Big data analysis and prediction method of multidimensional space-time features of energy internet based on fuzzy rough model

Nowadays, energy has become a hot issue of concern to the whole society. With the unbalanced distribution of resources in the world and more severe climate change, the constraints of resource conditions and environmental status on global energy development are becoming stronger and stronger. The rapid development of the Internet, as well as the proposal of the energy Internet, has a better application in the analysis of energy demand, which can effectively alleviate the contradiction between energy and environment. Aiming at the big data of energy Internet and based on the advantages of fuzzy rough model, this paper studies a method of big data analysis and prediction of multidimensional space-time characteristics of energy Internet based on fuzzy rough model. Firstly, according to the spatio-temporal characteristics of energy Internet data, extract the multidimensional spatio-temporal characteristics of energy internet. Secondly, rough set and fuzzy set are two commonly used mathematical tools, and the combination of the two fuzzy rough models can more fully mine data information. In view of the shortcomings of the commonly used fuzzy rough set reduction algorithm, a reduction algorithm based on conditional entropy is proposed. Finally, taking multidimensional space-time characteristics as input, combining the advantages of fuzzy rough model and neural network, a prediction model is established to analyze and forecast energy demand. The simulation experiments show that the design method is feasible and superior, and can achieve the prediction of energy demand well, so as to make more rational use of energy.

A Novel Integrated Interval Rough MCDM Model for Ranking and Selection of Asphalt Production Plants

Asphalt production plants play an important role in the field of civil engineering, but also in the entire economic system since the construction of roads enables uninterrupted functioning within it. In this paper, the ranking of asphalt production plants on the territory of the Autonomous Province of Vojvodina has been performed. The modern economy needs contemporary models and methods to solve complicated MCDM problems and, for these purposes, it has been developed an original Interval Rough Number (IRN) Multi-criteria decision-making (MCDM) model that implies an extension of two methods belonging to the field with interval rough numbers. After forming a list of eight most significant criteria for assessing the efficiency of asphalt production plants, the Interval Rough Number PIvot Pairwise RElative Criteria Importance Assessment (IRN PIPRECIA) method was developed to determine the significance of the criteria. A total of 21 locations with asphalt mixture installation were considered. For that purpose, seven asphalt production plants were included, and for their ranking, the IRN EDAS (Evaluation based on Distance from Average Solution) method was created. The aim of this paper is to develop a novel interval rough model that can be useful for determining the efficiency of asphalt production plants. Averaging in group decision-making (GDM) for both methods was performed using an IRN Dombi weighted geometric averaging (IRNDWGA) aggregator. The obtained results show that (A15) Ruma (SP)–Mačvanska Mitrovica–Zasavica has the best characteristics out of the set of locations considered in this study. However, Alternatives A6 and A19 are also variants with remarkably good characteristics since there is very little difference in values compared to the first-ranked alternative. Also, the obtained results have shown that the developed model is applicable, which is proven through a comparative analysis.

Three-way decision based on canonical soft sets of hesitant fuzzy sets

<abstract><p>The theory of three-way decision is built on the philosophy of thinking in threes. The essence of three-way decision is trisecting the whole and taking different strategies for different parts accordingly. The theory of three-way decision has been successfully implemented to diverse fields since it provides an elegant and efficient solution for solving complicated problems. In this paper, a useful representation for hesitant fuzzy sets is obtained by means of canonical soft sets. We also define unit interval parameterized soft sets and their derived hesitant fuzzy sets. Mutual representations and inner connections between hesitant fuzzy sets and soft sets are examined. With the help of soft rough sets, a generalized rough model based on hesitant fuzzy sets is established. A novel three-way decision method is presented for solving decision-making problems by means of hesitant fuzzy sets and canonical soft sets. Finally, a numerical example regarding peer review of research articles is given to illustrate the validity and efficacy of the proposed method.</p></abstract>

Electromagnetic full- gyrokinetics in the tokamak edge with discontinuous Galerkin methods

We present an energy-conserving discontinuous Galerkin scheme for the full- $f$ electromagnetic gyrokinetic system in the long-wavelength limit. We use the symplectic formulation and solve directly for $\unicode[STIX]{x2202}A_{\Vert }/\unicode[STIX]{x2202}t$ , the inductive component of the parallel electric field, using a generalized Ohm’s law derived directly from the gyrokinetic equation. Linear benchmarks are performed to verify the implementation and show that the scheme avoids the Ampère cancellation problem. We perform a nonlinear electromagnetic simulation in a helical open-field-line system as a rough model of the tokamak scrape-off layer using parameters from the National Spherical Torus Experiment (NSTX). This is the first published nonlinear electromagnetic gyrokinetic simulation on open field lines. Comparisons are made to a corresponding electrostatic simulation.

Preparation and properties of polyimide air-jet textured yarns and their woven fabrics

Two kinds of air-textured polyimide yarns with different overfeed ratios (0.5/2 and 8/8) were prepared by air-jet texturing technique, and three kinds of woven fabrics with similar tightness and thickness were produced with the same warp yarn but different weft yarns (polyimide flat yarn and the two air-jet textured polyimide yarns, respectively). The influence of air-jet textured yarn on the wearing properties of the fabrics was explored. The results show that, compared with the fabric containing polyimide flat yarn, the fabrics woven with polyimide air-jet textured yarns possess lower mechanical properties but have better air permeability, moisture permeability, heat resistance and luster. In addition, the smaller contact angle and larger wicking height of polyimide air-jet textured yarn fabrics indicate that the hydrophilicity of polyimide fabrics was improved. The effect of air-jet textured yarn on the hydrophilicity of polyimide fabrics was explained by Wenzel rough model and fabric surface roughness characteristics.

An Improved Constrained Order Optimization Algorithm for Uncertain SCUC Problem Solving

Studying the faster and more efficient method of solving the uncertain security-constrained unit commitment (SCUC) problem is an urgent need for the development of power systems under the background of large-scale wind power access and power dispatching. This study proposes an improved constrained order optimization (COO) algorithm to solve the uncertain SCUC problem. First, the data-driven discrete variable identification strategy is incorporated into the COO rough model, and then, the invalid security constraints identification strategy is incorporated into the COO accurate model. Finally, the improved COO algorithm combines the discrete variable identification with the invalid security constraint identification to make the uncertain SCUC decision. The results of the IEEE 118-bus test system showed that, compared with the traditional COO algorithm, the improved COO algorithm proposed has higher accuracy and better efficiency.

Guided Next Best View for 3D Reconstruction of Large Complex Structures

In this paper, a Next Best View (NBV) approach with a profiling stage and a novel utility function for 3D reconstruction using an Unmanned Aerial Vehicle (UAV) is proposed. The proposed approach performs an initial scan in order to build a rough model of the structure that is later used to improve coverage completeness and reduce flight time. Then, a more thorough NBV process is initiated, utilizing the rough model in order to create a dense 3D reconstruction of the structure of interest. The proposed approach exploits the reflectional symmetry feature if it exists in the initial scan of the structure. The proposed NBV approach is implemented with a novel utility function, which consists of four main components: information theory, model density, traveled distance, and predictive measures based on symmetries in the structure. This system outperforms classic information gain approaches with a higher density, entropy reduction and coverage completeness. Simulated and real experiments were conducted and the results show the effectiveness and applicability of the proposed approach.

Improving RANSAC for Efficient and Precise Model Fitting with Statistical Analysis

RANSAC (random sample consensus) has been widely used as a benchmark algorithm for model fitting in the presence of outliers for more than thirty years. It is robust for outlier removal and rough model fitting, but neither reliable nor efficient enough for many applications where precision and time is critical. Many other algorithms have been proposed for the improvement of RANSAC. However, no much effort has been done to systematically tackle its limitations on model fitting repeatability, quality indication, iteration termination, and multi-model fitting.A new paradigm, named as SASAC (statistical analysis for sample consensus), is introduced in this paper to relinquish the limitations of RANSAC above. Unlike RANSAC that does not consider sampling noise, which is true in most sampling cases, a term named as ? rate is defined in SASAC. It is used both as an indicator for the quality of model fitting and as a criterion for terminating iterative model searching. Iterative least square is advisably integrated in SASAC for optimal model estimation, and a strategy is proposed to handle a multi-model situation.Experiment results for linear and quadratic function model fitting demonstrate that SASAC can significantly improve the quality and reliability of model fitting and largely reduce the number of iterations for model searching. Using the ? rate as an indicator for the quality of model fitting can effectively avoid wrongly estimated model. In addition, SASAC works very well to a multi-model dataset and can provide reliable estimations to all the models. SASAC can be combined with RANSAC and its variants to dramatically improve their performance.