In this article, to explore the efficiency and precision of the 3-D finite volume method (FVM) for the logging while drilling (LWD) ultradeep resistivity measurements, we compared four different schemes: field-based total-field FVM, coupled potentials total-field FVM, field-based secondary-field FVM, and coupled potentials secondary-field FVM. The fast and accurate discretization of scattered current density in the secondary-field method is another issue we focus on. On the one hand, we improve the discretization accuracy of the scattered current density near the source by extracting the direct waves in the background electric field. On the other hand, based on the dyadic Green’s functions (DGFs) of vector potentials, the number of Sommerfeld integrals in the background electric field is reduced as much as possible through the background field library and interpolation. The numerical results show that the accuracy and stability of the secondary-field method are better than those of the total-field method and the efficiency of the background electric field is greatly improved through the library and interpolation. Based on the premises of the LWD ultradeep resistivity measurements and the direct solver, the accuracy of the field-based and coupled potentials methods is almost the same, however, the field-based method is much more efficient. Overall, we believe that the field-based secondary-field FVM and the direct solver constitute a more efficient modeling scheme with high precision for LWD ultradeep resistivity measurements.

With the development of computation methods and the requirement of data processing, it is often required to execute electromagnetic (EM) simulations in a lot of different complex formation models simultaneously. For this purpose, in this article, we advance a contraction high-order Born approximation (CHBA) of scattered EM fields from arbitrary perturbation in conductivity based on the 3-D finite volume method (FVM) of coupled potentials. We manage to apply the CHBA to efficiently and precisely simulate the logging while drilling (LWD) ultra-deep resistivity measurement in multiple perturbation models based on arbitrarily given anisotropic reference models. First, from the energy conservation of the EM fields, we derive the rigorous contraction operator about the modified scattered EM fields through the variable transformations. After that, the modified scattered EM fields are expanded into an unconditionally convergent series. All terms of the series can be obtained by solving the Helmholtz equation with recursively right-hand terms. Then, we apply the relative residuals of the modified scattered EM fields to determine the truncation order of the series and acquire the reliable CHBA solution. The Helmholtz equation is discretized by the 3-D FVM and solved by the parallel direct sparse solver (PARDISO). We thus obtain the EM fields of multiple sources in the multiple perturbation models simultaneously. Finally, the numerical results validate the algorithm and compare the EM responses in multiple perturbation models.

Global IPv6 scanning has always been a challenge for researchers because of the limited network speed and computational power. In this paper, we introduce 6GAI to implement more efficient target address generation. 6GAI is built with Generative Adversarial Net (GAN) integrated with Convolutional Bottleneck Attention Module (CBAM). 6GAI allows the discriminative net to leak generated address’s high-level features extracted by CBAM to the generative net, while the generative net incorporates such informative signals into all generation steps through an additional Manger module, which takes the extracted features of current generated address nybbles and outputs a latent vector to guide the Worker module for active IPv6 address generation. This work outperformed the state-of-the-art target generation algorithms on two datasets including one public dataset and one independently collected dataset.

The Encrypted Domain Name System (DNS) and Encrypted Server Name Indication (ESNI) are recently proposed to enhance network security and privacy protection; we refer to these schemes collectively as domain name encryption technologies. Previous research has shown that the destination IP address accessed by the user cannot be associated with common web services such as websites because a large number of websites are hosted through cloud or CDN over IPv6. However, encrypted DNS, as an internet infrastructure service, is typically deployed independently by the service provider rather than hosted through cloud or CDN. In this paper, we propose a method to discover the unique service provider of encrypted DNS resolvers on a large-scale encrypted traffic with TLS1.3 support over IPv6. The model utilizes a Siamese Network to determine whether two IPv6 resolver addresses belong to the same service provider of encrypted DNS, even if the DNS query is protected by ESNI. Through a comprehensive analysis of two real-world datasets, which include encrypted DNS data and common web data, we find that the implementation of TLS1.3, especially ESNI, does not impact the association of encrypted DNS server addresses. Our model achieves an accuracy rate of 95.29%.

Wireless short-hop communication is a crucial solution for information transmission between logging while drilling and measurement while drilling. It is important for closed-loop control in geosteering drilling. Compared with wired transmission, wireless short-hop communication offers a cost-effective and stable alternative. It furthermore reduces drilling risks and improves efficiency. Current research primarily concentrates on magnetic dipole antennas, where resistivity stands as the only continuous variable under investigation, and consideration of other parameters may be limited. It lacks complex models and fails to account for factors such as borehole mud and antenna slots. This paper utilizes the Finite Element Method to examine the transmitting and receiving characteristics of electric dipole and magnetic dipole antennas and investigate the effects of transmission conditions and instrument structure on the signal. Experimental validation in a water tank confirmed the reliability and practicality of the numerical simulation results. It suggests that using a coil number between 150 and 200 turns with a frequency of 5 to 8 kHz for both antenna structures in oil-based mud can achieve transmission distances of over 15 m. The antenna slot has minimal impact on the signal. Design considerations should prioritize overall structural stability and mechanical strength. These findings contribute valuable insights to the design and optimization of the instrument.

The article advances a 3-D adaptive regularization nonlinear inversion of the logging while drilling (LWD) ultradeep multicomponent resistivity (LWD-UDMCR) by the Gauss-Newton (GN) method. We manage to reconstruct the pixel-based horizontal and vertical conductivities simultaneously in a goal domain outside an arbitrary dipping borehole. The piecewise constant functions are used to describe the spatial distribution of the block-based and the pixel-based conductivity. The background formation is assumed as the horizontally layered transversely isotropic (TI) media, and the background electromagnetic (EM) fields are determined analytically by the transmission line method (TLM). We then use the 3-D finite volume method (FVM) of secondary field coupled potentials and parallel direct sparse solver (PARDISO) to simulate the tool responses and Fréchet derivatives simultaneously. Through the projection operator and OpenMP parallel technique, we further enhance the computational efficiency of the pixel-based explicit Fréchet derivatives and set up a complete normalization linearized response. After that, the large normal equation from the quadratic objective function is solved by the preconditioned conjugate gradient (PCG) to determine the gradient of the objective function. By properly controlling the maximum component of the gradient per iteration step, we acquire an adaptive regularization factor so that the stabilization of the inversion solution is assured as well as the realization of the best fit of the input data with the modeling logs. Finally, numerical tests validate the algorithm and antinoise ability.

The article proposes an efficient global optimization of adaptive boundary detection from the logging while drilling (LWD) azimuthal electromagnetic (EM) measurements. The goal is to realize real-time geo-steering in 1-D layered transversely isotropic (TI) formation with an arbitrarily deviated borehole. The method includes apparent resistivity (APR) extraction and 0-D inversion, 1-D adaptive regularized iterative inversion, and global optimization. The APR extraction and 0-D inversion are used to quickly determine the initial horizontal and vertical resistivities of the bed where the tool is located. Subsequently, the 1-D regularized inversion is performed to achieve a local minimum solution near an arbitrarily given initial model. For solving the non-unique problem and acquiring the globally optimal solution, several different initial values are selected according to the possible range per model parameter to construct a serial of initial models. The OpenMP parallel technique is applied for simultaneous inversions at all initial models. Multiple inversion solutions may be obtained due to the non-uniqueness. The one with the minimal residual function in all inversion results will become the globally optimal solution. Furthermore, the tool response and its exact explicit Fréchet derivative with respect to each model parameter are analytically calculated, while an adaptive regularization factor ensures a gradual reduction of the objective function. The correction of field data is required to reduce the mandrel effect. The inversion results of synthetic and field data demonstrated that the proposed inversion algorithm efficiently provides the reliable bed boundaries and resistivities around the wellbore.

As an effective tool for data analysis and knowledge processing, the theory of concept lattices has been studied extensively and applied to various fields. In order to discover useful knowledge, one often ignores some attributes according to a particular purpose and merely considers the subcontexts of a rather complex context. In this paper, we make a deep investigation on the theory of concept lattices of subcontexts. An approach to construct the concept lattice of a context is first presented by means of the concept lattices of its subcontexts. Then the concept lattices induced by all subcontexts of the context are considered as a set, and an order relation is introduced into the set. It is proved that the set together with the order relation is a complete lattice. Finally, the top element and the bottom element of the complete lattice are also obtained.

Rough sets theory has proved to be a useful mathematical tool for classification and prediction. However, as many real-world problems deal with ordering objects instead of classifying objects, one of the extensions of the classical rough sets approach is the dominance-based rough sets approach, which is mainly based on substitution of the indiscernibility relation by a dominance relation. In this article, we present a dominance-based rough sets approach to reasoning in incomplete ordered information systems. The approach shows how to find decision rules directly from an incomplete ordered decision table. We propose a reduction of knowledge that eliminates only that information that is not essential from the point of view of the ordering of objects or decision rules. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 13–27, 2005.

Theories of fuzzy sets and rough sets have emerged as two major mathematical approaches for managing uncertainty that arises from inexact, noisy, or incomplete information. They are generalizations of classical set theory for modelling vagueness and uncertainty. Some integrations of them are expected to develop a model of uncertainty stronger than either. The present work may be considered as an attempt in this line, where we would like to study fuzziness in probabilistic rough set model, to portray probabilistic rough sets by fuzzy sets. First, we show how the concept of variable precision lower and upper approximation of a probabilistic rough set can be generalized from the vantage point of the cuts and strong cuts of a fuzzy set which is determined by the rough membership function. As a result, the characters of the (strong) cut of fuzzy set can be used conveniently to describe the feature of variable precision rough set. Moreover we give a measure of fuzziness, fuzzy entropy, induced by roughness in a probabilistic rough set and make some characterizations of this measure. For three well-known entropy functions, including the Shannon function, we show that the finer the information granulation is, the less the fuzziness (fuzzy entropy) in a rough set is. The superiority of fuzzy entropy to Pawlak's accuracy measure is illustrated with examples. Finally, the fuzzy entropy of a rough classification is defined by the fuzzy entropy of corresponding rough sets. and it is shown that one possible application of it is lies in measuring the inconsistency in a decision table.

Abstract: The probability reasoning method, fuzzy reasoning method, evidential reasoning method and other reasoning methods are main techniques employed in intelligent systems for processing uncertain and vague information. The concept of inclusion degree was proposed earlier and it has been proved that the methods mentioned above are examples of inclusion degrees. In this paper, we introduce type S1 and type S2 inclusion degrees, discuss the relationship between them, and further propose inclusion degrees on interval numbers, divisions, vectors and set vectors. This paper addresses an uncertainty analysis method with different inclusion degrees for intelligent systems and other systems such as fuzzy relational databases.

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.

Abstract: The theory of rough sets is an extension of set theory for studying intelligent systems characterized by insufficient and incomplete information. We discuss the basic concept and properties of knowledge reduction based on inclusion degree and evidence reasoning theory, and propose a knowledge discovery approach based on inclusion degree and evidence reasoning theory.

This article deals with approaches to knowledge reductions in inconsistent information systems (ISs). The main objective of this work was to introduce a new kind of knowledge reduction called a maximum distribution reduct, which preserves all maximum decision classes. This type of reduction eliminates the harsh requirements of the distribution reduct and overcomes the drawback of the possible reduct that the derived decision rules may be incompatible with the ones derived from the original system. Then, the relationships among the maximum distribution reduct, the distribution reduct, and the possible reduct were discussed. The judgement theorems and discernibility matrices associated with the three reductions were examined, from which we can obtain approaches to knowledge reductions in rough set theory (RST). © 2003 Wiley Periodicals, Inc.

In this paper, a genetic-based method for training fuzzy classification systems is proposed. The genetic algorithm, called genetic algorithm with no genetic operators (GANGO), neither needs to use the conventional genetic operators nor to store the population throughout the evolution process, but still has the same search mechanisms as conventional genetic algorithms. The novelty of the proposed training approach lies in: 1) the new scheme of encoding a fuzzy system based on the interpretation of the values of the components of a fuzzy relationship matrix as the sample probabilities of genes, and this, together with no requirement on storing the population, contributes to a dramatic decrease in storage requirement and computational cost; and 2) the automatic elimination of irrelevant fuzzy rules using a fitness reassignment strategy at the gene level and a weight truncation strategy. The proposed training method is successfully applied to train a fuzzy system for the classification of real-world remote sensing data.

Extracting patterns and models of interest from large databases is attracting much attention in a variety of disciplines. Knowledge discovery in databases (KDD) and data mining (DM) are areas of common interest to researchers in machine learning, pattern recognition, statistics, artificial intelligence, and high performance computing. An effective and robust method, the regression class mixture decomposition (RCMD) method, is proposed for the mining of regression classes in large data sets, especially those contaminated by noise. A concept, called "regression class" which is defined as a subset of the data set that is subject to a regression model, is proposed as a basic building block on which the mining process is based. A large data set is treated as a mixture population in which there are many such regression classes and others not accounted for by the regression models. Iterative and genetic-based algorithms for the optimization of the objective function in the RCMD method are also constructed. It is demonstrated that the RCMD method can resist a very large proportion of noisy data, identify each regression class, assign an inlier set of data points supporting each identified regression class, and determine the a priori unknown number of statistically valid models in the data set. Although the models are extracted sequentially, the final result is almost independent of the extraction order due to a dynamic classification strategy employed in the handling of overlapping regression classes. The effectiveness and robustness of the RCMD method are substantiated by a set of simulation experiments and a real-life application showing the way it can be used to fit mixed data to linear regression classes and nonlinear structures in various situations.