Parallel Coordinate System Research Articles

Study and analysis of wastewater treatment in the refining and chemical industry based on multidimensional data analysis

Abstract To improve the efficiency of wastewater treatment in the refining chemical industry, this paper designs a wastewater treatment process in the refining chemical industry based on multidimensional data analysis to guarantee the optimal operation of wastewater treatment operations. In the process of multidimensional data analysis, triangular polynomials are introduced to calculate the robust distance of high-dimensional data and construct a multidimensional data model. The optimal multidimensional clustering levels are divided using data operations to obtain high-dimensional data sequences in the multidimensional data set to accelerate data clustering. A parallel coordinate system realizes the visual representation of the wastewater treatment process stems to improve the capacity representation of wastewater treatment data. Simulation analysis was conducted to verify the treatment effect of the wastewater treatment process based on multidimensional data analysis in the refining and chemical industries. The results showed that the process designed in this paper resulted in a dissolved organic matter fraction concentration of 16.28, and the humic acid-like fraction increased by 36.2%, which was significantly higher than the protein-like fraction of 21.6%. And among all protein-like fractions, only C4 was positively and significantly correlated with fluorescent organic matter at the P < 0.05 level. It can be seen that the multidimensional data analysis model is conducive to promoting the development of wastewater treatment technology in the refining chemical industry and ensuring that the refining chemical industry steps into a circular and sustainable development track.

Zebra-crossing detection based on cascaded Hough transform principle and vanishing point characteristics

Abstract In this study, a zebra-crossing detection method based on cascaded Hough transform (CHT) and vanishing point (VP) characteristics is proposed. In this method, the principle of detecting straight lines in the parallel coordinate system is applied to zebra-crossing detection. Each edge point of the image obtained by edge detection is represented in the parallel coordinate system to find the VP. Using the VP coordinate as the judgment condition, those straight lines that do not pass through the VP but meet the straight-line condition are excluded to obtain the straight lines passing through both sides of the zebra crossing, and finally fit the edge points on the straight line, and get the zebra-crossing fitting line segment. Experiments show that CHT has obvious advantages in detection accuracy and speed compared with the Hough transform. At the same time, VPs can be used to eliminate interference segments, which provide support for the accuracy of zebra-crossing detection. This method can get zebra-crossing location information without using region of interest extraction, which also provides a reference method for road detection in some specific cases.

MODELING OF TACTICAL INNOVATIVE TOOLS OF ECONOMIC DEVELOPMENT OF ENTERPRISES IN THE CONDITIONS OF EUROPEAN INTEGRATION

Purpose. Tactical innovative tools of the economic development of the enterprise in the conditions of European integration are considered in the article as short-term adaptive and relative means, intended for the practical implementation of irreversible, controlled and purposeful changes of a quantitative, qualitative and essential nature in the economic system of the enterprise, which occur under the influence of changes in the operating environment and condition the development means of production and socio-economic relations, increasing flexibility, achieving a state of optimal and efficient functioning of the enterprise and other strategic goals. For the purpose of their modeling, the article provides a conceptual formulation of the problem, defines a function that ensures their formation on alternative bases, forms compliance criteria and a set of modeling tasks: formation of modeling input data; substantiation of the criteria by which the tools of economic development of enterprises are optimized in the conditions of the course of European integration processes; ensuring the modeling of tactical innovative tools for the economic development of enterprises in the conditions of European integration by establishing the compliance of the group of tools for the economic development of enterprises with optimization criteria for the purpose of performing the function; identification of a set of tactical innovative tools for the economic development of enterprises in the conditions of European integration; making managerial decisions regarding the tactical management of the development of the enterprise in the conditions of European integration. Design/methodology/approach. Methods of analysis, synthesis, theoretical generalization, expert evaluations, coefficient analysis, method of pattern analysis. Findings. As a modeling toolkit, the article recommends the method of pattern analysis, which provides for the formation of homogeneous groups of objects as a result of cluster analysis in the following order: setting the problem; display of objects in the parallel coordinate system; direct connection of parameter values marked on the axes; analyzing the similarity of patterns; interpretation of the results, identification of tactical innovative tools for the economic development of enterprises in the conditions of European integration, which meet the criteria of optimality and restrictive criteria. Originality/value. The recommended procedure should be applied for the purpose of modeling tactical innovative tools of economic development, which will ensure their optimization in the conditions of European integration. Practical implications. The practical approbation of the method made it possible to identify 3 priority groups of tactical innovative tools of economic development of ShK Svitanok+ LLC: starting the activities of ShK Svitanok+ LLC in free economic zones, which involves its administrative support and providing employees with a social package; ensuring the participation of ShK Svitanok+ LLC in conferences, forums and round tables, promotion of the enterprise's exports, lending and expansion of analysis tools by spreading the practice of functional-cost and factor analysis in the mentioned conditions; implementation of targeting and ensuring the participation of ShK Svitanok+ LLC in grant projects.

Rigorous spherical bearing with Soldner coordinates and azimuth angles on sphere

Meridian systems, called Soldner coordinates (parallel coordinate) systems, have found wide application in geodesy. In particular, the meridian system constitutes a suitable base for the Gauss-Kruger projection of the ellipsoid and the sphere. Soldner coordinates can be used in Cassini-Soldner projection without any processing. As it is known, the directions of the edges are shown with azimuth angles in the geographic coordinate system and the bearing angles in the Soldner coordinate system. Bearing or azimuth angles are frequently used in geodetic calculations. These angles give the direction of sides in the clockwise direction from a certain initial direction. Both angle values range from 0 to 360 degrees and are usually calculated from the arctan function. But the arctan function returns an angle value between -90 and +90 degrees. Therefore, it is necessary to analyze the quarter for the angle found. For practical computations, the quadrants of the arctangents are determined by the signs of the numerator and denominator in the tangent formulas. Determining the quarter of the angles is done with if…, then…, end..., blocks on the computer. It should be noted that each comparison requires a separate processing time. This study will be given how to calculate both bearing and azimuth angles with direct formulas without any need to examine them. In addition, a solution proposal will be given against the division by zero errors in the bearing and azimuth angles calculations.

Novel Optimization Approach for Camera Calibration in Traffic Scenes

Currently, most camera calibration methods for traffic scenes are based on vanishing points and road geometry markings with simplified camera models, which can only be applied to scenes containing straight roads. However, in practical applications, cameras are usually installed with roll angles and scenes containing curved roads, to which existing methods are not applicable. To solve the above problems, we propose a novel optimization approach for camera calibration in traffic scenes, which can be applied to curved road scenes and predict camera roll angle. Firstly, a camera space model with a camera roll angle is established for image rotation. Secondly, vehicle trajectories are extracted for the best vanishing point by a parallel coordinate system and diamond space. Vehicle trajectories are also used to obtain calibration regions for extracting road markings and edges. The road markings, edges, and the best vanishing point obtained by the above two steps automatically are more accurate and stable, especially for curved road scenes. Based on the road markings and the best vanishing point, initial calibration can be conducted. Finally, by extracting redundant markings in the calibration region, the non-linear constraint of redundant markings on the road is proposed to obtain optimized calibration parameters and predict the camera roll angle. Through experimental validation on the public dataset BrnoCompSpeed and highway scenes, the proposed approach can achieve better calibration results in both straight and curved road scenes with the mean calibration error reduced by 30% compared with the previous calibration methods.

Ukrainian art since the early 2000s: the "second revival" in the world’s cultural field

The purpose of the article is to determine the place of Ukrainian art during the active transformations of the world art space. The scientific novelty lies in the study of the world's leading art fairs and the participation of Ukrainian artists in these platforms of the art market, as well as the analysis of modern leading Ukrainian art fairs and festivals. Methodology. The main methodological approach in determining the place of Ukrainian art during the active transformations of the world art space is the structural-system approach, and when analyzing the activities of leading Ukrainian art fairs in the context of world art development - the contextual method. The applied methods allowed to determine the main vectors of development of the private art segment of Ukrainian art of the 1990s - 2020s. Conclusions. In recent years, a new global art space has been formed and developed in the art world. Traditionally, the Western world dictates the rules of world art, but the exotic nature of everything that comes from the North and Eastis of interest to art lovers and professionals. Renewal of the cultural and artistic process in Ukraine at the beginning of the XXI century. and powerful social changes became the basis for many art festivals and art fairs of both regional and national scale. Initiatives of independent curators and gallerists' search for new formats for demonstrating the artistic achievements of Ukrainian artists have inspired the emergence of a number of interdisciplinary cultural projects of national importance. These artistic events set the vector for the development of communication in the modern cultural sphere. In general, Ukrainian art is developing several parallel coordinate systems that operate on different principles and offer different agendas. Among the latest trends that determine its development - digitalization, virtualization, commercialization, blurring the boundaries between elite and mass art, loss of integrity, polystylistic aspect, exemption from any rules of regulation. Like world cultural projects, Ukrainian festivals and art fairs are a type of artistic form of actualization of contemporary art and its interaction with society. For example, Kyiv Art Week is a landmark event in the cultural space of Ukraine, which brings together influential artists, collectors, and representatives of many worlds and Ukrainian cultural institutions.

Using of Parallel Coordinates in Finding Minimum Distance in Time-Space

Airspace domain may be represented by a time-space consisting of a three-dimensional Cartesian coordinate system and time as the fourth dimension. A coordinate system provides a scheme for locating points given its coordinates and vice versa. The choice of coordinate system is important, as it transforms data to geometric representation. Visualization of the three and more dimensional data on the two-dimensional drawing - computer monitor is usually done by projection, which often can restrict the amount of information presented at a time. Using the parallel coordinate system is one of possibilities to present multidimensional data. The aim of this article is to describe basics of parallel coordinate system and to investigate lines and their characteristics in time-space.

Application of Equivalence Principle for EM Scattering From Irregular Array of Arbitrarily Oriented PEC Scatterers Using Both Translation and Rotation Addition Theorems

Electromagnetic scattering from irregular array of arbitrarily oriented PEC scatterers is investigated using a novel domain decomposition method based on equivalence principle algorithm (EPA) in conjunction with addition theorems for spherical wave harmonics (SWHs). Each element is considered to be enclosed by a spherical equivalence surface as a single domain. The primary unknowns on each element are replaced by the equivalent electric and magnetic current distributions on the equivalence sphere in terms of RWG basis functions. From these current distributions, the scattered field outside its equivalence sphere is evaluated in terms of SWHs in the local coordinates system fixed to each element body. The SWHs in rotated local coordinates are translated as a new set of SWHs in other local coordinates with the same origin but axes parallel to the global coordinate’s axes using the rotation addition theorem. Furthermore, the mutual coupling effects among the separated domains are considered using the translation addition theorem for parallel local coordinate systems. It is shown that the proposed method effectively reduces the computational costs by reducing the number of unknowns versus the conventional EPA. The efficiency and validity of the proposed method are investigated through several numerical examples.

Online Road Detection under a Shadowy Traffic Image Using a Learning-Based Illumination-Independent Image

Shadows and normal light illumination and road and non-road areas are two pairs of contradictory symmetrical individuals. To achieve accurate road detection, it is necessary to remove interference caused by uneven illumination, such as shadows. This paper proposes a road detection algorithm based on a learning and illumination-independent image to solve the following problems: First, most road detection methods are sensitive to variation of illumination. Second, with traditional road detection methods based on illumination invariability, it is difficult to determine the calibration angle of the camera axis, and the sampling of road samples can be distorted. The proposed method contains three stages: The establishment of a classifier, the online capturing of an illumination-independent image, and the road detection. During the establishment of a classifier, a support vector machine (SVM) classifier for the road block is generated through training with the multi-feature fusion method. During the online capturing of an illumination-independent image, the road interest region is obtained by using a cascaded Hough transform parameterized by a parallel coordinate system. Five road blocks are obtained through the SVM classifier, and the RGB (Red, Green, Blue) space of the combined road blocks is converted to a geometric mean log chromatic space. Next, the camera axis calibration angle for each frame is determined according to the Shannon entropy so that the illumination-independent image of the respective frame is obtained. During the road detection, road sample points are extracted with the random sampling method. A confidence interval classifier of the road is established, which could separate a road from its background. This paper is based on public datasets and video sequences, which records roads of Chinese cities, suburbs, and schools in different traffic scenes. The author compares the method proposed in this paper with other sound video-based road detection methods and the results show that the method proposed in this paper can achieve a desired detection result with high quality and robustness. Meanwhile, the whole detection system can meet the real-time processing requirement.

Data Mining Analysis and High-dimensional Visualization Based on Electric Big Data

with the popularization of intelligent ammeter, collection of users’ power utility information can be more accurate and smart. Every other 15 minutes, backstage of power supply companies can read power utility data automatically. By collecting data based on optimized scatter diagram in non-linear polynomial form, power supply companies can accurately analyze and display users’ power utility habits in more dimensions and angles through parallel coordinate system based on convex hull optimization. They are also able to synthesize multiple users’ power utility information and geographical information of communities, so as to establish improved comprehensive assessment system for residential quarters. In addition, power supply companies can synthesize visualized analysis on the relation between power utility of various industries in urban areas and GDP data, so as to further explore the interactive function of industry power utility and regional development; thus can also provide significant reference value for regional development strategies.

AMOBH: Adaptive Multiobjective Black Hole Algorithm.

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called “adaptive multiobjective black hole algorithm” (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

Many-Objective Particle Swarm Optimization Using Two-Stage Strategy and Parallel Cell Coordinate System.

It is a daunting challenge to balance the convergence and diversity of an approximate Pareto front in a many-objective optimization evolutionary algorithm. A novel algorithm, named many-objective particle swarm optimization with the two-stage strategy and parallel cell coordinate system (PCCS), is proposed in this paper to improve the comprehensive performance in terms of the convergence and diversity. In the proposed two-stage strategy, the convergence and diversity are separately emphasized at different stages by a single-objective optimizer and a many-objective optimizer, respectively. A PCCS is exploited to manage the diversity, such as maintaining a diverse archive, identifying the dominance resistant solutions, and selecting the diversified solutions. In addition, a leader group is used for selecting the global best solutions to balance the exploitation and exploration of a population. The experimental results illustrate that the proposed algorithm outperforms six chosen state-of-the-art designs in terms of the inverted generational distance and hypervolume over the DTLZ test suite.

A data-driven multidimensional visualization technique for process fault detection and diagnosis

A multidimensional visualization technique is described for fault detection and diagnosis of a multivariate process by principal component analysis (PCA) of historical data. The technique uses a parallel coordinate system to visualize data that allows for monitoring of abnormal process events that lead to process faults, enabling the visualization of multiple principal components effectively and facilitating the study of how each principal component varies with respect to time. The principal component and residual space control limits are established for fault detection and the Random Forests machine learning tool is adopted for fault diagnosis. The key features of the methodology are demonstrated through a study of the benchmark Tennessee Eastman process.

Visual Performance in Multidimensional Data Characterisation with Scatterplots and Parallel Coordinates

We present a study on the visual assessment of relative data point distances in Parallel coordinate systems and scatterpiots in Cartesian coordinate systems Specifically, we assess the impact of coordinate system type, dimension, and relative point distance deviation. We performed an online pilot experiment with 100 participants using Amazon's MechanicalTurk. The experiment design and methodology are presented in detail and results indicate that there may indeed be a difference in human performance when visually assessing distances in the considered coordinate systems. We argue that further investigations are needed to draw stronger conclusions. These should consider inclusion of other factors into the experiment design, such as the relative angle between data points that is expected to have a significant impact on the outcomes.

Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System

Managing convergence and diversity is essential in the design of multiobjective particle swarm optimization (MOPSO) in search of an accurate and well distributed approximation of the true Pareto-optimal front. Largely due to its fast convergence, particle swarm optimization incurs a rapid loss of diversity during the evolutionary process. Many mechanisms have been proposed in existing MOPSOs in terms of leader selection, archive maintenance, and perturbation to tackle this deficiency. However, few MOPSOs are designed to dynamically adjust the balance in exploration and exploitation according to the feedback information detected from the evolutionary environment. In this paper, a novel method, named parallel cell coordinate system (PCCS), is proposed to assess the evolutionary environment including density, rank, and diversity indicators based on the measurements of parallel cell distance, potential, and distribution entropy, respectively. Based on PCCS, strategies proposed for selecting global best and personal best, maintaining archive, adjusting flight parameters, and perturbing stagnation are integrated into a self-adaptive MOPSO (pccsAMOPSO). The comparative experimental results show that the proposed pccsAMOPSO outperforms the other eight state-of-the-art competitors on ZDT and DTLZ test suites in terms of the chosen performance metrics. An additional experiment for density estimation in MOPSO illustrates that the performance of PCCS is superior to that of adaptive grid and crowding distance in terms of convergence and diversity.

Multi-objective Bacterial Foraging Optimization Algorithm Based on Parallel Cell Entropy for Aluminum Electrolysis Production Process

Environment-friendly aluminum electrolysis production process has long been a challenging industrial issue due to its built-in difficulty in optimizing numerous highly coupled and nonlinear parameters. This paper presents a multi-objective bacterial foraging optimization (MOBFO) algorithm to find optimal solutions that maximize the current efficiency and minimize the energy consumption and the production of perfluorocarbons (PFCs). Our method can be viewed as an enhanced version of the bacterial foraging optimization (BFO) in solving multi-objective optimization (MOO) problems (MOPs). We first propose a task-oriented optimization framework and model, and then parallel cell entropy and its difference are introduced to evaluate the evolutionary status of the Pareto solutions in a new objective space called parallel cell coordinate system (PCCS). In particular, the Pareto-archived evolution approach (PAEA) and the adaptive foraging strategy (AFS) are applied to balance the convergence and diversity of the Pareto front in the optimization procedure. Compared with traditional approaches, MOBFO not only increases speed of convergence toward the Pareto front, but also improves the diversity of the obtained solutions. Extensive experiment results on numerous benchmark problems and real-world aluminum electrolysis production process validated our proposed method’s effectiveness.

Imperfect interfaces as asymptotic models of thin curved elastic adhesive interphases

We obtain a limit model for a thin curved anisotropic interphase adherent to two elastic media. Our method is based on asymptotic expansions and energy minimization procedures. The model of perfect interface is obtained at the first order, while an imperfect interface model is obtained at the next order. The conditions of imperfect contact, given in a parallel orthogonal curvilinear coordinate system, involve the interphase material properties, the first order displacement and traction vectors, and their derivatives. An example of implementation of the imperfect interface condition is given for a composite sphere assemblage.

Research on Methods to Improve Parallel Coordinates Visualization

In parallel coordinate plots, the density could not be represented where the polylines were over intensive. Based on the problem, the aggregation degree conception was proposed. The three-dimension parallel coordinate system with the aggregation degree axis was built, and the mapping from aggregation degree to gray degree value was built. The three-dimension polylines with gray color were projected onto the original two-dimension plane, and the two-dimension parallel coordinate plots with the aggregation degree information were acquired. The results show that they are effective in representing the density change of polylines on parallel coordinate plots.

Visual clustering of multidimensional educational data from an intelligent tutoring system

AbstractData mining can be used in the educational field to make teaching more effective, to provide help for the student and teacher, and to analyze data. Data mining can be useful with educational software like intelligent tutoring systems which contain large quantities of data about students and their degree of success, scores, and errors. By analyzing these data, it is possible to obtain pedagogically reliable information as feedback for the teacher. The objectives of this study are to cluster the data available from an Intelligent Tutoring System (ITS) and to visualize the multidimensional data analysis results. In this study, the ITS data, which contain exam results on six different concepts, are clustered using k‐means and fuzzy c‐means algorithms, and the clustering performance of the two algorithms is compared. Cluster analysis results are visualized using a parallel coordinate system. Clustering and visualizing of the concept‐level scores is used to provide meaningful and nontrivial insights into the workings of a course. Such information is useful for the teacher to discover which concepts give students difficulty and which do not. The paper also describes the data mining software developed in this study and the analytical results obtained. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 375–382, 2010; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20272

On two models of arbitrarily curved three-dimensional thin interphases in elasticity

In boundary value problems involving thin interphases, it is often desirable to have a model of an interphase which makes possible to solve for the fields in the adjacent media without having to solve for the fields in it. This is usually achieved in the literature by replacing the interphase by a geometrical surface with appropriately designed “imperfect interface” conditions on it. In the present study, carried out in the setting of elasticity, another option is explored: the geometry of the interphase is left intact, and conditions are devised for the displacements and tractions pertaining to the media adjacent to the interphase and evaluated at both sides of it such that they will simulate the presence of the interphase. Those conditions do not involve the fields within the interphase, yet they depend on its material properties and on those of the adjacent media as well, and make possible to solve for the fields in the adjacent media without having to solve for the fields in the interphase. The formulation is given in a parallel orthogonal curvilinear coordinate system suitable for the modeling arbitrarily curved three-dimensional interphases of constant thickness. Both types of the above described interphase models are tested in the setting of a coated infinite fiber embedded in a matrix which is subjected to an anti-plane shear loading and an in-plane transverse shear loading at infinity, and their predictions are compared with the exact solutions for the fields in the three-phase configuration consisting of the interphase and its adjacent media. The model in which the interphase geometry is left intact is observed to perform generally better than the one in which the interphase is replaced by an interface.