- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[American Society of Agricultural and Biological Engineers 2017 Spokane, Washington July 16 - July 19, 2017 - ()] 2017 Spokane, Washington July 16 - July 19, 2017 - Design and modeling of grain impact sensor utilizing two crossed polyvinylidene fluoride films
摘要： In order to reduce the unavoidable grain losses during harvesting, the combine harvester’s operational parameters should be adjusted accordingly. So, it is important to develop a real-time sensor which can monitor the grain losses. A grain impact sensor utilizing crossed piezoelectric polyvinylidene fluoride (PVDF) films as sensitive material is described. This sensor is composed of two crossed layers of sensor unit arrays, a damping layer and a support plate. The two layers are insulated from each other but can detect the impact simultaneously. The sensor unit arrays of those two layers are perpendicular and the sensor units in each layer are independent and parallel. Each sensor unit has its independent signal processing circuit, which is composed of charge amplifier, band-pass filter, envelope detector and voltage comparator. Two signals from two layers presented a two-dimensional impact position information through multi-sensor fusion technology. The sensor can obtain the spatial distribution of grain loss accurately to reduce the error-recognition ratio. Moreover, the grain impact sensor was simulated by finite element method to obtain the best number and size of the sensor units for higher sensitivity, detection speed, stress transfer efficiency, deformation transfer efficiency.
关键词： double layers,grain impact sensor,multi-sensor fusion,grain loss detecting,PVDF film
更新于2025-09-23 15:22:29
-
Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
摘要： With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.
关键词： classification,feature extraction,multi-sensor fusion,remote sensors,deep learning,hyperspectral image
更新于2025-09-23 15:22:29
-
Recent Advances in Multifunctional Sensing Technology on a Perspective of Multi-Sensor System: A Review
摘要： Due to rapid advancement in technology, recently a significant amount of work has been carried out in the field of multi-mode sensor and multifunctional sensor. A single unit of multifunctional sensor provides multiple measurements which eventually reduces the cost of multiple sensors and makes the system compact. Though the risk of sensor failure and reliability of the multifunctional sensor is always imminent, researchers are working out different methods to make the sensor fault tolerant. Multifunctional sensor are getting widespread acceptance in variety of fields. This paper provides a review of recent advances in multifunctional sensor technology in the perspective of multi-sensor system. Different aspects of design and implementation of multifunctional sensor technology has been addressed in this paper. This paper can serve as a basic reading material for students and researchers pursuing research on multifunctional sensor.
关键词： Multi-Mode Sensor,Multi-sensor system,Multifunctional Sensor
更新于2025-09-23 15:21:21
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach
摘要： Estimating parameters for global climate models via combined active and passive microwave remote sensing data has been a subject of intensive research in recent years. A variety of retrieval algorithms has been proposed for the estimation of soil moisture, vegetation optical depth and other parameters. A novel attenuation-based retrieval approach is proposed here to globally estimate the gravimetric moisture of vegetation (????) and retrieve information about the amount of water [kg] per amount of wet vegetation [kg]. The parameter ???? is particularly interesting for agro-ecosystems, to assess the status of growing vegetation. The key feature of the proposed approach is that it relies on multi-sensor data from three sensor types (microwave radar, microwave radiometer, and lidar) to solve the physics equations and obtain ????-estimates. The comparability of these estimates to literature values as well as to results of a globally applied, retrieval approach of Grant [4], reveal the potential of the developed method.
关键词： lidar,radiometer,Multi-sensor,SMAP,vegetation water content,vegetation optical depth,radar
更新于2025-09-23 15:21:21
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Multi-Frequency Estimation of Canopy Penetration Depths from SMAP/AMSR2 Radiometer and Icesat Lidar Data
摘要： In this study, the ?? - ?? model framework is used to derive extinction coefficient and canopy penetration depths from multi-frequency SMAP and AMSR2 retrievals of vegetation optical depth together with ICESat LiDAR vegetation heights. The vegetation extinction coefficient serves as an indicator of how strong absorption and scattering processes within the canopy attenuate microwaves at L and C-band. Through inversion of the extinction coefficient, the penetration depth into the canopy can be obtained, which is analyzed on local (Sahel, Illinois) and continental scale (Africa, parts of North America) as well as for a one year time series (04/2015-04/2016). First analyses of the retrieved penetration depth estimates reveal strongest attenuation for densely forested areas, therefore vegetation attenuation should be accounted for when retrieving soil moisture in these areas. For the continents of North America and Africa penetration depths decrease in average with an increase in frequency from L- to C-band. Moreover penetration depth time series were found to match with expected seasonal variations (e.g. vegetation growth period & rainy season) for analyzed local regions.
关键词： ICESat,Vegetation attenuation,SMAP,AMSR2,canopy penetration,multi-sensor
更新于2025-09-23 15:21:01
-
High-Precision 3D Object Capturing with Static and Kinematic Terrestrial Laser Scanning in Industrial Applicationsa??Approaches of Quality Assessment
摘要： Terrestrial laser scanning is used in many disciplines of engineering. Examples include mobile mapping, architecture surveying, archaeology, as well as monitoring and surveillance measurements. For most of the mentioned applications, 3D object capturing in an accuracy range of several millimeters up to a few centimeters is sufficient. However, in engineering geodesy, particularly in industrial surveying or monitoring measurements, accuracies in a range of a few millimeters are required. Additional increased quality requirements apply to these applications. This paper focuses on the quality investigation of data captured with static and kinematic terrestrial laser scanning. For this purpose, suitable sensors, which are typically used in the approach of a multi-sensor-system, as well as the corresponding data capturing/acquisition strategies, are presented. The aim of such systems is a geometry- and surface-based analysis in an industrial environment with an accuracy of +/? 1–2 mm or better.
关键词： synchronization,quality analysis,laser tracker,high-precision terrestrial laser scanning,multi-sensor-systems,calibration,industrial surveying,kinematic laser scanning,forward modeling,backward modeling
更新于2025-09-23 15:19:57
-
The generation and validation of a CUF-based FEA model with laser-based experiments
摘要： Architectural structures today are increasingly complex and structural health monitoring plays an important role in guaranteeing their safety. How to improve the reliability of deformation analysis is, thus, one of the key problems. This article combines laser-based measurement technology and the Carrera unified formulation (CUF) method to investigate the deformation of engineering structures. Within this article, we simulate architectural structures using the CUF geometric model, which is consistent with the results of the laser tracker experiment. We aimed at constructing an intelligent and efficient CUF model which can be applied extensively in the monitoring of various constructs, such as tunnels and bridges. The innovation of this article is that high-accuracy laser tracker technology is integrated with an effective CUF model to investigate the load-displacement relationship considering lateral displacement.
关键词： multi-sensor,laser tracker,SHM,FEA,terrestrial laser scanning
更新于2025-09-12 10:27:22
-
Optimized free-form surface modeling of point clouds from laser-based measurement
摘要： Freeform parameterizations to reproduce structure deformation are increasingly important topics in laser-scanner-based deformation analyses. High-accuracy assurance of free-form surface approximation is extremely critical for reliable deformation analysis. One main challenge in this field is the model selection. Improper model complexity could result in under-fitting the real object shape or overfitting data noises, and thus a failure of deformation analysis. A multi-sensor system could integrate advantages of different sensors and improve the quality of mission completed. This paper combines terrestrial laser scanning (TLS) and laser tracker (LT) technologies, to enhance high-accuracy surface modeling in deformation analysis. A surface-based B-spline approximation and a multi-sensor system are investigated, the latter of which focuses mainly on the combination of TLS and LT technologies. The innovation of this paper is that the surface-based B-spline approximation is validated and optimized with LT corner cube reflectors. Hypothesis testing is adopted to select the best parameter setting by judging most consistency of TLS and LT in various epochs. In the B-spline surface modeling, both instrumental and numerical uncertainties are considered. We use the instrumental uncertainty model based on intensity value, as well as numerical uncertainty based on adjustment theories. A sampling strategy is proposed to avoid data gaps and obtain even distributed data points.
关键词： multi-sensor,laser tracker,Surface modeling,B-spline approximation,terrestrial laser scanning
更新于2025-09-11 14:15:04
-
[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Fusing non-contact vital sign sensing modalities - first results*
摘要： A wearable multi-sensor system allowing synchronized unobtrusive measurements of 4 vital signs at a dedicated location of interest is presented. The 4xU sensor is capable of synchronously measuring magnetic impedance, reflective photoplethysmography, capacitive electrocardiogram and seismocardiography (ballistocardiography). The hardware of all modalities is described and some preliminary results are reported.
关键词： ballistocardiography,vital signs,reflective photoplethysmography,magnetic impedance,seismocardiography,wearable multi-sensor system,capacitive electrocardiogram
更新于2025-09-10 09:29:36
-
[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments
摘要： Robust real-time object detection and tracking are challenging problems in autonomous transportation systems due to operation of algorithms in inherently uncertain and dynamic environments and rapid movement of objects. Therefore, tracking and detection algorithms must cooperate with each other to achieve smooth tracking of detected objects that later can be used by the navigation system. In this paper, we first present an efficient multi-sensor fusion approach based on the probabilistic data association method in order to achieve accurate object detection and tracking results. The proposed approach fuses the detection results obtained independently from four main sensors: radar, LiDAR, RGB camera and infrared camera. It generates object region proposals based on the fused detection result. Then, a Convolutional Neural Network (CNN) approach is used to identify the object categories within these regions. The CNN is trained on a real dataset from different ferry driving scenarios. The experimental results of tracking and classification on real datasets show that the proposed approach provides reliable object detection and classification results in maritime environments.
关键词： maritime environment,object detection,convolutional neural networks,region proposals,autonomous vessel,multi-sensor fusion
更新于2025-09-09 09:28:46