Simplification Method Research Articles

Abstraction for non-ground answer set programs

Abstraction is an important technique utilized by humans in model building and problem solving, in order to figure out key elements and relevant details of a world of interest. This naturally has led to investigations of using abstraction in AI and Computer Science to simplify problems, especially in the design of intelligent agents and automated problem solving. By omitting details, scenarios are reduced to ones that are easier to deal with and to understand, where further details are added back only when they matter. Despite the fact that abstraction is a powerful technique, it has not been considered much in the context of nonmonotonic knowledge representation and reasoning, and specifically not in Answer Set Programming (ASP), apart from some related simplification methods. In this work, we introduce a notion for abstracting from the domain of an ASP program such that the domain size shrinks while the set of answer sets (i.e., models) of the program is over-approximated. To achieve the latter, the program is transformed into an abstract program over the abstract domain while preserving the structure of the rules. We show in elaboration how this can be also achieved for single or multiple sub-domains (sorts) of a domain, and in case of structured domains like grid environments in which structure should be preserved. Furthermore, we introduce an abstraction-&-refinement methodology that makes it possible to start with an initial abstraction and to achieve automatically an abstraction with an associated abstract answer set that matches an answer set of the original program, provided that the program is satisfiable. Experiments based on prototypical implementations reveal the potential of the approach for problem analysis, by its ability to focus on the parts of the program that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details. This makes domain abstraction an interesting topic of research whose further use in important areas like Explainable AI remains to be explored.

Polygonal simplification and its use in DEM generalization for land surface segmentation

AbstractWe introduce a simplification method based on irregular triangular networks to create a generalized digital elevation model (DEM) for land surface segmentation. The quadric error metrics simplification (QEMS) algorithm was used to generalize the DEM as a method that follows the theory of optimal triangle for (land) surface representation. Its use for land surface modeling is novel, however, it is well known in computer graphics. Comparison of QEMS with the maximum z‐tolerance method shows priority in terms of root mean square error. Numerical expression of the value concentration of the third‐order variables (curvature changes) around zero (K0) was used as an indicator of elementary form representation in generalized DEMs. K0 was calculated for a set of DEMs at several generalization levels in two different territories, as well as for comparably generalized artificial surfaces. K0 values of real DEMs vary significantly with generalization levels and character of the landforms, and their maxima point to the generalization levels that best preserve the information about elementary forms of various hierarchical levels. Differences between K0 of real DEMs and artificial surfaces confirm the same.

High-volume point cloud data simplification based on decomposed graph filtering

Recent studies on three-dimensional (3D) point cloud data (PCD) simplification have played significant roles in computer-aided models for alleviating computational and storage burden. However, existing simplification methods are not suitable for the high-volume PCD with billions of point number, especially in construction industry. In this paper, a decomposed simplification method is developed to handle the high-volume buildings' PCD. Based on the divide-and-conquer philosophy, the new decomposed approach effectively reduces the memory usage. In the proposed approach, PCD is divided into several subsets according to the relationship of natural neighbor, and decomposed graph filtering with adaptive resampling rate is designed. It can be proved that the decomposed simplification results have no variance in the comparison with the optimal simplification of entire PCD. Verification experiments are conducted on different sizes of PCD, which indicate the effectiveness and feasibility of the proposed approach.

DFT study for the absorption spectra evolution of CdS magic-size clusters

Absorption spectroscopy is important and necessary for the study of magic-size clusters (MSCs). However, researches regarding the relationship between the structural change and absorption spectra variation are quite limited. Herein, we compared the structural variation and absorption evolution of MSCs by first-principles calculations in this paper. Firstly, we simplified the single crystal structure In37P20(OOCCH2Ph)51 of InP MSC to In37P20H68 and calculated its absorption spectrum using three different functionals (PBE, B3LYP, HSE06). By comparing the absorption spectra calculated from the In37P20H68 structure with those measured from the InP MSC sample, we evaluated the difference in absorption spectra calculated by different functionals and the rationality of this kind of method for structure simplification. Secondly, we validated the structural model Cd37S20 proposed for CdS MSC-311 by calculating the absorption spectrum of Cd37S20H68 and comparing the similarity between the absorption spectrum calculated and the experimental measured. Finally, we expanded structures based on the principal characteristics of Cd37S20 and calculated the corresponding absorption spectra of them. By comparing the difference among the absorption spectra of the expanded structures, we demonstrate that the absorption peak position shifts of MSCs are more sensitive to their anisotropy, instead of the number of atoms added and local structure change. These findings provide an in-depth understanding of the structure-absorption relationship.

A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding

According to the requirements of point cloud simplification for T-profile steel plate welding in shipbuilding, the disadvantages of the existing simplification algorithms are analyzed. In this paper, a point cloud simplification method is proposed based on octree coding and the threshold of the surface curvature feature. In this method, the original point cloud data are divided into multiple sub-cubes with specified side lengths by octree coding, and the points that are closest to the gravity center of the sub-cube are kept. The k-neighborhood method and the curvature calculation are performed in order to obtain the curvature features of the point cloud. Additionally, the point cloud data are divided into several regions based on the given adjustable curvature threshold. Finally, combining the random sampling method with the simplification method based on the regional gravity center, the T-profile point cloud data can be simplified. In this study, after obtaining the point cloud data of a T-profile plate, the proposed simplification method is compared with some other simplification methods. It is found that the proposed simplification method for the point cloud of the T-profile steel plate for shipbuilding is faster than the three existing simplification methods, while retaining more feature points and having approximately the same reduction rates.

Simplification method for nonlinear equations of monotone type in Banach space

Abstract. In a Banach space, we study an operator equation with a monotone operator T. The operator is an operator from a Banach space to its conjugate, and T=AC, where A and C are operators of some classes. The considered problem belongs to the class of ill-posed problems. For this reason, an operator regularization method is proposed to solve it. This method is constructed using not the operator T of the original equation, but a more simple operator A, which is B-monotone, B=C−1. The existence of the operator B is assumed. In addition, when constructing the operator regularization method, we use a dual mapping with some gauge function. In this case, the operators of the equation and the right-hand side of the given equation are assumed to be perturbed. The requirements on the geometry of the Banach space and on the agreement conditions for the perturbation levels of the data and of the regularization parameter are established, which provide a strong convergence of the constructed approximations to some solution of the original equation. An example of a problem in Lebesgue space is given for which the proposed method is applicable.

Scale free is not rare in international trade networks

Failing to consider the strong correlations between weights and topological properties in capacity-weighted networks renders test results on the scale-free property unreliable. According to the preferential attachment mechanism, existing high-degree nodes normally attract new nodes. However, in capacity-weighted networks, the weights of existing edges increase as the network grows. We propose an optimized simplification method and apply it to international trade networks. Our study covers more than 1200 product categories annually from 1995 to 2018. We find that, on average, 38%, 38% and 69% of product networks in export, import and total trade are scale-free. Furthermore, the scale-free characteristics differ depending on the technology. Counter to expectations, the exports of high-technology products are distributed worldwide rather than concentrated in a few developed countries. Our research extends the scale-free exploration of capacity-weighted networks and demonstrates that choosing appropriate filtering methods can clarify the properties of complex networks.

A Saliency-Based Sparse Representation Method for Point Cloud Simplification.

High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.

Quality-Aware Resource Model Discovery

Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems.

Quality-Aware Resource Model Discovery

Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems.

A Topology-Preserving Simplification Method for 3D Building Models

Simplification of 3D building models is an important way to improve rendering efficiency. When existing algorithms are directly applied to simplify multi-component models, generally composed of independent components with strong topological dependence, each component is simplified independently. The consequent destruction of topological dependence can cause unreasonable separation of components and even result in inconsistent conclusions of spatial analysis among different levels of details (LODs). To solve these problems, a novel simplification method, which considers the topological dependence among components as constraints, is proposed. The vertices of building models are divided into boundary vertices, hole vertices, and other ordinary vertices. For the boundary vertex, the angle between the edge and component (E–C angle), denoting the degree of component separation, is introduced to derive an error metric to limit the collapse of the edge located at adjacent areas of neighboring components. An improvement to the quadratic error metric (QEM) algorithm was developed for the hole vertex to address the unexpected error caused by the QEM’s defect. A series of experiments confirmed that the proposed method could effectively maintain the overall appearance features of building models. Compared with the traditional method, the consistency of visibility analysis among different LODs is much better.

A Topology‐Preserving Simplification Method for 3D Building Models

A Topology‐Preserving Simplification Method for 3D Building Models

Distributed state estimation for renewable energy microgrids with sensor saturations

In this paper, the distributed state estimation problem is studied for renewable energy microgrids with sensor saturations. A system model for the microgrids with sensor saturations is proposed. Attention is focused on the design of a distributed recursive estimation scheme such that, in the presence of the sensor saturations, an upper bound of the estimation error covariance is guaranteed. Subsequently, such an upper bound is minimized by appropriately designing the gain matrices of the corresponding state estimator. In particular, the sparsity of the gain matrices resulting from network topology is handled by using a matrix simplification method. Moreover, the performance evaluation of the designed distributed state estimator is conducted by analyzing the exponential boundedness of the estimation error in the mean square sense. Finally, simulation experiments under two cases are carried out on a renewable energy microgrid which contains two distributed generation units. The simulation results demonstrate that the developed state estimation scheme is effective.

Participatory Design of a Clinical Trial Eligibility Criteria Simplification Method.

Clinical trial eligibility criteria are important for selecting the right participants for clinical trials. However, they are often complex and not computable. This paper presents the participatory design of a human-computer collaboration method for criteria simplification that includes natural language processing followed by user-centered eligibility criteria simplification. A case study on the ARCADIA trial shows how criteria were simplified for structured database querying by clinical researchers and identifies rules for criteria simplification and concept normalization.

Text segmentation for patent claim simplification via Bidirectional Long‐Short Term Memory and Conditional Random Field

AbstractText simplification is a vital work for comprehending patent claims due to its complex syntactic structures and lengthy sentences. Therefore, almost all patent analysis practitioners cannot be able to directly and intuitively understand patent essence even through some common natural language processing (NLP) tools are applied to parse these patent claim paragraph or sentences. Universal text analysis tools above is almost useless, or even crashed when applied to some complex paragraphs of patent claims. Therefore, it is necessary to propose a patent text oriented simplification approach to help patent researchers grasp the essence of patent quickly and intuitively. Motivated by the above reason, we in this article propose a simplification method based on deep learning to segment patent claim into shorter and comprehensible sentences for downstream tasks of patent analysis. The proposed approach contains two stages: on one stage, we use a machine learning approach of conditional random field (CRF) to decompose syntactically complex paragraphs into coarse‐grained level sentences with simplified structures and complete semantics; on another stage, a deep Learning architecture of bidirectional long‐short term memory (Bi‐LSTM)‐CRF is applied to segment coarse‐grained and lengthy sentences of former stage into fined‐grained and shorter sentences. Compared with a series of baselines, our patent segmentation architecture based on deep learning of Bi‐LSTM‐CRF achieves higher performance than any other methods on the evaluation measures of precision, recall, and F1.

Fast Resilience Assessment of Distribution Systems With a Non-Simulation-Based Method

Traditional simulation-based method for resilience assessment of distribution systems is very time consuming since the network topology is characterized with a large number of scenario-specific optimization models. In this paper, we propose a methodology for fast resilience assessment of distribution systems with a non-simulation-based method, which can significantly improve the assessment accuracy and computational efficiency. First, a probabilistic metric is proposed to assess the system resilience against extreme events, which quantifies the system performance starting from the pre-event stage to the post-event stage. Then, a mixed-integer linear programming (MILP) is proposed to model the energization paths (EPs) with binary decision variables. Subsequently, the resilience metric-related probability events are built using the EPs. Last, the probabilistic resilience metric is explicitly expressed based on the total probability formula, conditional probability formula and EP-topology simplification methods. In the proposed method, the topology evolution along with the system degradation, restoration (part healed) and recovery (all healed) is characterized with a non-simulation-based method, rather than the multiple scenarios in traditional methods. The numerical tests validate the effectiveness of the proposed method and superiority over the simulation-based approach.

A Bibliometric Analysis of Articles on Text Simplification: Sample of Scopus Database

Literacy is a term generally used for adults and young people. Basically, it is an acquisition that includes the process of reading, writing and understanding symbols in any language. While this concept, whose definition and scope has expanded over time, refers to people who can only say their names in the past, today it refers to individuals who can perform more functional skills. What is expected from today’s literacy, which is also referred to as functional literacy, is not just saying the name, but also understanding what you read and harmonizing these information with the environment. It is critical to create texts that are easier to interpret, especially for poor readers and individuals learning foreign languages, in today’s world where reading comprehension and correct use of information have become extremely important. In this context, the text simplification method, which is one of the text modification methods, comes to the fore. In accordance with these information, this current study aims to presents the bibliometric analysis of articles on text simplification, which are published in journals indexed in Scopus database in the field of social sciences. The data set of the study consists of 194 articles on text simplification published in journals scanned in the field of social sciences and scopus database. These 194 articles were examined in terms of different variables. The research is generally a descriptive study and document analysis method was used as a method. In the data analysis stage of the research, VOSviewer visualization software version 1.6.16 was used. According to the results obtained from the study, the most articles on text simplification were written in 2020 (f: 21), most cited article is “Interpretation as Abduction” (f: 363) written by J.R Hobbs, M.E Stickel, D.E. Appelt and P. Martin. Findings obtained from the research were shared in the form of tables, graphs and figures. The most common keywords that preffered by authors is “Simplification” (f: 18). The most cited institution is “Artifical Intelligence Center” (f: 363). The most cited journal is “Artifical Intelligence” (f: 363). The most published country is United States of America (f: 30). The most cited country is United States of America (f: 863). All findings obtained from the research were shared as tables and figures in the findings section.

A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set

Multi-scale spatial representation has been widely used in geographic information and online mapping systems. Terrain contour, which provides a reference for understanding and monitoring the Earth’s surface, is an important data category. For the multi-scale representation of contour lines, simplification is a fundamental step in providing different levels of detail for linear features. However, achieving a global continuous multi-scale simplification of contours remains a challenge. Therefore, based on the concept of level set, a novel contour simplification method labeled the continuous changing surface model (CCSM) was proposed in this paper. The CCSM was built by using a non-uniform rational B-spline constrained with characteristics and was then intersected with a set of horizontal planes with progressive height values. The generated intersection lines are considered continuous multi-scale simplified contours. Experiments were conducted on a 1:50,000 real contour dataset to verify the effectiveness of CCSM. Results showed that the changes in the shape of the simplified contours generated by CCSM are more natural and progressive than those generated by two other significant simplification methods. CCSM can also effectively balance local and global structures and has potential applications in obtaining a continuous multi-scale representation of terrain contours.

On [formula omitted]-insensitive simplification of fuzzy rules for fetal distress assessment

Objective: The work introduces ε-insensitive distance based approach to simplification (by reducing the rules number) of the fuzzy classifier rule base. To obtain premises of the initial rules, a modified clustering with pairs of ε-hyperballs procedure is used. The goal of the presented solutions is to achieve high quality support for fetal distress assessment, based on cardiotocographic (CTG) signals classification with a reduced number of fuzzy rules.Methods: In the presented rule base simplification solution, two rules are considered similar (or contradictory) when the distances between their premises do not exceed the assumed value of ε. The proposed simplification process consists of two phases: the first with combining similar rules into representative rules, and the second (optional) with the removal of contradictory rules. In addition, two methods of determining conclusions for representative rules are considered: a representative rule retains the original (unchanged) conclusion, or from the conclusions of similar rules the one with the highest absolute value is chosen. In the introduced clustering with pairs of ε-hyperballs the sizes of object classes are taken into account, to reduce the potential adverse impact of the unbalanced classes in the considered research material. In experiments, two reference assessments for CTG signals based on the retrospective fetal state evaluation were considered.Results and conclusions: In the two-stage classification, the modified clustering outperformed the original procedure in terms of classification sensitivity and the QI index being the geometric mean of sensitivity and specificity. Among the examined rule base simplification methods, we consider the best to be the one based only on combining similar rules, with the unchanged value of conclusion for a representative rule. With the smallest number of rules (after simplification), an increased sensitivity, and in the case of pH-based reference assessment also increased QI value is obtained. Moreover, the achieved sensitivity and QI are higher in comparison to the reference methods and values reported in literature.Significance and main impact: The results confirmed the effectiveness of the ε-insensitive distance rule base simplification. The proposed methods can be applied to any fuzzy rules with premise membership functions with a defined center. Therefore, we believe that this work may have a positive impact on other studies concerning fuzzy rule-based systems.

Simplification approaches for multi-level load spectra by using equivalent damage rule

Reasonable and effective fatigue load spectrum is key of aircraft structural fatigue test. Two kinds of load spectra simplification approaches based on statistical consistent fatigue damage model are proposed in the paper. One method is to simplify the original multi-level load spectra to spectral load with fewer level and was verified by the experimental data of five levels and seven levels load spectra. The equivalent damage here is not equivalent fatigue life, but equivalent number of spectral blocks. Moreover, it was proved that a section of low stress load cannot be converted into the highest stress load considering equivalent damage. The other is to convert the life distribution under multi-level load spectra into that of constant amplitude spectrum according to a certain principle, that is, equivalent damage is achieved through equivalent fatigue life, and two groups of three levels spectral data were used for experimental verification. The results show that equivalent damage simplification approach of multi-level load spectra is feasible, and it achieve that the damage of one loading block before and after the simplification is equal in probability statistics. The life distribution of load simplified as constant amplitude spectrum is consistent with test data. The simplification method presented in this paper can be generalized and is a predictive engineering method which does not depend on experimental data.