Subsystem Models Research Articles

Study on The Supply Security of Fresh Products Under Public Health Emergencies

Strict control taken in response to public health emergencies (PHEs) can hinder fresh products supply, while stock-outs and the risk of epidemics can increase fresh products demand. To explore the interrelationship between outbreak control measures and the availability of fresh products, this paper establishes a dynamic synergistic model of epidemic transmission subsystem and fresh products supply-consumption subsystem based on system dynamics (SD). Taking the spread of COVID-19 in Shanghai from March to May 2022 as the actual background, the impact of different storage strategies and control measures on epidemic prevention and material supply was quantitatively evaluated. The results show that risk transmission factor (RTF) affects the number of infections and the intensity of community control contributes to controlling the spread of the epidemic, but it can continue to affect fresh product supply security. Therefore, increasing fresh product protection inputs and reducing in-transit time can reduce fresh product losses and improve fresh product supply security under PHEs. The findings can enable fresh products to effectively match supply and demand under PHEs, mitigate the impact of epidemic risks and provide decision support for relevant retailers.

Frequency-Based Estimation of the B-H Curve of Steel Strips in a Continuous Induction Furnace*

A parameter estimation strategy for continuous inductive heating of ferromagnetic steel strips is developed and investigated. During strip processing in an induction furnace, the parameters of the magnetization curve (B-H curve) of the strip material are identified by a moving horizon estimator (MHE). This estimator uses measurements of the resonance frequency of the electric circuit. A tailored 2D harmonic model of the electromagnetic subsystem is used to calculate the complex coil impedance. Other model parameters are identified in a scenario, where the magnetization curve of the strip is known. The approach does not require a thermal model of the induction furnace in favor of the computing time and the achieved accuracy. For model validation, the simulated resonance frequency is compared with measurements. To avoid a high computational effort, the effective magnetization approach is applied to approximate the solution of the nonlinear Maxwell equations. The effective B-H curve is mathematically expressed in terms of a linear and an exponential function and parametrized by two parameters. The MHE is validated in a test scenario using a strip with a known magnetization curve.

Decision Support System to Determine Higher Education Resource Needs Using Accreditation Assessment Standards

Accreditation is an activity to evaluate the eligibility of programs and academic units based on predetermined criteria. In an educational institution, accreditation is an important thing to do to improve quality in the field of education because the aim is to improve quality which includes all aspects of education in the form of science, administration, as well as teaching, and educational staff. College managers really need a decision support system that can produce several alternative strategic decisions quickly and accurately. The existence of decision support system tools used to make strategic decisions makes the manager's work lighter and the results are more accurate. In this study, a decision support system approach was applied to determine the resource requirements of tertiary institutions using accreditation assessment standards consisting of a management subsystem model, data management subsystem, and user interface management subsystem. The result of the research is a design of a decision support system that can be used in an educational institution for determining the need of higher education resources.

Spacecraft energy management using convex optimisation

This paper considers a spacecraft energy management strategy for optimally scheduling the power available for secondary tasks, such as communications and scientific payloads, while meeting the power demand for primary tasks, such as thermal management and propulsion, and adhering to operating constraints. A solution based on convex optimisation and model predictive control is proposed. Mathematical models of the various energy subsystems are used to formulate the energy management task over a future horizon as a constrained optimisation problem. We show that this problem can be formulated as a convex program and we derive an energy management strategy in the form of a model predictive control law. The convex problem formulation guarantees global optimality of the presented solution and opens the way for future onboard real-time implementations.

A case for somatic practices as embodied mindfulness

Presenting convergences between mindfulness and somatic practices, this article makes a case for somatic practices as embodied mindfulness. It discusses historical origins as well as brief comments supporting some shared value systems and qualities espoused in both practices. After giving an overview of embodied cognition theory and the Interacting Cognitive Subsystems model, I trace how developing expertise in both somatic and mindfulness practices create the same cognitive repatterning to support the argument that somatics is a field of embodied mindfulness practices.

Study of a local source of autonomous power supply on the basis of a diesel generator

A mathematical model of a local autonomous power supply source based on a diesel generator set is presented. The source of primary mechanical energy is a diesel internal combustion engine with an automatic speed controller, operating on the Polzunov–Watt’s principle. The electric energy converter is an asynchronous motor operating in a generator mode with capacitor self-excitation. The complete set of such devices is carried out from the available and normally working components of technological, electrical and transport equipment. They are formed by the personnel of the relevant enterprises in the period preceding the accident, during its development or at the end of its active phase. Therefore, mathematical models of subsystems of a diesel engine, an asynchronous machine operating in a generator mode, capacitive self-excitation and a number of typical electricity consumers are represented by separate structural blocks with functional relationships and connected according to the principle of subordinate regulation. This form of representation makes it possible to carry out large-scale studies of the qualitative and quantitative indicators of the operation of diesel generator sets with various types of both internal combustion engines and asynchronous machines. The coincidence of the results of numerical simulation and full-scale experiments allows to judge the adequacy of the proposed mathematical model of a local autonomous power supply source with a diesel generator. The presented model combines algorithmic simplicity and high computational precision and will make it possible to determine the criteria for the trouble-free operation of autonomous power supply sources to provide energy to consumers of different categories

Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo

The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polynomials) and (b) an constant runtime independent from the number of operation points requested because new optimization needs just to be performed in case of plant model changes. An experimental implementation of the algorithm is validated using a simscape simulation. For a batch of five requests, OptTopo needs 61min while the solvers Cobyla, SDPEN, and COUENNE need 0.3 min, 1.4 min, and 3.1 min, respectively. OptTopo achieves an efficiency improvement similar to that of established solvers. This paper demonstrates the general feasibility of the concept and fortifies further improvements to reduce computing time.

Structure–function subsystem model and computational lesions of the central nervous system’s rostral sector (forebrain and midbrain)

The craniote central nervous system has been divided into rostral, intermediate, and caudal sectors, with the rostral sector containing the vertebrate forebrain and midbrain. Here, network science tools were used to create and analyze a rat hierarchical structure-function subsystem model of intrarostral sector neural connectivity between gray matter regions. The hierarchy has 109 bottom-level subsystems and three upper-level subsystems corresponding to voluntary behavior control, cognition, and affect; instinctive survival behaviors and homeostasis; and oculomotor control. As in previous work, subsystems identified based on their coclassification as network communities are revealed as functionally related. We carried out focal perturbations of neural structural connectivity comprehensively by computationally lesioning each region of the network, and the resulting effects on the network's modular (subsystem) organization were systematically mapped and measured. The pattern of changes was found to be correlated with three structural attributes of the lesioned region: region centrality (degree, strength, and betweenness), region position in the hierarchy, and subsystem distribution of region neural outputs and inputs. As expected, greater region centrality results, on average, in stronger lesion impact and more distributed lesion effects. In addition, our analysis suggests that strongly functionally related regions, belonging to the same bottom-level subsystem, exhibit similar effects after lesioning. These similarities account for coherent patterns of disturbances that align with subsystem boundaries and propagate through the network. These systematic lesion effects and their similarity across functionally related regions are of potential interest for theoretical, experimental, and clinical studies.

МОДЕЛЮВАННЯ АВТОМАТИЧНОГО ПОЛЬОТУ МАЛОГО БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТУ НАД ЛІНІЄЮ-МАРКЕРОМ

The subject of study is the process of automating the behavior of an unmanned aerial vehicle during a flight following a given marker inside a room. The goal is to reduce the distance of deviation from the course and reduce the time to complete the flight along the trajectory set by the contrasting marker on the ground. The task: to conduct an analysis of existing libraries capable of performing automatic flight simulation and adaptation of the received code to the hardware platform of modern unmanned aerial vehicles; review the structure of the selected library, and modify the components of the library, which are necessary for the formation of automatic flight; create a model of the video processing and control system, check its operational parameters in a simulation environment. The methods used are: system analysis used to compare existing libraries of UAV flight simulation information systems within the framework of the assigned tasks, artificial intelligence methods for the development of a video stream data analysis subsystem and recognition of the provided marker during UAV flight simulation, system programming methods for automatic control of UAV flight based on the data obtained from the visual information analysis system, a method of simulation modeling to check the correctness of the developed algorithms in collaborative work. The following results were obtained. The choice of the library for the development and modeling of subsystems of automatic flight of a small unmanned aerial vehicle in a room is justified, a subsystem for processing video information obtained from the built-in camera of a UAV is created, an automatic flight control subsystem is developed taking into account the data of the video information analysis subsystem; a series of experiments was conducted on a simulation model to check the system's performance. Conclusions. In the course of the study, a model of the autonomous flight of a small unmanned aerial vehicle capable of flying over a contrasting color marker placed on the surface over which the UAV flies was improved. Unlike existing models, the system does not require the use of additional equipment, all computing operations must be performed on board the UAV, only the data of the video stream of the built-in camera and the coordinates of the UAV are transmitted to the control station, in addition, the minimum number of additional libraries necessary for creating a simulation model was used simulation modeling. This will reduce the time it takes to create a model, and, accordingly, conduct more experiments on improving parameters to achieve the goal of research.

Ontological Support for Harmonization and Integration of Ukrzaliznytsia Information Systems Data

Purpose. The development strategy of Ukrzaliznytsia includes the following areas: integration and standardization of information systems, increasing the truthfulness of data and automating business processes. The integration of railway information systems is possible by ontological means without changing their structure. In this work, the main aim is the analysis of existing transportation ontological developments and determination of approaches to the application of related domains developments to the objectives of Ukrzaliznytsia’s development. Methodology. Ontological developments are systematized according to the type and format of their resources, the level of data integration, and the goals of ontology-based software. Methods of system analysis are used. Findings. The analysis showed that European Union railway transport ontologies are used to integrate infrastructure description data, train timetables, and others. At the same time, insufficient attention is paid to the regulatory support of the transportation process. There are software tools for annotating texts, extracting knowledge from tables and developing ontologies, but they are not used to support the Ukrainian railway transportation process. It has been determined that the actual problem is normative documentation annotation to establish a link between the ontology and the regulation texts. Originality. The basis for achieving the development goals of Ukrzaliznytsia by ontological means was laid, using the analysis and systematization of existing ontological developments of transport and related domains. The possibilities of using ontological means in railway transport are scientifically substantiated for: formalization of regulatory support; data transformations; data integration; checking the consistency of information systems data and regulations. Practical value. The work made it possible to identify the most significant ontological projects in transport. The foundations for the implementation of the conceptualization of the tabular representation of knowledge and the development of an ontology for the integration of models of railway subsystems have been laid.

Adaptive neural network control for uncertain dual switching nonlinear systems

Dual switching system is a special hybrid system that contains both deterministic and stochastic switching subsystems. Due to its complex switching mechanism, few studies have been conducted for dual switching systems, especially for systems with uncertainty. Usually, the stochastic subsystems are described as Markov jump systems. Based upon the upstanding identity of RBF neural network on approaching nonlinear data, the tracking models for uncertain subsystems are constructed and the neural network adaptive controller is designed. The global asymptotic stability almost surely (GAS a.s.) and almost surely exponential stability (ES a.s.) of dual switching nonlinear error systems are investigated by using the energy attenuation theory and Lyapunov function method. An uncertain dual switching system with two subsystems, each with two modes, is studied. The uncertain functions of the subsystems are approximated well, and the approximation error is controlled to be below 0.05. Under the control of the designed adaptive controller and switching rules, the error system can obtain a good convergence rate. The tracking error is quite small compared with the original uncertain dual switching system.

Dynamic response assessment and multi-objective optimization of organic Rankine cycle (ORC) under vehicle driving cycle conditions

Organic Rankine cycle (ORC) can effectively utilize the waste heat energy of internal combustion (IC) engine. The ORC system has obvious large time-varying characteristic, strong fluctuation, and hysteresis under the synergistic influence of frequent fluctuation of vehicle speed and operating parameters in the system. The power system, waste heat recovery system, transmission system, and other subsystem models are constructed in this study. The key components are verified. An integrated system model of IC engine–ORC for driving cycle is constructed on the basis of the subsystem model. The dynamic response of ORC system is evaluated under different road conditions. The synergistic influence of multiple variables on system performance is analyzed. Obvious strong coupling and nonlinear characteristics are observed among different performances of ORC system under road conditions. Therefore, a multi-objective cooperative optimization framework of ORC system considering actual road conditions is proposed on the basis of multidimensional data. In the process of collaborative optimization, thermal efficiency and emissions of CO2 equivalent show a clear trade-off under different road conditions. The limit value of thermal efficiency under new European driving cycle is also 6.48% higher than that under worldwide harmonized light vehicles test cycle. Frequent fluctuation of vehicle speed is not conducive to obtaining the limit value of thermal efficiency. The evaluation and optimization of the comprehensive performance of ORC system under complex road conditions can provide a reference for the practical application of vehicle ORC system.

Modeling of the dynamic behavior of an integrated fuel cell system including fuel cell stack, air system, hydrogen system, and cooling system

1-dimentional (1-D) physical modeling methods of the dynamic behavior of an integrated fuel cell (FC) system are investigated, which consist of the models of the FC stack and subsystems of the air, hydrogen (H2), and cooling systems. To ensure numerical simulation of the entire FC system in life-long system operation (> 10 years) in acceptable calculation time, a proper model resolution is selected. The subsystem models are integrated with the FC stack model and controllers developed in our previous research to build a closed-loop simulator of an integrated FC system. It demonstrated that the impacts of the FC stack material properties on the overall FC system performance can be investigated by numerical simulation.

A two-layer transformation for zeros analysis of networked dynamical systems

Input–output properties are studied for a linear network model with homogeneous single-input single-output subsystems. Specifically, the finite- and infinite- zero structure of a channel in the network, defined by a single external actuation and measurement, is characterized. This is done by developing a two-layer transformation for the input–output model, which expresses the zero structure in terms of the subsystem model, global network interactions (graph), and the input and output locations. This algebraic characterization then provides a means to: (1) distinguish structural properties of the input–output channel (minimum and nonminimum behavior) in terms of the network’s graph, and (2) design of the graph to shape the input–output dynamics. Similar characterizations are also briefly explored for a multiple-input multiple-output (MIMO) network model with collocated measurements and actuations.

Development of a comprehensive transient fuel cell-battery hybrid system model and rule-based energy management strategy

ABSTRACT In the study, a comprehensive transient fuel cell-battery hybrid power system model is established to explore the output performances and dynamic responses of fuel cell electric vehicles (FCEVs). A fuel cell system sub-model, a one-dimensional transient Li-ion battery sub-model, two direct current/direct current (DC/DC) sub-models as well as a vehicle load sub-model are integrated. The coupled heat and mass transfer processes are considered inside fuel cell system and Li-ion battery. After rigorous model validation, the overall performances under several typical China standard vehicle road conditions are investigated, including power distribution between different power units, battery state of charge (SOC), battery charge/discharge rate, fuel cell operating duration, temperature, and other key parameters. During the 100 km h−1 acceleration processes under the preliminary rule-based energy management strategy (EMS), the accelerated speed is found to be the largest in the beginning, resulting in the fact that the discharge power for battery increases sharply. The shorter the acceleration time, the larger the maximum power demand for Li-ion battery. During the combined 1300 s high-speed operating conditions under the on-off switch EMS, the total operating duration of fuel cell system is calculated to be about 531 s, and the total output power is calculated to be 11,556 kJ. In comparison, the vehicle saves about 1044 kJ energy and increases the fuel cell operation duration by 333.7% under the power-following EMS. The present study provides important guidance in terms of dynamic simulation analysis and energy management strategy for FCEVs.

Ідентифікація матриці суміжності у моделі імпульсних процесів з різнотемповою дискретизацією в когнітивній карті застосування криптовалют

Застосування криптовалюти на фінансових ринках характеризується складною динамікою, яка відрізняється нестаціонарністю процесів і невизначеністю ситуації. На процеси застосування криптовалюти діють різні збурення, направлені на зменшення рівня довіри до використання криптовалюти. Тому при операціях з криптовалютою виникають ризики втрати користувачів, що призводить до зниження ціни біткоїна, що пов’язано з хибними загальними одночасними сподіваннями багатьох користувачів, які створюються маніпулюваннями трейдерів на фінансових біржах; різкого обвалу курсу криптовалюти в результаті звичайних махінацій на біржах, до яких можна віднести так званий високочастотний трейдинг, який полягає в перевазі певної групи користувачів у швидкості купівлі грошових активів раніше за більшість інвесторів і продажу їх повільним користувачам, поки інформація про купівлю дійде до повільного інвестора. Ці дії в поєднанні з алгоритмічним трейдингом, механізмом деривативів і квартальних ф’ючерсів, реалізованих на біржах, створюють реальну небезпеку значної зміни курсу від доволі незначних збурень, пов’язаних з відсутністю гарантії на збереження капіталу, вкладеного в купівлю криптовалюти, який призводить до певної істерії користувачів у процесі торгів на біржах. Для опису впливу даних ризиків розглянуто когнітивну карту (КК) застосування криптовалюти на фінансовому ринку, на основі якої описано динамічну модель імпульсних процесів КК у вигляді систем різницевих рівнянь (рівняння Робертса) з різнотемповою дискретизацією. При цьому виконана декомпозиція вихідної теоретичної моделі імпульсних процесів КК з однотемповою дискретизацією на підсистеми з швидковимірюваними і повільновимірюваними координатами вершин КК. Для цього моделі підсистем представлені з різнотемповою дискретизацією координат і взаємопов’язані між собою. Розроблені алгоритми ідентифікації коефіцієнтів матриці суміжності імпульсних процесів КК для підсистем на основі рекурентного методу найменших квадратів відповідно у швидкозмінному і повільнозмінному масштабах часу. На основі цифрового моделювання виконані експеримен­тальні дослідження швидкодії і точності оцінювання вагових коефіцієнтів матриць суміжності в моделях імпульсних процесів підсистем КК.

Dual Mode Localization Assisted Beamforming for mmWave V2V Communication

This study is motivated by the fact that localization in millimeter wave(mmWave) Vehicle-to-Vehicle communication becomes a more critical problem because both the terminals of the communication link are in motion. The positional awareness merely based on GPS or local sensors has an error margin of around 10 meters, which can worsen in uncertain real-time conditions such as road topology and highway traffic. The paper analyses the relation between vehicle dynamics, beamforming, and beam alignment for highly directive antennas. When the subsystem models presented in this paper are taken into consideration, the joint vehicle dynamics-beamforming approach will optimize the link connectivity and SNR. The vehicle dynamics model is designed to be more realistic considering the non-linear acceleration based on the throttle-brake jerks due to induced noises. The Unscented Kalman Filter based prediction subsystem predicts the alignment values even during non-linear acceleration and jerks. The feasibility of antenna design for the beamforming strategies is supported by the requirements of phase shifters and the number of elements.

Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations

Energy consumption of subway stations has become a focus of scientific research. For energy modeling, complete coverage of inputs makes it difficult to collect information for large-scale engineering application. To this consideration, this study conducted sensitivity analysis (SA) to determine key inputs and simplify energy model of subway stations. Firstly, four commonly-used SA methods (PEAR, SPEA, Morris and Sobol) were compared. Secondly, according to sensitivity indices, key inputs were extracted for ventilation and air-conditioning (VAC), lighting (LIGHT) and vertical transport (TRANS) model. For VAC model, the most important factors are humidity and temperature of the outside air and mechanical fresh air volume, contributing 40%, 33% and 11% of the change on energy simulation results, respectively. For LIGHT model, lighting power density of the hall exerts the greatest impact, with 50% of the change. For TRANS model, key parameters include train departure density and number of passengers, contributing 45% and 32% of the change, respectively. These outcomes provide guidance for engineers in the field of energy-saving operation for subway stations. Eventually, simplified VAC and TRANS energy models were investigated. Results indicate that CV-RMSE for simplified models are within 7.6%. The simplification greatly reduces the workload of data collection and model simulation.

Partially Coupled Stochastic Gradient Estimation for Multivariate Equation-Error Systems

This paper researches the identification problem for the unknown parameters of the multivariate equation-error autoregressive systems. Firstly, the original identification model is decomposed into several sub-identification models according to the number of system outputs. Then, based on the characteristic that the information vector and the parameter vector are common among the sub-identification models, the coupling identification concept is used to propose a partially coupled generalized stochastic gradient algorithm. Furthermore, by expanding the scalar innovation of each subsystem model to the innovation vector, a partially coupled multi-innovation generalized stochastic gradient algorithm is proposed. Finally, the numerical simulations indicate that the proposed algorithms are effective and have good parameter estimation performances.

Digital twins for prediction of atherosclerosis progression and stroke

Background and Aims : The etiology behind atherosclerosis is complex, involving many different processes, and develops over several years, often without symptoms, before it leads to more acute conditions such as stroke. One way of dealing with this complexity is through mathematical modelling. There already exist models for relevant organs and sub-systems. However, none have combined these models for different sub-systems and personalized them into digital twins.