Search Results (6)

Onshore oil spills are directly related to soil contamination and significantly impact groundwater, vegetation, and human life. Immediate cleanup work is carried out when an oil spill occurs, but the currently used preventive measures are insufficient. Therefore, this study independently developed a smart liner that allows general groundwater flow but blocks groundwater in the event of a spill to prevent further spread, and aims to verify the excellence of the product through verification. Because the verification of the smart liner performance in real-life conditions is difficult for various reasons, large-scale experiments were simulated using a container. The Roll Spreading and Inserting Method (RSIM) and Panel Injecting Method (PIM) were used as installation methods due to the properties of the material employed. Through rainfall simulations, the discharge amount and groundwater levels before and after an oil spill were measured, and a reaction diagram was created following the smart liner’s demolition. From the results, it was found that both installation methods successfully blocked more than 99% of the drainage, and soil contaminants were not detected outside the installation area. These results confirm the effectiveness of the smart liner. Additionally, the reaction diagram indicated that the RSIM and PIM installation reaction areas were identical, validating the suitability of both methods. By conducting this study, the performance of the smart liner was verified, demonstrating its potential as an effective preventive measure against the spread of oil contamination in soil. Full article

The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim—an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed. Full article

Soil is an important factor for public health, and when a soil contaminant occurs by oil spill, it has a great impact on the ecosystem, including humans. Accordingly, the area is blocked using a vertical barrier, and various remediation methods are being applied when an oil spill occurs. This study intends to use a smart liner to prevent and detect the spreading of soil contaminants in a situation in which oil spill detection is important. However, the smart liner is in the form of a fiber, so it is impossible to construct it in a general method. Therefore, the roll spreading and inserting method (RSIM) is proposed for smart liner construction. RSIM is a method of installing a supporting pile after excavating the ground and connecting the smart liner vertically to the ground surface. This method is the first method proposed in this study, and the design and concept have not been established. In this study, a conceptual design was established to apply RSIM in the actual field, and a scale model experiment was performed to prove it. As a result of the scale model experiment, the applicability of RSIM was confirmed. Finally, numerical analysis using Abaqus/CAE was performed to carry out the detailed design of RSIM (installation conditions such as dimensions). Analysis parameters were embedded depth, thickness, diameter, and material properties of a supporting pile according to the ground type. As a result of the analysis, it was confirmed that the results of RSIM analysis were interacting with all parameters according to the ground conditions. Therefore, it was confirmed that the actual design should be based on ground investigation and economic conditions, not standardized regulations. Full article

Deep learning (DL)-based architectures have shown a strong capacity to identify changes. However, existing change detection (CD) networks still suffer from limited applicability when it comes to multi-scale targets and spatially misaligned objects. For the sake of tackling the above problems, a region-based feature fusion network (RFNet) for CD of very high spatial resolution (VHR) remote sensing images is proposed. RFNet uses a fully convolutional Siamese network backbone where a multi-stage feature interaction module (MFIM) is embedded in the dual encoder and a series of region-based feature fusion modules (RFFMs) is used to generate change information. The MFIM fuses features in different stages to enhance the interaction of multi-scale information and help the network better distinguish complex ground objects. The RFFM is built based on region similarity (RSIM), which measures the similarity of bitemporal features with neighborhoods. The RFFM can reduce the impact of spatially offset bitemporal targets and accurately identify changes in bitemporal images. We also design a deep supervise strategy by directly introducing RSIM into loss calculation and shortening the error propagation distance. We validate RFNet with two popular CD datasets: the SECOND dataset and the WHU dataset. The qualitative and quantitative comparison results demonstrate the high capacity and strong robustness of RFNet. We also conduct robustness experiments and the results demonstrate that RFNet can deal with spatially shifted bitemporal images. Full article

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use. Full article

Signal injection method (SIM) is widely applied to the insulation parameters’ measurement in distribution network for its convenience and safety. It can be divided into two kinds of patterns: injecting a specific frequency signal or several frequencies’ groups, and scanning frequency in a scheduled frequency scope. In order to avoid the disadvantages in related researches, improved signal injection method (ISIM), in which the frequency characteristic of the transformer magnetizing impedance is taken into consideration, is proposed. In addition, optimization for signal injection position has been accomplished, and the corresponding three calculation methods of line-to-ground capacitance has been derived. Calculations are carried out through the vector information (vector calculation method), the amplitude information (amplitude calculation method), the phase information (phase calculation method) of voltage and current in signal injecting port, respectively. The line-to-ground capacitance is represented by lumped parameter capacitances in high-voltage simulation test. Eight different sinusoidal signals are injected into zero-sequence circuit, and then line-to-ground capacitance is calculated with the above-mentioned vector calculation method based on the voltage and the current data of the injecting port. The results obtained by the vector calculation method show that ISIM has a wider application frequency range compared with signal injection method with rated parameters (RSIM) and SIM. The RSIM is calculated with the rated transformer parameters of magnetizing impedance, and the SIM based on the ideal transformer model, and the relative errors of calculation results of ISIM are smaller than that for other methods in general. The six groups of two-frequency set are chosen in a specific scope which is recommended by vector calculation results. Based on ISIM, the line-to-ground capacitance calculations through the amplitude calculation method and phase calculation method are compared, and then its application frequency range, which can work as a guidance for line-to-ground capacitance measurement, is concluded. Full article