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In the field of molecular nonequilibrium transports, physical mechanisms of multiple reaction dynamics of these systems are the core of deep understanding complex reactions and transport mechanisms. In order to explore related mechanisms, establishing multiple systems coupled with tremendous exit dynamics and studying their exit dynamics properties are quite vital. Beyond previous researches, new stochastic transport processes are emphasized here. Multiple new exit dynamic systems are established, which are motivated by the multiplicity of paths and products of real biochemical processes in organisms. In order to ensure research universality, core system modeling factors are fully considered. Countable parallel orbits, uniform connection with external sources, countable parallel orbits as subsystems in middle lattices and influences of all lattices on transport trajectories on dynamic properties are analyzed. Dynamic properties of different particles located in orbits are explored by deeply studying average exit time and time scale. Quantitative spatiotemporal impacts are extensively studied. The rationality of average exit time as a time scale in the universal exit dynamic system is proved. Main findings and fruitful results can not only serve as theoretical bases for broadening reaction path modeling, but also be helpful to support understanding nonequilibrium transport mechanisms, especially stochastic biochemical processes.

In this paper, a bifurcation prediction approach is proposed based on dynamic recognition and further applied to predict the period-doubling bifurcation (PDB) of power systems. Firstly, modeling of the internal dynamics of nonlinear systems is obtained through deterministic learning (DL), and the modeling results are applied for constructing the dynamic training pattern database. Specifically, training patterns are chosen according to the hierarchical structured knowledge representation based on the qualitative property of dynamical systems, which is capable of arranging the dynamical models into a specific order in the pattern database. Then, a dynamic recognition-based bifurcation prediction approach is suggested. As a result, perturbations implying PDB on the testing patterns can be predicted through the minimum dynamic error between the training patterns and testing patterns by recalling the knowledge restored in the pattern database. Finally, the second-order single-machine to infinite bus power system model is introduced to check the effectiveness of this prediction approach, which implies PDB under small periodic parameter perturbations. The key point that determines the prediction effect mainly lies in two methods: (1) accurate approximation of the unknown system dynamics through DL guarantees the feasibility of the prediction process; (2) the qualitative property of PDB and the generalization ability of DL algorithm ensure the validity of the selected training patterns. Simulations are included to illustrate the effectiveness of the proposed approach.

The transformation of taxation processes and the optimization and modeling of their management systems are hot topics in many disciplines such as public management and computer science. Therefore, the intelligent tax management is used to implement the tax process. Meanwhile, tax indicators are adopted as independent variables, and the Logistic regression model is applied to check the selected cases to determine corporate information, which further eliminates less relevant indicators based on the selection results to obtain a more accurate model. In addition, the system modularization design method is applied to design and plan the specific function process of system realization in detail, establishing the necessary business function which can be formed. Moreover, the test process is performed according to the simulation test of the function and the performance of the tax management module. It is proved that the application level of the design module in the paper can meet the current needs, and has the ability of intelligent data analysis and discrimination, which can be promoted within the scope of tax management.

Decision making is a vital function in the age of machine learning and artificial intelligence; however, its physical realization and theoretical fundamentals are not yet well understood. In our former study, we demonstrated that single photons can be used to make decisions in uncertain, dynamically changing environments. The two-armed bandit problem was successfully solved using the dual probabilistic and particle attributes of single photons. In this study, we present a category theoretic modeling and analysis of single-photon-based decision making, including a quantitative analysis that agrees well with the experimental results. The category theoretic model unveils complex interdependencies of the entities of the subject matter in the most simplified manner, including a dynamically changing environment. In particular, the octahedral structure and the braid structure in triangulated categories provide better understandings and quantitative metrics of the underlying mechanisms for the single-photon decision maker. This study provides insight and a foundation for analyzing more complex and uncertain problems for machine learning and artificial intelligence.

Many industrial thermal processes belong to distributed parameter systems (DPSs), which have two coupled nonlinear blocks. Dual least square support vector machines (LS-SVM) has been proposed to model such systems. However, due to the use of two LS-SVM, this method often leads to heavy computation and long learning time, which does not suit for online application. In this study, a dual extreme learning machine (ELM)-based spatiotemporal modeling method is proposed for such two nonlinearities embedded DPSs. Firstly, the KL method is applied to reduce the dimension of the original system and obtain the spatial basis functions (BFs). Then, dual ELM is designed to match the two nonlinear structures. Finally, through the reconstruction of space–time synthesis, the approximate spatiotemporal distribution model of the original system is obtained. In addition, simulations on a curing process is studied to confirm the effectiveness of the proposed method.

People who live in low-income and hard-to-reach regions are usually the most affected ones by high incidences of arboviral diseases, increasing morbidity and mortality rates, and public health costs. We present the modeling of hardware and software components of an unmanned aerial vehicle (UAV) system by mathematical tools, focusing on monitoring foci of arboviral diseases transmitted by Aedes aegypti mosquito, e.g., Zika, Chikungunya, and Dengue. We used restriction equations and the colored Petri nets formal modeling language to represent the flight dynamics and the software components of the system, respectively. We evaluated the specification of desired behaviors of the monitoring system using simulations and the model checking technique. The results showed the completeness and correctness of the specification. The design of such a system is challenging due to the potential risks to people and the environment. Therefore, this study provides insights into the development of an UAV system for such an application scenario. The monitoring system has the potential of improving the efficiency in identifying foci of arboviral diseases.

System modeling and analysis is used to validate assumptions, increase understanding, synchronize views, and support decisions. By measuring indirect related quantities and commonalities of different modeling techniques in practice we can get an indication of the value of modeling. In this paper, we discuss how to increase modeling value and provide more effective model-assisted communication by understanding critical success factors of modeling. We analyze models used to support production line design at Volvo Aero Norge AS.

Volvo Aero Norge AS manufactures jet engine components for commercial and military engine suppliers. Flight safety is fundamental in the domain which translates to comprehensive component quality and traceability requirements. Long-term engine programs make production line development and process improvements important for staying competitive and maintaining a profitable production that supports the required quality level.

System modeling and analysis is applied to communicate insight between stakeholders and visualize different aspects of production lines and processes. In this paper we present impact factors the architect can use to increase a model's ability to assist communication. We argue how balancing and utilizing the right quantity of these factors increase modeling value.

This paper is presented for the Special Session “Workshop on Soft Robotic Systems — Locomotion, posture and motion control”.

The purpose of this work is to present a novel control approach for a tendon driven soft robotic system using a Fractional Order (FO) controller. As the complex system is difficult to model, an advanced robust controller is applied based on a simplified model. The FO controller is used to meet the control specifications, taking advantage of the introduction of its fractional order α. Simulation and experimental data are presented to validate the approach.

Whereas in classical robotics the goal is to construct stiff actuators in order to achieve a high precision, current research also focusses on compliant drives. These can be used in bioinspired robotic systems as well as in classical robotic scenarios where the focus lies on safety – for example in human-machine interactions. Due to the elasticity of the compliant drives, the forces during impacts can be reduced compared to stiff systems. Usually, the compliance is achieved by pure control or by integration of steel springs. In this work, an approach is presented that utilizes a sensorized elastomer coupling. The advantages over the mentioned systems is the inherent damping due to the elastomer properties and the possibility to build smaller and therefore lighter systems as compared to steel spring based approaches. A disadvantage is the need for a more complex model in order to derive the acting torque from torsion measurements. Based on experimental data, a lumped physical model has been developed that is able to estimate the torques with a high accuracy, yet simple enough to be implemented on a microcontroller.