intelligent Transport System“. Building of Informatics, Technology and Science (BITS) 4, Nr. 2 (22.09.2022): 562–69. http://dx.doi.org/10.47065/bits.v4i2.1974.

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The implementation of intelligent transportation systems will produce a large amount of data. The resulting data is critical in the design and implementation of ITS in the transportation system. This study discusses the performance of the Support Vector Machine algorithm on an accident monitoring tool by utilizing the Intelligent Transportation System that works in real-time using an Android-based application. This experiment simulates accident monitoring with a multisensor accident monitoring device. Multisensor technology consists of MPU 6050 sensor, sound sensor, vibration sensor, and camera. In an experiment, the measured variables are location, slope, accuracy, and time of the traffic accident monitoring system. The results of monitoring traffic accidents in testing using the Support Vector Machine algorithm can work well by classifying data based on the type of accident.

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Zhang, Xin, Longfu Deng und Na Li. „Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things“. Computational Intelligence and Neuroscience 2022 (18.05.2022): 1–13. http://dx.doi.org/10.1155/2022/2099268.

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The international community has paid extensive attention to the numerous engineering problems faced by karst areas caused by the increasingly frequent human activities. China has a wide variety of karst forms. Among them, carbonate karst is the most widely distributed, and the development of carbonate karst is relatively strong in many areas. Countless property losses are caused by karst disasters every year. This article aims to study the real-time monitoring and timely early warning of karst collapse through the use of multisensor Internet of Things technology. To this end, this article proposes an improved method for multisensor data fusion. It optimizes and improves the transmission and delivery efficiency of its data. This makes the improved multisensor more in line with the research content of this article in terms of monitoring efficiency. At the same time, related experiments and analyses are designed to compare and analyze the karst collapse and the monitoring efficiency of the sensor. The experimental results of this article show that after the improvement, the anti-interference ability of the monitoring system is increased by 34%. The frequency of early warning has also been improved by 24%, which has high practical application value.

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Zhou, Xinliang, und Shantian Wen. „Monitoring and Analysis of Physical Exercise Effects Based on Multisensor Information Fusion“. Journal of Sensors 2022 (10.01.2022): 1–12. http://dx.doi.org/10.1155/2022/4199985.

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In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.

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Anand, Inder S., W. H. Wilson Tang, Barry H. Greenberg, Niranjan Chakravarthy, Imad Libbus und Rodolphe P. Katra. „Design and Performance of a Multisensor Heart Failure Monitoring Algorithm: Results From the Multisensor Monitoring in Congestive Heart Failure (MUSIC) Study“. Journal of Cardiac Failure 18, Nr. 4 (April 2012): 289–95. http://dx.doi.org/10.1016/j.cardfail.2012.01.009.

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Zhang, Zhonglin, Jiajia Xu, Chenggen Peng und Yuping Chen. „Information Collection, Analysis, and Monitoring System of Children’s Physical Training Based on Multisensor“. Applied Bionics and Biomechanics 2022 (12.05.2022): 1–10. http://dx.doi.org/10.1155/2022/6455841.

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In order to obtain more children’s physical training information and improve the accuracy of children’s physical training monitoring, a multisensor-based children’s physical training information collection, analysis, and monitoring system is proposed. In the process of physical training and sports training, people’s physical training information collection is directly related to the level and effectiveness of physical training. With the combination of multisensor concept and sports training information collection, it can collect the key index data of sports mobilization in real time with the help of multiple sensors and information technology. Taking children’s physical training as the object, this paper designs a multisensor physical training data information acquisition terminal, collects different training characteristic data with the help of multisensor equipment, and then comprehensively analyzes and monitors the physical information with the help of certain fusion technology, so as to construct a human posture recognition algorithm based on children’s physical training information acquisition. Support vector machine and decision tree are used to classify children’s different physical exercise states, and a relatively perfect algorithm architecture of human posture recognition is constructed. The results show that for two decision trees, each decision tree is trained with a total of 675 groups of data, and a total of 342 groups of data are verified and pruned. The two decision trees take 7.17 s and 7.32 s to complete the training process, respectively. It can be seen that when the number of training groups is equal, the training time of the two placement methods is close, so it can be considered that the two placement methods have little effect on the training speed of decision tree. The experimental data show that the design of children’s physical training monitoring system in this paper has a certain market value.

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Huang, S., und F. Siegert. „ENVISAT multisensor data for fire monitoring and impact assessment“. International Journal of Remote Sensing 25, Nr. 20 (Oktober 2004): 4411–16. http://dx.doi.org/10.1080/01431160412331269670.

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RYMARCZYK, Tomasz. „Area monitoring using the ERT method with multisensor electrodes“. PRZEGLĄD ELEKTROTECHNICZNY 1, Nr. 1 (05.01.2019): 155–58. http://dx.doi.org/10.15199/48.2019.01.39.

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Hautefeuille, Mathieu, Conor O’Mahony, Brendan O’Flynn, Krimo Khalfi und Frank Peters. „A MEMS-based wireless multisensor module for environmental monitoring“. Microelectronics Reliability 48, Nr. 6 (Juni 2008): 906–10. http://dx.doi.org/10.1016/j.microrel.2008.03.007.

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Ogaja, Clement, Jinling Wang, Chris Rizos und James Brownjohn. „Multivariate Monitoring with GPS Observations and Auxillary Multisensor Data“. GPS Solutions 5, Nr. 4 (April 2002): 58–69. http://dx.doi.org/10.1007/pl00012912.

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Oprea, A., J. Courbat, D. Briand, N. Bârsan, U. Weimar und N. F. de Rooij. „Environmental monitoring with a multisensor platform on polyimide foil“. Sensors and Actuators B: Chemical 171-172 (August 2012): 190–97. http://dx.doi.org/10.1016/j.snb.2012.02.095.

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Bott, B., und T. A. Jones. „The use of multisensor systems in monitoring hazardous atmospheres“. Sensors and Actuators 9, Nr. 1 (Februar 1986): 19–25. http://dx.doi.org/10.1016/0250-6874(86)80003-8.

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Gordon, Grant A., und Clark A. Moose. „Multisensor monitoring of gear tooth fatigue for predictive diagnostics“. Tribotest 4, Nr. 4 (Juni 1998): 393–406. http://dx.doi.org/10.1002/tt.3020040406.

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Rani, Sonia, Mohit Sahni, Lalit Chauhan, Ram Mohan Mehra, Sunil Chauhan, Pankaj Gupta und Piyush Kumar Gupta. „Arduino Based Multisensor Smart Monitoring Device for Electrical Appliances“. Macromolecular Symposia 407, Nr. 1 (Februar 2023): 2100363. http://dx.doi.org/10.1002/masy.202100363.

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32

Qu, Chong, Zhiguo Zhou, Zhiwen Liu, Shuli Jia, Liyong Ma und Mary Immaculate Sheela L. „A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment“. Advances in Materials Science and Engineering 2022 (11.08.2022): 1–6. http://dx.doi.org/10.1155/2022/4632137.

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Great changes have been brought about by the coastal environment when the economy develops rapidly. Coastal environmental monitoring is the basis and technical guarantee for coastal environmental protection supervision and management. It is one of the important tasks to detect and timely discover coastal seawater anomalies. Usually, a single sensor cannot determine whether the coastal environment or ship operation is an anomaly. Recently, an unmanned surface vehicle for coastal environment monitoring was developed, and stacked autoencoders are used for seawater anomaly detection using multisensor data fusion methods. The multisensor data of pH, conductivity, and ammonia nitrogen are employed to judge the anomaly of seawater. The mean, standard deviation, mean square root, and normalized power spectrum features of multisensor data are extracted, and a stacked autoencoder is employed to fuse these features for anomaly detection. The proposed method is feasible and effective for anomaly detection of coastal water quality and ship operation. Compared with other commonly used methods, the proposed method has a higher recall, precision, and F1 score performance.

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Lin, Jen-Yung, Huan-Liang Tsai und Wen-Chi Sang. „Implementation and Performance Evaluation of Integrated Wireless MultiSensor Module for Aseptic Incubator of Cordyceps militaris“. Sensors 20, Nr. 15 (31.07.2020): 4272. http://dx.doi.org/10.3390/s20154272.

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This paper originally proposes a wireless multisensor module with illuminance, temperature, relative humidity (RH) and carbon dioxide (CO2) sensors in an aseptic jar incubator for a solid-state fermentation (SSF) of Cordyceps militaris culture. The light intensity, ambient temperature, RH and CO2 are the critical cultivation factors of C. militaris. First, these sensors are integrated in a multisensor platform which is installed inside a lid and covered with a high-efficiency particulate air (HEPA) membrane of class H14 for sterilization of bacteria and viruses. The observations of sensors are then transmitted by a wireless XBee network where the slave sensor node is fixed at the top of jar lid and the master radio node receives data and uploads to an on-site monitoring node. The acquired information is further transmitted to an iCloud database and displayed in a web-based monitoring system. The results illustrate the proposed wireless multisensor module was validated with sufficient accuracy, reliable confidence and well-tolerance for C. militaris cultivation biotechnology under aseptic conditions.

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Dai, Yibo. „Endurance Monitoring Method for Rock Climbers Based on Multisensor FDA Model“. Mathematical Problems in Engineering 2022 (31.08.2022): 1–15. http://dx.doi.org/10.1155/2022/2683399.

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The quality of sports performance mainly depends on the scientific degree of sports training. The best monitoring and control of the whole process of sports training is an important guarantee for improving the scientific degree of training. For the scientific monitoring of sports training process, many scholars have carried out research from many aspects. Current research still finds that athletes still have many problems with the protection of their own physical endurance. (1) The monitoring of the sports training process by coaches and athletes is still mainly spontaneous. The monitoring of athletes has not yet formed a system. (2) At present, the monitoring of athletes is mainly carried out from a single discipline or a certain field, and there is no systematic monitoring system. (3) There are many common problems in the monitoring items of the training process in the existing research. Existing studies have relatively little content on specific characteristics. In this paper, a dynamic monitoring system for the training process of elite rock climbers is established, and a training operation mode for rock climbers is established. (1) This paper sorts out the various subsystems of the training system. The monitoring system of sports training process mainly includes target system, personnel organization system, and software and hardware system. In addition, the sports training system proposed in this paper also includes monitoring content system, implementation system, and evaluation system. The sports training process monitoring system also includes a subsystem for monitoring the development of athletic ability of athletes. We take on the training load condition monitoring subsystem; daily physical function state and psychological state monitoring subsystem; and physical and mental health monitoring subsystem. (2) In addition, this paper establishes an operation mode dominated by “scientific research leaders.” The model includes three parts: “Administrative Managers, Scientific Research Leaders, and Coaches.” (3) Finally, this paper establishes five basic guiding ideologies for the evaluation of training process monitoring results. This paper establishes three reference standards for the evaluation of monitoring indicators.

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Andrizal, Andrizal, Lifwarda, Yul Antonisfia, Zulharbi und Yuhefizar. „Sistem Kontrol Berbasis Pemrograman LabVIEW MyRIO untuk Monitoring Kualitas Udara Dalam Ruangan“. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, Nr. 5 (30.10.2020): 930–36. http://dx.doi.org/10.29207/resti.v4i5.2391.

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A multisensor control system based on the LabVIEW myRIO programming has been created for monitoring indoor air quality. The purpose of this research is to create a system to monitor and control the levels of CO and CO2 in the room so that it remains within the threshold of healthy air and does not endanger users. The research phase began with the manufacture of a multisensor circuit and a relay module for the air purifier system and connected to the input and output ports of the myRIO module as a processor programmed with LabVIEW. The process of testing the multisensor response and the activation response of the air purifier on-off is carried out in open areas and indoors by adding artificial air pollution. Besides, air quality control and monitor is also carried out when the level of CO or CO2 gas exceeds the threshold by increasing the number of users and set the air conditioner activation. From the results and data analysis, it was found that the system could be used as a monitor and control the indoor air quality as expected. The range of CO sensor readings is 7.46 ppm - 27.65 ppm and CO2 296.8 ppm - 1190.5 ppm. Air purifier on-off control response time to change of CO and CO2 are 7 and 6 seconds. The air purifier system is able to clean indoor air with a long activation time depending on the number users and the room air conditioner activation settings.

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Zanon, Mattia, Martin Mueller, Pavel Zakharov, Mark S. Talary, Marc Donath, Werner A. Stahel und Andreas Caduff. „First Experiences With a Wearable Multisensor Device in a Noninvasive Continuous Glucose Monitoring Study at Home, Part II: The Investigators’ View“. Journal of Diabetes Science and Technology 12, Nr. 3 (16.11.2017): 554–61. http://dx.doi.org/10.1177/1932296817740591.

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Background: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. Method: To test previous findings also in uncontrolled conditions, a long term at home study has been organized to collect multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking multisensor data to glucose applied to estimate glucose levels noninvasively. Results: The algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone AR+BR. Conclusions: The multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.

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Faruolo, Mariapia, Alfredo Falconieri, Nicola Genzano, Teodosio Lacava, Francesco Marchese und Nicola Pergola. „A Daytime Multisensor Satellite System for Global Gas Flaring Monitoring“. IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–17. http://dx.doi.org/10.1109/tgrs.2022.3143167.

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Bignami, Christian, Stefano Corradini, Luca Merucci, Marcello de Michele, Daniel Raucoules, Gianfilippo De Astis, Salvatore Stramondo und Juan Piedra. „Multisensor Satellite Monitoring of the 2011 Puyehue-Cordon Caulle Eruption“. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, Nr. 7 (Juli 2014): 2786–96. http://dx.doi.org/10.1109/jstars.2014.2320638.

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39

Gade, Martin, Vittorio Barale und Helen M. Snaith. „Multisensor monitoring of plume dynamics in the northwestern Mediterranean Sea“. Journal of Coastal Conservation 9, Nr. 1 (2003): 91. http://dx.doi.org/10.1652/1400-0350(2003)009[0091:mmopdi]2.0.co;2.

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40

Lingfei Mo, Shaopeng Liu, R. X. Gao, D. John, J. W. Staudenmayer und P. S. Freedson. „Wireless Design of a Multisensor System for Physical Activity Monitoring“. IEEE Transactions on Biomedical Engineering 59, Nr. 11 (November 2012): 3230–37. http://dx.doi.org/10.1109/tbme.2012.2208458.

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41

Kim, Miae, Jungho Im, Hyangsun Han, Jinwoo Kim, Sanggyun Lee, Minso Shin und Hyun-Cheol Kim. „Landfast sea ice monitoring using multisensor fusion in the Antarctic“. GIScience & Remote Sensing 52, Nr. 2 (04.03.2015): 239–56. http://dx.doi.org/10.1080/15481603.2015.1026050.

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42

Li, Deguang, Tianhao Wu, Xiaohui Li, Qiurui He und Zhanyou Cui. „A Wireless Multisensor Node for Long-Term Environmental Parameters Monitoring“. Journal of Electrical and Computer Engineering 2020 (28.12.2020): 1–12. http://dx.doi.org/10.1155/2020/8872711.

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Environmental quality is a great concern to everyone, in order to realize the collection, upload, management, and visualization of parameters of atmospheric environment in real time. We propose a cheap, low-power, and fast deployment wireless sensor node for environmental monitoring, consisting of STM32 MCU, ESP8266, light sensor, rain sensor, UV sensor, seven-in-one sensor (including temperature, humidity, PM2.5, PM10, CO2, formaldehyde, and TVOC), and solar automatic tracking module. A customized μC/OS-III runs on the node, which controls the transmission of environment parameters collected by each sensor to the cloud server through the wireless network, and then the server receives, stores, and visualizes the data. In actual test, the node collects data once an hour, and the running power of the node is low and stable. Experimental results show that the node could achieve accurate collection and transmission and display the environmental data, and solar automatic tracking module could meet long-term running of the node in the night and continuous rainy days.

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Andò, B., S. Baglio, V. Marletta und S. Medico. „A smart multisensor system for volcanic ash fall-out monitoring“. Sensors and Actuators A: Physical 202 (November 2013): 13–22. http://dx.doi.org/10.1016/j.sna.2013.03.027.

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44

Jetzki, S., M. Weinzierl, I. Krause, S. Hahne, H. Rehbaum, M. Kiausch, I. Kozubek et al. „A Multisensor Implant for Continuous Monitoring of Intracranial Pressure Dynamics“. IEEE Transactions on Biomedical Circuits and Systems 6, Nr. 4 (August 2012): 356–65. http://dx.doi.org/10.1109/tbcas.2012.2183131.

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45

Choi, Subin, Dae Jung Kim, Yun Young Choi, Kyeonghwan Park, Sung-Woo Kim, Sung Hun Woo und Jae Joon Kim. „A Multisensor Mobile Interface for Industrial Environment and Healthcare Monitoring“. IEEE Transactions on Industrial Electronics 64, Nr. 3 (März 2017): 2344–52. http://dx.doi.org/10.1109/tie.2016.2626239.

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46

Andò, Bruno, Salvatore Baglio und Vincenzo Marletta. „A Smart Multisensor System for the Ash Fall-Out Monitoring“. Procedia Engineering 47 (2012): 766–69. http://dx.doi.org/10.1016/j.proeng.2012.09.260.

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47

Sousa, Daniel, und Christopher Small. „Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models“. Remote Sensing 11, Nr. 2 (18.01.2019): 181. http://dx.doi.org/10.3390/rs11020181.

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Rice is the staple food for more than half of humanity. Accurate prediction of rice harvests is therefore of considerable global importance for food security and economic stability, especially in the developing world. Landsat sensors have collected coincident thermal and optical images for the past 35+ years, and so can provide both retrospective and near-realtime constraints on the spatial extent of rice planting and the timing of rice phenology. Thermal and optical imaging capture different physical processes, and so provide different types of information for phenologic mapping. Most analyses use only one or the other data source, omitting potentially useful information. We present a novel approach to the mapping and monitoring of rice agriculture which leverages both optical and thermal measurements. The approach relies on Temporal Mixture Models (TMMs) derived from parallel Empirical Orthogonal Function (EOF) analyses of Landsat image time series. Analysis of each image time series is performed in two stages: (1) spatiotemporal characterization, and (2) temporal mixture modeling. Characterization evaluates the covariance structure of the data, culminating in the selection of temporal endmembers (EMs) representing the most distinct phenological cycles of either vegetation abundance or surface temperature. Modeling uses these EMs as the basis for linear TMMs which map the spatial distribution of each EM phenological pattern across study area. The two metrics we analyze in parallel are (1) fractional vegetation abundance (Fv) derived from spectral mixture analysis (SMA) of optical reflectance, and (2) land surface temperature (LST) derived from brightness temperature (Tb). These metrics are chosen on the basis of being straightforward to compute for any (cloud-free) Landsat 4-8 image in the global archive. We demonstrate the method using a 90 × 120 km area in the Sacramento Valley of California. Satellite Tb retrievals are corrected to LST using a standardized atmospheric correction approach and pixelwise fractional emissivity estimates derived from SMA. LST and Tb time series are compared to field station data in 2016 and 2017. Uncorrected Tb is observed to agree with the upper bound of the envelope of air temperature observations to within 3 °C on average. As expected, LST estimates are 3 to 5 °C higher. Soil T, air T, Tb and LST estimates can all be represented as linear transformations of the same seasonal cycle. The 3D temporal feature spaces of Fv and LST clearly resolve 5 and 7 temporal EM phenologies, respectively, with strong clustering distinguishing rice from other vegetation. Results from parallel EOF analyses of coincident Fv and LST image time series over the 2016 and 2017 growing seasons suggest that TMMs based on single year Fv datasets can provide accurate maps of crop timing, while TMMs based on dual year LST datasets can provide comparable maps of year-to-year crop conversion. We also test a partial-year model midway through the 2018 growing season to illustrate a potential real-time monitoring application. Field validation confirms the monitoring model provides an upper bound estimate of spatial extent and relative timing of the rice crop accurate to 89%, even with an unusually sparse set of usable Landsat images.

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Antonini, Andrea, Samantha Melani, Alberto Ortolani, Maurizio Pieri und Bernardo Gozzini. „Qualitative weather radar mosaic in a multisensor rainfall monitoring approach“. Journal of Applied Remote Sensing 6, Nr. 1 (12.09.2012): 063572–1. http://dx.doi.org/10.1117/1.jrs.6.063572.

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Müller, J., F. Kaufmann, M. Dandel, K. Kalanaki, M. Hummel, M. Bettmann, H. Bieda, F. Speroni und R. Hetzer. „Non-invasive Rejection Monitoring with a New Implantable Multisensor Device.“ Transplantation 67, Nr. 7 (April 1999): S267. http://dx.doi.org/10.1097/00007890-199904150-01065.

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Gade, M., V. Barale und H. M. Snaith. „Multisensor monitoring of plume dynamics in the Northwestern Mediterranean Sea“. Journal of Coastal Conservation 9, Nr. 2 (September 2003): 190. http://dx.doi.org/10.1007/bf02838090.

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