Journal articles: 'Multisensor'

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Zenevich, A. O., S. V. Zhdanovich, H. V. Vasilevski, A. A. Lagutik, T. G. Kovalenko, and T. M. Lukashik. "Research of multisensor characteristics based on optical fiber." Doklady BGUIR 19, no. 1 (February 23, 2021): 70–78. http://dx.doi.org/10.35596/1729-7648-2021-19-1-70-78.

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The research results of multisensors based on optical fiber, the principle of which is to change the conditions of propagation of optical radiation in the optical fiber in the places where macro-bends are formed at the points of impact, are presented in the paper. The formation of macro-bends leads to an additional attenuation of the power of optical radiation propagating through the optical fiber. A single-mode optical fiber was used with the parameters, which are supported by numerous manufacturers and comply with the recommendations of ITU-T G.655. The measurements were carried out for four wavelengths of optical radiation (1310, 1490, 1550, 1625 nm), corresponding to the transparency windows of the optical loss spectrum of the optical fiber. Using optical reflectometry methods, it was determined that the amount of attenuation of optical radiation of each macro-bend formed at the point of action of the multisensor does not depend on the number of simultaneously formed macro-bends and also does not depend on the location of the point of action along the length of the multisensor. The dependences of the attenuation of the optical radiation power introduced by the macro-bends of the optical fiber on the radius, length, or angle of the macro-bends formed at the multisensory impact points are determined experimentally. The obtained dependences also allow one to determine the optimal parameters of the formed macro-bends of the multisensor to obtain the maximum range of attenuation change for each value of the wavelength. The values of the minimum distance between the impact points, the maximum number of impact points and the optimal values of the radius and angle of the optical fiber macro-bend at the impact points are determined. The results obtained provide opportunities to continue the development of multisensors that allow us to receive information about parameters from several impact points, that are located on a single optical fiber, simultaneously.

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Lin, Tsun-Kuo. "PCA/SVM-Based Method for Pattern Detection in a Multisensor System." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6486345.

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This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine- (SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multiple sensing devices when concurrently detecting different patterns. Therefore, to resolve this issue, this study proposes a novel framework for establishing indicators and corresponding thresholds to identify patterns in the system; it employs a feature-based scheme that integrates principal component analysis (PCA) with an SVM for effectively detecting patterns in the system. Experiments were conducted using a tactile and optical measurement system. The experimental results demonstrated that the proposed method can effectively identify patterns in multisensor systems by using a feature-based algorithm that combines PCA and SVM classification for detecting various patterns. Moreover, the proposed framework established alarm indicators and corresponding thresholds that can be used for pattern detection.

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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 (September 13, 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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Bogomolov, Andrey. "Developing Multisensory Approach to the Optical Spectral Analysis." Sensors 21, no. 10 (May 19, 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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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 (June 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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Han, Youkyung, Jaewan Choi, Jinha Jung, Anjin Chang, Sungchan Oh, and Junho Yeom. "Automated Coregistration of Multisensor Orthophotos Generated from Unmanned Aerial Vehicle Platforms." Journal of Sensors 2019 (April 14, 2019): 1–10. http://dx.doi.org/10.1155/2019/2962734.

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Image coregistration is a key preprocessing step to ensure the effective application of very-high-resolution (VHR) orthophotos generated from multisensor images acquired from unmanned aerial vehicle (UAV) platforms. The most accurate method to align an orthophoto is the installation of air-photo targets at a test site prior to flight image acquisition, and these targets were used as ground control points (GCPs) for georeferencing and georectification. However, there are time and cost limitations related to installing the targets and conducting field surveys on the targets during every flight. To address this problem, this paper presents an automated coregistration approach for orthophotos generated from VHR images acquired from multisensors mounted on UAV platforms. Spatial information from the orthophotos, provided by the global navigation satellite system (GNSS) at each image’s acquisition time, is used as ancillary information for phase correlation-based coregistration. A transformation function between the multisensor orthophotos is then estimated based on conjugate points (CPs), which are locally extracted over orthophotos using the phase correlation approach. Two multisensor datasets are constructed to evaluate the proposed approach. These visual and quantitative evaluations confirm the superiority of the proposed method.

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Tibbetts, Jake, Bethany L. Goldblum, Christopher Stewart, and Arman Hashemizadeh. "Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks." Journal of Nuclear Engineering 3, no. 4 (September 22, 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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Chen, Guo, Zhigui Liu, Guang Yu, and Jianhong Liang. "A New View of Multisensor Data Fusion: Research on Generalized Fusion." Mathematical Problems in Engineering 2021 (October 15, 2021): 1–21. http://dx.doi.org/10.1155/2021/5471242.

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Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.

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Semenova, V. A., and E. M. Petrenko. "Identification and Comparative Analysis of Alkaloids by Inversion Voltammetry Method." Herald of the Bauman Moscow State Technical University. Series Natural Sciences, no. 6 (87) (December 2019): 113–21. http://dx.doi.org/10.18698/1812-3368-2019-6-113-121.

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The alkaloids concurrent detection and identification in the samples by the method of multisensory inversion voltammetry is the purpose of the work. To achieve this goal, the proposed method has been substantiated, the composition of the electrochemical test system has been optimized taking into account the specifics of the detected substances, and informative signs that characterize the presence of alkaloids in the studied sample have been found. A new approach, consisting in the use of an electrochemical multisensor test system in the form of a solution containing a set of metal ions that can form complex compounds with organic substances, has been developed and scientifically approved. The results showed that each organic substance has a different effect on the electrochemical behavior of the multisensory test system. The use of such a test system made it possible to model the principle currently defined by the term "Electronic tongue". An electronic database has been prepared according to the results of the electroanalytical studies, which made it possible to identify the detected substance by comparing it with analyzed sample. The proposed electrochemical method, which is based on multisensor inversion voltammetry, allows the detection and identification of both narcotic drugs and psychotropic substances with high confidence for a small mass of the sample.

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10

Liu, Bao Jun. "Study on Multisensor Data Fusion of Ultrasonic Sensor." Advanced Materials Research 722 (July 2013): 44–48. http://dx.doi.org/10.4028/www.scientific.net/amr.722.44.

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Aiming at data fusion of Autonomous car multisensors experiment at many times on distances, a novel fusion method is proposed based on the approach degree and weights. The method calculate mean and variance based on the measured sensors data, Using the maximum and minimum approach degree of this fuzzy set , the approach degree of the measured data from various sensors is processed quantitatively ,eliminating outlier data by Grubbs method, assigned the weights of data measured in the fusion process reasonably , so that the final expression of the data fusion is obtained, thus the data fusion of multisensor is realized.Test results demonstrate that this method can bring higher fusion precision, and more suitable for microcontroller and embedded systems applications.

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11

Henderson, T., E. Weitz, C. Hansen, and A. Mitiche. "Multisensor Knowledge Systems." International Journal of Robotics Research 7, no. 6 (December 1988): 114–33. http://dx.doi.org/10.1177/027836498800700609.

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12

Polla, D. L., R. S. Muller, and R. M. White. "Integrated multisensor chip." IEEE Electron Device Letters 7, no. 4 (April 1986): 254–56. http://dx.doi.org/10.1109/edl.1986.26363.

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13

Stewart, Robert E. "Vibration accelerometer‐multisensor." Journal of the Acoustical Society of America 84, no. 6 (December 1988): 2303. http://dx.doi.org/10.1121/1.397012.

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Stewart, Robert E. "Vibration accelerometer‐multisensor." Journal of the Acoustical Society of America 84, no. 6 (December 1988): 2303. http://dx.doi.org/10.1121/1.397013.

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Nannuru, Santosh, Stephane Blouin, Mark Coates, and Michael Rabbat. "Multisensor CPHD filter." IEEE Transactions on Aerospace and Electronic Systems 52, no. 4 (August 2016): 1834–54. http://dx.doi.org/10.1109/taes.2016.150265.

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Guinea, D., L. J. Barrios, and A. Ruiz. "Multisensor information integration." Sensor Review 10, no. 3 (March 1990): 133–36. http://dx.doi.org/10.1108/eb007826.

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Varshney, P. K. "Multisensor data fusion." Electronics & Communication Engineering Journal 9, no. 6 (December 1, 1997): 245–53. http://dx.doi.org/10.1049/ecej:19970602.

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Nelson, Brian R., D.-J. Seo, and Dongsoo Kim. "Multisensor Precipitation Reanalysis." Journal of Hydrometeorology 11, no. 3 (June 1, 2010): 666–82. http://dx.doi.org/10.1175/2010jhm1210.1.

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Abstract Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings.

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Rachkov, Michael Yu, Lino Marques, and AnÍbal T. de Almeida. "Multisensor Demining Robot." Autonomous Robots 18, no. 3 (May 2005): 275–91. http://dx.doi.org/10.1007/s10514-005-6840-y.

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Alfonsi, Alfonso, Raiza Yánez, and Alfonso R. Alfonsi. "Modelo de sistema multisensor con enfoque de muestreo multifrecuencia." RECIBE, REVISTA ELECTRÓNICA DE COMPUTACIÓN, INFORMÁTICA, BIOMÉDICA Y ELECTRÓNICA 10, no. 1 (May 13, 2021): E1—E25. http://dx.doi.org/10.32870/recibe.v10i1.163.

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En la fase temprana de diseño de un multisensor están las especificaciones funcionales que establecen el comportamiento sus señales, donde es importante analizar la influencia de los tiempos de muestreo en el despliegue temporal entre los datos de entrada y salida disponibles del multisensor, como del enlace de comunicación que pudieran utilizar. Por tanto, en este trabajo se desarrolla un modelo de sistema multisensor considerando múltiples señales con periodos de muestreo que pueden ser diferentes del proceso y datos de estado, incluyendo la limitación temporal del enlace de comunicación. El sistema consta de los componentes: multisensor y estación base. Además, se desarrolló un modelo de simulación en Scilab/Xcos para ejercitar diferentes escenarios. El modelo ofrece un perfil temporal de la secuencia de datos sensoriales bajo diferentes esquemas de muestreo, permitiendo presentar la evolución de las señales con pérdidas o ausencias de muestras, las cuales al ser tratadas sistemáticamente con operadores multifrecuencia y funciones de interpolación para su reconstrucción, conducen a resultados utilizables para la abstracción del mundo real. Finalmente, la efectividad del modelo se ilustra mediante simulaciones numéricas.

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Di, Peng, Xuan Wang, Tong Chen, and Bin Hu. "Multisensor Data Fusion in Testability Evaluation of Equipment." Mathematical Problems in Engineering 2020 (November 30, 2020): 1–16. http://dx.doi.org/10.1155/2020/7821070.

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The multisensor data fusion method has been extensively utilized in many practical applications involving testability evaluation. Due to the flexibility and effectiveness of Dempster–Shafer evidence theory in modeling and processing uncertain information, this theory has been widely used in various fields of multisensor data fusion method. However, it may lead to wrong results when fusing conflicting multisensor data. In order to deal with this problem, a testability evaluation method of equipment based on multisensor data fusion method is proposed. First, a novel multisensor data fusion method, based on the improvement of Dempster–Shafer evidence theory via the Lance distance and the belief entropy, is proposed. Next, based on the analysis of testability multisensor data, such as testability virtual test data, testability test data of replaceable unit, and testability growth test data, the corresponding prior distribution conversion schemes of testability multisensor data are formulated according to their different characteristics. Finally, the testability evaluation method of equipment based on the multisensor data fusion method is proposed. The result of experiment illustrated that the proposed method is feasible and effective in handling the conflicting evidence; besides, the accuracy of fusion of the proposed method is higher and the result of evaluation is more reliable than other testability evaluation methods, which shows that the basic probability assignment of the true target is 94.71%.

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Wei, Daozhi, Zhaoyu Zhang, Jiahao Xie, Liang fu Yao, and Ning Li. "Multisensor Hybrid Dynamic Alliance Formation Problem Using Sensitive Particle-Based Dynamic Discrete PSO." Mathematical Problems in Engineering 2021 (December 9, 2021): 1–14. http://dx.doi.org/10.1155/2021/2997983.

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In recent years, with the wide application and popularization of artificial intelligence algorithm in the field of multisensor information processing, it has been a research hotspot to solve the problem of sensor alliance formation in the battlefield environment by using multisensor cross-cueing technology. Based on the establishment of the multisensor hybrid dynamic alliance model and objective function, a multisensor cross-cueing algorithm based on dynamic discrete particle swarm optimization (DDPSO) with sensitive particles is proposed and a mechanism of “predict re-predict” is proposed in the process of sensor handover. Simulations have verified the good convergence effect and small detection error of multisensor cross-cueing technology in solving alliance formation problems. Meanwhile, compared with “measurement and then update” and “predict and update” mechanisms, the proposed mechanism is more suitable to the changing combat environment. At the same time, to some extent, it also shows that the artificial intelligence algorithm is more suitable for multisensor information processing.

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Wang, Changhui. "Sample Density Clustering Method Considering Unbalanced Data Distribution." Mobile Information Systems 2022 (September 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/7580468.

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The data distribution of the multidimensional array sensor is unbalanced in data sample collection. To improve the clustering ability of data samples, a data density clustering method of sparse scattered points and multisensor array sensor samples based on the analysis of unbalanced data distribution characteristics is proposed. The sparse scattered multisensor array network’s sample data collection structure is created using the Voronoi polygon topology. By analyzing the unbalanced parameters between data classes and reconstructing the characteristic space of data sample sequence, the time series of sample data collected by sparse scattered multisensor array is reorganized, and the statistical characteristic quantity and high-order cumulant of sample data collected by sparsely scattered multisensor array are extracted. Combined with the learning algorithm of unbalanced data distribution sample feature fusion, the fuzzy clustering of sample data information flow collected by sparse scattered multisensor array elements is realized. According to the feature clustering and convergence analysis, the sparse scattered feature detection method is adopted to realize the data density clustering and data structure optimization configuration of sparse scattered multisensor array elements. The test results show that the method in this paper has good convergence, strong spectrum expansion ability, and low error rate of data clustering when collecting samples with sparse scattered points and multisensor arrays.

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Lei, Yang, Yong Zou, Bo Jiang, and Tian Tian. "Optimal Defect Detection and Sensing System of Railway Tunnel Radar considering Multisensor System Combined with Active Interference Suppression Algorithm." Computational Intelligence and Neuroscience 2022 (April 25, 2022): 1–13. http://dx.doi.org/10.1155/2022/2459996.

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With the rapid development of science and technology, testing equipment and testing methods are constantly updated. Radar detectors have the advantages of losslessness, high efficiency, high resolution, and high-speed radar image capture. They can accurately locate defects in railway tunnels, respond to hidden dangers in time, and provide strong technical support for transportation. This paper proposes to optimize the defect detection of railway tunnel radar through the combination of multisensor technology and active interference suppression algorithm and designs the corresponding sensor system according to the content. This article analyzes several factors that affect the radar detection effect and makes a detailed summary from the detection environment and other aspects. At the same time, it uses the multisensor system combined with active interference suppression algorithm to design a railway tunnel detection simulation experiment. Experimental results show that the use of multisensors combined with active interference suppression algorithm to optimize radar detection can effectively improve the accuracy of railway tunnel defect detection. Through the analysis of the results of tunnel defect detection, the detection accuracy of this paper has reached 98.8%, which can provide an effective reference for the detection of railway tunnels.

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Lei, Yang, Yong Zou, Bo Jiang, and Tian Tian. "Optimal Defect Detection and Sensing System of Railway Tunnel Radar considering Multisensor System Combined with Active Interference Suppression Algorithm." Computational Intelligence and Neuroscience 2022 (April 25, 2022): 1–13. http://dx.doi.org/10.1155/2022/2459996.

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With the rapid development of science and technology, testing equipment and testing methods are constantly updated. Radar detectors have the advantages of losslessness, high efficiency, high resolution, and high-speed radar image capture. They can accurately locate defects in railway tunnels, respond to hidden dangers in time, and provide strong technical support for transportation. This paper proposes to optimize the defect detection of railway tunnel radar through the combination of multisensor technology and active interference suppression algorithm and designs the corresponding sensor system according to the content. This article analyzes several factors that affect the radar detection effect and makes a detailed summary from the detection environment and other aspects. At the same time, it uses the multisensor system combined with active interference suppression algorithm to design a railway tunnel detection simulation experiment. Experimental results show that the use of multisensors combined with active interference suppression algorithm to optimize radar detection can effectively improve the accuracy of railway tunnel defect detection. Through the analysis of the results of tunnel defect detection, the detection accuracy of this paper has reached 98.8%, which can provide an effective reference for the detection of railway tunnels.

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Zhou, Yuqing, and Wei Xue. "A Multisensor Fusion Method for Tool Condition Monitoring in Milling." Sensors 18, no. 11 (November 10, 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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Romanov, Volodymyr, Igor Galelyuka, Oleksandr Voronenko, Oleksandra Kovyrova, Hanna Antonova, and Anna Kedych. "Wireless Networks of Smart Multisensors and Biosensor Devices for Express Diagnostics of the State of Grape and Fruit-Berry Crops and Quality Control of Winemaking Products." Cybernetics and Computer Technologies, no. 1 (April 28, 2023): 58–73. http://dx.doi.org/10.34229/2707-451x.23.1.6.

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The application of information and wireless technologies, multisensor and biosensor devices, as well as sensor networks based on them to control the parameters of technological processes in different industries makes it possible to greatly improve the quality of final products and reduce losses of raw materials at all stages of production and storage. This covers both the industrial production of consumer goods and the food and processing industry. The article describes the developing and testing of network of wireless smart biosensor devices for food industry, namely for controlling the quality of wine, wine materials and food products in the production process. It is considered the technological process of production of wine materials and wine, as well as parameters that are subject of controlling. The requirements for smart multisensor devices, which are reasonable to use in such technological productions, are formulated. Measuring nodes are built on the basis of amperometric sensors and wireless data transmission units. Structural, functional and principle schemas of measuring devices were developed. The design documentation was created for their production at contract factories. The integration of wireless data transmission units into measuring nodes made it possible to build wireless sensor networks based on them. Several structures of wireless networks were developed on the base of wireless nodes with different functionality and with using of different wireless data transmission protocols. Hardware and software for wireless network nodes were developed. It was fulfilled the testing of the multisensor measuring node in autonomous mode, as well as the whole network consisting of several wireless nodes. The test results showed both the weaknesses of the developed devices, which should be eliminated in further researches, and the reasonability of using smart multisensor devices and networks based on them to control the quality of wine materials and wine at different stages of their production and storage. The work was performed with the support and within the Complex program of scientific research of the National Academy of Sciences of Ukraine "Smart" sensor devices of the new generation based on modern materials and technologies." Keywords: biosensors, amperometric transducers, smart multisensors, wireless sensor network, wine and food quality control.

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Wenhao, Dong. "Multisensor Information Fusion-Assisted Intelligent Art Design under Wireless Virtual Reality Environment." Journal of Sensors 2021 (December 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/6119127.

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Under the background of intelligent technologies, art designers need to use information technology to assist the design of art factors and fully realize the integration of art design and information technology. Multisensor information fusion technology can more intuitively and visually carry out a more comprehensive grasp of the objectives to be designed, maximize the positive effects of art design, and achieve its overall optimization and can also help art designers get rid of the traditional monolithic and obsolete design concepts. Based on multisensor information fusion technology under wireless virtual reality environment, principles of signal acquisition and preprocessing, feature extraction, and fusion calculation, to analyze the information processing process of multisensor information fusion, conduct the model construction and performance evaluation for intelligent art design, and propose an intelligent art design model based on multisensor information fusion technology, we discuss the realization of multisensor information fusion algorithm in intelligent art design and finally carry out a simulation experiment and its result analysis by taking the environment design of a parent-child restaurant as an example. The study results show that using multisensor information fusion in the environmental design of parent-child restaurant is better than using a single sensor for that; at the same time, using force sensors has a better environmental design effect than using vibration sensors. The multisensor information fusion technology can automatically analyze the observation information of several sources obtained in time sequence under certain criteria and comprehensively perform information processing for the completion of the decision-making and estimation tasks required for intelligent art design.

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Karimi, Meysam, Mohammad Rabiee, Mojgan Abdolrahim, Mohammadreza Tahriri, Daryoosh Vashayee, and Lobat Tayebi. "High Sensitivity Electrochemical Multisensors based on Graphene-PANI Nanocomposite for Simultaneous Detection of Glucose and Urea." Journal of New Materials for Electrochemical Systems 19, no. 3 (September 7, 2016): 145–50. http://dx.doi.org/10.14447/jnmes.v19i3.311.

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We present a study of the effect of graphene–PANI nanocomposites on the sensitivity of the urea and glucose multisensory. We used an electroctrochemical multisensor based on two electrodes located in a reservoir with two separate channels. The urease and glu-cose oxidase (GOD) were employed for detecting the urea and glucose, respectively. We characterized the graphene and graphene-PANI samples with X-ray Diffraction (XRD) analysis and scanning electron microscopy (SEM) observations. We further performed the Cyclic voltammetry and Amperometry tests. The collected experimental results revealed that the intensity of the peak significantly increases with the concentration of the urea and glucose.

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OLIVEIRA, Élcio Jeronimo de. "MULTISENSOR DATA FUSION TECHNOLOGY." Periódico Tchê Química 07, no. 14 (August 20, 2010): 30–34. http://dx.doi.org/10.52571/ptq.v7.n14.2010.31_periodico14_pgs_30_34.pdf.

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The data fusion from multiple sensors is one of the areas and technologies of interest to national defense (Ministry of Defense of Brazil). Approximately 20 years ago, several methods of data fusion were introduced. Such methods are generally related to the defense sector, but data fusion can also be applied in other areas such as medical diagnostics, industry, etc. A simple way to explain its operation is to state that this technology combines information from several sensors in order to produce other, more accurate information.

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Richardson, John M., and Kenneth A. Marsh. "Fusion of Multisensor Data." International Journal of Robotics Research 7, no. 6 (December 1988): 78–96. http://dx.doi.org/10.1177/027836498800700607.

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Hussain, A. M. "Multisensor distributed sequential detection." IEEE Transactions on Aerospace and Electronic Systems 30, no. 3 (July 1994): 698–708. http://dx.doi.org/10.1109/7.303740.

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Onwubiko, Cyril. "Multisensor message exchange mechanism." International Journal of Electronic Security and Digital Forensics 1, no. 4 (2008): 387. http://dx.doi.org/10.1504/ijesdf.2008.021456.

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Castella, F. R. "Multisensor, multisite tracking filter." IEE Proceedings - Radar, Sonar and Navigation 141, no. 2 (1994): 75. http://dx.doi.org/10.1049/ip-rsn:19941010.

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Chung, Soon-Yeong, and Nam Kee Lee. "A Multisensor Deconvolution Problem." Journal of Fourier Analysis and Applications 11, no. 4 (August 2005): 389–406. http://dx.doi.org/10.1007/s00041-005-4029-6.

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Trömel, Silke, Alexander V. Ryzhkov, Malte Diederich, Kai Mühlbauer, Stefan Kneifel, Jeffrey Snyder, and Clemens Simmer. "Multisensor Characterization of Mammatus." Monthly Weather Review 145, no. 1 (January 1, 2017): 235–51. http://dx.doi.org/10.1175/mwr-d-16-0187.1.

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Abstract Multisensor observations of anvil mammatus are analyzed in order to gain a more detailed understanding of their spatiotemporal structure and microphysical characterization. Remarkable polarimetric radar signatures are detected for the Pentecost 2014 supercell in Northrhine Westfalia, Germany, and severe storms in Oklahoma along their mammatus-bearing anvil bases. Radar reflectivity at horizontal polarization ZH and cross-correlation coefficient ρHV decrease downward toward the bottom of the anvil while differential reflectivity ZDR rapidly increases, consistent with the signature of crystal depositional growth. The differential reflectivity ZDR within mammatus exceeds 2 dB in the Pentecost storm and in several Oklahoma severe convective storms examined for this paper. Observations from a zenith-pointing Ka-band cloud radar and a Doppler wind lidar during the Pentecost storm indicate the presence of a supercooled liquid layer of at least 200–300-m depth near the anvil base at temperatures between −15° and −30°C. These liquid drops, which are presumably generated in localized areas of vertical velocities of up to 1.5 m s−1, coexist with ice particles identified by cloud radar. The authors hypothesize that pristine crystals grow rapidly within these layers of supercooled water, and that oriented planar ice crystals falling from the liquid layers lead to high ZDR at precipitation radar frequencies. A mammatus detection strategy using precipitation radar observations is presented, based on a methodology so far mainly used for the detection of updrafts in convective storms. Owing to the presence of a supercooled liquid layer detected above the mammatus lobes, the new detection strategy might also be relevant for aviation safety.

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Kypris, Dedalos, Georgios Nikolaou, Eustathios Evangellides, and Damianos Neocleous. "A Hydraulic Evapotranspiration Multisensor." AgriEngineering 4, no. 4 (November 21, 2022): 1164–70. http://dx.doi.org/10.3390/agriengineering4040072.

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An exclusively mechanical stand-alone automatic device, self-adjusting to weather changes for controlled irrigation, that operates only on the energy of piped water, without electricity, is the described low-cost “Hydraulic Evapotranspiration Multisensor-HEM”. It is composed of an Evaporation Pan with water left to evaporate, a Floater with a Magnet floating in this water, a Hydraulic Device managing a Hydraulic Water Valve having means to adjust irrigation frequency, and a system that returns water to said Pan, through an Adjustable Dripper, to replace that lost by evaporation. During the Evaporation Phase, gradually the water level is lowered to a predetermined level, at which the floating Magnet acts on said Hydraulic Device to start irrigation. Water from the irrigation line is returned to the Evaporation Pan at the proper for the irrigation time rate. When the lost water is replaced irrigation is terminated and the system resets. On installation Irrigation Frequency and Irrigation Time are set with two graduated screws, for normal weather and the conditions of the particular plantation. HEM responding to weather changes modifies the irrigation schedule set, either by shortening, at a high evaporation rate, the time interval between consecutive irrigation cycles to protect plantations from water deficit stress or extending this time interval at a low evaporation rate to save water. Assessing the performance of HEM, by taking the estimations of evapotranspiration from the Penman–Monteith method shows high accuracy in the studied site. Considering the advantages of the product against the programmable irrigation controller devices, HEM provides optimum irrigation control in field crops and makes it a powerful “green tool” to be used in Mediterranean greenhouses.

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Rautenberg, Jens, Klaus Beringer, Achim Stark, Rudolf Braun, Stefan Rüger, Wolfgang Schleemilch, and Michael Münch. "Konzentrationsmessung mit akustischem Multisensor." JOT Journal für Oberflächentechnik 55, no. 10 (September 2015): 56–58. http://dx.doi.org/10.1007/s35144-015-0752-1.

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Xu, Tao. "Performance of VR Technology in Environmental Art Design Based on Multisensor Information Fusion under Computer Vision." Mobile Information Systems 2022 (April 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/3494535.

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Multisensor information fusion technology is a symbol of scientific and technological progress. This paper is aimed at discussing the performance of virtual reality (VR) technology in the environmental art design of multisensor information fusion technology. This paper prepares some related work in the early stage and then lists the algorithms and models, such as the multisensor information fusion model based on VR instrument technology, and shows the principle of information fusion and GPID bus structure. This paper describes the multisensor information fusion algorithm to analyze DS evidence theory. In the evidence-based decision theory, the multisensor information fusion process is the calculation of the qualitative level and/or confidence level function, generally calculating the posterior distribution information. In addition to showing its algorithm, this paper also shows the data flow of the multisensor information fusion system through pictures. Then, this paper explains the design and construction of garden art environment based on active panoramic stereo vision sensor, shows the relationship of the four coordinates in an all-round way, and shows the interactive experience of indoor and outdoor environmental art design. Then, this paper conducts estimation simulation experiments based on EKF and shows the results, and it is concluded that the fusion data using the extended Kalman filter algorithm is closer to the actual target motion data and the accuracy rate is better than 92%.

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Jiang, Lan. "Artificial Intelligence Algorithms for Multisensor Information Fusion Based on Deep Learning Algorithms." Mobile Information Systems 2022 (April 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/3356213.

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Artificial intelligence (AI) has been widely used all over the world. AI can be applied not only in mechanical learning and expert system but also in knowledge engineering and intelligent information retrieval and has achieved amazing results. This article aims to study the relevant knowledge of deep learning algorithms and multisensor information fusion and how to use deep learning algorithms and multisensor information fusion to study AI algorithms. This paper raises the question of whether the improved multisensor information fusion will affect the AI algorithm. From the data in the experiment of this article, the accuracy of the neural network before the improvement was 4.1%. With the development of society, the traditional algorithm finally dropped to 1.3%. The accuracy of the multisensor information fusion algorithm before the improvement was 3.1% at the beginning; with the development of society, it finally dropped to 1%; it can be known that the accuracy of the improved neural network is 4.6%, and with continuous improvement, it finally increased to 9.8%. The improved multisensor information fusion algorithm is the same, the accuracy at the beginning was 3.9%, and gradually increased to 9.5%. From this set of data, it can be known that the improved convolutional neural network (CNN) algorithm, and the improved multisensor information fusion algorithm should be used to study AI algorithms.

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Xie, Jiahao, Daozhi Wei, Shucai Huang, and Xiangwei Bu. "A Sensor Deployment Approach Using Improved Virtual Force Algorithm Based on Area Intensity for Multisensor Networks." Mathematical Problems in Engineering 2019 (February 27, 2019): 1–9. http://dx.doi.org/10.1155/2019/8015309.

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Sensor deployment is one of the major concerns in multisensor networks. This paper proposes a sensor deployment approach using improved virtual force algorithm based on area intensity for multisensor networks to realize the optimal deployment of multisensor and obtain better coverage effect. Due to the real-time sensor detection model, the algorithm uses the intensity of sensor area to select the optimal deployment distance. In order to verify the effectiveness of this algorithm to improve coverage quality, VFA and PSOA are selected for comparative analysis. The simulation results show that the algorithm can achieve global coverage optimization better and improve the performance of virtual force algorithm. It avoids the unstable coverage caused by the large amount of computation, slow convergence speed, and easily falling into local optimum, which provides a new idea for multisensor deployment.

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Wang, Cui. "Advanced Intelligent English Translation Based on Multisensor Data Fusion Optimization." Journal of Sensors 2022 (September 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/5951127.

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English translation activity course is of great significance to cultivate students’ English translation level. In the context of multisensor data fusion, how to effectively carry out English translation activity course in colleges and universities has become an important topic. The educational value and intellectual property of advanced Intelligent English translation activity course are analyzed. From multisensor data fusion and the improvement of translation, translators psychological changes of the boot and prominent features, English translation activity and translation, the generality of the multisensor data fusion of multisensor data fusion personalization features this four aspects, which is under the background of the sensor data fusion of the practice for college English translation activity. Firstly, the theory of data fusion estimation is elaborated, and various data fusion structures in multisensor systems are summarized. Then, the data fusion estimation model based on Kalman filter is established, and the Kalman filtering algorithms of centralized, sequential, parallel, and joint structures are given, respectively. Simulation experiments are carried out on the algorithms. Experimental results show that the estimation accuracy of the system can be improved by multisensor data fusion. Then a rule-based lexical analyzer is designed. Combined with the system model, a rule-based lexical analyzer and a comprehensive dictionary, the linguistic knowledge source throughout the whole machine translation process, are researched and designed. A hashing algorithm for lexical retrieval is designed, and various rules and data structures related to the lexical analyzer are described in formal language. The analysis algorithms of morphological preprocessing, morphological analysis, unincluded word processing, phrase analysis, and part of speech tagging are introduced.

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Caduff, Andreas, Mattia Zanon, Pavel Zakharov, Martin Mueller, Mark Talary, Achim Krebs, Werner A. Stahel, and 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, no. 3 (January 14, 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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Ma, Zhonglin, Zhihao Yu, and Jingshan Zhang. "Sports Information Acquisition and Functional Training System Based on Multisensor Information Fusion." Journal of Sensors 2021 (September 1, 2021): 1–12. http://dx.doi.org/10.1155/2021/6941258.

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Since entering the 21st century, multisensor information fusion technology has developed rapidly, and multisensor information fusion is a trend in the coming years and even decades. This article is aimed at studying how the traditional sports information acquisition and functional training system can be implemented under the multisensor information fusion. This article proposes the use of multisensor information fusion technology to conduct an in-depth study on the traditional ways of acquiring sports information and the cognition of sports information by different groups of people, as well as the use of multisensor information fusion technology to update the functional training system and discuss whether this new functional training system is in line with the speed and rhythm of the current era compared with the traditional training system. The results of obtaining sports information from different groups of people show that more people know about sports events at home and abroad, reaching 22.5%. Under the same circumstances, 60% of the elderly are willing to pay attention to sports information, while only 30% of the young people are willing to pay attention to sports information. And from 2016 to 2021, the number of people in functional training nationwide has increased from 200,000 to 1.5 million.

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Li, Wangyan, Zidong Wang, Guoliang Wei, Lifeng Ma, Jun Hu, and Derui Ding. "A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks." Discrete Dynamics in Nature and Society 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/683701.

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Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development of multisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.

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Wang, Xin, Qi Dan Zhu, and Ye Bin Wu. "A Measurement Fusion Fault-Tolerating PID Control for Time-Delay System with Colored Noise Disturbance." Key Engineering Materials 419-420 (October 2009): 589–92. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.589.

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The designing method of filtering, fault-tolerating, and fusing PID control is put forward, concerning multisensor time-delay system with colored noise disturbance. First of all, this method detects fault and isolate the data by the weighted square sum of residuals (WSSR) method which is measured by multisensors, then the data which is detected right will be measurement fused, and the fused data will be optimally filtered basing on modern time series analysis method. Finally, the global optimal estimation of measured data will be got, which will be brought back to the input endian in order to improve PID controlling accuracy. A 3-sensor servomotor control example shows the effectiveness of the method.

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Oleneva, Ekaterina, Maria Khaydukova, Julia Ashina, Irina Yaroshenko, Igor Jahatspanian, Andrey Legin, and Dmitry Kirsanov. "A Simple Procedure to Assess Limit of Detection for Multisensor Systems." Sensors 19, no. 6 (March 18, 2019): 1359. http://dx.doi.org/10.3390/s19061359.

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Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach for LOD estimation in multisensor analysis. The suggested approach is based on the assessment of evolution of mean relative error values in calibration series with growing analyte concentration. The LOD value is estimated as the concentration starting from which MRE values become stable from sample to sample. This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems.

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Zhang, Xiang, Xinming Tang, Xiaoming Gao, and Hui Zhao. "Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data." Advances in Meteorology 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/7914581.

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The objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. The Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. The solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. This research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. The disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. The optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. The agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm3/cm3 for both methods. The optimized Alpha model shows an obvious improvement for soil moisture retrieval. The results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

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Chen, Lequn, Xiling Yao, Kui Liu, Chaolin Tan, and 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 (June 19, 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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Bose, Sam C., and Glenn E. Thobe. "Multisensor Navigation Systems Multicore Architecture." Journal of Aerospace Information Systems 16, no. 11 (November 2019): 442–53. http://dx.doi.org/10.2514/1.i010718.

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Journal articles on the topic 'Multisensor'

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