Space Model Research Articles

Linear control strategy of the dilution rate for stability in the anaerobic digestion process

Anaerobic digestion is a biochemical process of decomposition of organic matter, mediated by anaerobic bacteria, in an oxygen-free environment, from which biogas is produced. Biogas is used to generate thermal and electrical energy, where environmental conditions such as pH, temperature, concentration of volatile fatty acids, have a great influence on the efficiency and performance of the anaerobic digestion process, and can cause the process to be ruined. This document presents a control strategy that seeks to regulate the conditions of the anaerobic digestion process to keep it within the stability zone, using a linearization technique of the model in the state space for the design of controllers. In this work, a compensator is designed, for which the approximate linearization technique of the AM2 nonlinear model is proposed, to obtain the linearized model from which a classic lag-lead control strategy is proposed, using the dilution rate as a control variable. As a result, an improvement in system performance is obtained by reducing the maximum overshoot and reducing the settling time, evaluated at the operating point, which is validated through simulation and analysis of the response provided by the nonlinear model at the point of operation.

A Bayesian non-stationary heteroskedastic time series model for multivariate critical care data.

We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non-stationary time series data. Model comparison can then be easily performed using the WAIC.

Wind and Wave-Induced Vibration Reduction Control for Floating Offshore Wind Turbine Using Delayed Signals

Active vibration control is a critical issue of the wind turbine in the field of marine energy. First, based on a three-degree-of-freedom wind turbine, a state space model subject to wind and wave loads is obtained. Then, a delayed state feedback control scheme is illustrated to reduce the vibration of platform pitch angle and tower top foreaft displacement, where the control channel includes time-delay state signals. The designed controller’s existence conditions are investigated. The simulation results show that the delayed feedback H∞ controller can significantly suppress wind- and wave-induced vibration of the wind turbine. Furthermore, it presents potential advantages over the delay-free feedback H∞ controller and the classic linear quadratic regulator in two aspects: vibration control performance and control cost.

Sediment Cover Modulates Landscape Erosion Patterns and Channel Steepness in Layered Rocks: Insights From the SPACE Model

AbstractErosional perturbations from changes in climate or tectonics are recorded in the profiles of bedrock rivers, but these signals can be challenging to unravel in settings with non‐uniform lithology. In layered rocks, the surface lithology at a given location varies through time as erosion exposes different layers of rock. Recent modeling studies have used the Stream Power Model (SPM) to highlight complex variations in erosion rates that arise in bedrock rivers incising through layered rocks. However, these studies do not capture the effects of coarse sediment cover on channel evolution. We use the “Stream Power with Alluvium Conservation and Entrainment” (SPACE) model to explore how sediment cover influences landscape evolution and modulates the topographic expression of erodibility contrasts in horizontally layered rocks. We simulate river evolution through alternating layers of hard and soft rock over million‐year timescales with a constant and uniform uplift rate. Compared to the SPM, model runs with sediment cover have systematically higher channel steepness values in soft rock layers and lower channel steepness values in hard rock layers. As more sediment accumulates, the contrast in steepness between the two rock types decreases. Effective bedrock erodibilities back‐calculated assuming the SPM are strongly influenced by sediment cover. We also find that sediment cover can significantly increase total relief and timescales of adjustment toward landscape‐averaged steady‐state topography and erosion rates.

MBSE Analysis & Update of U.S. Infrastructure Data Taxonomy (IDT) Using U.S. National Critical Functions (NCFs)

AbstractIn this paper, we will demonstrate the application of Model‐Based Systems Engineering (MBSE) tools and techniques we have used to assist the U.S. Department of Homeland Security (DHS) / Cybersecurity Infrastructure Security Agency (CISA) in analysis of open‐source Infrastructure Data Taxonomy (IDT) data to analyze and contribute to the updates required for their IDT. We will show how we have used Functional Analysis, Logical Structure Taxonomies, and MBSE tools to analyze the IDT alongside the DHS National Critical Functions (NCFs) to recommend and improve these updates for their IDT. We have also proposed updates to the NCFs themselves where obvious improvements could be made. Our future recommendations are to continue this effort as a team of INCOSE Critical Infrastructure Protection and Recovery (CIPR) team members, along with other INCOSE Working Groups (WGs), and to explore other use cases based on this model data, as well as inviting other Subject Matter Experts (SMEs) to assist in the enhancements. This modeling effort demonstrated the use of these MBSE tools while working together with other organizations to enable improvements to the Critical Infrastructure (CI) space leveraging systems theory & thought leadership. We will outline further avenues for exploration using this provided MBSE capability in multiple areas that could be useful to the DHS, and will discuss a potential longer‐term engagement with DHS in an annual challenge. This challenge similar to those led by multiple other projects will include collaborations with universities and other CI / CIPR entities interested in this modeling of the CI space. Other opportunities to extend the model further include, but are not limited to security / cybersecurity, operational, and services modeling use cases, involving Industrial Control Systems (ICS) and much more. We have shown how these tools are effective and efficient for use in the water / wastewater sector, but will also yield similar benefits when applied across all identified sectors and across the international community as well (i.e., transportation, telecommunications, etc.). Interestingly we have seen in our initial analyses that the various taxonomies across sectors, subsectors, segments, subsegments, and assets vary slightly between regions and countries internationally for various reasons.

Theme park greening VR design based on entertainment robots and genetic algorithm optimization: Digital entertainment design experience

Theme parks should provide attractive green design and digital interactive experiences to attract tourists and improve their satisfaction. Therefore, this study aims to achieve a digital interactive experience in theme park greening design through the use of entertainment robots and genetic algorithm optimization. The study analyzed the characteristics of experiential theme parks and introduced landscape 3D modeling techniques for creating interactive 3D models of green spaces in theme parks. By using technologies such as sensors and cameras, entertainment robots can perceive the presence and behavior of tourists. Entertainment robots can interact with tourists in real-time, customize them according to their interests and needs, and provide unique and enjoyable experiences for tourists. By collecting and analyzing feedback and behavioral data from tourists, the advantages and improvement points of digital interactive experiences can be identified. Through comparative experimental analysis with traditional experience methods, it was found that digital interactive experience can significantly improve tourist participation and satisfaction. Through user data analysis of interactive experience, this design can effectively improve the greening design of theme parks, enhance tourist participation and entertainment experience.

Exploring the Evolution of a Dwarf Spheroidal Galaxy with Smoothed-particle Hydrodynamics Simulations. I. Stellar Feedback

A fundamental question regarding the evolution of dwarf spheroidal galaxies is the identification of the key physical mechanisms responsible for gas depletion. Here, we focus on the study of stellar feedback in isolated dwarf spheroidal galaxies by performing numerical simulations using a modified version of the smoothed-particle hydrodynamics code GADGET-3. The Milky Way satellite Leo II (PGC 34176) in the Local Group was considered as our default model dwarf galaxy. The parameter space for the stellar feedback models was explored to match observational constraints of Leo II, such as residual gas mass, total mass within the tidal radius, star formation history, final stellar mass, stellar ages, and metallicity. Additionally, we examined the impact of the binary fraction of stars, initial mass function, dark matter halo mass, and initial gas reservoir. Many simulations revealed recent star formation quenching due to stellar feedback. In general, the gas depletion, expected star formation history, total mass of stars, and total mass within the tidal radius were adequately reproduced in the simulations when compared to observational estimates. However, there were discrepancies in the distribution of stellar ages and metallicities, which suggested that the cosmic gas infall would play a more complex role in our dwarf spheroidal galaxy than captured by a monolithic infall scenario. Our results suggest that currently quenched dwarf galaxies may not necessarily need to evolve within clusters or groups and that stellar feedback alone could be a sufficient factor in shaping at least some of these galaxies as we observe them today.

LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.

Masked Multiple State Space Model Identification Using FRD and Evolutionary Optimization

Masked Multiple State Space Model Identification Using FRD and Evolutionary Optimization

Design and implementation of a collaborative modeling architecture based on MDE (Multi-DDS Edge)

Abstract Virtual space is an integral part of the Metaverse. Typical virtual spaces are modeled step by step using artificial means. With the development needs of Metaverse modeling and simulation technology and the need for simulation fidelity and content richness in virtual space construction, efficient and correct modeling methods are required. It has become a bottleneck restricting the efficiency of virtual space modeling. This paper designs a multi-person collaborative virtual space modeling method, which improves the modeling efficiency, communication security, and interaction stability of the virtual space collaborative modeling method. Compared with existing collaborative modeling methods, this paper proposes a virtual space collaborative modeling method based on multi-edge DDS technology. By constructing MDE (multi-DDS edges) and MDE on user migration, collaborative construction in multiple virtual spaces is completed. Model data communication extends collaborative modeling technology from the virtual space creation stage to the virtual space simulation and deduction stage, and designs and verifies collaborative content, collaborative architecture, and collaborative communication. Experiments show that the architecture synergy efficiency is 145.6%, the architecture synergy efficiency is 212.5%, and the architecture synergy efficiency is 105.4%. As the number of people in collaborative modeling increases, the operational complexity and demonstration take a logarithmic form, meeting the needs of the future development of the metaverse.

Experimental vibration-based output-only damage localization of mechanical systems

Vibration-based Structural Health Monitoring (SHM) is becoming increasingly widespread in a variety of engineering sectors as its variability enables diverse applications. It enables full-time assessment of the status of a structure, allowing for early maintenance and repair to prevent further damage or even failure. The presented study is part of the SPP100+ program, which has the objective of extending the usability of complex structures. This study presents an experimental investigation of the damage localization of mechanical structures while considering varying environmental and operational conditions (EOC). For this, a testing field was designed and applied, enabling one to study the effects of the varying conditions in a comparable way. The research is based on the approach to modify the state projection estimation error (SP2E) method to account for the influence of EOC. The proposed method utilizes output-only system identification and state space parameterization to estimate the current structural state. Stochastic subspace identification (SSI) and H_∞ -estimation are employed to identify the state space model during a learning phase. Subsequently, the SP2E method is applied during the monitoring phase to localize structural alterations. Moreover, an algorithm is introduced to estimate the weighting factors of different environmental and operational conditions. To verify the efficacy of the proposed method, an extensive experimental study is conducted. The numerical analysis successfully demonstrates the applicability of the method for localizing mass perturbation and stiffness degradation under various EOC. This work contributes to proactive maintenance and damage detection for mechanical structures under varying conditions.

Spatio-temporal monitoring of plant water status using optical remote sensing data and in situ measurements

Water content as an important physiological status variable in plants, is closely linked to transpiration, photosynthesis, water stress, and biomass productivity. Obtaining plant water information that provides sufficient spatial and temporal resolution for such applications remains a challenge. In this study, the data distribution space of fractional vegetation cover (FVC) and shortwave infrared transformed reflectance (STR) with linear and nonlinear edges was used to monitor plant water content. Four indicators of plant water status, equivalent water thickness (EWT), leaf water content (LWC), relative water content (RWC), and leaf water potential (LWP), were measured in a 10-ha corn field at the Karkaj Agricultural Research Station of Tabriz University, Tabriz, Iran. Furthermore, the Sentinel-2 Level 2 prototype processor (SL2P) was utilized to estimate EWT and compare the results with FVC-STR space. The FVC-STR space with nonlinear edges (FSNLE) provided better estimation accuracy of plant water status than the FVC-STR space with linear edges (FSLE). The root mean square errors of the EWT, LWC, RWC and LWP estimates for FSLE were 0.00306 g/cm2, 4.03 %, 6.56 %, and 0.38 bar, respectively, while those for FSNLE were 0.00303 g/cm2, 3.75 %, 5.57 %, and 0.37 bar, respectively. In addition, the R2 value for FSNLE was higher than that for FSLE (0.43–0.70 vs. 0.39–0.64). The RMSE and R2 for SL2P were 0.0041 g/cm2 and 0.401, respectively. Among the four measured indicators, the highest and lowest estimation accuracies in both FSLE and FSNLE were obtained with EWT and RWC, respectively. It can be concluded that FVC-STR space model based on Sentinel-2 imagery data provide acceptable accuracy for estimating plant water content. The FVC-STR space with nonlinear edges provided a better estimation accuracy for plant water indicators than the FVC-STR space with linear edges.

State Space Modelling for detecting and characterising gravitational waves afterglows

We propose the usage of an innovative method for selecting transients and variables. These sources are detected at different wavelengths across the electromagnetic spectrum spanning from radio waves to gamma-rays. We focus on radio signals and use State Space Models, which are also referred to as Dynamic Linear Models. State Space Models (and more generally parametric auto-regressive models) have been the mainstay of economic modelling for some years, but rarely they have been used in Astrophysics.The statistics currently used to identify radio variables and transients are not sophisticated enough to distinguish different types of variability. These methods simply report the overall modulation and significance of the variability, and the ordering of the data in time is insignificant. State Space Models are much more advanced and can encode not only the amount and significance of the variability but also properties, such as slope, rise or decline for a given time t.In this work, we evaluate the effectiveness of State Space Models for transient and variable detection including classification in time-series astronomy. We also propose a method for detecting a transient source hosted in a variable active galaxy, whereby the time-series of a static host galaxy and the dynamic nature of the transient in the galaxy are intertwined. Furthermore, we examine the hypothetical scenario where the target transient we want to detect is the gravitational wave source GW170817 (or similar).

System identification based on the cross Gramian

In order to make mechanical structures more reliable, research is being carried out into methods of detecting and localizing damage to structures, known as Structural Health Monitoring (SHM). One method for continuously monitoring a structure is called State Projection Estimation Error (SP2E) and was developed at the University of Applied Sciences Leipzig. This method is based on state space systems, which are generated by output-only system identification. It is state-of-the-art to create a stabilization diagram to choose the best model order. Since the monitoring process is continuous, a state space model is created every 10 minutes, and therefore it is not possible to look at a stabilization diagram. Thus, the system identification process has to be automated. A novel approach developed in the SPP 100+ research program takes place via the theory of the cross Gramian. The cross Gramian is defined by the Hankel operator, which maps the past input to the future output. This Gramian indicates the interaction between the states and the input/output of a system and can be seen as the energy capacity of a system. By comparing the power of the measurement with the identified energy capacity, the model order selection can be automated. There are limits to the existence of the cross Gramian for multiple-input, multiple-output (MIMO) systems. The system has to be square, stable and symmetric. The covariance driven system identification never provides a symmetric system. A way to create a symmetric system out of the asymmetric measurement data is presented by the use of a symmetrizer, such that the cross Gramian can always be computed. With this symmetrizer a method to estimate the system matrix A by a stable Lyapunov equation, which is classically computed by a numerically unstable pseudo inverse, is presented.

LQR controller design for portable power supply based on flyback converter

Abstract The stability of the power repair equipment guarantees the reliability of the repair of electrical equipment. The following problems exist in the power supply of the emergency equipment: the load equipment randomly cuts in the power supply network; load equipment requires fast dynamic performance of the power supply. This challenges the stability of the portable power supply with the Flyback converter as the core. In this paper, an optimal feedback control strategy based on linear quadratic regulator (LQR) theory is proposed to improve the dynamic and steady-state performance of the Flyback converter. First, the state-averaged space model of the Flyback is derived and established. Second, an output voltage feedback integral controller is introduced to eliminate the steady-state error of the output voltage. Next, according to the LQR optimal control theory, the control model of the Flyback converter has been established, and the parameter design of the controller has been carried out by obtaining the optimal feedback gain matrix of the system. Finally, the simulation models are implemented with an output power of 120 W and a switching frequency of 50 kHz. The simulation results prove that the LQR controller provides superior performance than the traditional PI controller.

Individualized survival predictions using state space model with longitudinal and survival data.

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

Analyzing the Nuclear Structure of 13O-13B and 13N-13C Mirror Nuclei

In the context of the shell model, electromagnetic transitions were used to analyze the nuclear structure of a mirror nucleus with the same mass number, A = 13. The shell model investigation was performed by calculating the root mean square (rms) for the proton, neutron, matter and charge radii, occupancies, the excitation energies, as well as electromagnetic moments, using the elements of a body density matrix of the PSDMOD two-body effective interactions carried out in the psdpn-shell model space. The present results adopted the harmonic oscillator's single particle eigen functions and Hartree-Fock approximation. In addition, the effect of core polarization was added using the effective charge and effective g factors to calculate electromagnetic moments. To acquire a fair analysis of the information, the core polarization has to be present. The outcomes were compared with experimental data

Exploring The Impact of Stemming on Text Topic-Based Classification Accuracy

Text classification attempts to assign written texts to specific group types that share the same linguistic features. One class of features that have been widely employed for a wide range of classification tasks is lexical features. This study explores the impact of stemming on text classification using lexical features. To explore, this study is based on a corpus of thirty texts written by six authors with topics that focus on politics, history, science, prose, sport, and food. These texts are stemmed using a light stemming algorithm. In order to classify these texts according to the topic by means of lexical features, linear hierarchical clustering and non-linear clustering (SOM) is carried out on the stemmed and unstemmed texts. Although both clustering methods are able to classify texts by topic with two models produce accurate and stable results, the results suggest that the impact of a light stemming on the accuracy of text classification by topic is ineffectual. The accuracy is neither increased nor decreased on the stemmed texts, whereby the stemming algorithm helped reducing the dimensionality of feature vector space model.

Deteksi Tingkat Kematangan Buah Tomat Dengan Transformasi Ruang Warna HSI

Among the vegetables most commonly consumed by people around the world are tomatoes. One of the potential vegetable commodities to be developed is tomato plants. This plant can thrive in rice fields, dry land, and highlands. Use of Technology Digital images are images that can be processed by computers directly. A matrix with M columns and N rows can be used to describe a digital image. The smallest element in an image is called a pixel or image element, and is the intersection between columns and rows. image processing is the process of processing an image numerically; in this case, each pixel or point in the image is treated. One method of image processing is to use computer software to process each pixel in the image. It is easier for object recognition applications in image processing to identify objects based on differences in hue values when the hue values of objects are limited to a certain value. The color space system that mimics the capabilities of the human eye is called the HSI color space model. HSI incorporates the grayscale or color components of an image. The test image of Tomato fruit with a value of H = 32 S = 0.675 I = 83 can be considered ripe, according to the range of fruit reference values that have been established through the use of the HSI method.

Explicit unsupervised feature selection based on structured graph and locally linear embedding

Numerous redundant and irrelevant features are usually contained in high-dimensional data whose presence, if ignored, can bring detrimental effects on the performance of data processing tasks. Unsupervised feature selection (UFS) as a trending topic in a great deal of domains has been greatly concerned, which can effectively filter out redundant and irrelevant features in the unlabeled data. The majority of existing UFS methods mainly focus on building the parameterized model in the original feature space or some certain low-dimensional feature subspace, which cannot fully capture the information of the target space. In this work, we propose a novel UFS method that unifies the explicit feature selective matrix and structured graph into a learning framework. An explicit selection matrix, whose scale, bound of the elements and structure are considered, is tailored for UFS. Furthermore, we take into account the local consistency so that the low dimensional manifold structures can be preserved to a better degree. Instead of using the pre-defined similarity matrix to construct graph, we make use of the way of learning to obtain the adaptive graph. Besides, for the purpose of exploiting the cluster structure of data, we impose a rank constraint on the graph. Notably, both the explicit selection matrix and intrinsic structure are learned in the target feature subspace, and thus the proposed methodology is more straightforward and effective. We present an effective and efficient algorithm for the resulting problem based on alternative optimization strategy, and establish its convergence and computational complexity analysis. The experimental results show that our proposed approach has improved by over 5.28% and 5.79% on average in terms of clustering accuracy (ACC) and Normalized Mutual Information (NMI) compared with comparison methods, which demonstrates its superiority.