Dynamic General Model Research Articles

Technoeconomic assessment of solar technologies for the hybridization of industrial process heat systems using deterministic global dynamic optimization

The industrial process heat (IPH) sector provides an opportunity to replace terawatts of fossil fuel energy with solar alternatives, such as parabolic trough collectors (PTC) or photovoltaics (PV) with resistive heating and energy storage (ES) for off-peak deployment. PTCs offer significantly higher thermal efficiency, but have stagnated in cost and efficiency improvements over the past decade. In contrast, PVs have seen significant improvements in cost and efficiency. Additionally, the energy generated by PV can be stored electrochemically in batteries. Current optimization-based approaches for design and investment decision making lack guarantees of global optimality that are necessary to ensure the best-possible solutions are obtained for these general nonconvex dynamical models. We present dynamical models for the formal deterministic global optimization-based design and technoeconomic assessment of solar systems to hybridize IPH and reduce natural gas combustion that serve as a general and flexible framework. We compare hybridization strategies of PTC with thermal ES, PV with thermal ES, and PV with battery ES and explore the impacts of natural gas pricing and process scale. For the low- to medium-temperature IPH, PTC with thermal ES is currently at least four times more economically favorable than alternative technologies regardless of location or process scale.

Collaborative robot dynamics with physical human–robot interaction and parameter identification with PINN

Collaborative robots are increasingly being used in dynamic and semi-structured environments because of their ability to perform physical Human–Robot Interaction (pHRI) to ensure safety. Therefore, it is crucial to model the dynamics of collaborative robots during pHRI to gain valuable insights into the system’s behavior when in contact with humans. In this work, a generic dynamic model is proposed for the quasi-static contact phase of pHRI, which considers the interaction dynamics and the complete structural dynamics of the collaborative robot. Moreover, a hybrid physics-informed neural network (PINN) is proposed, which utilizes a recurrent neural network (RNN) and the Runge–Kutta method to identify the joint dynamic parameters without complicated regressor construction. Experiments are conducted using a UR3e collaborative robot, and the PINN is trained using the acquired data. The results demonstrate the effectiveness of the PINN in identifying joint dynamics without prior knowledge, and the dynamic simulation of pHRI is consistent with the experimental results. The proposed model and PINN-based identification approach have the potential to improve safety and productivity in industrial environments by facilitating the control of pHRI.

Theoretical Study on the Nonlinear Dynamic Response of Propulsion Shafting under Time-Varying Hull Motion

Due to the effect of external elements such sea waves and sea wind, the ship will unavoidably produce 6 degrees of freedom of movement, with roll motion being the most likely to occur and having the greatest impact. This paper establishes a general dynamic model of the propulsion shafting based on Timoshenko beam theory, the concentrated mass method, and Lagrange’s equation under roll motion and translation motion to study the dynamic behaviour of the propulsion shafting under roll motion and translation motion. FFT, axis trajectory, Poincaré map, and 3D spectrogram were utilized to examine the impact of motion amplitude, frequency, and rotation speed on the propulsion shafting’s dynamic response. The results demonstrated that base motion significantly affects the dynamics of the propulsion shafting, resulting in many components appearing in the system’s response spectrum, and the system changes from quasi-periodic to chaotic motion when base motion amplitude or frequency rises.

Research on Collision Avoidance Algorithm of Unmanned Surface Vehicle Based on Deep Reinforcement Learning

To solve the problem of active collision avoidance for unmanned surface vehicle (USV) in complex maritime environment, a method of deep reinforcement learning (DRL) based on proximal policy optimization (PPO) is proposed. In order to master the collision avoidance policy by self-learning, the mathematical model of USV, dynamic obstacle generation model and reward mechanism were established. Using the relative position between obstacle and USV and the information of closest point of approach (CPA), high-dimensional state features including the collision track layer and collision threat layer, were constructed respectively. On this basis, combined with low-dimensional states such as navigation state and path error, a deep convolutional neural network(CNN) structure fused in multi-feature and multi-scale was designed. The proposed DRL network was trained through repeated collision avoidance simulations in random environments. Simulation results reveal that the proposed algorithm can output effective decisions in complex scenarios and avoid dynamic obstacles quickly and safely.

Applying the AD-AS Model to the Housing Market of Post-Socialist Economies

We propose applying the standard aggregate demand and aggregate supply model (AD-AS) to the housing market. It is a very simple and intuitive tool that can help shed light on the major forces at play in the market and that can supplement the use of the general equilibrium and dynamic stochastic equilibrium models (DSGE). The latter models are very sophisticated and aim to cover many aspects of the economy, but they require a significant number of long time series to estimate the model parameters. However, in many countries, such as post-socialist countries, the time series are short. Those models moreover cover only real house prices, but in certain situations we should consider real and nominal prices at the same time.

Fault-tolerant attitude tracking control for carrier-based aircraft using RBFNN-based adaptive second-order sliding mode control

This paper proposes a fault-tolerant attitude tracking control for carrier-based aircraft with external disturbances and actuator faults, which is based on backstepping control, second-order sliding mode control (SOSMC) and the radial basis function neural network (RBFNN) adaptation algorithm. First, the generic attitude dynamic model of carrier-based aircraft is presented. Based on backstepping method, the attitude control system is divided into two simple and low-order subsystems. Then, the basic SOSMC using the type of proportional integral (PI) sliding mode variable are designed respectively for two subsystems, which can guarantee the system stable with no chattering in the finite time. To compensate for disturbances and faults more accurately, we design RBFNN-based adaptive second-order sliding mode control (RASOSMC) where RBFNN algorithm is utilized to approximate the lumped perturbation, which attenuates the impact of disturbances and faults more effectively and improves attitude tracking performance. Furthermore, the adaptive online algorithms for the RBFNN weights are proposed based on Lyapunov theory. Finally, numerical simulation results are given to indicate the effectiveness and robustness of the SOSMC scheme and RASOSMC scheme.

CEG: A joint model for causal commonsense events enhanced story ending generation.

With the success of pre-trained language models, the performance of story ending generation has been dramatically improved while remaining challenging due to the lack of commonsense reasoning ability. Most previous works mainly focus on using commonsense knowledge to enhance the implicit correlations between words but ignore the hidden causality of sentences or events. In this paper, we propose Causal commonsense Enhanced joint model for story ending Generation (CEG), which incorporates causal commonsense events knowledge to generate a reasonable story ending. Specifically, we first develop a commonsense events inference model trained on GLUCOSE, which converts static knowledge into a dynamic generation model to discover unseen knowledge. It uses prompts to produce various commonsense events behind the stories as pseudo-labels of the dataset. Then, we propose a joint model for the causal events inference task and the story ending generation task to inject inference knowledge into the generation, which consists of a shared encoder, an inference decoder, and a generation decoder. In the causal events inference task, we use the shared encoder and the inference decoder to reason the causal events behind each sentence of the story context to help the model better understand the story and provide long-distance dependencies for the story ending generation. In story ending generation, we combine the hidden states of the causal events with the story context to generate the story ending by the shared encoder and the generation decoder. We jointly train the model on two tasks so that the generation decoder produces the story endings that better match the clues. Experimental results on the ROCStories dataset show that our model outperforms the previous works, demonstrating the effectiveness of the joint model and the generated causal events.

Hypergraphx: a library for higher-order network analysis

Abstract From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where hyperedges encode interactions among an arbitrary number of units. Here, we present an open-source python library, hypergraphx (HGX), providing a comprehensive collection of algorithms and functions for the analysis of higher-order networks. These include different ways to convert data across distinct higher-order representations, a large variety of measures of higher-order organization at the local and the mesoscale, statistical filters to sparsify higher-order data, a wide array of static and dynamic generative models, and an implementation of different dynamical processes with higher-order interactions. Our computational framework is general, and allows to analyse hypergraphs with weighted, directed, signed, temporal and multiplex group interactions. We provide visual insights on higher-order data through a variety of different visualization tools. We accompany our code with an extended higher-order data repository and demonstrate the ability of HGX to analyse real-world systems through a systematic analysis of a social network with higher-order interactions. The library is conceived as an evolving, community-based effort, which will further extend its functionalities over the years. Our software is available at https://github.com/HGX-Team/hypergraphx.

Anomaly detection and string stability analysis in connected automated vehicular platoons

In this study, we develop a comprehensive framework to model the impact of cyberattacks on safety, security, and head-to-tail stability of connected and automated vehicular platoons. First, we propose a general platoon dynamics model with heterogeneous time delays that may originate from the communication channel and/or vehicle onboard sensors. Based on the proposed dynamics model, we develop an augmented state extended Kalman filter (ASEKF) to smooth sensor readings, and use it in conjunction with an anomaly detector to detect sensor anomalies. Specifically, we consider two detectors: a parametric detector, the χ2-detector, and a learning-based detector, the one class support vector machine (OCSVM). We investigate the detection power of all combinations of vehicle dynamics models (EKF and ASEKF) and detectors (χ2 and OCSVM). Furthermore, we introduce a novel concept in string stability, namely, pseudo string stability, to measure a platoon’s string stability under cyberattacks and model uncertainties. We demonstrate the relationship between the pseudo string stability of a platoon and its detection rate, which enables us to identify the critical detection sensitivity/recall that the platoon’s members should meet for the platoon to remain pseudo string stable.

Toward 3D Integration of Highly See‐Through Photonic Circuits in Glass

AbstractEmbedding naked‐eye‐invisible electronic and optical elements in transparent panels is at the heart of enabling mobile transparent accessories by making see‐through smart screens. Here, a novel invisible photonic element, highly see‐through (HST) waveguide, is reported, which is written by femtosecond laser in glass. A general synergistic control of the thermodynamic and dynamic behavior over the matter fluid in the laser irradiated confined region to tune the cross‐section of waveguides and suppress the generation of scattering centers in the waveguides is established. An effective reduction of light leakage (covering red, green, and blue coupled light) by an order of magnitude compared to conventional waveguides is achieved, making it highly see‐through at bright illumination of >100 lux. A general dynamical model based on a frozen‐in shock wave diffusion process is proposed, which is applicable to various glasses regardless of their compositions. Ultra‐wide tuning of HST waveguide mode diameters from 4.9 to 26.5 µm is demonstrated, making it versatile for functionalizing various transparent screens by mode‐matching with fiber sources and integrated planar waveguides of different working wavelengths.

Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks

Traditional control and monitoring of water quality in drinking water distribution networks (WDNs) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known. In contrast, system identification (SysID) algorithms for generic dynamic system models seek to approximate such models using only input-output data without relying on network parameters. The objective of this paper is to investigate SysID algorithms for water quality model approximation. This research problem is challenging due to (1) complex water quality and reaction dynamics; and (2) the mismatch between the requirements of SysID algorithms and the properties of water quality dynamics. In this paper, we present the first attempt to identify water quality models in WDNs using only input-output experimental data and classical SysID methods without knowing any WDN parameters. Properties of water quality models are introduced, the ensuing challenges caused by these properties when identifying water quality models are discussed, and remedial solutions are given. Through case studies, we demonstrate the applicability of SysID algorithms, show the corresponding performance in terms of accuracy and computational time, and explore the possible factors impacting water quality model identification.

Human-Aware Path Planning With Improved Virtual Doppler Method in Highly Dynamic Environments

Human-aware path planner is essential for achieving harmonious coexistence between humans and robots in highly dynamic environments. In this paper, we propose an integrated framework to find the optimal path in the complex environment with considering collision risk, social norms, and crowded areas. In the proposed framework, a general dynamic group model (g-space) based on the Gaussian Mixed Model (GMM) is proposed as the social norms of dynamic groups, which not only considers the factors of humans (e.g., pose, quantity, distribution, psychology) but also establishes the proximity and human interacting constraints of dynamic groups. An integrated Collision Risk and Human Space (CR&HS) model is applied to achieve human-acceptable behaviors, in which both collision avoidance, human comfort, and interference-free constraints have been involved. Moreover, an Improved Virtual Doppler Method (IVDM) has been used to realize safety navigation to avoid the robot falling into the crowded area. Finally, the proposed framework has been utilized with the sampling-based rapidly-exploring random tree. Experimental results demonstrate that the proposed method can generate the optimal human-aware collision-free path in complex environments. Note to Practitioners—This paper aims to plan an optimal trajectory for the robot in highly dynamic environments. In this field, it is still a challenging task to plan a trajectory with collision-free, human-aware, and crowd-aware. To do that, we present an integrated framework to generate the optimal trajectory by involving the collision risk, social norms, and human density. First, the g-space model is adopted as interference-free constraints of dynamic groups. The integrated knowledge fusion model (CR&HS) then penalizes the manners which have higher collision risk and adverse effects on human interaction or human comfortable. Besides, human motion and density are provided to a robot by IVDM. The proposed framework is utilized in the sampling-based rapidly-exploring random tree as the evaluation module. Finally, the feasibility and reliability of the proposed method have been verified by experiments in different simulated environments. The proposed framework can be applied in most mobile service robots to achieve human-friendly manners.

Utilization-adjusted TFP across countries: Measurement and implications for international comovement

This paper develops estimates of TFP growth adjusted for movements in unobserved factor utilization for a panel of 29 countries and up to 37 years. When factor utilization changes are unobserved, the commonly used Solow residual mismeasures actual changes in TFP. We use a general equilibrium dynamic multi-country multi-sector model to derive a production function estimating equation that corrects for unobserved factor usage. We compare the properties of utilization-adjusted TFP series to the standard Solow residual, and quantify the roles of both TFP and utilization for international business cycle comovement. Utilization-adjusted TFP is virtually uncorrelated across countries, and does not generate much GDP comovement through its propagation. Shocks to factor utilization can more successfully account for international comovement.

Theoretical and experimental studies on vibration characteristics of bolted joint multi-plate structures

The bolted joint multi-plate structures are widely used in practical engineering, but their theoretical dynamic model is hard to find in literature. The main purpose of this paper is to establish a general dynamic model of bolted joint multi-plate structures with flanges, and to investigate its vibration characteristics from both theoretical and experimental aspects. Both the flange effect and contact pressure distribution in the bolted joint affected region (BJAR) are considered. Artificial spring sets with variable stiffness are adopted to simulate the non-uniform distributed contact pressure in BJAR. The governing equations are derived based on the Kirchhoff plate theory and Euler-Bernoulli beam theory. The Chebyshev polynomials are used as admissible displacement functions. Then, the free vibration and forced vibration under base excitation are solved by means of the Rayleigh-Ritz method and Newmark-beta method, respectively. The experimental studies, including modal test and vibration response test, are conducted on a bolted joint two-plate specimen to verify the accuracy of the theoretical model. Results show that the present model is capable of realizing reliable vibration prediction and can be conveniently extended to the structure with an arbitrary number of plates and bolts. For the bolted structures, considering the pressure distribution in the connection interfaces can significantly improve the accuracy of theoretical results.

A generative design of collaborative innovation space

A workplace for collaboration can be a powerful tool for fostering collaborative innovation in an organization. However, many organizations have failed in realizing the benefits of collaborative innovation workplaces. Applying a sociomaterial lens to an empirical investigation of the creation and genesis of workplaces for collaborative innovation in six organizations, we expand the focus beyond identifying workplace's material and social elements to the emergence of a collaborative innovation space as an effective workplace for collaboration. We develop a dynamic generative design model for collaborative innovation spaces. This model draws the attention to practices involved in the creation of such space instead of spatial characteristics only. It presents three dimensions for creating collaborative innovation spaces: the collaborative workplace consisting of collaborative spatial layout, work practices, and organizational structures. All are created and manifested by means of a collaborative–participatory design approach and the practice of generative reflection instead of conventional evaluation measures. Ultimately, a mindset shift is set in motion, generating a sustainable emergence of a collaborative innovation space. We conclude that a collaborative innovation space as an in‐between space cannot be deliberately designed but rather evolves over time. Using our generative design model, organizations and stakeholder can actively become part of this emergence process.

‘As a Sunni, I Naturally Cannot Understand Anything Like Temporary Marriage’

Abstract Identity construction involves drawing a rhetorical boundary between one’s own group and others. Using data gathered from a Finnish online forum for Muslims, I study how the practice of temporary (mutʿa) marriage is used to mark a boundary between Sunni and Shiʿa Muslims. Here, I examine the rhetorical devices used by Sunni and Shiʿa Muslims, combining the principle of meta-contrast with the concept of boundary-markers to propose a general model of intergroup boundary dynamics.

Barrier Lyapunov function-based adaptive fuzzy control for general dynamic modeling of affine and non-affine systems

Barrier Lyapunov function-based adaptive fuzzy control for general dynamic modeling of affine and non-affine systems

The COVID-19 pandemic in various restriction policy scenarios based on the dynamic social contact rate

The social contact rate has influenced the transmission of COVID-19, with more social contact resulting in more contagion cases. We chose 18 countries with the most confirmed cases in the first 200 days after the Wuhan lockdown. This was the first study using the dynamic social contact rate to simulate the epidemic under diverse restriction policies over 500 days since the COVID-19 outbreak. The developed General Dynamic Model suggested that the probability of contagion ranged from 12.52% to 39.39% in the epidemic. The geometric mean of the social contact rates differed from 18.21% to 96.00% between countries. The restriction policies in developed economies were 3.5 times more efficient than in developing economies. We compare the effectiveness of different policies for disease prevention and discuss the influence of policy adjustment frequency for each country. Maintaining the tightest restriction or alternate tightening and loosening restrictions was recommended, with each having an average 72.45% and 79.78% reduction in maximum active cases, respectively.

Structural controllability of general edge dynamics in complex network

Dynamic processes that occur on the edge of complex networks are relevant to a variety of real-world systems, where states are defined on individual edges, and nodes are active components with information processing capabilities. In traditional studies of edge controllability, all adjacent edge states are assumed to be coupled. In this paper, we release this all-to-all coupling restriction and propose a general edge dynamics model. We give a theoretical framework to study the structural controllability of the general edge dynamics and find that the set of driver nodes for edge controllability is unique and determined by the local information of nodes. Applying our framework to a large number of model and real networks, we find that there exist lower and upper bounds of edge controllability, which are determined by the coupling density, where the coupling density is the proportion of adjacent edge states that are coupled. Then we investigate the proportion of effective coupling in edge controllability and find that homogeneous and relatively sparse networks have a higher proportion, and that the proportion is mainly determined by degree distribution. Finally, we analyze the role of edges in edge controllability and find that it is largely encoded by the coupling density and degree distribution, and are influenced by in- and out-degree correlation.

Semi-analytical orbital model around an oblate body with an inclined eccentric perturber

The semi-analytical model (based on the averaging technique) for long-term orbital evolution has proven to be useful in both astrophysical and astrodynamical contexts. In this secular approximation, orbits exhibit rich evolutionary behaviors under the effects of various perturbations. For example, in the hierarchical three-body systems, the Lidov-Kozai mechanism based on the quadrupole-level third-body perturbation model can produce large-amplitude oscillations of the eccentricity and inclination. In recent years, the octupole order has been found to induce dramatically new features when the perturbing body’s orbit is eccentric, including extremely high eccentricities and orbit flips between prograde and retrograde. Motivated by the striking effects of the octupole-order terms, we intend to derive a more general dynamical model by incorporating J2 of the central body and the inclined eccentric third-body perturbation to the hexadecapole order with its non-spherical gravity also included. This issue can be relevant for astrophysical and astrodynamical systems such as planets in stellar binaries, irregular satellites in planetary systems, and artificial probes about binary asteroid systems. Applications to the binary asteroid system 4951 Iwamoto and a fictitious exoplanetary system are illustrated as examples to validate our secular model. From these numerical results, we show the high accuracy of our secular model. Also, we show the important role of these high-order terms and the effects of the third-body’s inclination and eccentricity. Besides, we have found several different secular effects that could drive large eccentricities.