Space Model Research Articles

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.

Predicting single-cell cellular responses to perturbations using cycle consistency learning.

Phenotype-based drug screening emerges as a powerful approach for identifying compounds that actively interact with cells. Transcriptional and proteomic profiling of cell lines and individual cells provide insights into the cellular state alterations that occur at the molecular level in response to external perturbations, such as drugs or genetic manipulations. In this paper, we propose cycleCDR, a novel deep learning framework to predict cellular response to external perturbations. We leverage the autoencoder to map the unperturbed cellular states to a latent space, in which we postulate the effects of drug perturbations on cellular states follow a linear additive model. Next, we introduce the cycle consistency constraints to ensure that unperturbed cellular state subjected to drug perturbation in the latent space would produces the perturbed cellular state through the decoder. Conversely, removal of perturbations from the perturbed cellular states can restore the unperturbed cellular state. The cycle consistency constraints and linear modeling in the latent space enable to learn transferable representations of external perturbations, so that our model can generalize well to unseen drugs during training stage. We validate our model on four different types of datasets, including bulk transcriptional responses, bulk proteomic responses, and single-cell transcriptional responses to drug/gene perturbations. The experimental results demonstrate that our model consistently outperforms existing state-of-the-art methods, indicating our method is highly versatile and applicable to a wide range of scenarios. The source code is available at: https://github.com/hliulab/cycleCDR.

Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph Completion

Knowledge graphs (KGs) have become a cornerstone for structuring vast amounts of information, enabling sophisticated AI applications across domains. The progression to temporal knowledge graphs (TKGs) introduces time as an essential dimension, allowing for a dynamic representation of entity relationships. Despite their potential, TKGs often suffer from incompleteness, necessitating the development of temporal knowledge graph completion (TKGC) techniques. These methods, particularly focusing on interpolation within the known timeframe, aim to infer missing temporal facts and enhance the predictive capabilities of TKGs. The prevalent reliance on Euclidean space modeling in TKGC methods presents challenges in capturing the complex, hierarchical, and time-varying nature of TKGs. To overcome these limitations, we introduced the attention-based geometry interaction embedding (ATGIE) method, a novel approach that leverages the strengths of multiple geometric spaces, i.e., Euclidean, hyperbolic, and hypersphere, to model the intricacies of TKGs more effectively. ATGIE employs an attention mechanism to dynamically weigh the contributions of different geometric spaces, allowing it to adaptively form reliable spatial structures based on interactive geometric information. This multi-space modeling not only captures the diverse relationships within TKGs but also facilitates a nuanced understanding of how entities and their relationships evolve over time. Through extensive experiments, we demonstrate ATGIE’s superiority in TKGC tasks, showcasing its improvement over existing methods, robustness to noise, and sensitivity to temporal dynamics. The results highlight ATGIE’s potential to advance the state-of-the-art in TKGC, offering a promising direction for research and application in the field.

Tongue Disease Prediction Based on Machine Learning Algorithms

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.

Various Approaches for Software vulnerability Assessment and Risk Identification using soft computing Techniques

The detection of software flaws is a viable technique for enhancing the quality of technology and testing management by providing rapid detection of deficiency simulation models until the actual testing phase starts. These prediction outcomes help designers of technology effectively devote their available resources to components that are more vulnerable to deficiencies. In this research investigation, a software bug prediction model is proposed using Deep Learning (DL) approach. The Recurrent Neural Network (RNN) is used for classification of source code including numerous soft computing techniques. Numerous preprocessing and data filtration techniques have been carried out for data balancing and normalization. The Term Frequency and Inverse Document Frequency (TF-IDF) and relation features extraction techniques is used to generate Vector Space Model (VSM). The classification has been done using RNN on both training and validation dataset. The evaluation of performance of proposed work is performed using various real-time and synthetic accessible databases. It is observed from the experimental results that the proposed framework performs better when different evaluated with different datasets

Trajectory tracking control of wearable upper limb rehabilitation robot based on Laguerre model predictive control

Wearable rehabilitation robots have become an important auxiliary tool in rehabilitation therapy, providing effective rehabilitation training and helping to recover damaged muscles and joints. In response to the difficulty of traditional control methods in solving various constraints in the trajectory tracking process of the Upper Limb Rehabilitation Robot (ULRR), this study uses model predictive control to study the trajectory tracking problem of the upper limb rehabilitation robot. Firstly, based on the Lagrangian dynamic model of wearable rehabilitation robots, an extended state space model with pseudo linearization of the system was established. Given the performance indicators and various constraints of the system, a corresponding model predictive controller is designed based on the Laguerre model to ensure system performance while greatly reducing the computational complexity of predictive control. Secondly, the stability of the model predictive controller is demonstrated, and a disturbance observer is introduced into the controller to achieve compensation for slow-varying perturbations; a joint space sliding mode variable is also introduced to achieve simultaneous tracking of the joint’s desired position and desired velocity. Finally, taking a planar two bar robot as an example, comparative simulation verification was conducted on unconstrained joint trajectory tracking and constrained joint trajectory tracking. The simulation results show that the model predictive controller can achieve simultaneous tracking of joint expected trajectory and expected speed while meeting various constraints. It has good effects in improving patient motion control ability and reducing patient fatigue, providing new research ideas and methods for the field of rehabilitation therapy.

A Survey on Visual Mamba

State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, medical visual tasks (e.g., 2D/3D segmentation, classification, image registration, etc.), and remote sensing visual tasks. In particular, we introduce general visual tasks from two levels: high/mid-level vision (e.g., object detection, segmentation, video classification, etc.) and low-level vision (e.g., image super-resolution, image restoration, visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision.

Integrating distributive justice in system dynamics models of sustainability transitions

AbstractSocial‐ecological‐technological transitions for decarbonisation and sustainable development goals can perpetuate or introduce environmental and socio‐economic injustices. The just transitions literature provides useful perspectives for assessing transition pathways. This paper explores integrating distributive justice into system dynamics modelling for sustainability transitions. For this purpose, we adapt existing requirements to enable the evaluation of distributive justice in model‐based support for climate planning to the specific case of system dynamics modelling. We test the requirements by using a previously developed system dynamics model to (1) gain insights into the application of the requirements and (2) reflect on their further development. Our findings underscore the importance of transparency in model assumptions and results. They suggest exploring the behaviour space of models to account for uncertainty and plurality of stakeholder values. Furthermore, they emphasise the need to discuss the implications of model results and the plurality of stakeholder values to foster deliberation and inclusive decision‐making.

A novel modulation for four‐switch Buck‐boost converter to eliminate the right half plane zero point

AbstractThe four‐switch Buck‐Boost (FSBB) converter are widely used in combination with other isolated converter to extend the voltage range capability of the overall structure. When the converter operates in buck‐boost mode and boost mode, it exhibits a right half plane zero (RHPZ) in the control to output transfer function. This characteristic would cause negative impact to stability of the converter. In order to eliminate the RHPZ, a novel modulation method is proposed in this paper. The buck mode is introduced to the modulation to adjust the voltage gain, and the corresponding average state space modeling for the FSBB with the proposed modulation is established. The simulation of the converter with the proposed modulation method and traditional modulation are presented. Finally, experiment results by hardware in the loop (HIL) platform is employed to verity the correctness of theoretical analysis results.

Variational Model with Nonstandard Growth Condition in Image Restoration and Contrast Enhancement

AbstractWe propose a new variational model in Sobolev-Orlicz spaces with non-standard growth conditions of the objective functional and discuss its applications to the simultaneous contrast enhancement and denoising of color images. The characteristic feature of the proposed model is that we deal with a constrained non-convex minimization problem that lives in variable Sobolev-Orlicz spaces where the variable exponent is unknown a priori and it depends on a particular function that belongs to the domain of the objective functional. In contrast to the standard approach, we do not apply any spatial regularization to the image gradient. We discuss the consistency of the variational model, give the scheme for its regularization, derive the corresponding optimality system, and propose an iterative algorithm for practical implementations.

PROBLEMS OF SOCIALIZATION OF DISPLACED CHILDREN WITH DISABILITIES

The REACH project at Kh. Dosmukhamedov Atyrau University is a unique experience that has created a model of educational space and programs for displaced children with disabilities. The children’s parents, school teachers, school psychologists, and volunteers took part in the project and provided methodological support. The purpose of the article is to identify the problems of socialization of refugee children with special educational needs on the basis of the research conducted in the framework of the research project. In the framework of an international partnership project for 2021-2024, scientists from Simon Fraser University (Canada), Yarmouk University (Jordan), and Canakkale University (Turkey) carried out significant work with displaced children from the war zone for their successful socialization, education, and provision of necessary educational resources. Project participants from Atyrau University (RK) conducted a series of research activities with special children from several refugee families who returned from Syria. On the base of Kids University (Kh. Dosmukhamedov Atyrau University) student volunteers demonstrated the effectiveness of introducing elements of fine art into the learning process for displaced children with disabilities. In addition, the use of musical and dramatic elements enhances interest in learning and motivates the expression of immediate emotions, contributing to the overall therapy of students. This article emphasizes the importance of advisory and methodological support for refugee children, and the results of the scientific research will become valuable material for scientists in this field.

Weyl Points on Nonorientable Manifolds.

Weyl fermions are hypothetical chiral particles that can also manifest as excitations near three-dimensional band crossing points in lattice systems. These quasiparticles are subject to the Nielsen-Ninomiya "no-go" theorem when placed on a lattice, requiring the total chirality across the Brillouin zone to vanish. This constraint results from the topology of the (orientable) manifold on which they exist. Here, we ask to what extent the concepts of topology and chirality of Weyl points remain well defined when the underlying manifold is nonorientable. We show that the usual notion of chirality becomes ambiguous in this setting, allowing for systems with a nonzero total chirality. This circumvention of the Nielsen-Ninomiya theorem stems from a generic discontinuity of the vector field whose zeros are Weyl points. Furthermore, we discover that Weyl points on nonorientable manifolds carry an additional Z_{2} topological invariant which satisfies a different no-go theorem. We implement such Weyl points by imposing a nonsymmorphic symmetry in the momentum space of lattice models. Finally, we experimentally realize all aspects of their phenomenology in a photonic platform with synthetic momenta. Our work highlights the subtle but crucial interplay between the topology of quasiparticles and of their underlying manifold.

Design and Simulation of Torque Control for Hot Rolling Steel Mill

Lateral movement of a strip in the hot rolling process is an important unsolved technical problem. To minimize the strip thickness variations, the state space model formulation is usually used. It results in reducing productivity and, in the extreme case, strip tearing during tailing out at the finishing mill. This movement is caused by various asymmetric factors such as mechanical asymmetry of the mill and different temperatures along the strip width direction. Furthermore, the difference in the temperature and the thickness of steel strip resulting in deformation of the steel, which affects the bending torque of the steel strip. This study aims to understand the influences of these various factors on lateral movement. In order to reduce the lateral movement and the disturbance terms, that act on uncertain inter-connected systems, a new control approach, based on a combination of Integral Sliding Mode Control (ISMC) and Linear Matrix Inequalities (LMI) technique, is proposed. Simulation results of hot rolling system with three interconnected systems are presented to validate the new controller performance.