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Keywords = prior dataset repair

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17 pages, 1132 KiB  
Article
Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty
by Daniel Olson and Sean Yaw
Energies 2025, 18(4), 926; https://doi.org/10.3390/en18040926 - 14 Feb 2025
Viewed by 468
Abstract
Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly [...] Read more.
Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions. Full article
(This article belongs to the Section B3: Carbon Emission and Utilization)
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25 pages, 14578 KiB  
Article
Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning
by Danyang Liu, Weiming Cheng, Zhen Qian, Jiayin Deng, Jianzhong Liu and Xunming Wang
Remote Sens. 2023, 15(16), 4036; https://doi.org/10.3390/rs15164036 - 15 Aug 2023
Cited by 3 | Viewed by 2140
Abstract
Detecting impact craters on the Martian surface is a critical component of studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological mapping. The Martian topography is more complex than [...] Read more.
Detecting impact craters on the Martian surface is a critical component of studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological mapping. The Martian topography is more complex than that of the Moon, making the accurate detection of impact crater boundaries challenging. Currently, most techniques concentrate on replacing impact craters with circles or points. Accurate boundaries are more challenging to identify than simple circles. Therefore, a boundary delineator for Martian crater instances (BDMCI) using fusion data is proposed. First, the optical image, digital elevation model (DEM), and slope of elevation difference after filling the DEM (called slope of EL_Diff to highlight the boundaries of craters) were used in combination. Second, a benchmark dataset with annotations for accurate impact crater boundaries was created, and sample regions were chosen using prior geospatial knowledge and an optimization strategy for the proposed BDMCI framework. Third, the multiple models were fused to train at various scales using deep learning. To repair patch junction fractures, several postprocessing methods were devised. The proposed BDMCI framework was also used to expand the catalog of Martian impact craters between 65°S and 65°N. This study provides a reference for identifying terrain features and demonstrates the potential of deep learning algorithms in planetary science research. Full article
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14 pages, 3864 KiB  
Article
Reconstructing Floorplans from Point Clouds Using GAN
by Tianxing Jin, Jiayan Zhuang, Jiangjian Xiao, Ningyuan Xu and Shihao Qin
J. Imaging 2023, 9(2), 39; https://doi.org/10.3390/jimaging9020039 - 8 Feb 2023
Cited by 6 | Viewed by 3324
Abstract
This paper proposed a method for reconstructing floorplans from indoor point clouds. Unlike existing corner and line primitive detection algorithms, this method uses a generative adversarial network to learn the complex distribution of indoor layout graphics, and repairs incomplete room masks into more [...] Read more.
This paper proposed a method for reconstructing floorplans from indoor point clouds. Unlike existing corner and line primitive detection algorithms, this method uses a generative adversarial network to learn the complex distribution of indoor layout graphics, and repairs incomplete room masks into more regular segmentation areas. Automatic learning of the structure information of layout graphics can reduce the dependence on geometric priors, and replacing complex optimization algorithms with Deep Neural Networks (DNN) can improve the efficiency of data processing. The proposed method can retain more shape information from the original data and improve the accuracy of the overall structure details. On this basis, the method further used an edge optimization algorithm to eliminate pixel-level edge artifacts that neural networks cannot perceive. Finally, combined with the constraint information of the overall layout, the method can generate compact floorplans with rich semantic information. Experimental results indicated that the algorithm has robustness and accuracy in complex 3D indoor datasets; its performance is competitive with those of existing methods. Full article
(This article belongs to the Special Issue Geometry Reconstruction from Images)
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17 pages, 2482 KiB  
Article
Molecular Background of Toxic-Substances-Induced Morphological Alterations in the Umbilical Cord Vessels and Fetal Red Blood Cells
by Szabolcs Zahorán, Ágnes Márton, Krisztina Dugmonits, Payal Chakraborty, Ali Khamit, Péter Hegyi, Hajnalka Orvos and Edit Hermesz
Int. J. Mol. Sci. 2022, 23(23), 14673; https://doi.org/10.3390/ijms232314673 - 24 Nov 2022
Cited by 4 | Viewed by 2076
Abstract
The relationship between smoking and human health has been investigated mostly in adults, despite the fact that the chemicals originating from sustained maternal smoking disrupt the carefully orchestrated regulatory cascades in the developing fetus. In this study, we followed molecular alterations in the [...] Read more.
The relationship between smoking and human health has been investigated mostly in adults, despite the fact that the chemicals originating from sustained maternal smoking disrupt the carefully orchestrated regulatory cascades in the developing fetus. In this study, we followed molecular alterations in the umbilical cord (UC) vessels and fetal red blood cells (RBCs), which faithfully reflect the in vivo status of the fetus. We showed evidence for the decreased level of DNA-PKcs-positive nuclei in samples with smoking origin, which is associated with the impaired DNA repair system. Furthermore, we pointed out the altered ratio of MMP-9 metalloproteinase and its endogenous inhibitor TIMP-1, which might be a possible explanation for the morphological abnormalities in the UC vessels. The presented in vivo dataset emphasizes the higher vulnerability of the veins, as the primary target for the toxic materials unfiltered by the placenta. All these events become amplified by the functionally impaired fetal RBC population via a crosstalk mechanism between the vessel endothelium and the circulating RBCs. In our ex vivo approach, we looked for the molecular explanation of metal-exposure-induced alterations, where expressions of the selected genes were upregulated in the control group, while samples with smoking origin showed a lack of response, indicative of prior long-term in utero exposure. Full article
(This article belongs to the Special Issue Molecular Biology of Human Fertility)
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15 pages, 4324 KiB  
Article
Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
by Zhaoyu Liu, Shi Liu, Yaofang Zhang and Pengbo Yao
Appl. Sci. 2022, 12(21), 10814; https://doi.org/10.3390/app122110814 - 25 Oct 2022
Cited by 3 | Viewed by 1894
Abstract
For the purpose of solving the large temperature field reconstruction error caused by different measuring point arrangements and the problem that the prior dataset cannot be built due to data loss or distortion in actual measurement, a three-dimensional temperature profile reconstruction optimization algorithm [...] Read more.
For the purpose of solving the large temperature field reconstruction error caused by different measuring point arrangements and the problem that the prior dataset cannot be built due to data loss or distortion in actual measurement, a three-dimensional temperature profile reconstruction optimization algorithm is proposed to repair the empirical dataset and optimize the arrangement of temperature measuring points based on Tucker decomposition, the minimum condition number method, the greedy algorithm, and the hill climbing algorithm. We used the Tucker decomposition algorithm to repair the missing data and obtain the complete prior dataset and the core tensor. By optimizing the dimension of the core tensor and the number and position of the measuring points calculated by the minimum condition number method, the greedy algorithm, and the mountain climbing algorithm, the real-time three-dimensional distribution of the temperature field is reconstructed. The results show that the Tucker decomposition optimization algorithm could accurately complete the prior dataset, and compared with the original algorithm, the proposed optimal placement algorithm improves the reconstruction accuracy by more than 20%. At the same time, the algorithm has strong robustness and anti-noise, and the relative error is less than 4.0% and 6.0% with different signal-to-noise ratios. It indicates that the proposed method can solve the problem of building an empirical dataset and 3D temperature distribution reconstruction more accurately and stably in industry. Full article
(This article belongs to the Special Issue Advances in Applied Signal and Image Processing Technology)
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19 pages, 3591 KiB  
Article
Boosting Intelligent Data Analysis in Smart Sensors by Integrating Knowledge and Machine Learning
by Piotr Łuczak, Przemysław Kucharski, Tomasz Jaworski, Izabela Perenc, Krzysztof Ślot and Jacek Kucharski
Sensors 2021, 21(18), 6168; https://doi.org/10.3390/s21186168 - 14 Sep 2021
Cited by 8 | Viewed by 2960
Abstract
The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prior knowledge with learning from examples, thus allowing sensor devices to be [...] Read more.
The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prior knowledge with learning from examples, thus allowing sensor devices to be used for the successful execution of machine learning even when the volume of training data is highly limited, using compact underlying hardware. The proposed architecture comprises two interacting functional modules arranged in a homogeneous, multiple-layer architecture. The first module, referred to as the knowledge sub-network, implements knowledge in the Conjunctive Normal Form through a three-layer structure composed of novel types of learnable units, called L-neurons. In contrast, the second module is a fully-connected conventional three-layer, feed-forward neural network, and it is referred to as a conventional neural sub-network. We show that the proposed hybrid structure successfully combines knowledge and learning, providing high recognition performance even for very limited training datasets, while also benefiting from an abundance of data, as it occurs for purely neural structures. In addition, since the proposed L-neurons can learn (through classical backpropagation), we show that the architecture is also capable of repairing its knowledge. Full article
(This article belongs to the Collection Machine Learning and AI for Sensors)
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15 pages, 3659 KiB  
Article
Iterative Online Fault Identification Scheme for High-Voltage Circuit Breaker Utilizing a Lost Data Repair Technique
by Gang Zhou, Zhongjie Han, Jin Fu, Guan Hua Xu and Chengjin Ye
Energies 2020, 13(13), 3311; https://doi.org/10.3390/en13133311 - 28 Jun 2020
Cited by 4 | Viewed by 1993
Abstract
Most of the prior-art electrical noninvasive monitoring systems adopt Zigbee, Bluetooth, or other wireless communication infrastructure. These low-cost channels are often interrupted by strong electromagnetic interference and result in monitoring anomalies, particularly packet loss, which severely affects the precision of equipment fault identification. [...] Read more.
Most of the prior-art electrical noninvasive monitoring systems adopt Zigbee, Bluetooth, or other wireless communication infrastructure. These low-cost channels are often interrupted by strong electromagnetic interference and result in monitoring anomalies, particularly packet loss, which severely affects the precision of equipment fault identification. In this paper, an iterative online fault identification framework for a high-voltage circuit breaker utilizing a novel lost data repair technique is developed to adapt to low-data quality conditions. Specifically, the improved efficient k-nearest neighbor (kNN) algorithm enabled by a k-dimensional (K-D) tree is utilized to select the reference templates for the unintegrated samples. An extreme learning machine (ELM) is utilized to estimate the missing data based on the selected nearest neighbors. The Softmax classifier is exploited to calculate the probability of the repaired sample being classified to each of the preset status classes. Loop iterations are implemented where the nearest neighbors are updated until their labels are consistent with the estimated labels of the repaired sample based on them. Numerical results obtained from a realistic high-voltage circuit breaker (HVCB) condition monitoring dataset illustrate that the proposed scheme can efficiently identify the operation status of HVCBs by considering measurement anomalies. Full article
(This article belongs to the Section F: Electrical Engineering)
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