The relaxation spectrum is a fundamental viscoelastic characteristic from which other material functions used to describe the rheological properties of polymers can be determined. The spectrum is recovered from relaxa...
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The relaxation spectrum is a fundamental viscoelastic characteristic from which other material functions used to describe the rheological properties of polymers can be determined. The spectrum is recovered from relaxation stress or oscillatory shear data. Since the problem of the relaxation spectrum identification is ill-posed, in the known methods, different mechanisms are built-in to obtain a smooth enough and noise-robust relaxation spectrum model. The regularization of the original problem and/or limit of the set of admissible solutions are the most commonly used remedies. Here, the problem of determining an optimally smoothed continuous relaxation time spectrum is directly stated and solved for the first time, assuming that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. The relaxation time spectrum model that reproduces the relaxation modulus measurements and is the best smoothed in the class of continuous square-integrable functions is sought. Based on the Hilbert projection theorem, the best-smoothed relaxation spectrum model is found to be described by a finite sum of specific exponential-hyperbolic basis functions. For noise-corrupted measurements, a quadratic with respect to the Lagrange multipliers term is introduced into the Lagrangian functional of the model's best smoothing problem. As a result, a small model error of the relaxation modulus model is obtained, which increases the model's robustness. The necessary and sufficient optimality conditions are derived whose unique solution yields a direct analytical formula of the best-smoothed relaxation spectrum model. The related model of the relaxation modulus is given. A computational identification algorithm using the singular value decomposition is presented, which can be easily implemented in any computing environment. The approximation error, model smoothness, noise robustness, and identifiability of the
This paper gives new insights on Laser Range Profiling. In this paper we explore the advantages of Laser Range Profiling or High Temporal Resolution Ladar (Laser Detection and Ranging) to identify objects at long rang...
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ISBN:
(纸本)9781510654631;9781510654624
This paper gives new insights on Laser Range Profiling. In this paper we explore the advantages of Laser Range Profiling or High Temporal Resolution Ladar (Laser Detection and Ranging) to identify objects at long ranges with high probability. In the first part of the paper, we explain the concept of Laser Range Profiling. In the second part of the paper, we focus on the electromagnetic simulation of laser range profiles of different objects. In the third part of the paper, we study the identification function of the system, and describe an algorithm which correlates the measured signatures of the unknown object with the closest range profile related to the aspect angles of the object in the database.
Real risk status detection is an effective way to reflect risky or dangerous driving behaviors and therefore to prevent road traffic accidents. However, a driver's risk status is not only difficult to define but a...
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Real risk status detection is an effective way to reflect risky or dangerous driving behaviors and therefore to prevent road traffic accidents. However, a driver's risk status is not only difficult to define but also uncontrollable and uncertain. In this study, a simulated experiment with 30 drivers was conducted using a driving simulator to collect the multi-sensor data of road conditions, humans, and vehicles. The driving risk status was classified into three states (0 - incident, 1 - near crash, or 2 - crash) on the basis of the playback system of the driving simulator. The experimental data were pre-processed using the cubic spline interpolation method and the time-windows theory. A driving risk status identification model was established using the C5.0 decision tree algorithm, and the receiver operating characteristic curve (ROC) was adopted to evaluate the performance of the identification model. The results indicated that respiration (RESP), vehicle speed (SPE), SM_FATIGUE, distance to the left lane (LLD), course angle (CA), and skin conductivity (SC) had a significant correlation (p < 0.05) with the driving risk status. The identification accuracy of the C5.0 decision tree algorithm was 78%, and the areas under the ROC were 0.934, 0.77, and 0.845, respectively. Moreover, compared with other four identification algorithms, the algorithm performance evaluation indexes TPR (0.780), precision (0.753), recall (0.78), F-measure (0.756), and kappa (0.884) of the C5.0 decision tree were all the best. The conclusion can provide reference evidence for danger warning systems and intelligent vehicle design.
The reaction-diffusion equations have been studied in various aspects of nature, such as heat transfer and biological dynamic modelling. In this paper, we study a nonlocal reaction-diffusion model with time delay asso...
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The reaction-diffusion equations have been studied in various aspects of nature, such as heat transfer and biological dynamic modelling. In this paper, we study a nonlocal reaction-diffusion model with time delay associated with the density and diffusion behaviour of biological populations. Based on our previous conclusions and numerical strategy of the direct problem, we continue to study the inverse problem about the diffusion coefficient as well as the parameters in the birth function, and also perform numerical simulations with error analysis. In addition, we further generalize the constant diffusion coefficient to the position-dependent diffusion rate, which is intended to improve the generalizability to multiple organisms diffusion in nature.
With the growing demand for energy and increasing environmental concerns, accurate modeling and efficient optimization of thermodynamic systems have become key research areas in engineering. In recent years, the appli...
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With the growing demand for energy and increasing environmental concerns, accurate modeling and efficient optimization of thermodynamic systems have become key research areas in engineering. In recent years, the application of machine learning techniques in thermodynamic systems has demonstrated significant potential, greatly improving modeling precision and optimization efficiency. However, existing methods face challenges in handling temperature delay phenomena, limiting the accuracy and effectiveness of system modeling and control. This paper introduces a temperature delay identification algorithm to enhance the accuracy of delay representation. Additionally, it develops a predictive control model for thermodynamic systems that incorporates temperature delay. These two components aim to improve the precision and efficiency of thermodynamic system modeling, providing new insights and methods for the intelligent management of complex thermodynamic systems.
In forensic science, particularly in the context of latent fingermarks detection, forensic scientists are often faced with the need to assess the quality of the detected fingermarks to quantitatively interpret their r...
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In forensic science, particularly in the context of latent fingermarks detection, forensic scientists are often faced with the need to assess the quality of the detected fingermarks to quantitatively interpret their results and express conclusions. Today this process is mainly carried out by human examiners referring to guidelines or provided quality scales. The largest the set of fingermarks (e.g., hundreds, thousands), the longest and the most labor-intensive this task becomes. Moreover, it is difficult to guarantee a fully objective process since the subjectivity of each individual is almost impossible to avoid, especially with regards to the interpretation of the quality scale levels or when facing fingermarks detected in an inhomogeneous manner. In this paper, the possibility of automatizing the quality assessment step is explored. The choice has been made to consider the use of quality assessment algorithms currently applied in an identification context. 150 natural fingermarks from ten donors were deposited on three different supports. These marks were detected using 1,2-indanedione/zinc or cyanoacrylate fumigation depending on the support. Then, their quality was assessed by five examiners, according to the UNIL scale, and by seven algorithms (i.e., Lights Out, Latent Fingerprint Image Quality 1 and 2, Latent Quality Metric, Expected Score Likelihood Ratio, NIST Fingerprint Image Quality, MINDTCT). Spearman and Pearson correlations were calculated, and the distribution of scores for each algorithm was charted (using boxplots) against the results provided by the human examiners. The most promising results were obtained with the LQM algorithm, more specifically with the fingermark clarity metric.
The relaxation spectra, from which other material functions used to describe mechanical properties of materials can be uniquely determined, are important for modeling the rheological properties of polymers used in che...
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The relaxation spectra, from which other material functions used to describe mechanical properties of materials can be uniquely determined, are important for modeling the rheological properties of polymers used in chemistry, food technology, medicine, cosmetics, and many other industries. The spectrum, being not directly accessible by measurement, is recovered from relaxation stress or oscillatory shear data. Only a few models and identification methods take into account the non-negativity of the real spectra. In this paper, the problem of recovery of non-negative definite relaxation spectra from discrete-time noise-corrupted measurements of relaxation modulus obtained in the stress relaxation test is considered. A new hierarchical identification scheme is developed, being applicable both for relaxation time and frequency spectra. Finite-dimensional parametric classes of models are assumed for the relaxation spectra, described by a finite series of power-exponential and square-exponential basis functions. The related models of relaxation modulus are given by compact analytical formula, described by the products of power of time and the modified Bessel functions of the second kind for the time spectrum, and by recurrence formulas based on products of power of time and complementary error functions for frequency spectrum. The basis functions are non-negative. In result, the identification task was reduced to a finite-dimensional linear-quadratic problem with non-negative unknown model parameters. To stabilize the solution, an additional smoothing constraint is introduced. Dual approach was used to solve the stated optimal identification task resulting in the hierarchical two-stage identification scheme. In the first stage, dual problem is solved in two levels and the vector of non-negative model parameters is computed to provide the best fit of the relaxation modulus to experiment data. Next, in second stage, the optimal non-negative spectrum model is determined. A co
In this article, an automatic near-field measurement system is introduced to record the electromagnetic emission (EME) of equipment under testing (EUT). First, a camera is used to capture the measurement area, which i...
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In this article, an automatic near-field measurement system is introduced to record the electromagnetic emission (EME) of equipment under testing (EUT). First, a camera is used to capture the measurement area, which is combined with an image positioning method to achieve fast and accurate positioning of the area. Then, based on the wavelet transform and autocorrelation transform of the EME signal identification algorithm, the system further identifies the broadband and narrowband characteristics of the signal and obtains the power distributions of the corresponding characteristics. In performance tests, the near-field probe moves with a minimum stepping accuracy of 0.1 mm. The movement error of the consistency and stability are guaranteed as less than 1% of the set value. The image positioning error in the 30 x 30 cm scanned area is less than 0.5 mm. Finally, the system identifies EME signals from a circuit with multiple crystal oscillators and a sine signal, predictive identification of the circuit functions under the metal shell of a router system. The proposed measurement system therefore offers great measurement accuracy, stability, and efficiency.
The viscoelastic relaxation spectrum provides deep insights into the complex behavior of polymers. The spectrum is not directly measurable and must be recovered from oscillatory shear or relaxation stress data. The pa...
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The viscoelastic relaxation spectrum provides deep insights into the complex behavior of polymers. The spectrum is not directly measurable and must be recovered from oscillatory shear or relaxation stress data. The paper deals with the problem of recovery of the relaxation spectrum of linear viscoelastic materials from discrete-time noise-corrupted measurements of relaxation modulus obtained in the stress relaxation test. A class of robust algorithms of approximation of the continuous spectrum of relaxation frequencies by finite series of orthonormal functions is proposed. A quadratic identification index, which refers to the measured relaxation modulus, is adopted. Since the problem of relaxation spectrum identification is an ill-posed inverse problem, Tikhonov regularization combined with generalized cross-validation is used to guarantee the stability of the scheme. It is proved that the accuracy of the spectrum approximation depends both on measurement noises and the regularization parameter and on the proper selection of the basis functions. The series expansions using the Laguerre, Legendre, Hermite and Chebyshev functions were studied in this paper as examples. The numerical realization of the scheme by the singular value decomposition technique is discussed and the resulting computer algorithm is outlined. Numerical calculations on model data and relaxation spectrum of polydisperse polymer are presented. Analytical analysis and numerical studies proved that by choosing an appropriate model through selection of orthonormal basis functions from the proposed class of models and using a developed algorithm of least-square regularized identification, it is possible to determine the relaxation spectrum model for a wide class of viscoelastic materials. The model is smoothed and robust on measurement noises;small model approximation errors are obtained. The identification scheme can be easily implemented in available computing environments.
During the operation of air separation units(ASU), planned short-term shutdowns are often required to ensure a balance between supply and demand of downstream air separation products, and maintain equipment performanc...
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During the operation of air separation units(ASU), planned short-term shutdowns are often required to ensure a balance between supply and demand of downstream air separation products, and maintain equipment performance and ensure production safety. Making the device shut down smoothly and safely is a key measure in air separation production. However, this process highly relies on manual participation and carries risks and uncertainties. In response to this practical demand, we propose an ASU Shutdown operation flow assistance system(SOFAS). We firstly build a standard logical model based on the actual shutdown process and develop algorithms to assist in executing standardized and safe shutdown operations. Then, we propose an operation flow identification algorithm to identify the shutdown operation flow, and an evaluation algorithm to quantitatively analyze the efficiency and safety of the operation flow using two performance indicators. Finally, the effectiveness of the method was verified through experimental analysis using the actual operational data of ASU.
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