State Space Of Model Research Articles

A dynamic migration route planning optimization strategy based on real-time energy state observation considering flexibility and energy efficiency of thermal power unit

New energy resources compromising intermittent and fluctuating natures have been integrated into the power grid on a large scale, and thermal power units are obliged to promote the depth of deep peak shaving and flexible response-ability to cope with this new scenario. The safety, flexibility, and high efficiency of coal-fired units are the ultimate goals of its transformation, and there is a mutual influence and mutual restriction relationship among these indicators. To exploit the flexibility of thermal power units, take into account efficiency and safety, and play a supporting role in the new power system, a control strategy that weighs various indicators needs to be developed. Based on heat flow model and dynamic state space model, and updated matrix in real time, the key process parameters affecting the flexibility of heat transfer can be fully characterized. By using the observed state and calculation, the load response index and the energy efficiency index are optimized under the boundary constraint. The results show that the multi-objective optimization methods can avoid over regulation, reduce the flue gas flow by 3.23%–3.59%, and reduce the fluctuation of state quantity under the premise of satisfying the load response rate, about 2.32%–2.52% away from the safety boundary of the design temperature parameter to achieve the flexibility of frequency modulation, high efficiency of energy transmission and operation safety.

Compensation States Approach in the Hybrid Diabatization Scheme: Extension to Multidimensional Data and Properties.

The diabatization of reactive systems for more than just a couple of states is a very demanding problem and generally requires advanced diabatization techniques. Especially for dissociative processes, the drastic changes in the adiabatic wave functions often would require large diabatic state bases, which quickly become impractical. Recently, we addressed this problem by the compensation states approach developed in the context of our hybrid diabatization scheme. This scheme utilizes wave function as well as energy data in combination with a diabatic potential model. In regions where the initial diabatic state basis becomes insufficient for an appropriate representation of the adiabatic states, new model states are generated. The new model states compensate for the state space not spanned by the initial diabatic basis. Such a compensation state is obtained by projecting the initial diabatic state space out of the adiabatic wave function. This yields a very efficient basis representation of the electronic Hamiltonian. The present work presents two new aspects. First, it is shown how other operators like the spin-orbit operator in the framework of the Effective Relativistic Coupling by Asymptotic Representation (ERCAR) can be evaluated in this compact model state space without losing the correct wave function information and accuracy. Second, the extension of the approach to multidimensional potential energy surface models is presented for methyl iodide including the C-I dissociation coordinate and the angular H3C-I bending coordinates.

Situation modeling and evaluation for complex systems: A case study of satellite attitude control system

Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit satellite failure inevitable. Modeling and evaluation of satellite conditions can identify potential problems of key system components in time. Firstly, in order to accurately describe the system components and measure the reliability, the correlative damage coefficient is introduced to describe the component degradation process, and the component reliability model of the satellite attitude control system is established. Then, to solve the problem of large state space and changeable condition of system reliability model, the concept of tangent order pair is introduced into the multi-state dynamic fault tree of system. The depth-first search algorithm is used to sort the underlying event components of the fault tree, and the multi-state multi-value decision graph model of different state events is constructed. The minimum cut set of the model is obtained to evaluate the system state. The experimental results show that the proposed method can effectively simulate the system situation under different fault modes, effectively reduce the state space of the system reliability model, and improve the convergence and accuracy of the system fault probability evaluation.

Reliability evaluation method for PID feedback control system considering performance degradation

The reliability of proportional-integration-derivative (PID) feedback control systems is crucial for products due to their critical functions. Traditional reliability evaluation methods of PID feedback control systems usually pose difficulties in comprehensively considering the performance compensation property of feedback mechanisms and the degradation phenomenon in operating processes. This paper proposes a new reliability evaluation method for the PID control system under the framework of belief reliability theory to consider both deterministic and uncertain laws regarding functional logics and degradation effects. For deterministic laws, the interdisciplinary, degradation, and margin equations are established with respect to the steady state error. Various internal and external influencing factors are integrated into these equations by modeling state space and characterizing degradation mechanisms. For uncertain laws, the metric equation is constructed accordingly considering diverse uncertain factors. A Monte Carlo-based algorithm and an analysis framework are further developed to calculate the belief reliability. Finally, the proposed method is implemented to an aero-power hydraulic servo control system as a specific illustration, demonstrating its effectiveness and rationality.

Design and real-time implementation of a robust backstepping non-singular integral terminal sliding mode attitude control for a quadrotor aircraft

This paper concerns the problem of robust attitude control of a quadrotor aircraft subjected to modeling inaccuracies and wind disturbances. First, the model dynamics and state space representation of the quadrotor system are designed by considering the impact of disturbances and uncertainties. Then, a novel robust backstepping non-singular integral terminal sliding mode control (BNITSMC) is synthesized for the quadrotor attitude with the aim of enhancing the tracking accuracy. To strengthen the system’s robustness and guarantee the reachability of the sliding surface while reducing chattering, the supertwisting algorithm (ST) is used. Closed-loop stability and state convergence are guaranteed via the Lyapunov theory. Some experimental flight tests have been executed to validate the workability of the developed flight control. The obtained results prove that the recommended technique can provide improved performance in terms of stabilization and tracking precision.

Modelling and analysis of LCC‐HVDC converter station for harmonic coupling of AC/DC power grid during geomagnetic storm

AbstractIn this paper, the problem of harmonic coupling between the converter transformer and the converter under the action of geomagnetically induced current (GIC) is proposed to be investigated. Firstly, a model of a 12‐pulse line commutated converter high voltage direct current (LCC‐HVDC) converter station based on harmonic state space (HSS) theory is established. Secondly, the harmonic components of the interaction between the AC‐side harmonic current and the DC‐side harmonic voltage are qualitatively analyzed by using the harmonic modulation theory. Through the simulation model and harmonic state space (HSS) calculation model, the harmonic coupling characteristics of power grid are analyzed. Finally, the harmonic current and voltage amplitude of AC / DC power grid under different GIC are calculated, and the characteristics and rules of AC / DC harmonic propagation are revealed. Among them, the amplitude of the 2nd harmonic current on the AC side and the 3rd harmonic current on the DC side increases linearly with the GIC. In addition, the 3rd and 9th harmonics on the AC side are also greatly affected by the magnetic bias of the converter.

Brain model state space reconstruction using an LSTM neural network

Objective. Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to electroencephalography (EEG). However, this approach lacks a reliable method to determine the initial filter conditions and assumes that the distribution of states remains Gaussian. This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically a long short-term memory (LSTM) neural network. Approach. An LSTM filter was trained on simulated EEG data generated by a NMM using a wide range of parameters. With an appropriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As a result, it can output the state vector and parameters of NMMs given observation data as the input. Main results. Test results using simulated data yielded correlations with R squared of around 0.99 and verified that the method is robust to noise and can be more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate. As an example of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures, and revealed changes in connectivity strength parameters at the beginnings of seizures. Significance. Tracking the state vector and parameters of mathematical brain models is of great importance in the area of brain modelling, monitoring, imaging and control. This approach has no need to specify the initial state vector and parameters, which is very difficult to do in practice because many of the variables being estimated cannot be measured directly in physiological experiments. This method may be applied using any NMM and, therefore, provides a general, novel, efficient approach to estimate brain model variables that are often difficult to measure.

Separation of spherically and translationally covariant finite quantum spaces within the XXX model

A method of separation of subspaces with definite values of the total spin, its z-projection and quasimomentum, within the state space of the XXX model, i.e. the N-th tensor power of a single qubit, is presented. We construct a complete system of orthogonal projectors onto such subspaces by use of the basis of wavelets and chosen measuring operators based on the total spin. We also demonstrate, on examples of N=6 and 8 magnetic spin rings, that the constructed projectors can be further decomposed into the orthogonal sum of density matrices of eigenstates of the XXX model in the corresponding subspaces. This decomposition exhibits a Galois symmetry, stemming from roots of indecomposable factors of characteristic polynomial of the Heisenberg Hamiltonian. In this way, we propose an alternative for Bethe Ansatz for moderate lengths of spin chains, by a return to the original spectral problem, avoiding the highly nonlinear system of Bethe Ansatz equations. Such results are easy to adapt in quantum information processing.

Symmetry and complexity in object-centric deep active inference models.

Humans perceive and interact with hundreds of objects every day. In doing so, they need to employ mental models of these objects and often exploit symmetries in the object's shape and appearance in order to learn generalizable and transferable skills. Active inference is a first principles approach to understanding and modelling sentient agents. It states that agents entertain a generative model of their environment, and learn and act by minimizing an upper bound on their surprisal, i.e. their free energy. The free energy decomposes into an accuracy and complexity term, meaning that agents favour the least complex model that can accurately explain their sensory observations. In this paper, we investigate how inherent symmetries of particular objects also emerge as symmetries in the latent state space of the generative model learnt under deep active inference. In particular, we focus on object-centric representations, which are trained from pixels to predict novel object views as the agent moves its viewpoint. First, we investigate the relation between model complexity and symmetry exploitation in the state space. Second, we do a principal component analysis to demonstrate how the model encodes the principal axis of symmetry of the object in the latent space. Finally, we also demonstrate how more symmetrical representations can be exploited for better generalization in the context of manipulation.

Fractional Order, State Space Model of the Temperature Field in the PCB Plate

Abstract In the paper the fractional order, state space model of a temperature field in a two-dimensional metallic surface is addressed. The proposed model is the two dimensional generalization of the one dimensional, fractional order, state space of model of the heat transfer process. It uses fractional derivatives along time and length. The proposed model assures better accuracy with lower order than models using integer order derivatives. Elementary properties of the proposed model are analysed. Theoretical results are experimentally verifed using data from industrial thermal camera.

Anytime Tree-Based Trajectory Planning for Urban Driving

The personal mobility of the future will be changed significantly by autonomous driving. To realize this vision, the complex task of trajectory planning needs to be solved. In this article, a novel planning concept, CarPre trajectory planning, based on Monte-Carlo tree search, is presented. Using a speed-dependent steering angle transformation, the state space of a kinematic single track model is discretized. The planner can then choose between different actions, each consisting of a discrete-value pair of an acceleration and a steering rate. With this, an equitemporal search tree is created to compute the future trajectory. Using Monte-Carlo simulations, the influence of short-term actions of the vehicle can be evaluated over a longer planning horizon. Thus, the current best solution can be accessed at any point during computation, enabling real-time applications. Furthermore, the discretized search tree enables easy checking of complex constraints dependent on binary or continuous variables. The concept is verified on a real test vehicle in a lane keeping maneuver. Through initial testing, a pleasant driving experience is perceived, which indicates future acceptance of the real-time capable algorithm.

Machine learning with nonlinear state space models

Abstract In this dissertation, a novel class of model structures and associated training algorithms for building data-driven nonlinear state space models is developed. The new identification procedure with the resulting model is called local model state space network (LMSSN). Furthermore, recurrent neural networks (RNNs) and their similarities to nonlinear state space models are elaborated on. The overall outstanding performance of the LMSSN is demonstrated on various applications.

Short Term Forecast of COVID-19 cases in Japan Using Time Series Analysis Models

The new strain of coronavirus (COVID-19) was found to have started in Wuhan, China in late December 2019. The virus has spread to countries all over the world including Japan. The World Health Organization (WHO) declared COVID-19 as a pandemic on 11 March 2020 due to the increasing number of confirmed cases and deaths daily. The COVID-19 outbreak has impacted the nation of Japan adversely and the number of confirmed cases in Japan continues to increase day by day. On 7 April 2020, Japan declared a state of emergency to prevent the pandemic from worsening. This study is conducted to forecast new daily confirmed cases of COVID-19 in Japan over a short-term period. Four univariate time series models were applied: the Naïve Model, Mean Model, Autoregressive Integrated Moving Average (ARIMA) Model and Exponential State Space Model. This study analyses daily data from 22 January to 10 April 2020 collected from the Our World in Data website. The prediction involves five phases of data analysis and five different partitions of estimation and evaluation parts in every model to ensure the accuracy of forecast values. R and R Studio software were used in this study to analyze the data. The results reveal that Naïve model with 99 percent of estimation part and 1 percent evaluation part produces the lowest value of error measures for Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE).

Bounded Invariant Checking for Stateflow

Stateflow models are complex software models, often used as part of industrial safety-critical software solutions designed with Matlab Simulink. Being part of safety-critical solutions, these models require the application of rigorous verification techniques for assuring their correctness. In this paper, we propose a refutation-based formal verification approach for analyzing Stateflow models against invariant properties, based on bounded model checking (BMC). The crux of our technique is: i) a representation of the state space of Stateflow models as a symbolic transition system (STS) over the symbolic configurations of the model, and ii) application of incremental BMC, to generate verification results after each unrolling of the next-state relation of the transition system. To this end, we develop a symbolic structural operational semantics (SSOS) for Stateflow, starting from an existing structural operational semantics (SOS), and show the preservation of invariant properties between the two. We define bounded invariant checking for STS over symbolic configurations as a satisfiability problem. We develop an automated procedure for generating the initial and next-state predicates of the STS, and a prototype implementation of the technique in the form of a tool utilising standard, off-the-shelf satisfiability solvers. Finally, we present preliminary performance results by applying our tool on an illustrative example and two industrial models.

MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model.

With the continuous development of deep learning, more and more domains use deep learning technique to solve key problems. The security issues of deep learning models have also received more and more attention. Nowadays, malware has become a huge security threat in cyberspace. Traditional signature-based malware detection methods are not adaptable to the current large-scale malware detection. Thus many deep learning-based malware detection models are widely used in real malware detection scenarios. Therefore, we need to secure the deep learning-based malware detection models. However, model testing currently focuses on image and natural language processing models. There is no related work to test deep learning-based malware detection models specifically. Therefore, to fill this gap, we propose MalFuzz. MalFuzz uses the idea of coverage-guided fuzzing to test deep learning-based malware detection models. To solve the model state representation problem, MalFuzz uses the first and last layer neuron values to approximately represent the model state. To solve the new coverage calculation problem, MalFuzz uses the fast approximate nearest neighbor algorithm to compute the new coverage. The mutation strategy and seed selection strategy in image model or natural language processing model testing is not appropriate in deep learning-based malware detection model testing. Hence MalFuzz designs the seed selection strategy and seed mutation strategy for malware detection model testing. We performed extensive experiments to demonstrate the effectiveness of MalFuzz. Based on MalConv, Convnet, and CNN 2-d, we compared the modified TensorFuzz and MAB-malware with MalFuzz. Experiment results show that MalFuzz can detect more model classification errors. Likewise, the mutation operation of MalFuzz can retain the original functionality of malware with high probability. Moreover, the seed selection strategy of MalFuzz can help us explore the model state space quickly.

Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model

AbstractAccurate initial conditions play a critical role in improving predictive accuracy of hydrological models for quantities such as streamflow generation. Streamflow observations from in situ gauging sites have been assimilated in a wide range of past research to improve lumped catchment streamflow. However, spatio‐temporal state updating in distributed hydrological models through streamflow data assimilation (DA) remains a challenge due to the large dimensional disparity between model state space and observation space. This study explores the use of model spatio‐temporal variance‐covariance from simulated climatology as a surrogate for model error, as a way of distributing constraint from gauge measurements spatially. In the approach developed here, grid cells within and surrounding gauged catchments were updated simultaneously. The proposed DA method leads to a substantial improvement in performance and reliability compared to open‐loop (OL) estimates of streamflow. In particular, for “ungauged” catchments updated using streamflow observations from neighboring catchments, the mean absolute error (MAE) was reduced by 20% on average after the assimilation. In addition, improvements from using the analyzed states as initial conditions were found to persist for more than 1 week, and even longer for catchments with high annual runoff. The improvement in streamflow forecasts using initial conditions from DA can be more than 40% in terms of MAE compared to OL results for 1 day lead time, and more than 10% for 5 days lead time.

An Offloading Algorithm based on Markov Decision Process in Mobile Edge Computing System

In this paper, an offloading algorithm based on Markov Decision Process (MDP) is proposed to solve the multi-objective offloading decision problem in Mobile Edge Computing (MEC) system. The feature of the algorithm is that MDP is used to make offloading decision. The number of tasks in the task queue, the number of accessible edge clouds and Signal-Noise-Ratio (SNR) of the wireless channel are taken into account in the state space of the MDP model. The offloading delay and energy consumption are considered to define the value function of the MDP model, i.e. the objective function. To maximize the value function, Value Iteration Algorithm is used to obtain the optimal offloading policy. According to the policy, tasks of mobile terminals (MTs) are offloaded to the edge cloud or central cloud, or executed locally. The simulation results show that the proposed algorithm can effectively reduce the offloading delay and energy consumption.

Robust static output feedback control for hidden Markov jump linear systems

This paper studies the robust static output feedback control of discrete-time Markov jump systems considering that the mode of operation cannot be directly measured. It is assumed that the only available information concerning the main jump process comes from a detector, so that the jump and detector processes can be modelled as a hidden Markov model. Initially, it is considered that the system's dynamic matrix is subject to parametric uncertainties. By assuming a full row rank condition for the output matrix and employing a clusterisation technique for the state space of the hidden Markov model, it is derived a set of linear matrix inequalities (LMIs) that guarantees robust stability in the mean-square sense of the closed-loop system, as well as a bound on its norm. It is also shown that this design technique is linked to the bounded real lemma, so that a new set of LMIs conditions can be obtained for the ‘pure’ control problem, which can be extended to cover the robust polytopic case. The paper is concluded with an illustrative example.

Optimization Approaches for Nonlinear State Space Models

The Local Model State Space Network (LMSSN) is a recently developed black box algorithm in nonlinear system identification. It has proven to be an appropriate tool on benchmark problems as well as for real-world processes. A severe shortcoming though is the long computation time that is necessary for model training. Therefore, a different optimization strategy, the adaptive moment estimation (ADAM) method with mini batches is used for the LMSSN and compared to the current Quasi-Newton (QN) optimization method. It is shown on a numerical Hammerstein example and on a well known Wiener-Hammerstein benchmark that the use of ADAM and mini batches does not limit the performance of the LMSSN algorithm and speeds up the nonlinear optimization per investigated split by more than 30 times. The price to be paid, however, is higher parameter variance (less interpretability) and more tedious hyperparameter tuning.

Abstraction NBTI model

Abstract Negative Bias Temperature Instability (NBTI) is one of the major transistor aging effects, possibly leading to timing failures during run-time of a system. Thus one is interested in predicting this effect during design time. In this work an Abstraction NBTI model is introduced reducing the state space of trap-based NBTI models using two abstraction parameters, applying a state transformation to incorporate variable stress conditions. This transformation is faster than traditional approaches. Currently the conversion into estimated threshold voltage damages is a very time consuming process.