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This paper proposes EMA-Net, a fully convolutional neural network, to improve the effectiveness of sea-land segmentation on Gaofen-2 images. The aim is to address the issue of low segmentation accuracy in sea-land boundary regions when using remote sensing images for sea-land segmentation. The MA-Net network structure is enhanced by splitting the EfficientNet-B0 benchmark network into five convolutional blocks. The five downsampled convolutional blocks in MA-Net are then sequentially replaced. Furthermore, an extra loss term for the sea-land boundary region is incorporated through the introduction of a boundary region enhancement loss function. This approach encourages the network to focus on learning the boundary region between the sea and land. This improves the accuracy of its prediction. The study presents the results of segmentation experiments conducted on a constructed Gaofen-2 image dataset. The improved EMA-Net model, utilizing the boundary region enhancement loss, achieves better performance than other methods for both the overall region and the sea-land boundary region. The LR (Land Recall), LP (Land Precision), SR (Sea Recall), SP (Sea Precision), F1 Score (F1-Score), mIoU (Mean Intersection over Union), and EA (Edge Accuracy) are averaged over multiple experiments to reach 97.78%, 96.63%, 97.65%, 98.48%, 97.62%, 95.37%, and 87.08% respectively. Additional experiments on the IKONOS images also confirmed the adaptability of the proposed method to high-resolution imagery. [ABSTRACT FROM AUTHOR]

The confinement effect in micro- and nanopores gives rise to distinct flow characteristics in fluids. Clarifying the fluid migration pattern in confined space is crucial for understanding and explaining the abnormal flow phenomena in unconventional reservoirs. In this study, flow characteristics of water and oil in alumina nanochannels were investigated with diameters ranging from 21 nm to 120 nm, and a heterogeneous viscosity flow model considering boundary fluid was proposed. Compared with the prediction of the HP equation, both types of fluids exhibit significant flow suppression in nanochannels. As the channel size decreases, the deviation degree increases. The fluid viscosity of the boundary region displays an upward trend as the channel size decreases and the influence of the interaction between the liquid and solid walls intensifies. The thickness of the boundary region gradually decreases with increasing pressure and eventually reaches a stable value, which is primarily determined by the strength of the interaction between the liquid and solid surfaces. Both the pore size and wettability are essential factors that affect the fluid flow. When the space scale is extremely small, the impact of wettability becomes more pronounced. Finally, the application of the heterogeneous flow model for permeability evaluation has yielded favorable fitting results. The model is of great significance for studying the fluid flow behavior in unconventional reservoirs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The scope of this paper is to give introduction and basic on intuitionistic fuzzy nano topology, intuitionistic fuzzy nano topological spaces, Basic properties of intuitionistic fuzzy nano open sets and intuitionistic fuzzy nano closed sets are studied. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of clustering algorithms in detecting arbitrary clusters, such as low recognition accuracy, large number of iterations, and poor detection effect, this paper proposes a density clustering algorithm of boundary point division based on local center measure (named DBLCM). Under the limitation of the local center measure, data points are divided into core areas or boundary areas. The points of core region are grouped together to form initial clusters according to the allocation mode of the priority of mutual nearest neighbors, and the points in the boundary region are allocated according to the clusters of the nearest points among its mutual nearest neighbors to obtain the final cluster structure. To verify the algorithm effectiveness, DBLCM is compared with three classic algorithms and three outstanding algorithms newly proposed in recent years on the two-dimensional datasets containing arbitrary shapes and arbitrary densities as well as the multi-dimensional datasets with arbitrary dimensions. In addition, to verify the sensitivity of the parameter k in the DBLCM algorithm, a correlation test is conducted between the k value and the cluster quality on different types of datasets. The experimental results show that the DBLCM algorithm has the advantages such as high recognition accuracy, good ability to det ect arbitrary clusters, and no iteration, and it has bet ter comprehensive performance than the six comparison algorithms. [ABSTRACT FROM AUTHOR]

The confinement effect in micro- and nanopores gives rise to distinct flow characteristics in fluids. Clarifying the fluid migration pattern in confined space is crucial for understanding and explaining the abnormal flow phenomena in unconventional reservoirs. In this study, flow characteristics of water and oil in alumina nanochannels were investigated with diameters ranging from 21 nm to 120 nm, and a heterogeneous viscosity flow model considering boundary fluid was proposed. Compared with the prediction of the HP equation, both types of fluids exhibit significant flow suppression in nanochannels. As the channel size decreases, the deviation degree increases. The fluid viscosity of the boundary region displays an upward trend as the channel size decreases and the influence of the interaction between the liquid and solid walls intensifies. The thickness of the boundary region gradually decreases with increasing pressure and eventually reaches a stable value, which is primarily determined by the strength of the interaction between the liquid and solid surfaces. Both the pore size and wettability are essential factors that affect the fluid flow. When the space scale is extremely small, the impact of wettability becomes more pronounced. Finally, the application of the heterogeneous flow model for permeability evaluation has yielded favorable fitting results. The model is of great significance for studying the fluid flow behavior in unconventional reservoirs.

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The concept and feasibility of deploying higher-order space harmonics (HSHs) in 1-D periodic leaky-wave antennas (1-D periodic LWAs) is proposed, studied, analyzed, and demonstrated in this article. This can lead to the creation of multiband multibeam antennas (MBMBAs) with the capability of wide scanning ranges. To show the proposed concept, the excitation and flexibility of odd (n ε [−1, −3]) and even (n ε [−2, −4]) HSHs based on the analysis of a Brillouin diagram along with the effect of unit-cell geometrical structures of substrate integrated waveguide (SIW) for different propagating media are investigated. The proposed analysis is experimentally validated by prototyping and measuring a 1-D periodic LWA in SIW technology in which two HSHs (n ε [−1, −2]) are utilized for achieving a dual-band multibeam LWA with wide scanning range capability. In addition, a triple-band multibeam LWA design based on this concept is also analyzed and demonstrated where we take advantage of the characteristics of four HSHs (n ε [−1, −2, −3, −4]). Furthermore, the band edge issue of different stop-bands of HSH is also examined, and a method is proposed and used to address this issue where we use a quarter wavelength technique to overcome the matching problem at different frequencies related to different stop-bands of various HSHs (n ε [−1, −2, −3, −4]). Another design of a SIW-based 1-D periodic LWA is also discussed to extend and generalize the proposed concept in solving the mismatch issues of HSHs focusing on broadside radiation of the n = −2 space harmonic. The measured results are found to agree very well with their simulation and analysis counterparts. [ABSTRACT FROM AUTHOR]

3-D-TCAD results are presented showing current density profiles for triggered GGNmosts with substantial, and with minimal, drain ballast resistance. That same variation is repeated on two more GGNmosts with reduced source ballast resistance, which reduces the electron–hole current ratio. The simulated current density profiles fill only part of the device, while in the complementary part of the device no current flows. Between these parts, boundary regions exist in which the current density decreases from its peak value to zero. The width of these boundary regions is set by carrier diffusion, and can therefore not be arbitrarily small. As this article shows, the result is that the boundary regions can contribute to GGNmost current spreading. Moreover, the boundary regions can exist in a stable manner over time, and this article shows the physical mechanism by which this happens. Finally, this article shows how boundary regions manifest themselves in TLP I (V)-curves: features relating to boundary regions are identified in TLP I (V)-curves simulated by 3-D-TCAD, and then replicated in measured TLP I (V-)-curves. Bottom line is that boundary regions can help spread current in a GGNmost. [ABSTRACT FROM AUTHOR]

In recent years, three-way decisions have received much attention in uncertain decision and cost-sensitive learning communities. However, in many real applications, labeled samples are usually far from sufficient. In this case, it is a reasonable choice to defer the decision rather than make an immediate decision without sufficient supported information, thus it constructs a boundary region. In order to label more available samples, a traditional co-training method employs two classifiers on two complementary views to extend the existing training sets. However, the wrong predictions of new labels may lead to a high misclassification cost, especially when few labeled samples are available. To address this problem, a co-training method is incorporated into three-way decisions, which can label new samples with higher confidence. When we obtain sufficient labeled samples, the non-commitment decisions are directly decided to a positive or a negative region, which finally generates a two-way decisions result. Experiments on several face databases are conducted to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The training of the support vector machine (SVM) classifier has high computational complexity and is not suitable for large data classification. Since the classification hyperplane is determined by the support vectors and the other instances do not have an effect on the classifier, a method is introduced that does not use all instances for training. Data set may include inappropriate instances such as noisy and outlier instances. In this paper, a novel method is introduced in which at the first step using the belief function theory, the instances uncertainty such as noisy and outlier instances are identified and discarded, at the second step using the geometric method, called, boundary region, the boundary instances are determined. Finally, at the last step, by using the obtained boundary instances, the training of the SVM classifier is done. In the proposed method BF–BR (Belief Function–Boundary Region), the computational cost of the classification training is reduced without losing classification accuracy. The performance has been evaluated on real world data sets from UCI repository by the tenfold cross validation method. The results of the experiments have been compared with the other methods, which indicate superiority of the proposed method in terms of the number of training instances and training time while good classification accuracy for SVM training is achieved. [ABSTRACT FROM AUTHOR]

We introduce a new order on rough sets inspired by the notion of accuracy measure. Rough sets ordered by this accuracy-type order form a complete lattice, which is isomorphic to the lattice where rough sets are ordered traditionally by the inclusion relation. Although the lattices are order-isomorphic, the elements can be totally differently located in them. The isomorphy of the lattices is verified by introducing the so-called boundary representation of rough sets. Some basic properties of the introduced order are also presented and an illustrative example demonstrates the relationship between the different structures. [ABSTRACT FROM AUTHOR]

In this paper, cat optimization algorithm for feature extraction in satellite image has been proposed. In cat optimization, cost function computes the pixel in the satellite image to preserve the boundary shape and avoid non-convex part of the contour of the image. However, the existing feature extraction optimization algorithm measures the distinct data framework and thematic information to insight land cover such as waterbody, urban and vegetation. The land cover is obtained from different optimized feature extraction algorithms never provide proper boundary shape and land feature. Furthermore, the proposed cat optimized algorithm distinguishes the inner, outer and extended boundary along with the land cover. The cat-optimised algorithm for low and high-resolution satellite image shows the better result of 85%, with the preserved convex region when compared with the existing feature extraction algorithm such as fuzzy and Particle Swarm Optimization (PSO). [ABSTRACT FROM AUTHOR]

Database may contain pivotal records-small chunks of records or instances consist of important information specific to the domain. These chunks of instances may contain crucial information which assists in decision making by assigning labels to pivotal records, unlabeled data instances and improves accuracy of the classification model. Our work suggests the heuristic Rough Set Boundary detection for approximating the boundary set efficiently from the large database to reduce the search space substantially for finding critical records. The use of Rough Set Boundary detection has the advantage of obtaining rough set from the original data set which confines the search space only to the boundary. It uses the concept of pivotal score for each instance in the boundary to isolate the critical records. The method also exploits Feature Selection technique for reduced set of attributes in order to obtain less computational time. The proposed work retrieves the pivotal records from the boundary set and also improves the classification accuracy by increasing true positive and true negative errors. Experiments are carried out for real—world medical data sets with numeric values and various classification algorithms are executed to validate the results. Result shows that the identification of pivotal records from rough boundary set helps for improved classification accuracy using less computational time and which are validated using real-world data sets. [ABSTRACT FROM AUTHOR]

Prediction of fault proneness of modules in software is one of the ways to ensure the achievement of software quality and reliability. Software delivered cannot not always be bug free, the more the number of bug the more the dis-satisfaction among client due to degradation in software quality and reliability. Though we have few models for detecting software fault prone modules, the intend of our work is to increase the reliability of the software by using an approach named Rough Fuzzy c-means (RFCM) clustering algorithm to analyse the fault proneness of the software modules under test. This helps in an efficient analysis of the modules having an ambiguous behaviour using rough set boundary which is not possible using traditional clustering methods. A dataset from PROMISE software engineering repository has been taken for the experimental analysis. The results were promising enough to determine software modules which fall in the boundary region of ambiguity emphasizing the software team to focus on those modules to achieve higher reliable system. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Most existing FS methods based on rough set theory use the dependency function for evaluating the goodness of a feature subset. However, these FS methods may unsuccessfully be applied on dataset with noise, which determine only information from a positive region but neglect a boundary region. This paper proposes a criterion of the maximal lower approximation information (Max-Certainty) and minimal boundary region information (Min-Uncertainty), based on neighborhood rough set and mutual information for evaluating the goodness of a feature subset. We combine this proposed criterion with neighborhood rough set, which is directly applicable to numerical and heterogeneous features, without involving a discretization of numerical features. Comparing it with the rough set based approaches, our proposed method improves accuracy over various experimental data sets. Experimental results illustrate that much valuable information can be extracted by using this idea. This proposed technique is demonstrated on discrete, continuous, and heterogeneous data, and is compared with other FS methods in terms of subset size and classification accuracy. [ABSTRACT FROM AUTHOR]

In this article a mass transfer model for multiphase flow in naturally fractured reservoirs was derived. The scaling analysis is presented in order to identify the characteristic scales where the fractured porous media can be studied. The system under consideration consists of two regions and an inter-region. One region is porous medium saturated with multiphase flow (-region) and the other region is multiphase flow in the fracture (-region). The multiphase flow analyzed in both regions is water-oil-gas. The one-equation model for the fractured system was obtained considering thermodynamic equilibrium, which can be applied in-region,-region, and-inter-region due to the non-local averaging approximation. [ABSTRACT FROM PUBLISHER]