Modeling Problems Research Articles

Semi-supervised anchorless single-engine grip detection

The extensive application of artificial intelligence in wireless communication, 3D reconstruction, and target location has successfully solved the modeling problem. Grasping specific objects in a stacked scene is a difficult task to achieve robot grasping. In this paper, a semi-supervised anchor-less single-gun grasping detection framework is proposed to help robots grasp objects easily. The framework first designs the AFMaskDetDep (AFMDD) network and then predicts the contours of objects by looking at sensing modules. Finally, the boundary value of the minimum boundary rectangle is obtained by judging the optimal target and the optimal grasping point of the inference module, and the final result is obtained by rotating back to the coordinate output on the original image area. Our proposed approach yields state-of-the-art performance with improved accuracy and computational speed on the VMRD and Cornell datasets, respectively. Experiments show that the algorithm can help the robot grasp a single specific object and has a high success rate in overlapping scenes. The success rate of each grip exceeds 93.5%.

Exact parameter identification in PET pharmacokinetic modeling using the irreversible two tissue compartment model * *This work was partly funded by the NIH Grants P41-EB017183 and R01-EB031199-02.

Objective. In quantitative dynamic positron emission tomography (PET), time series of images, reflecting the tissue response to the arterial tracer supply, are reconstructed. This response is described by kinetic parameters, which are commonly determined on basis of the tracer concentration in tissue and the arterial input function. In clinical routine the latter is estimated by arterial blood sampling and analysis, which is a challenging process and thus, attempted to be derived directly from reconstructed PET images. However, a mathematical analysis about the necessity of measurements of the common arterial whole blood activity concentration, and the concentration of free non-metabolized tracer in the arterial plasma, for a successful kinetic parameter identification does not exist. Here we aim to address this problem mathematically. Approach. We consider the identification problem in simultaneous pharmacokinetic modeling of multiple regions of interests of dynamic PET data using the irreversible two-tissue compartment model analytically. In addition to this consideration, the situation of noisy measurements is addressed using Tikhonov regularization. Furthermore, numerical simulations with a regularization approach are carried out to illustrate the analytical results in a synthetic application example.Main results. We provide mathematical proofs showing that, under reasonable assumptions, all metabolic tissue parameters can be uniquely identified without requiring additional blood samples to measure the arterial input function. A connection to noisy measurement data is made via a consistency result, showing that exact reconstruction of the ground-truth tissue parameters is stably maintained in the vanishing noise limit. Furthermore, our numerical experiments suggest that an approximate reconstruction of kinetic parameters according to our analytic results is also possible in practice for moderate noise levels. Significance. The analytical result, which holds in the idealized, noiseless scenario, suggests that for irreversible tracers, fully quantitative dynamic PET imaging is in principle possible without costly arterial blood sampling and metabolite analysis.

Generating Mortality Rate Intensity for Life Insurance Applications through Novel Method of Successive Differencing Under the Parsimonious Generalised Makeham’s Framework

Developing and implementing age dependent mortality rates in functional forms have presented critical modelling problems for life insurance and annuity firms thereby creating serious research gaps. A recurring problem with the existing life tables is that their underlying generating functions are not capable of showing any evidence that mortality at 10 declines. This observed irregularity in the age pattern of mortality is the prime motivation in the search for analytical functions which fully capture the observed variations of mortality with age. To fill the gaps identified, the study aims to develop specific life table function under the Generalised Makeham’s framework. The objectives of this study are to (i) compute the mortality rate intensities (ii) compute the curve of deaths densities (iii) compute the probability of deaths and then compare the common domain of definition of these measures. From the method of successive differencing employed to model, the male ageing parameter value fall within the globally accepted interval for the. This method is superior to the method of maximum likelihood estimation which mostly violates the permissible interval of validity. Computational evidence from our analysis proves that under the Generalised Makeham’s law, the mortality rate intensity declines at .

Association between Prenatal Control and the Incidence of Preeclampsia in Ecuadorian Pregnant Women: A Cross-Sectional Study

Hypertensive disorders are one of the main complications that affect pregnancy, which makes them one of the most important causes of high maternal and fetal morbimortality. Approximately 80% of cases occur at term gestational age, while the remaining percentage begin at earlier gestational ages. Most cases of preeclampsia occur in healthy pregnant women. Therefore, it is important to establish the risk factors in prenatal control visits. Prenatal control is fundamental in which comprehensive surveillance of the pregnant woman is performed. It is known that hypertension in pregnancy is a frequent and potentially dangerous complication for the mother, fetus and newborn. For this reason, one of the objectives of prenatal care is to detect pre-pregnancy hypertension and pregnancy-induced hypertension syndrome (preeclampsia). A nationally representative sample of 20648 mothers from the National Health and Nutrition Survey 2018 (ENSANUT) was used. We used multicollinearity tests to rule out possible statistical modeling problems and a binary logistic linear regression model where Odds Ratio (OR) with their 95% confidence intervals (95% CI) were estimated for each of the independent variables. In addition, we used specificity tests to test the fit between our dependent and independent variables. Our results reveal that prenatal control reduced 2 times (OR= -2.0005; CI=-1.981;-2.055) the probability of suffering complications during pregnancy such as preeclampsia. This result is statistically significant (p<0.05). It was also demonstrated that women from rural areas, with lower income and low schooling are more susceptible to suffer complications during pregnancy compared to the rest of the population. Preeclampsia-eclampsia continues to be one of the pathologies that has the greatest impact on maternal and perinatal morbidity and mortality, so work should continue to find ways to prevent its onset or modify its evolution, avoiding eclampsia or other severe forms of the disease. The results found in this study showed that adequate prenatal care (more than five visits starting in the first trimester of pregnancy) has an impact on the prevention of pregnancy complications such as preeclampsia, since identifying and avoiding it is one of the main purposes of prenatal care.

TCohPrompt: task-coherent prompt-oriented fine-tuning for relation extraction

Prompt-tuning has emerged as a promising approach for improving the performance of classification tasks by converting them into masked language modeling problems through the insertion of text templates. Despite its considerable success, applying this approach to relation extraction is challenging. Predicting the relation, often expressed as a specific word or phrase between two entities, usually requires creating mappings from these terms to an existing lexicon and introducing extra learnable parameters. This can lead to a decrease in coherence between the pre-training task and fine-tuning. To address this issue, we propose a novel method for prompt-tuning in relation extraction, aiming to enhance the coherence between fine-tuning and pre-training tasks. Specifically, we avoid the need for a suitable relation word by converting the relation into relational semantic keywords, which are representative phrases that encapsulate the essence of the relation. Moreover, we employ a composite loss function that optimizes the model at both token and relation levels. Our approach incorporates the masked language modeling (MLM) loss and the entity pair constraint loss for predicted tokens. For relation level optimization, we use both the cross-entropy loss and TransE. Extensive experimental results on four datasets demonstrate that our method significantly improves performance in relation extraction tasks. The results show an average improvement of approximately 1.6 points in F1 metrics compared to the current state-of-the-art model. Codes are released at https://github.com/12138yx/TCohPrompt.

Analytical Calculation of Magnetic Field and Analysis of Rotor Permeability Effects on Permanent Magnet Synchronous Motor with Fractional Slot Concentrated Winding

Accurate calculation of the flux and the magnetic field distribution of fractional slot concentrated winding permanent magnet synchronous motor (FSCW PMSM) is the basis for motor performance analysis, and rapid calculation is key. In this paper, to solve the problem of difficult modeling and accuracy guarantee of the flux linkage differential method, a method is proposed to calculate the flux and the no-load back EMF by the slotless subdomain model. By introducing the leakage flux calculation link, the calculation accuracy is improved, the analytical method results are compared with the finite element method results, and the effectiveness of the proposed method is verified. On this basis, the nonlinear variations of the magnetic field and the no-load back EMF with rotor permeability are determined, and the influence mechanism of rotor length and rotor permeability on the main magnetic circuit is revealed. Finally, an experiment of the prototype is carried out, and the correctness and accuracy of the analytical method and the finite element method is verified by comparing with the experimental results.

Building up a model family for inflammations

The paper presents an approach for overcoming modeling problems of typical life science applications with partly unknown mechanisms and lacking quantitative data: A model family of reaction–diffusion equations is built up on a mesoscopic scale and uses classes of feasible functions for reaction and taxis terms. The classes are found by translating biological knowledge into mathematical conditions and the analysis of the models further constrains the classes. Numerical simulations allow comparing single models out of the model family with available qualitative information on the solutions from observations. The method provides insight into a hierarchical order of the mechanisms. The method is applied to the clinics for liver inflammation such as metabolic dysfunction-associated steatohepatitis or viral hepatitis where reasons for the chronification of disease are still unclear and time- and space-dependent data is unavailable.

Method of Identification of Combat Vehicles Based on Yolo

A method for recognizing contours of objects in a video data stream is proposed. Data will be uploaded using a video camera in real time and object recognition will be performed. We will use the YOLO network – a method of identifying and recognizing objects in real time. Recognized objects will be recorded in a video sequence showing the contours of the objects. The approach proposed in the project reasonably synthesizes methods of artificial intelligence, theories of computer vision on the one hand, and pattern recognition on the other; it makes it possible to obtain control influences and mathematical functions for decision-making at every moment of time with the possibility of analyzing the influence of external factors and forecasting the flow of processes, and refers to the fundamental problems of mathematical modeling of real processes. The installation of the neural network is shown in detail. The characteristics of the neural network are shown and its capabilities are substantiated. Approaches to computer vision for object extraction are shown. Well-known methods are methods of expanding areas, methods based on clustering, contour selection, and methods using a histogram. The work envisages building a system for rapid identification of combat vehicles based on the latest image filtering methods developed using deep learning methods. The time spent on identifying the machine will be 10 –20 % shorter, thanks to the developed new information technology for detecting objects in conditions of rapidly changing information.

F-PICNN: A physics-informed convolutional neural network for partial differential equations with space-time domain

The physics and interdisciplinary problems in science and engineering are mainly described as partial differential equations (PDEs). Recently, a novel method using physics-informed neural networks (PINNs) to solve PDEs by employing deep neural networks with physical constraints as data-driven models has been pioneered for surrogate modelling and inverse problems. However, the original PINNs based on fully connected neural networks pose intrinsic limitations and poor performance for the PDEs with nonlinearity, drastic gradients, multiscale characteristics or high dimensionality in which the complex features are hard to capture. This leads to difficulties in convergence to correct solutions and high computational costs. To address the above problems, in this paper, a novel physics-informed convolutional neural network framework based on finite discretization schemes with a stack of a series of nonlinear convolutional units (NCUs) for solving PDEs in the space-time domain without any labelled data (f-PICNN) is proposed, in which the memory mechanism can considerably speed up the convergence. Specifically, the initial conditions (ICs) are hard-encoded into the network as the first time-step solution and used to extrapolate the next time-step solution. The Dirichlet boundary conditions (BCs) are constrained by soft BC enforcement while the Neumann BCs are hard enforced. Furthermore, the loss function is designed as a set of discretized PDE residuals and optimized to conform to physics laws. Finally, the proposed auto-regressive model has been proven to be effective in a wide range of 1D and 2D nonlinear PDEs in both space and time under different finite discretization schemes (e.g., Euler, Crank Nicolson and fourth-order Runge-Kutta). The numerical results demonstrate that the proposed framework not only shows the ability to learn the PDEs efficiently but also provides an opportunity for greater conceptual simplicity, and potential for extrapolation from learning the PDEs using a limited dataset.

The Effect of Pollutantmigration on the Environmental Situation in the Zone of Chemical Weaponinfluence

The article is devoted to the issues of monitoring the environment state in the influence zone of chemically hazardous objects on the example of the environmental monitoring system of a chemical weapons storage and destruction facility (CWDF) in the city of Kambarka, Udmurt Republic. Among the components there are: production control systems and site monitoring, environmental monitoring and health monitoring, however, it is environment in the zone of the chemical waste disposal facility influence which hasthe largest area and is the most difficult to control. Here, due to large areas, the process of migration of pollutants over considerable distances is observedclearly. The importance of the system for arranging sampling points on the topographic map of the waste disposal facility is shown in order to take into account the process of pollutantmigration. Vertical and horizontal migration of pollutants can lead to the emergence of unobvious local areas of pollutantaccumulation both within and outside the influence zone of the chemical waste disposal facility. A solution to the problem of migration process modeling on a conditionally selected area of the territory within the facility influence zone is presented to show the effect pollutantmigration on the dynamics of the environmental situation within the chemical waste disposal area. It has been proven that within the area under consideration there will be a significant amount of pollutant, which, subject to further evaporation of rainwater runoff, will remain in this area. This indicates the inevitability of the appearance of local areas of pollutantaccumulation in the influence zone of chemically-disposable substances in excess of permissible standards for their concentration, which requires a revision of the arranging sampling pointsystem, taking into account the application of migration processmodeling methods. The need for dynamic observations in individual sites of the area, selected with respect to the analysis results of the pollutantmigration routes within the entire influence zone of the chemically-disposable waste disposal facility, in order to assess the degree of impact of the non-toxic waste disposal facility on the environment, is substantiated.

Research Progress of EEG-Based Emotion Recognition: A Survey

Emotion recognition based on electroencephalography (EEG) signals has emerged as a prominent research field, facilitating objective evaluation of diseases like depression and motion detection for heathy people. Starting from the basic concepts of temporal-frequency-spatial features in EEG and the methods for cross-domain feature fusion, this survey then extends the overfitting challenge of EEG single-modal to the problem of heterogeneous modality modeling in multimodal conditions. It explores issues such as feature selection, sample scarcity, cross-subject emotional transfer, physiological knowledge discovery, multimodal fusion methods, and modality missing. These findings provide clues for researchers to further investigate emotion recognition based on EEG signals.

Admittance stability model and equivalent circuit analysis of three‐phase AC systems in different coordinate systems

AbstractIn recent years, with a large number of distributed energy sources connected to the grid, the distribution network has shown a clear trend towards the use of power electronics. As a power conversion interface, the stability of the converter's interaction with the grid has received considerable attention. Adequate admittance (impedance) modeling of the three‐phase grid‐connected converter is a prerequisite for analyzing the stability and resonance characteristics of the grid‐connected system. Understanding the internal mechanisms and proposing effective solutions are crucial to enable power systems to effectively cope with the impact of power electronics. Therefore, by studying the admittance equivalence relationship and equivalent circuit of three‐phase AC systems in various coordinate systems, this paper focuses on solving the non‐uniqueness problem of impedance admittance modeling of three‐phase converter system. It is proposed that the input admittance of the three‐phase asymmetrical system in the dq and fb rotating coordinate systems is a 2‐order matrix and equivalent. The input admittance of the three‐phase unbalanced system in the αβ and “12” static coordinate systems is a 4‐order matrix and equivalent. From both theoretical analysis and simulation verification, it is shown that the number of eigenvalues of the three‐phase asymmetrical system in the stationary coordinate system is twice that in the rotating coordinate system, and the overall damping of the system is improved. The control strategy based on the stationary coordinate system will make the system more stable. The second contribution of this paper is to establish the equivalent circuit of the three‐phase symmetric converter system by the node admittance matrix and extend it to the multi‐machine system to explain the system instability mechanism by the circuit resonance mechanism. Finally, the frequency coupling phenomenon is studied for the three‐phase asymmetrical converter system, and it is explained that it cannot establish an equivalent circuit similar to the three‐phase symmetrical converter system.

How to protect those who protect: psychosocial risks in child welfare workers during the pandemic

ABSTRACT Child welfare professionals face elevated psychosocial risks, especially during the COVID-19 pandemic, yet they are often overlooked in specialized literature, especially for Spanish workers. Most studies use linear models, neglecting nonlinear relationships. This study examined the role of job demands, resources, and COVID-19 impact on burnout, psychosomatic problems, and work engagement in 196 Spanish child welfare professionals. Demands predicted burnout and psychosomatic problems in both Hierarchical Regression Modelling (HRM) and Qualitative Comparative Analysis (QCA). Resources positively predicted engagement in both analyses. COVID-19 impact was significantly related to psychosomatic problems in QCA, highlighting nuances not captured by HRM.

On the relationship of mental and biological in the problem of modeling consciousness

The problem of mental modelling and creation of thinking devices is one of the most significant and at the same time, the least studied in modern science. The main difficulties in solving it are associated with insufficient elaboration of the relationship between the mental and the biological, primarily from the conceptual apparatus and a unified methodological base. The article discusses the issues of the definition of alive, the emergence of the psyche, the concept of pan-interiorism, existing models of consciousness and their criticism from the position of non-disjunctive logic and proposes a new semiotic model based on a cultural substrate. The work is an attempt at an interdisciplinary synthesis of views on the above problems of the largest scientists of the XX and XXI centuries: V.I. Vernadsky, S.L. Rubinshtein, A.V. Brushlinsky, M.M. Bakhtin, Yu.M. Lotman and others. The possibility of building artificial intelligence based on cultural substrate is substantiated, as it is proposed to use semiotic systems, whose properties in many respects resemble the properties of a living organism: they possess the mechanism of autopoiesis, follow the Redi principle, are organized in the form of cholarchy, and exist only in a communicative environment.

Attention Guide Axial Sharing Mixed Attention (AGASMA) Network for Cloud Segmentation and Cloud Shadow Segmentation

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation.

Solar Models and Astrophysical S-factors Constrained by Helioseismic Results and Updated Neutrino Fluxes

The ratio of metal abundance to hydrogen abundance of the solar photosphere, (Z/X) s , has been revised several times. Standard solar models, based on these revised solar abundances, are in disagreement with seismically inferred results. Recently, Magg et al. introduced a new value for (Z/X) s , which is still under debate in the community. The solar abundance problem or solar modeling problem remains a topic of ongoing debate. We constructed rotating solar models in accordance with various abundance scales where the effects of convection overshoot and enhanced diffusion were included. Among these models, those utilizing Magg’s abundance scale exhibit superior sound speed and density profiles compared to models using other abundance scales. Additionally, they reproduce the observed frequency separation ratios r 02 and r 13. These models also match the seismically inferred surface helium abundance and convection zone depth within the 1σ level. Furthermore, the calculated neutrino fluxes from these models agree with detected ones at the level of 1σ. We found that neutrino fluxes and density profile are influenced by nuclear reactions, allowing us to use the combination of detected neutrino fluxes and seismically inferred density for diagnosing astrophysical S-factors. This diagnostic approach shows that S 11 may be underestimated by 2%, while S 33 may be overestimated by about 3% in previous determinations. The S-factors favored by updated neutrino fluxes and helioseismic results can lead to significant improvements in solar models.

KINETIC MODELLING OF BIOCHEMICAL REACTIONS USING MATHСAD ANALYTICAL TOOLKIT

Background. The study of the kinetics of biochemical reactions provides a better understanding of how biological processes occur in living organisms. Understanding the peculiarities of such reactions is important for the development of new technologies, in particular for the production of biologically active substances and for the synthesis of drugs. A powerful tool for solving problems in biochemical reaction kinetics is mathematical modelling, which can be carried out using computer mathematical systems, in particular the MATHCAD analytical toolkit. Aim: to substantiate the feasibility and effectiveness of using the MATHCAD analytical toolkit to solve problems of kinetic modelling of biochemical reactions in pharmaceutical research, and to review the capabilities of MATHCAD for computer modelling in pharmacy. Materials and methods. In the context of studying the rate of enzymatic reactions and developing models, such as the Michaelis-Menten model, to describe reactions in which enzymes catalyze the transformation of substrates, the use of a computer mathematical system (CMS) is considered. CMS is a software package and environment for performing mathematical computations, modelling and visualization. The possibilities of using the MATHCAD system to create mathematical models of biochemical reactions based on kinetic equations are demonstrated. This involves the creation of differential equations describing changes in reagent concentrations over time. These equations were solved using numerical methods in MATHCAD. In addition, the results obtained are visualized using 3D graphics in MATHCAD. The stages of using the MATHCAD analytical toolkit in the kinetic modelling of biochemical reactions have been determined. Results. The use of MATHCAD in the kinetic modelling of biochemical reactions is effective for the study of: (1) the kinetics of enzymatic reactions, e.g. reactions in which an enzyme catalyzes the conversion of a substrate into a product; (2) biochemical reactions that take place in reaction vessels in which reagents mix and interact; (3) modelling of reactions in reaction vessels based on the solution of differential equations of reaction kinetics; (4) the effect of inhibitors or activators on enzymatic reactions; (5) scenarios of interaction of reagents to determine changes in the kinetics of reactions that occur when different active substances are introduced; (6) kinetics of biochemical reactions in cases where reactions are accompanied by diffusion of reagents through membranes or other semi-permeable barriers; (7) modelling the effect of diffusion processes on the kinetics of biochemical reactions; (8) models describing the kinetics of decomposition of substances, for example the decomposition of biologically active compounds in the body or in the environment; (9) predicting the effect of changes in the conditions of the reaction medium (temperature, pH, concentration of reagents) on the kinetics of biochemical reactions. It is substantiated that model descriptions of the kinetics of biochemical reactions are important for forming an understanding of the functions of biological systems, including metabolism, enzymatic reactions, and other physiological phenomena. Tools have been used to visualize the modelling results in the form of three-dimensional MATHCAD graphics, which improves the understanding of the reaction mechanism and allows a more thorough analysis of its kinetics. Conclusion. MATHCAD provides an optimized environment for kinetic modelling of biochemical reactions through its ergonomic interface. Particular advantages are the ability to work with symbolic expressions and to use a wide range of built-in functions and tools for exploring mathematical models and visualizing results. The obtained results may be important both for further scientific pharmaceutical research and for implementation in the training of future Masters of Pharmacy in the discipline of "Computer Modelling in Pharmacy" in higher medical education institutions.

Mathematical modeling of heat exchange for efficient automated control systems

The article deals with the problem of mathematical modeling of heat transfer processes, taking into account a large number of factors affecting these processes and a variety of mathematical modeling methods. The article discusses the idea of developing a universal and flexible model that could quickly adapt to different initial modeling conditions and take into account various parameters for further use of the mathematical model in the synthesis of automatic control systems. The paper discusses the main aspects of mathematical modeling of heat transfer and the creation of a mathematical model in distributed heat exchanger parameters with the possibility of changing the properties and parameters of the equipment. The paper analyzes previous studies and identifies the problems of existing methods of mathematical modeling of heat transfer, such as lack of versatility, complexity, and insufficient accuracy. The paper goes on to review different approaches to mathematical modeling of heat transfer, such as models with a group of parameters, models in distributed parameters, and models based on artificial intelligence. The article provides an overview of model implementation methods and their advantages and disadvantages. Based on this analysis, the authors propose a new mathematical model of heat transfer in distributed parameters, which should be universal, easy to use, and accurate in accordance with the requirements of synthesizing automated control systems. The authors also outline the methodology for achieving this goal, which includes the implementation of the geometry of the heat exchanger, the implementation of conditions and mathematical modeling, as well as the study of the influence of parameters and the formation of transients. The general conclusion of the article is that the developed mathematical model of heat transfer can be used to synthesize automatic control systems for heat exchange processes in various industries.

Version [1.1] - [2D-xtDCR-LTBEM: A Fortran code for numerical solutions to the unsteady anisotropic-DCR equation of spatio-temporal coefficients]

Variable coefficient equations are usually used as governing equations for modeling problems which are associated with Functionally Graded Materials (FGM). Version 1.1 - 2D-xtDCR-LTBEM is a Fortran code that can be used to find numerical solutions of two-dimensional (2D) initial–boundary value problems which are governed by the unsteady anisotropic-diffusion convection reaction (DCR)-type and the anisotropic-diffusion reaction (DR)-type equations with a class of space–time dependent coefficients, by utilizing a combined Laplace Transform-Boundary Element Method (LTBEM). This code may provide valuable help for simulation of problems of FGM.

Efficient state change detection method for modeling stateful time series data

AbstractTime series analysis is an important research area and has wide real applications. Time series modeling has been identified and well known as the key fundamental problem in time series analysis, and it has received lots of research attentions. The hardness of achieving sophisticated and efficient time series models depends on the characteristic of the data met. In practical, time series data are usually generated by the physical process with several working states, which are determined by the system design. In such cases, the data characteristics of each state is quite different from the other states, where the modeling problem becomes much harder and it is more challenging to design modeling methods for stateful time series data. Therefore, this paper focuses on the stateful time series data, and studies the state change detection problem, which is identified as a key fundamental problem for modeling stateful data. To achieve effective and efficient state change detection, the proposed method utilizes two essential ideas. Since the discrepancy (a.k.a. histogram) based method has been proposed and verified to be more effective by previous works, the first idea is to improve its performance by involving more temporal information and utilizing the encoder–decoder techniques to reform the data. In view of the efficiency issue of the discrepancy‐based method, the second idea is to integrate both the Bayesian and discrepancy‐based detection methods, such that a perfect tradeoff between time efficiency and detection performance can be achieved. Experiments on both real and synthetic datasets show that the proposed methods are both effective and efficient, and the state change detection problem can be solved well by them on stateful time series data.