Complex Process Models Research Articles

Study of the soil temperature effect on the operation of a low-capacity power plant’s condenser

Every year, the share of decentralized energy generation in Russia is increasing. The following factors contribute to the development of this scenario: increased wear of the country’s energy system equipment, energy shortages, and lack of centralized energy supply in a number of regions and constantly rising tariffs. One of the methods of decentralized energy generation is the use of low-capacity power plants based on the Rankine cycle with an organic working fluid. The operation of such plants requires cooling and condensation of the working fluid by transferring its heat to the environment. This study discusses the design of such a power plant and the heat removal system to a cold source. is the authors consider the design of a condenser which is a horizontal pipeline placed in the ground. Seasonal fluctuations of the soil temperatures affect the operation of the condenser. Thereby, to ensure the stable operation of the power plant, it is necessary to quantitatively assess the effect of the annual dynamics of the soil temperature state on cooling and condensation of the coolant. The study of the temperature fields of the soil, pipeline and working fluid, as well as the lengths required for cooling and condensation of the working fluid, was carried out in the ANSYS CFX software package for numerical hydrodynamic modeling. A homogeneous flow model was chosen to simulate the momentum and condensation of a vapor-liquid medium. Also, the calculations were conducted in a one-dimensional formulation using an engineering method. A methodology for modeling complex processes of heat transfer to the soil using numerical modeling has been developed and verified. 12 calculations were made; the distributions of the steam dryness and temperature in the simulated region depending on the time of the year were obtained. The functions of the total length of the pipeline, cooling and condensation lengths on the soil temperature are analyzed. It has been established that the harmonic change in the temperature of the soil set as the initial condition determines a similar change in the lengths required for cooling and condensation of the working fluid. Using this technique, it is possible to calculate pipelines of more complex shapes. The obtained temperature distributions in cross sections allow to establish the optimal distance between the axes of the pipes when designing a condenser in the form of a bundle of horizontal pipes or a bent pipeline.

Исследование эквивалентности иллюзии резиновой руки в классическом варианте и в условиях виртуальной реальности

Computer technologies implementation into the body illusions research is increasing because they allow to controllably model complex processes that cannot be realised in ordinary life. It was previously demonstrated that the rubber hand illusion may be reconstructed in the virtual setting and cause similar changes in the somatoperception when the virtual hand begins to feel like your own. This result suggests that the phenomenological experience obtained in the classical illusion and in its virtual reality version has much in common. However, a direct experimental comparison of the two illusion variants has not been made, therefore, in this research we studied the equivalence of the rubber and virtual hand illusions (RHI and VHI). The sample consisted of 16 subjects (18—25 years). As registration methods we used a subjective sense of ownership of an artificial limb and the proprioceptive drift of the real hand towards the illusory hand. The analysis has proved the equivalence of illusions.

Arched Vaults Made of Corrugated Sheets: Natural Vibration Frequencies

According to the recent Russian norms, when designing building structures, it is necessary to conduct a dynamic analysis of wind loads, which previously was required not in all cases. In arched vaults of profiled arched self-supporting flooring, it was not always necessary to determine the frequencies and forms of natural vibrations. These parameters can be established using the finite element method. Taking into consideration the complex and lengthy modeling process of arched vaults, in particular the contact areas in regular transverse joints, a large number of finite elements in the models and, as a consequence, considerable time for their calculation, it was necessary to identify a sufficient level of detailing of a finite element model for correct calculations of frequencies and forms of vibrations considered in the operation of structures. The influence of the detailing of the finite element model of arched vaults made of profiled flooring on the determination of their natural frequency is revealed. To substantiate the parameters of the finite element model, it was studied how the results of the calculation was influenced by edge effects, the presence of friction in the joints of corrugations of the profiled flooring, the influence of rubber gaskets in the joints on the work of the arched vault. Much attention is paid to the features of the design scheme associated with the choice of the number of sheets of profiled flooring, taking into account the contact nodes, friction forces, etc. It is established that the first vibration frequency is little dependent on the number of sheets of corrugated flooring and the presence of rubber gaskets in the joints. For subsequent frequencies, the difference can be significant.

Reinforcement learning for batch bioprocess optimization

Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to their unsteady-state operation modes and stochastic behaviours. Furthermore, biological systems are highly complex, therefore plant-model mismatch is often present. To address the aforementioned challenges we propose a Reinforcement learning based optimization strategy for batch processes.In this work we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network. We assume that a preliminary process model is available, which is exploited to obtain a preliminary optimal control policy. Subsequently, this policy is updated based on measurements from the true plant. The capabilities of our proposed approach were tested on three case studies (one of which is nonsmooth) using a more complex process model for the true system embedded with adequate process disturbance. Lastly, we discussed advantages and disadvantages of this strategy compared against current existing approaches such as nonlinear model predictive control.

Modeling of Complex Information Processes in the Oversaturated Media Environment (to the article by A.P. Sukhodolov, I.V. Anokhov, V.A. Marenko Informational Impulse-Wave Interaction between the Media and Society)

The article discusses promising areas of modeling informational interaction between the media and the society from the standpoint of the Paradigm of Complexity. Increasing complexity of the society’s media system, turbulence in the media environment, transformation of the functions of traditional media, changing methods of information production and consumption, and development of Web 2.0 technologies are critical challenges for media theorists. Basing on the theory of behavioral economics, the authors prove that in the context of a significant growth of information volume, most people demonstrate a shift of thinking from System 1 to System 2. System 1 is a sub-system of the brain responsible for making rational, well-considered and balanced decisions, whereas System 2 is the area of automatic quick, spontaneous decision-making. This shift results in the dominance of populism, post-truth and fake news in the media space. The authors show that Web 2.0 technologies, which help people, regardless their occupation, to generate info-content, extends the boundaries of journalism and media. This, particularly, leads to development of citizen journalism and the increasing role of social communities (networks) as isolated media-systems that fragment the media space. The article substantiates that traditional media’s abilities to manipulate mass consciousness in the current supersofisticated media environment are extremely limited. The authors put forward a hypothesis about a radical change in the media’s key role, namely, a transition from content production to creating a semantic context. The methodological reflections presented in the article may serve as new impulses to building up synergetic, nonequilibrium, evolutionary and other models of complex adaptive media systems.

Modeling of polygraphic web-service using colored Petri nets

The use of Petri Networks as a tool for graphical and mathematical modeling of complex systems and processes has recently been widespread. Visual representation techniques and simulations, such as Petri colored networks, are effective at the development stage of complex systems, since they allow formally to describe and model the system at different levels of abstraction and investigate them dynamically. An example of a dynamic system is web-services. Web services and their components can interact with different applications that meet the standards of web services. As a rule, one service does not meet the needs of users, and services are becoming more and more complex. In fact, a modern web service is created by combining different web services and their components to create a component service that offers a set of new functional services. When combining and sharing Web services the most critical is the interaction of Web services and their components among themselves, which requires a detailed study of the functioning of the processes and modeling their behavior to improve their efficiency.Polygraphic web-service is a complex program system that organizes the provision of printing services. It works with the client through the Internet and provides an opportunity to find the necessary service at the printing centers for the best possible means, to make an order, to use various services, to pay for services, to choose a means of payment and delivery of printed products. The complex structure of the web-service requires the study and modeling of the interaction of its components to ensure the effectiveness of the operation.To model the composite web service system, it is necessary to identify the main and auxiliary subsystems by means of structural analysis. The block diagram of a web-service is presented in fig. 1. As a structural analysis tool, we used a data flow diagram (DFD) in the notation of a similar Heine-Sarson notation. A top-level contextual chart contains a set of subsystems connected by data streams.A model of a polygraphic web-service in the form of Petri's network in a hierarchical form was developed and presented for the purpose of analysis of separate networks of the second level. This enables to analyze all parts of the network separately and use the results to formulate conclusions about the correctness of the construction of the entire network. In the presence of links between networks of the second level, it is necessary to add additional criteria for the analysis of networks, which are connected with the addition of the main network of cities and transitions between networks of the second level, the number of which depends on the number of possible states of interaction between networks of the second level.

On application of dynamical system methods in biomedical engineering

The spectrum of various methods and tools used for solving bioengineering problems is sufficiently wide. Dynamical systems (including the symbolic ones) in many cases become a base for design and implementation of methods of investigation and computer modeling complex processes. Whereas for solving direct problems we have many well-developed methods, results for inverse problems are much more modest. We discuss two methods for such tasks: Takens’ method for reconstruction attractor by a time series, and the based on ideas of symbolic dynamics method for digital image analysis using stationary flow on graph and weighted entropy. The results of numerical experiments are given.

FROM LIDAR DATA TOWARDS HBIM FOR STRUCTURAL EVALUATION

Abstract. In HBIM processes, the extraction of geometric components from 3D point clouds data can sometimes be a complex process. The so-called Scan to BIM process has been widely utilized: deriving 3D models from point clouds often a local modelling of geometric components is necessary. This leads in most cases to use external modelling tools or complex local modelling processes. In both cases, we often get a model that cannot be reused for other items belonging to the same category, contravening the BIM philosophy. Vaulted systems are a typical example of complex elements that we can find in historical architecture. The paper presents the first results of an ongoing research on geometric modelling and structural evaluation of masonry ribbed vaults. An algorithm is developed to generate a NURBS surface of masonry vaults that, starting from the data extrapolated from the point cloud, allows to obtain an HBIM family. The research aims to overcome the inability to reference to standardised objects in local modelling of historical architecture elements. Directed to a standardization in the geometric modelling process of 3D laser scan data, the developed workflow is a possible alternative to commonly used workflows. Particular attention is focused on a case study of stellar vaults, a special class of masonry ribbed vaults whose three-dimensional geometry features a star-shaped projection on the horizontal plane. The work is carried out to verify that this family can be used for the structural analysis of stellar masonry vaults.

Gaussian process modelling with Gaussian mixture likelihood

Gaussian Process (GP), as a probabilistic non-linear multi-variable regression model, has been widely used in nonparametric Bayesian framework for the data based modelling of complex processes. The noise dynamics in standard GP regression is assumed to follow a Gaussian distribution. In this setting, the point estimation of the model parameters can be obtained analytically using the maximum likelihood (ML) approach in a straight forward fashion. However, in practical scenarios, processes may have been corrupted by the outliers and other disturbances or have multiple modes of operation, resulting a non-Gaussian data likelihood. In this work, to model such scenarios, we propose to employ a mixture of two Gaussian distributions as the noise model to capture both regular noise and irregular noise, thereby enhancing the robustness of the regression model. Further, we present an Expectation Maximization (EM) algorithm-based approach to obtain the optimal parameters set of the proposed GP regression model. The predictive distribution can then be found according to the estimated hyperparameters from the EM algorithm. The efficacy and practicality of the proposed method are illustrated with two sets of synthetic data, a simulated example, as well as an industrial dataset.

Application of Transformation Matrices to the Solution of Population Balance Equations

The development of algorithms and methods for modelling flowsheets in the field of granular materials has a number of challenges. The difficulties are mainly related to the inhomogeneity of solid materials, requiring a description of granular materials using distributed parameters. To overcome some of these problems, an approach with transformation matrices can be used. This allows one to quantitatively describe the material transitions between different classes in a multidimensional distributed set of parameters, making it possible to properly handle dependent distributions. This contribution proposes a new method for formulating transformation matrices using population balance equations (PBE) for agglomeration and milling processes. The finite volume method for spatial discretization and the second-order Runge–Kutta method were used to obtain the complete discretized form of the PBE and to calculate the transformation matrices. The proposed method was implemented in the flowsheet modelling framework Dyssol to demonstrate and prove its applicability. Hence, it was revealed that this new approach allows the modelling of complex processes involving materials described by several interconnected distributed parameters, correctly taking into consideration their interdependency.

Performance of an automated process model discovery – the logistics process of a manufacturing company

Abstract The simulation and modelling paradigms have significantly shifted in recent years under the influence of the Industry 4.0 concept. There is a requirement for a much higher level of detail and a lower level of abstraction within the simulation of a modelled system that continuously develops. Consequently, higher demands are placed on the construction of automated process models. Such a possibility is provided by automated process discovery techniques. Thus, the paper aims to investigate the performance of automated process discovery techniques within the controlled environment. The presented paper aims to benchmark the automated discovery techniques regarding realistic simulation models within the controlled environment and, more specifically, the logistics process of a manufacturing company. The study is based on a hybrid simulation of logistics in a manufacturing company that implemented the AnyLogic framework. The hybrid simulation is modelled using the BPMN notation using BIMP, the business process modelling software, to acquire data in the form of event logs. Next, five chosen automated process discovery techniques are applied to the event logs, and the results are evaluated. Based on the evaluation of benchmark results received using the chosen discovery algorithms, it is evident that the discovery algorithms have a better overall performance using more extensive event logs both in terms of fitness and precision. Nevertheless, the discovery techniques perform better in the case of smaller data sets, with less complex process models. Typically, automated discovery techniques have to address scalability issues due to the high amount of data present in the logs. However, as demonstrated, the process discovery techniques can also encounter issues of opposite nature. While discovery techniques typically have to address scalability issues due to large datasets, in the case of companies with long delivery cycles, long processing times and parallel production, which is common for the industrial sector, they have to address issues with incompleteness and lack of information in datasets. The management of business companies is becoming essential for companies to stay competitive through efficiency. The issues encountered within the simulation model will be amplified through both vertical and horizontal integration of the supply chain within the Industry 4.0. The impact of vertical integration in the BPMN model and the chosen case identifier is demonstrated. Without the assumption of smart manufacturing, it would be impossible to use a single case identifier throughout the entire simulation. The entire process would have to be divided into several subprocesses.

Fabrication of 3D printed garments using flat patterns and motifs

PurposeThe purpose of this paper is to encourage the generation of 3D printed garments by developing a new software to open new potential of whole garment production in the fashion field.Design/methodology/approachThe authors developed a 3D printed garment design software using flat patterns and design motifs, to allow fast and easy generation of a 3D printed garment. Users can generate motif-based design easily using various user interfaces.FindingsA 3D printed garment could be printed not only by expensive SLS type printers but also by affordable FDM type printers with a simple assembly process using the software developed in this study.Originality/valueNewly developed 3D printed garment design software enables the users to generate 3D printed garments easily using flat patterns and design motifs, without having to go through a complex and arduous 3D modeling process using commercial CAD systems.

Stage-based discovery of business process models from event logs

An automated process discovery technique generates a process model from an event log recording the execution of a business process. For it to be useful, the generated process model should be as simple as possible, while accurately capturing the behavior recorded in, and implied by, the event log. Most existing automated process discovery techniques generate flat process models. When confronted to large event logs, these approaches lead to overly complex or inaccurate process models. An alternative is to apply a divide-and-conquer approach by decomposing the process into stages and discovering one model per stage. It turns out, however, that existing divide-and-conquer process discovery approaches often produce less accurate models than flat discovery techniques, when applied to real-life event logs. This article proposes an automated method to identify business process stages from an event log and an automated technique to discover process models based on a given stage-based process decomposition. An experimental evaluation shows that: (i) relative to existing automated process decomposition methods in the field of process mining, the proposed method leads to stage-based decompositions that are closer to decompositions derived by human experts; and (ii) the proposed stage-based process discovery technique outperforms existing flat and divide-and-conquer discovery techniques with respect to well-accepted measures of accuracy and achieves comparable results in terms of model complexity.

Visual presentation of life cycle control model of the cross-platform onboard software

The visual presentation of the software life cycle control model is considered in this scientific paper. A realization of the control model is connected with identifying the semantics, properties and attributes of formation stages, implementation and maintenance of software. For research themethodology IDEF0 is applied. The software lifecycle control model is obtained in a graphical notation. Functional modeling of complex processes, associated not only with development, but also with testing and long-term maintenance of onboard software, is described and presented. It is shown that visual modeling tools are able to be as an effective mean of making control decisions.

Design and Implementation of High Efficiency Control Scheme of Dual Active Bridge Based 10 kV/1 MW Solid State Transformer for PV Application

One promising topology for solid state transformer (SST) is a modular multilevel cascaded converter, in which submodule is composed of dual active bridge (DAB) and H-bridge. For SST application in PV system, the efficiency could be severely affected especially for DAB due to the wide voltage and power range of PV panels. Thus, the motivation of this paper is to deal with the control strategy to improve DAB efficiency inside SST for PV application. Instead of utilizing time-domain based analysis method, which requires complex modeling process, this paper models DAB under frequency domain by fully considering the effect of both fundamental and harmonic frequency component. To achieve high efficiency operation, a multiorder reactive-current suppression (MRS) strategy is proposed by controlling phase shift angle as well as the duty cycles of primary and secondary side H-bridges. In terms of DAB controller design inside SST, the small signal model under MRS is established and a notch filter is implemented to suppress the second order line frequency fluctuation in the control loop. Finally, a 10 kV/1 MW SST prototype is introduced along with the system control structure and implementation method. The experiment of the submodule and SST confirms the effectiveness of proposed method.

Numerical Calculation of Stator End Leakage Reactance of Permanent Magnet Machines with Concentric Winding

Concentric winding has been widely used in small capacity AC motors due to its excellent performance. In this paper, a numerical calculation method based on the magnetic vector potential is proposed to calculate the stator end leakage reactance of concentric winding. In this case, the basic unit of numerical calculation becomes the coil group rather than the coil since concentric windings have different coil sizes. The calculated stator end leakage reactance of a prototype three-phase permanent magnet machine with concentric winding is validated using the finite element method. Compared to 3D electromagnetic field calculation, the proposed method does not require a complex modeling process and is therefore highly efficient computationally, requiring only a fraction of the calculation time.

Information analysis and synthesis of the organizational structure of the unique project in the classical control theory

The formation of the organizational structure of a unique project (without prototypes) refers to the general problem of optimizing the management of processes in irreversible systems of various types. At the same time, an increase in the dimension of the system requires the construction of models not only for particular situations of the objective environment, but also for models that take into account the nature of the dynamics of changes in the parameters of the management process throughout the planning horizon. The article considers the problem from the perspective of information theory, which allows the most abstract method (not limited to a specific subject area) to carry out modeling and optimization of the structure of a control system with a complex, unparalleled project. Information in the economic system is heterogeneous, probabilistic, and has various quantitative characteristics, used to varying degrees in the modeling of complex management processes. The problem of synthesizing the structure of an organizational management system includes the choice of the number of levels and subsystems, as well as the principles of management organization; synthesis of the structure of the system for collecting, transmitting and processing information; addressing issues related to optimizing the hierarchy of the system, ensuring the effective performance of management functions and the achievement of goals. In the tasks of analysis and synthesis of the organizational structure of unique projects, the apparatus of the transfer functions of the classical control theory is used. The organizational structure was modeled at the most abstract level in terms of information theory. The proposed method of optimizing the organizational structure, taking into account the principle of the information value of the resources of the management system. The representation of the management process as a transitional process (a dynamic process of information transformation) allows to reconcile the dynamic characteristics of the control system hardware and the human factor operating in it.

Numerical solutions for point masses sliding over analytical surfaces: Part 1

In this study, we introduce a system of differential equations describing the motion of a single point mass or of two interacting point masses on a surface, that is solved by a fourth-order explicit Runge–Kutta (RK4) scheme. The forces acting on the masses are gravity, the reaction force of the surface, friction, and, in case of two masses, their mutual interaction force. This latter is introduced by imposing that the geometrical distance between the coupled masses is constant. The solution is computed under the assumption that the point masses strictly slide on the surface, without leaping or rolling. To avoid complications stemming from numerical errors related to real topographies that are only known over discrete grids, we restrict our attention to simulations on analytical continuous surfaces. This study sets the basis for a generalization to more complex systems of masses, such as chains or matrices of blocks that are often used to model complex processes such as landslides and rockfalls. The results shown in this paper provide a background for a companion paper in which the system of equations is generalized, and different geometries are presented.

Stance Detection of Microblog Text Based on Two-Channel CNN-GRU Fusion Network

In recent years, stance detection has become an important topic in the field of natural language processing. In earlier work, researchers have used feature engineering for stance detection but they need to define and extract appropriate features according to the particular application. This leads to poor generalization and a complex modeling process. Other researchers have applied deep learning methods. However, the popular convolutional neural network (CNN) method has the problem of information loss and a single-size CNN filter cannot accurately extract features that have different lengths from text, and so cannot deal with the diverse nature of features. In order to address these problems, we propose a two-channel CNN-GRU fusion network. First, a convolution layer with two filters with different window sizes is used to extract local features within the topic content and text content. Then, a gated recurrent unit (GRU) network is used to extract their timing characteristics. After that, the intermediate features are spliced and input to a classifier to complete the stance detection. Our method is validated using data from NLPCC 2016. The experimental results show that ACC and average F1 score of this method are 13.1% and 15.6% better than SVM method, 6.2% and 11.6% better than CNN method, 5.6% and 3.3% better than GRU method, and 1.1% and 2.2% better compared with hybrid model proposed by Nanyu, respectively, which is used as a baseline with no increase in run-time, and achieves the same accuracy with less run-time than another baseline of a semantic attention-based model proposed by Zhou. In addition, our method allows better classification than the single channel model. Finally, we find that the operation time of a multi-channel CNN-GRU increases gradually with increasing number of channels, but the classification accuracy does not improve, so a two-channel CNN-GRU is the most appropriate choice.

A Profile Clustering Based Event Logs Repairing Approach for Process Mining

Process discovery, as the most crucial learning task in the process mining, builds some highly complex process models such as “spaghetti-like” from event logs contained large amounts of data. To enhance the process discovery method for all of flexible environments, many researchers tried to exploit trace clustering approaches to split the logs into several homogeneous sub-logs, which are used to generate the corresponding sub-process model, respectively. However, their works are based on the assumption that the event logs are complete without missing any data values. On the contrary, the data in an event log may be lost due to some reasons such as system failure and human error. In this paper, we propose a method to deal with incomplete logs so as to discover the process model. First, we split up the event logs based on trace clustering. Then, the missing traces are assigned to the most similar clustering results, respectively. After that, with supplementing the missing data in the trace, a corresponding sub-process model is mined using the proposed method. At last, some experimental results on three real-life complex event logs demonstrate the feasibility and effectiveness of our method.