Boundary Uncertainty Research Articles

Evaluating the Current Situation of River and Lake Shoreline Planning and Utilization Using an Improved Matter-Element Extension Model

Based on the analysis of the influencing factors of the current planning and utilization of river and lake shorelines, an index system of the current planning and utilization of river and lake shorelines is constructed. Based on the triangular fuzzy number method and the improved CRITIC method, the evaluation indicators are weighted and analyzed and then integrated to obtain the combined weight. An improved matter-element extension model was constructed to evaluate the current situation of shoreline planning and utilization in typical regions. At the same time, the TOPSIS evaluation model and the cloud theory evaluation model were compared and analyzed to verify the accuracy of the model. The improved matter-element model accounts for indicator incompatibility and the uncertainty of evaluation boundaries, offering a clearer reflection of the planning and utilization status.

Stochastic two-stage multi-objective unit commitment of distributed resource energy systems considering uncertainties and unit failures

Compared to centralized generation technology, distributed energy resource systems are susceptible to energy risks caused by boundary uncertainties and unit failures. This study introduces a stochastic two-stage multi-objective optimization method to address reliability-based unit commitment issues. In the day-ahead stage, operational state and reserve capacity are determined to minimize prescheduled operation costs based on forecasted parameters. In the real-time stage, a decision-dependent stochastic reliability method is proposed to simulate outage scenarios. Reserve resources within available units are allocated to mitigate forecasting errors and unit failures. Additionally, the grid interaction ratio and penalty cost are added to restrict the depth and frequency access to the grid. Four comparative cases analyze the effects of the proposed methodology. This method innovatively achieves the simulation of stochastic multi-unit outages and delete faulty units in the operation scheme. The optimal results show that the risks of electricity and cooling supply are underestimated, while the risks of heating are overestimated, compared to N-1 reliability. Furthermore, Pareto analysis of the multi-objective problem enhances independent operational capacity through utilization of reserve resources. Grid dispatch pressure is reduced since purchased power can be used as day-ahead planning. Thus, the methodology achieves collaborative optimization of reliability with a reduction of operation costs, offering effective guidance for engineering applications.

PDE-Based Boundary Adaptive Consensus Control of Multiagent Systems With Input Constraints.

The leader-follower adaptive consensus control problem is addressed for partial differential equations (PDEs) multiagent systems (MASs), and these agents are composed of flexible manipulator systems with input nonlinearity, boundary uncertainties, and time-varying disturbances. Because of the spatial variables in the model, the design of adaptive protocols is more difficult than that of ordinary differential equation (ODE) MASs. By designing the Lyapunov function, a novel distributed boundary control (BC) protocol is constructed, which not only ensures the consensus of angular positions but also suppresses the boundary vibration of each agent. The hybrid effects of dead zones and input saturation on flexible manipulator systems are addressed using the approximation properties of neural networks (NNs). In addition, the disturbance adaptive laws are proposed to provide a control solution for bounded and time-varying disturbances. Furthermore, by applying the Lyapunov stability theory, the uniformly bounded stability of the multiflexible manipulator can be ensured. Finally, the feasibility of the presented control approach is verified using numerical examples.

FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation

In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of segmentation boundaries and the fuzziness of regions, resulting in the instability and inaccuracy of the segmentation results. To solve this problem, a denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation (FDiff-Fusion) is proposed in this paper. By integrating the denoising diffusion model into the classical U-Net network, this model can effectively extract rich semantic information from input medical images, thus providing excellent pixel-level representation for medical image segmentation. In this paper, a fuzzy learning module is designed on the skip path of U-Net network because of the widespread boundary uncertainty and region blurring of medical image segmentation. The module sets several fuzzy membership functions for the input encoded features to describe the similarity degree between the feature points, and applies fuzzy rules to the fuzzy membership functions, thus enhancing the modeling ability of the model for uncertain boundaries and fuzzy regions. In addition, in order to improve the accuracy and robustness of the model segmentation results, we introduced an iterative attention feature fusion method in the test phase, which added local context information to the global context information in the attention module to fuse the prediction results of each denoising time step. Finally, to validate the effectiveness of FDiff-Fusion, we compare it with existing advanced segmentation networks on the BRATS 2020 brain tumor dataset and the BTCV abdominal multi-organ dataset. The results show that FDiff-Fusion significantly improves the Dice scores and HD95 distance on these two datasets, demonstrating its superiority in medical image segmentation tasks.

Safe contact-based robot active search using Bayesian optimization and control barrier functions.

In robotics, active exploration and learning in uncertain environments must take into account safety, as the robot may otherwise damage itself or its surroundings. This paper presents a method for safe active search using Bayesian optimization and control barrier functions. As robot paths undertaken during sampling are continuous, we consider an informative continuous expected improvement acquisition function. To safely bound the contact forces between the robot and its surroundings, we leverage exponential control barrier functions, utilizing the derivative of the force in the contact model to increase robustness to uncertainty in the contact boundary. Our approach is demonstrated on a fully autonomous robot for ultrasound scanning of rheumatoid arthritis (RA). Here, active search is a critical component of ensuring high image quality. Furthermore, bounded contact forces between the ultrasound probe and the patient ensure patient safety and better scan quality. To the best of our knowledge, our results are both the first demonstration of safe active search on a fully autonomous robot for ultrasound scanning of rheumatoid arthritis and the first experimental evaluation of bounding contact forces in the context of medical robotics using control barrier functions. The results show that when search time is limited to less than 60 s, informative continuous expected improvement leads to a 92% success, a 13% improvement compared to expected improvement. Meanwhile, exponential control barrier functions can limit the force applied by the robot to under 5 N, even in cases where the contact boundary is specified incorrectly by -1 or +4 mm.

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation.

Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, AS-Net, Swin-Unet, and TransUNet).

Sliding mode control for the stabilization of fractional heat equations subject to boundary uncertainty

By adopting the sliding mode control (SMC) and the generalized Lyapunov method, the boundary feedback stabilization issue is studied for the fractional diffusion system subject to boundary control matched disturbance. The classical sliding surface and the fractional integral sliding function are constructed and sliding mode controllers are designed respectively to realize the Mittag-Leffler (M-L) stabilization of the considered system. The controller based on the newly-introduced fractional integral sliding function not only helps to relax the constraints on the disturbance but also realizes the same stabilization effect as that of the classical one. The well-posedness result of the solution is also obtained for discontinuous fractional heat equations. Besides, a numerical experiment validates the theoretical outcomes.

Неопределенность темпоральных границ в исследованиях исторической реконструкции

The article examines the emerging field of reenactment studies. Three key components of reenactment are analyzed: the political, cultural and cognitive. It is proved that the latter is extremely poorly reflected in reenactment studies, which borrow from popular culture the leveling of any oppositions, including the opposition of “high” and “low”, “professional” science and “popular” opinion. It is the weak demand for the cognitive component, combined with the playful nature of popular culture, that determines the uncertainty of temporal boundaries both in historical reconstruction itself and in reenactment studies, which maximally equate the researcher with participants in reconstruction practices. Next, a comparative analysis of the methodological settings of reconstruction and memory studies is carried out. The main thesis of the article is to prove the multidirectionality of their temporal settings: memory studies seek to clarify the boundaries between past and present, and reenactment studies seek to use and aesthetically play up their uncertainty. The authors declare no conflicts of interests.

ETU-Net: edge enhancement-guided U-Net with transformer for skin lesion segmentation

Objective. Convolutional neural network (CNN)-based deep learning algorithms have been widely used in recent years for automatic skin lesion segmentation. However, the limited receptive fields of convolutional architectures hinder their ability to effectively model dependencies between different image ranges. The transformer is often employed in conjunction with CNN to extract both global and local information from images, as it excels at capturing long-range dependencies. However, this method cannot accurately segment skin lesions with blurred boundaries. To overcome this difficulty, we proposed ETU-Net. Approach. ETU-Net, a novel multi-scale architecture, combines edge enhancement, CNN, and transformer. We introduce the concept of edge detection operators into difference convolution, resulting in the design of the edge enhanced convolution block (EC block) and the local transformer block (LT block), which emphasize edge features. To capture the semantic information contained in local features, we propose the multi-scale local attention block (MLA block), which utilizes convolutions with different kernel sizes. Furthermore, to address the boundary uncertainty caused by patch division in the transformer, we introduce a novel global transformer block (GT block), which allows each patch to gather full-size feature information. Main results. Extensive experimental results on three publicly available skin datasets (PH2, ISIC-2017, and ISIC-2018) demonstrate that ETU-Net outperforms state-of-the-art hybrid methods based on CNN and Transformer in terms of segmentation performance. Moreover, ETU-Net exhibits excellent generalization ability in practical segmentation applications on dermatoscopy images contributed by the Wuxi No.2 People’s Hospital. Significance. We propose ETU-Net, a novel multi-scale U-Net model guided by edge enhancement, which can address the challenges posed by complex lesion shapes and ambiguous boundaries in skin lesion segmentation tasks.

Delineation of a PFOA Plume and Assessment of Data Gaps in its Conceptual Model Using PlumeSeeker™.

An accurate conceptual site model (CSM) and plume-delineation at contamination sites are pre-requisites for successful remediation and for satisfying regulators and stakeholders. PlumeSeeker™ is well-suited for assessing data gaps in CSMs by using available site data and for identifying the optimal number and locations of sampling locations to delineate contaminant plumes. It is an enhancement of a university research code for plume delineation using geostatistical and stochastic modeling integrated with the groundwater modeling software MODFLOW-SURFACT™. PlumeSeeker™ increases the overall confidence in the location of the plume boundary through a variance-reduction approach that selects existing- or new monitoring wells for sampling based on minimizing the uncertainty in plume boundary and on new field information. Applicable at sites with or without existing monitoring wells, PlumeSeeker™ is particularly powerful for optimally allocating project resources (labor, well installation, and laboratory costs) between existing wells and sampling at new locations. An application of PlumeSeeker™ at Lakehurst, the naval component of Joint Base McGuire-Dix-Lakehurst in New Jersey, demonstrates how the cost of delineating the migration pathway of a perfluorooctanoic acid (PFOA) plume can be minimized by requiring only 9 new sampling locations in addition to samples from 2 existing wells for achieving a 70% reduction in plume uncertainty. In addition, the use of available site data in three different scenarios identified CSM data-gaps in the source area and in the interaction between Manapaqua Branch and groundwater, where the observed high concentration in this area could have resulted from a combination of groundwater migration and induced infiltration.

Boundary uncertainty aware network for automated polyp segmentation

Recently, leveraging deep neural networks for automated colorectal polyp segmentation has emerged as a hot topic due to the favored advantages in evading the limitations of visual inspection, e.g., overwork and subjectivity. However, most existing methods do not pay enough attention to the uncertain areas of colonoscopy images and often provide unsatisfactory segmentation performance. In this paper, we propose a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. Specifically, considering that polyps vary greatly in size and shape, we first adopt a pyramid vision transformer encoder to learn multi-scale feature representations. Then, a simple yet effective boundary exploration module (BEM) is proposed to explore boundary cues from the low-level features. To make the network focus on the ambiguous area where the prediction score is biased to neither the foreground nor the background, we further introduce a boundary uncertainty aware module (BUM) that explores error-prone regions from the high-level features with the assistance of boundary cues provided by the BEM. Through the top-down hybrid deep supervision, our BUNet implements coarse-to-fine polyp segmentation and finally localizes polyp regions precisely. Extensive experiments on five public datasets show that BUNet is superior to thirteen competing methods in terms of both effectiveness and generalization ability.

Graph- and transformer-guided boundary aware network for medical image segmentation

Background and objectiveDespite the considerable progress achieved by U-Net-based models, medical image segmentation remains a challenging task due to complex backgrounds, irrelevant noises, and ambiguous boundaries. In this study, we present a novel approach called U-shaped Graph- and Transformer-guided Boundary Aware Network (GTBA-Net) to tackle these challenges. MethodsGTBA-Net uses the pre-trained ResNet34 as its basic structure, and involves Global Feature Aggregation (GFA) modules for target localization, Graph-based Dynamic Feature Fusion (GDFF) modules for effective noise suppression, and Uncertainty-based Boundary Refinement (UBR) modules for accurate delineation of ambiguous boundaries. The GFA modules employ an efficient self-attention mechanism to facilitate coarse target localization amidst complex backgrounds, without introducing additional computational complexity. The GDFF modules leverage graph attention mechanism to aggregate information hidden among high- and low-level features, effectively suppressing target-irrelevant noises while preserving valuable spatial details. The UBR modules introduce an uncertainty quantification strategy and auxiliary loss to guide the model's focus towards target regions and uncertain “ridges”, gradually mitigating boundary uncertainty and ultimately achieving accurate boundary delineation. ResultsComparative experiments on five datasets encompassing diverse modalities (including X-ray, CT, endoscopic procedures, and ultrasound) demonstrate that the proposed GTBA-Net outperforms existing methods in various challenging scenarios. Subsequent ablation studies further demonstrate the efficacy of the GFA, GDFF, and UBR modules in target localization, noise suppression, and ambiguous boundary delineation, respectively. ConclusionsGTBA-Net exhibits substantial potential for extensive application in the field of medical image segmentation, particularly in scenarios involving complex backgrounds, target-irrelevant noises, or ambiguous boundaries.

Combining process-based model and machine learning to predict hydrological regimes in floodplain wetlands under climate change

Climate change has remarkable effects on the hydrological regime in floodplain wetlands. However, the hydrodynamic simulation under climate change conditions suffers from the uncertainty of the downstream water level boundary. In this study, an effective approach was developed by integrating an advanced machine learning algorithm and a process-based watershed hydrological model to predict the lake level under climate change. The developed approach was validated in a vital conservation wetland in the Ramsar Convention, the Poyang Lake floodplain wetland in China and utilized to simulate the hydrological regime in climate change. We found that the Long Short–term Memory (LSTM) performed relatively well in the simulation of water level, with coefficients of determination (R2) of 0.94 and 0.83 in the training and testing periods, respectively. The mean cumulative flooding time in the future period was slightly less than that in the baseline period, with a decreased range of 1.01 ∼ 12.05 days/year. The highest and mean water levels rose significantly in the wet season, with a risen range of 1.01 ∼ 3.3 and 1.56 ∼ 2.44 m, respectively, and the appearance of extreme flood events became more frequent. The maximum, minimum, and mean inflows for Poyang Lake in the dry season exhibited a remarkable reduction trend, especially for the minimum inflow, with a reduced range of 59.37%∼79.22%, indicating that more serious extreme drought events would appear in Poyang Lake. The developed approach has good performance for water level simulation and extreme runoff prediction, thus providing a viable way for future water resources management in floodplain wetlands.

The heterogeneous crustal architecture of the Falkland Plateau Basin

AbstractContinental break‐up can be oftentimes associated with intracontinental wrenching that can lead to the generation of transform margins and transform marginal plateaus. The wrenching phase can be accompanied by complicated processes, which result in heterogeneous structural and crustal architectures. This makes understanding the evolution of such tectonic settings challenging. The Falkland Plateau is an example of a transform marginal plateau where regional wrenching accompanied the incipient stages of Gondwanan continental break‐up to result in a mosaic of crustal types underlying its largest basin: the Falkland Plateau Basin (FPB). The uncertainties in crustal boundaries have led to several models for the evolution of the plateau, which hinder the development of a reliable plate reconstruction of Southern Gondwana. We integrate seismic reflection, gravity and magnetic data to propose an updated crustal architecture of the FPB. The results show that extended continental crust underlies the basin in the west and north. The eastern and central parts consist of a juxtaposition of intruded and underplated continental crust which transitions southwards to a thick oceanic domain. The basin is crosscut by three main NE–SW trending shear zones which facilitated the development of the contrasting crustal and structural domains interpreted across the plateau. This integrated reassessment of the FPB provides new insights into the tectonic evolution of the plateau, the deformation associated with wrenching and transform margin formation and our understanding of the tectono‐stratigraphic evolution of such areas.

Learning to segment fetal brain tissue from noisy annotations.

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI.

Biologically Important Areas II for cetaceans within U.S. and adjacent waters - Updates and the application of a new scoring system

Building on earlier work identifying Biologically Important Areas (BIAs) for cetaceans in U.S. waters (BIA I), we describe the methodology and structured expert elicitation principles used in the “BIA II” effort to update existing BIAs, identify and delineate new BIAs, and score BIAs for 25 cetacean species, stocks, or populations in seven U.S. regions. BIAs represent areas and times in which cetaceans are known to concentrate for activities related to reproduction, feeding, and migration, as well as known ranges of small and resident populations. In this BIA II effort, regional cetacean experts identified the full extent of any BIAs in or adjacent to U.S. waters, based on scientific research, Indigenous knowledge, local knowledge, and community science. The new BIA scoring and labeling system improves the utility and interpretability of the BIAs by designating an overall Importance Score that considers both (1) the intensity and characteristics underlying an area’s identification as a BIA; and (2) the quantity, quality, and type of information, and associated uncertainties upon which the BIA delineation and scoring depend. Each BIA is also scored for boundary uncertainty and spatiotemporal variability (dynamic, ephemeral, or static). BIAs are region-, species-, and time-specific, and may be hierarchically structured where detailed information is available to support different scores across a BIA. BIAs are compilations of the best available science and have no inherent regulatory authority. BIAs may be used by international, federal, state, local, or Tribal entities and the public to support planning and marine mammal impact assessments, and to inform the development of conservation and mitigation measures, where appropriate under existing authorities. Information provided online for each BIA includes: (1) a BIA map; (2) BIA scores and label; (3) a metadata table detailing the data, assumptions, and logic used to delineate, score, and label the BIA; and (4) a list of references used in the assessment. Regional manuscripts present maps and scores for the BIAs, by region, and narratives summarizing the rationale and information upon which several representative BIAs are based. We conclude with a comparison of BIA II to similar international efforts and recommendations for improving future BIA assessments.

Paced-curriculum distillation with prediction and label uncertainty for image segmentation.

In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress. While both methods heavily rely on the ability to score the difficulty of data samples, an optimal scoring function is still under exploration. Distillation is a knowledge transfer approach where a teacher network guides a student network by feeding a sequence of random samples. We argue that guiding student networks with an efficient curriculum strategy can improve model generalization and robustness. For this purpose, we design an uncertainty-based paced curriculum learning in self-distillation for medical image segmentation. We fuse the prediction uncertainty and annotation boundary uncertainty to develop a novel paced-curriculum distillation (P-CD). We utilize the teacher model to obtain prediction uncertainty and spatially varying label smoothing with Gaussian kernel to generate segmentation boundary uncertainty from the annotation. We also investigate the robustness of our method by applying various types and severity of image perturbation and corruption. The proposed technique is validated on two medical datasets of breast ultrasound image segmentation and robot-assisted surgical scene segmentation and achieved significantly better performance in terms of segmentation and robustness. P-CD improves the performance and obtains better generalization and robustness over the dataset shift. While curriculum learning requires extensive tuning of hyper-parameters for pacing function, the level of performance improvement suppresses this limitation.

Modeling the impact of uncertainties in geological boundaries for high-temperature aquifer thermal energy storage cases with the help of GMSH

Modeling the impact of uncertainties in geological boundaries for high-temperature aquifer thermal energy storage cases with the help of GMSH

Uncertainty quantification of phase boundary in a composition-phase map via Bayesian strategies

Phase boundary indicates the conditions of transition between phase regions, which is a key constituent of a phase diagram. We propose an approach to determine the phase boundary and its uncertainty in a phase-composition map based on the data from high-throughput experimentation. Bayesian logistics regression combined with the domain knowledge of phase diagrams was applied in the approach. For a typical ternary isothermal section, both the linear and nonlinear phase boundaries as well as the vertices of a ternary tie triangle were modeled to quantify the uncertainty with consideration of a couple of affecting factors such as data-point density, noise in the data, data coverage, etc. The effectiveness of the present approach was demonstrated by the Fe-Cr-Ni isothermal section data from database and the Fe-Co-Ni composition-phase map data by experiment. Moreover, the uncertainty of the phase boundary in the ternary system can be further reduced by incorporating the available data from the subbinary systems. The data-driven nature of the developed approach can further guide the efficient implementation of high-throughput experiments and provide confidence measures for decision-making in materials design and further Computer Coupling of Phase Diagrams and Thermochemistry (CALPHAD) method modeling.

A confidence level estimation technique for reliable Data-Driven FRA interpretation in air-core winding faults

Frequency response analysis (FRA) data interpretation is among highly challenging tasks in transformer fault diagnosis. Among different quantitative methods, employing statistical indicators on FRA results for mechanical integrity assessment is well-stablished. However, the boundary values of indices that differentiate normal/healthy, suspicious, and abnormal transformer operating conditions are not yet agreed upon. This will make the FRA data interpretation using SIs subjective and unreliable. In this study, data-driven critical boundaries of the most frequently used SIs are introduced. To address the uncertainty of the decision boundaries, a confidence level estimation technique is developed to quantify the probability of a transformer being in a particular operating condition. Reporting a confidence level along with the classified conditions facilitates objective decision-making that can support utility operators during on-site transformer evaluation. The confidence level estimation technique proposed in this work was inspired by bolstered error estimation method widely applied in pattern recognition. Moreover, this study provides a new comprehensive technique of transformer winding fault modeling using SPICE netlists. The proposed analytical winding model is validated through practical FRA measurements with high precision. It is further used to generate various scenarios of common active part faults, such as disk space variation, radial displacement, and disk tilting. Consequently, the proposed method of confidence level estimation is examined on different winding deformations and demonstrated a high potential for comprehensive transformer winding diagnosis.