- 标题
- 摘要
- 关键词
- 实验方案
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Remote sensing-based water quality assessment for urban rivers: a study in linyi development area
摘要： Nowadays, urban rivers play an important role in city development and make great contributions to urban ecology. Most urban rivers are the drinking water sources and water quality is extremely critical. The current assessment method in national standard of China has multiple limitations; therefore, this paper introduces an advanced assessment, that is, Canadian Water Quality Index (CWQI). This method can help to provide comprehensive and objective water quality assessment for the urban rivers. Moreover, CWQI can prevent waste of the water resource, since current assessment is pessimistic and tent to underestimate water samples to a lower grade. Linyi development area is selected as study region and CWQI method is applied to assess two major urban rivers within the area. The water monitoring data from 2014 to 2017 is acquired in 24 parameters. Since the CWQI calculation is still based on traditional water quality measurement in parameters, there will be a huge cost when increasing research scale and accuracy. In this paper, remote sensing technique is employed to develop models of CWQI scores from satellite data. By utilizing 23 selected monitoring instances and matching satellite data, linear regression analysis shows that red band data has highest correlation with CWQI in both two urban rivers in the study region. In addition, two testing datasets with five instances for each river are used to validate the RS-based CWQI models and the results show that testing datasets can be fitted well. With the models, CWQI distribution diagrams are generated and assist both spatial and temporal analysis. Experimental results show that the proposed approach can indicate actual water quality pattern which is validated by field visit. The proposed approach in this paper has satisfying effectiveness and robustness.
关键词： Remote sensing,Urban rivers,Water quality index,Spatial temporal analysis
更新于2025-09-23 15:22:29
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Wavelength and Polarization Effects in Strong-field Ionization of Diatomic Molecules Driven by Mid-infrared Laser Pulses
摘要： The solar power penetration in distribution grids is growing fast during the last years, particularly at the low-voltage (LV) level, which introduces new challenges when operating distribution grids. Across the world, distribution system operators (DSO) are developing the smart grid concept, and one key tool for this new paradigm is solar power forecasting. This paper presents a new spatial–temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers. The scope is 6-h-ahead forecasts at the residential solar photovoltaic and medium-voltage (MV)/LV substation levels. This framework has been tested in the smart grid pilot of évora, Portugal, and using data from 44 microgeneration units and 10 MV/LV substations. A benchmark comparison was made with the autoregressive forecasting model (AR—univariate model) leading to an improvement on average between 8% and 10%.
关键词： spatial–temporal,forecasting,solar power,smart metering,smart grid,Distribution network
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE) - Osaka, Japan (2019.10.15-2019.10.18)] 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE) - Design of a Real-Time Visible Laser Light Communication System with Basedband in FPGA for High Definition video Transmission
摘要： The solar power penetration in distribution grids is growing fast during the last years, particularly at the low-voltage (LV) level, which introduces new challenges when operating distribution grids. Across the world, distribution system operators (DSO) are developing the smart grid concept, and one key tool for this new paradigm is solar power forecasting. This paper presents a new spatial–temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers. The scope is 6-h-ahead forecasts at the residential solar photovoltaic and medium-voltage (MV)/LV substation levels. This framework has been tested in the smart grid pilot of évora, Portugal, and using data from 44 microgeneration units and 10 MV/LV substations. A benchmark comparison was made with the autoregressive forecasting model (AR—univariate model) leading to an improvement on average between 8% and 10%.
关键词： spatial–temporal,forecasting,solar power,smart metering,smart grid,Distribution network
更新于2025-09-19 17:13:59
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Hierarchical Spatial-Temporal Modeling and Monitoring of Melt Pool Evolution in Laser-Based Additive Manufacturing
摘要： Melt pool dynamics reflect the formulation of microstructural defects in part during laser-based additive manufacturing (LBAM). The thermal images of melt pool collected during the LBAM process provide unique opportunities for modeling and monitoring its evolution. The recognized anomalies are evidence of part defects that are to be eliminated for higher product quality. A unique concern in analyzing thermal image is spatial-temporal correlations – the heat transfer within melt pool causes spatial correlations among pixels in an image, and the evolution of melt pool causes temporal correlations across images. The objective of this study is to develop a LBAM modeling-monitoring framework that incorporates spatial-temporal effect in characterizing and monitoring melt pool behavior. Spatial-Temporal Conditional Autoregressive (STCAR) models are explored. STCAR-AR is identified as the best candidate among the numerous STCAR variants. A novel two-level control chart is constructed on top of the STCAR-AR model to monitor the melt pool dynamics. A hierarchical structure underlies the two-level control chart in the sense that global anomalies recognized in Level II can be traced in Level I for further inspection. A comparison with other recently developed in-situ monitoring approaches shows that the proposed framework achieves the best detection power and false positive rate.
关键词： spatial-temporal conditional autoregressive model,Additive manufacturing,hierarchical control charts
更新于2025-09-12 10:27:22
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[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 - Land Cover Change Detection Based on Spatial-Temporal Sub-Pixel Evolution Mapping: A Case Study for Urban Expansion
摘要： In the past decades, land cover change detection (LCCD) has been dramatically developed, since it provides corroborative support for policy decision, regulatory actions, and subsequent urban-rural activities. Satellite remote sensing image is the major source of LCCD since it is able to revisit the Earth’s surface regularly and provide time series images for monitoring and space-time analysis. However, there is always a trade-off between spatial scale and temporal scale, i.e., finer spatial resolution image generally has a lower revisit frequency, leading to an observation omission; while higher revisit frequency image usually has a lower spatial resolution, resulting in a deficiency in detecting finer scale change information. In this paper, a spatial-temporal sub-pixel mapping (SSM) algorithm is proposed on the premise that one pair of fine spatial resolution image with low frequency revisit period and coarse spatial resolution with high frequently revisit period are available, and SSM is taken to restore the coarse image to a finer scale thematic map which can be then compared to the fine image, realizing a frequency and detailed LCCD. SSM is an extension of traditional mono-temporal sub-pixel mapping (SPM) algorithm, and is improved by incorporating temporally fine distribution patterns for a more appropriate restoration of coarse image. A study case for urban expansion LCCD were carried out to verify the ability of the proposed algorithm to handle change detection based on one pair of china-made Gaofen-2 image (GF-2) and Landsat-8 image, the result demonstrate that the proposed SSM algorithm outperform the other traditional SPM, achieving both fine temporal resolution and spatial resolution LCCD for further applications.
关键词： Swarm intelligent theory,spatial-temporal sub-pixel mapping (SSM),Land cover change detection (LCCD),sub-pixel mapping (SPM)
更新于2025-09-10 09:29:36