The diversity of designs and optimization methods for elastic systems makes evaluating their performance challenging. This paper proposes a method based on xMAS and high-level modeling to analyze the performance, func...
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The diversity of designs and optimization methods for elastic systems makes evaluating their performance challenging. This paper proposes a method based on xMAS and high-level modeling to analyze the performance, functional and timing verification of regular and early evaluation synchronous elastic circuits while considering process variations. The xMAS framework provides modularity, precise semantics, and executable models, enhancing formal verification and high-level analysis capabilities over existing approaches. The proposed platform calculates the throughput value, which is the most critical performance factor in elastic circuits. The power, delay, and PDP of all early evaluation elastic components are evaluated under process variations and compared to those of regular elastic circuits. The results indicate that early evaluation properties increase the sensitivity of circuit components to process variations, making their performance less predictable. modeling results of the Elastic DLX microprocessor highlight these findings by demonstrating that process variations can cause a 26% reduction in throughput and lead to a 0.2% chance of synchronization errors between data and control signals. These findings underscore the critical need to account for process variations when designing and verifying early evaluation elastic circuits to maintain performance reliability.
Models, representing a system under study with respect to problems such as process design, processcontrol, product synthesis and many more, are at the core of most computer-aided solution techniques. The representati...
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Models, representing a system under study with respect to problems such as process design, processcontrol, product synthesis and many more, are at the core of most computer-aided solution techniques. The representation of a system through a model is done in different ways, such as, symbols, data, mathematical equations, and/or some combination of these. The workflow or process of creating a proxy mathematical representation (model) of a given target system is referred to as modeling. Model-based software tools incorporate the developed models within the steps of their systematic workflow through simultaneous or decomposed solution strategies related to synthesis, design, analysis, etc., of specific systems. In this perspective paper we highlight the various ways systems can be represented by models, the different ways the required models are developed through modeling techniques, and examples of model-based software tools developed to solve different process and product engineering problems. Two types of systems - process systems and chemical systems, are considered. Important issues and challenges are highlighted and perspectives on how they can be addressed are presented.
In the pursuit of ecological validity, current business process simulation methods are calibrated to data from existing processes. This is important for realistic what-if analysis in the context of these processes. Ho...
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In the pursuit of ecological validity, current business process simulation methods are calibrated to data from existing processes. This is important for realistic what-if analysis in the context of these processes. However, this is not always the "right tool for the job." To test hypotheses in the area of predictive process monitoring, it can be more helpful to simulate event-log data from a theoretical process, where all aspects can be manipulated. One example is when assessing the influence of process complexity or variability on the performance of a new prediction method. In this case, the ability to include control variables and systematically change process characteristics is a key to fully understanding their influence. Calibrating a simulation model from observed data alone can in these cases be limiting. This paper proposes a simulation framework, Synthetic Business process Simulation (SynBPS), a Python library for the generation of event-log data from synthetic processes. Aspects such as process complexity, stability, trace distribution, duration distribution, and case arrivals can be fully controlled by the user. The overall architecture is described in detail, and a demonstration of the framework is presented.
This study proposes a modeling optimization method based on three-dimensional graphics for the modeling problem of complex geometric structures in the intelligent construction process. This method uses the Marching Cu...
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In a closed-loop system, feedback control may transfer process faults from source variables to other variables that obscure the diagnosis of the root cause of the fault. Taking the fused magnesium furnace (FMF) as an ...
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In a closed-loop system, feedback control may transfer process faults from source variables to other variables that obscure the diagnosis of the root cause of the fault. Taking the fused magnesium furnace (FMF) as an example, the effects of process faults, e.g., the semimolten condition,may propagate through the controlled system. To address this issue, this article proposes a new data-driven process fault diagnosis method for dynamic processes in the presence of feedback control. A new residual analysis (RA) method is first proposed to extract features of process faults in the feedback-invariant subspace. Moreover, a new fault diagnosis algorithm, which incorporates support vector machines (SVM) with leaky integrate-and-fire neurons (LIF), named LIF-SVM, is proposed. Unlike traditional diagnosis methods which classify each element independently, LIF-SVM efficiently takes into account the fault dynamics. The features of process faults extracted by RA are used by LIF-SVM for diagnosis to constitute the new RALIF-SVM method. Results from experimental studies on a simulated 4x4 dynamic process and a real FMF show that the diagnosis accuracies using the proposed method increase by 10.9% and 9.15%, respectively, compared to the traditional subspace reconstruction method.
The dynamic model equations are essential in system analysis and control system design. In adaptive control systems, the mathematical equations of the controlled system are utilized to compute the corresponding contro...
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The dynamic model equations are essential in system analysis and control system design. In adaptive control systems, the mathematical equations of the controlled system are utilized to compute the corresponding control signals based on the current dynamic conditions of the system. The challenge intensifies when dealing with nonlinear systems, as the equation discovery process becomes more intricate. This paper proposes the Recursive Sparse Identification of Nonlinear Dynamics (R-SINDy) using Least Square-Assisted LASSO (Least Absolute Shrinkage and Selection Operator), an extension of the previous SINDy method that processed data in batches for online equation discovery. This method aims to generate mathematical equations for both linear and nonlinear dynamic systems in online streaming data. The proposed method is tested on two nonlinear systems: Lorenz Chaotic System with parameter changes and dataset of KUKA robotic manipulator. The results indicate that the proposed method has the ability to quickly adjust the gain when changes occur because of the adjustment in the forgetting factor, achieving an accuracy of up to 100 % on the Lorenz system and 92.02 % on the robotic manipulator, with a sparsity coefficient of up to 87.59 % from a total of 282 available matrix coefficients.
Regarding the modeling problem of grinding process in mining, taking the operating load of semi-automatic grinding mill as the research object, starting from industrial big dataanalysis, using data mining methods to ...
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ISBN:
(纸本)9798350366907;9789887581581
Regarding the modeling problem of grinding process in mining, taking the operating load of semi-automatic grinding mill as the research object, starting from industrial big dataanalysis, using data mining methods to establish decision tree models and random forest models, and evaluating and predicting the constructed models. Through testing, decision tree models with different effects can be obtained, indicating that the decision tree model has a certain degree of flexibility, and selecting parameters can lead to a model with good practicality. In addition, under the same conditions, a random forest model was constructed, and simulation results showed that the combination model constructed using the random forest algorithm was superior to a single decision tree model, with better prediction performance.
Background: Patient-specific lumbar spine models are crucial for enhancing diagnostic accuracy, preoperative planning, intraoperative navigation, and biomechanical analysis of the lumbar spine. However, the methods cu...
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Background: Patient-specific lumbar spine models are crucial for enhancing diagnostic accuracy, preoperative planning, intraoperative navigation, and biomechanical analysis of the lumbar spine. However, the methods currently used for creating and analyzing these models primarily involve manual operations, which require significant anatomical expertise and often result in inefficiencies. To overcome these challenges, this study introduces a novel method for automating the creation and analysis of subject-specific lumbar spine models. Methods: This study utilizes deep learning algorithms and smoothing algorithms to accurately segment CT images and generate patient-specific three-dimensional (3D) lumbar masks. To ensure accuracy and continuity, vertebral surface models are then constructed and optimized, based on these 3D masks. Following that, model accuracy metrics are calculated accordingly. An automated modeling program is employed to construct structures such as intervertebral discs (IVD) and generate input files necessary for Finite Element (FE) analysis to simulate biomechanical behavior. The validity of the entire lumbar spine model produced using this method is verified by comparing the model within vitro experimental data. Finally, the proposed method is applied to a patient-specific model of the degenerated lumbar spine to simulate its biomechanical response and changes. Results: In the test set, the neural network achieves an average Dice coefficient (DC) of 97.8%, demonstrating high segmentation accuracy. Moreover, the application of the smoothing algorithm reduces model noise substantially. The smoothed model exhibits an average Hausdorff distance (HD) of 3.53 mm and an average surface distance (ASD) of 0.51 mm, demonstrating high accuracy. The FE analysis results agree closely within vitro experimental data, while the simulation results of the degradation lumbar model correspond with trends observed in existing literature.
Count data are common in practice. Statistical processcontrol for count data thus has attracted much attention in recent years. Most existing methods on this topic focus on the detection of mean shifts of count data ...
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Count data are common in practice. Statistical processcontrol for count data thus has attracted much attention in recent years. Most existing methods on this topic focus on the detection of mean shifts of count data based on parametric modeling. However, their assumed parametric models (e.g., the Poisson probability model) are often invalid in practice due mainly to the potential impact of some latent confounding risk factors, which would lead to unreliable performance of the related control charts. In addition, it is highly desirable and important to monitor the dispersion of count data when the Poisson probability model is invalid. To this end, new nonparametric cumulative sum control charts and their corresponding self-starting versions are suggested in this paper for monitoring the dispersion of count data based on data categorization and categorical dataanalysis. Numerical results show that the proposed method can provide more effective and robust monitoring of count data in comparison with some representative existing methods. A real-data example is used to demonstrate its implementation and application.
this article presents a method of applying machine learning methods in the process of modeling the rating system of films. A brief overview of the subject area is presented. Some features of modeling rating systems ar...
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