The purpose of this study is to determine the heat flux distribution and to estimate the workpiece temperature in creep feed grinding. The sequential algorithm of the inverse heat transfer was used for determining the...
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The purpose of this study is to determine the heat flux distribution and to estimate the workpiece temperature in creep feed grinding. The sequential algorithm of the inverse heat transfer was used for determining the heat flux distribution. The amount of heat flux to the workpiece, the energy partition and the convective heat transfer coefficients both at the front and at the back of the heat flux were determined. Three heat source models using the determined amount of heat flux were applied to estimate the workpiece temperature. The workpiece temperatures estimated by the heat source models were compared with that measured by the embedded thermocouple. The scalene triangle model correlated best with measured and theoretical temperature profiles obtained for creep feed grinding. (c) 2006 Elsevier Ltd. All rights reserved.
In practical applications, there may exist a disparity between real values and optimal results due to uncertainties. This kind of disparity may cause violations of some probabilistic constraints in a reliability based...
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In practical applications, there may exist a disparity between real values and optimal results due to uncertainties. This kind of disparity may cause violations of some probabilistic constraints in a reliability based design optimization (RBDO) problem. It is important to ensure that the probabilistic constraints at the optimum in a RBDO problem are insensitive to the variations of design variables. In this paper, we propose a novel concept and procedure for reliability based robust design in the context of random uncertainty and epistemic uncertainty. The epistemic uncertainty of design variables is first described by an info gap model, and then the reliability-based robust design optimization (RBRDO) is formulated. To reduce the computational burden in solving RBRDO problems, a sequential algorithm using shifting factors is developed. The algorithm consists of a sequence of cycles and each cycle contains a deterministic optimization followed by an inverse robustness and reliability evaluation. The optimal result based on the proposed model satisfies certain reliability requirement and has the feasible robustness to the epistemic uncertainty of design variables. Two examples are presented to demonstrate the feasibility and efficiency of the proposed method. [DOI: 10.1115/1.4005442]
When the State of Charge, State of Health, and parameters of a Lithium-ion battery are estimated simultaneously, estimation accuracy is hard to be ensured due to uncertainties in the estimation process. A sequential a...
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When the State of Charge, State of Health, and parameters of a Lithium-ion battery are estimated simultaneously, estimation accuracy is hard to be ensured due to uncertainties in the estimation process. A sequential algorithm, which uses frequency-scale separation and estimates parameters/states sequentially by injecting currents with different frequencies, is proposed in this paper to improve estimation performance. Specifically, by incorporating a high-pass filter, the parameters can be independently characterized by injecting high-frequency and medium-frequency currents, respectively. Using the estimated parameters, battery capacity and State of Charge can then be estimated concurrently. Experimental results show that the estimation accuracy of the proposed sequential algorithm is much better than the concurrent algorithm where all parameters/states are estimated simultaneously, and the computational cost can also be reduced. Finally, experiments are conducted at different temperatures to verify the effectiveness of the proposed algorithm for varying battery capacities. (C) 2019 Elsevier Ltd. All rights reserved.
The existing methods for Chinese sentiment Labeling mainly relies on the artificial sentiment corpus, but a sentiment word in the corpus may not be sentiment words in different sentences. This paper proposes a new met...
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The existing methods for Chinese sentiment Labeling mainly relies on the artificial sentiment corpus, but a sentiment word in the corpus may not be sentiment words in different sentences. This paper proposes a new method to label the words in the sentences by combining deep convolution neural network with sequential algorithm., We first extract the aspects comprised by words vectors, part of speech vectors, dependent syntax vectors to train the deep convolution neural network, and then employ the sequential algorithm to obtain the sentiment annotation of the sentence. Experimental results verify that our method is effective for sentiment labeling. Considering that the identification of the implicit aspects can improve the completeness of sentiment analysis, we suggest to construct the tuples including aspect, sentiment shifter, sentiment intensity, sentiment words after obtaining the sentiment labels for each word in the sentence. We develop new algorithm for implicit aspect identification by taking the two key factors of the aspects as a topic and the match degree of aspects and sentiment words, and the human language habit. The experiment demonstrates that the algorithm can effectively identify the implicit aspect. In this paper, we solve the problem of sentiment labeling and implicit aspect recognition in sentiment analysis. As a new tool for sentiment analysis, our method can be applied to the enterprise management information analysis, such as product online review, product online reputation, brand image and consumer preference management, and can also be used for the sentiment analysis of large-scale text data.
Support vector machines (SVMs), though accurate, are not preferred in applications requiring high classification speed or when deployed in systems of limited computational resources, due to the large number of support...
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Support vector machines (SVMs), though accurate, are not preferred in applications requiring high classification speed or when deployed in systems of limited computational resources, due to the large number of support vectors involved in the model. To overcome this problem we have devised a primal SVM method with the following properties: (1) it solves for the SVM representation without the need to invoke the representer theorem, (2) forward and backward selections are combined to approach the final globally optimal solution, and (3) a criterion is introduced for identification of support vectors leading to a much reduced support vector set. In addition to introducing this method the paper analyzes the complexity of the algorithm and presents test results on three public benchmark problems and a human activity recognition application. These applications demonstrate the effectiveness and efficiency of the proposed algorithm. (C) 2012 Published by Elsevier Ltd.
Background and aims With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence sharply increasing globally, there is an urgent need for non-invasive diagnostic tests to accurately scree...
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Background and aims With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence sharply increasing globally, there is an urgent need for non-invasive diagnostic tests to accurately screen high-risk MAFLD patients for liver inflammation and fibrosis. We aimed to develop a novel sequential algorithm based on N-terminal propeptide of type 3 collagen (PRO-C3) for disease risk stratification in patients with MAFLD. Methods A derivation and independent validation cohort of 327 and 142 patients with biopsy-confirmed MAFLD were studied. We compared the diagnostic performances of various non-invasive scores in different disease states, and a novel sequential algorithm was constructed by combining the best performing non-invasive scores. Results For patients with high-risk progressive steatohepatitis (i.e., steatohepatitis + NAFLD activity score >= 4 + F >= 2), the AUROC of FAST score was 0.801 (95% confidence interval (CI): 0.739-0.863), and the negative predictive value (NPV) was 0.951. For advanced fibrosis (>= F3) and cirrhosis (F4), the AUROCs of ADAPT and Agile 4 were 0.879 (95%CI 0.825-0.933) and 0.943 (95%CI 0.892-0.994), and the NPV were 0.972 and 0.992. sequential algorithm of ADAPT + Agile 4 combination was better than other combinations for risk stratification of patients with severe fibrosis (AUROC = 0.88), with similar results in the validation cohort. Meanwhile, in all subgroup analyses (stratifying by sex, age, diabetes, NAS, BMI and ALT), ADAPT + Agile 4 had a good diagnostic performance. Conclusions The new sequential algorithm reliably identifies liver inflammation and fibrosis in MAFLD, making it easier to exclude low-risk patients and recommending high-risk MAFLD patients for clinical trials and emerging pharmacotherapies.
In this paper, we propose a computationally efficient algorithm for solving mixed-integer sampled optimization problems involving a large number of constraints. The proposed algorithm has a sequential nature. Specific...
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In this paper, we propose a computationally efficient algorithm for solving mixed-integer sampled optimization problems involving a large number of constraints. The proposed algorithm has a sequential nature. Specifically, at each iteration of the algorithm, the feasibility of a candidate solution is verified for all the constraints involved in the sampled optimization problem and violating constraints are identified. As a second step, an optimization problem is formed whose constraint set involves the current basis the minimal set of constraints defining the current candidate solution and a limited number of the observed violating constraints. We prove that the algorithm converges to the optimal solution in finite time. Additionally, we establish the effectiveness of the proposed algorithm using mixed-integer linear, and quadratically constrained quadratic programming problems. Copyright (C) 2020 The Authors.
The translocation of a polymer belongs to a class of important bio-chemical processes. We propose a sequential algorithm designed to reduce the complexity involved in the dynamics of polymer molecular transport. The k...
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ISBN:
(纸本)9789881925244
The translocation of a polymer belongs to a class of important bio-chemical processes. We propose a sequential algorithm designed to reduce the complexity involved in the dynamics of polymer molecular transport. The key concept behind our algorithm is the sequentialization of a polymer movement between its consecutive conformations into a sequence of steps, a picture borrowed from the definition of an optimal strategy within the theory of games played sequentially. As an example we apply our algorithm to study, in two dimensions, the driven translocation of a polymer-like structure with the length of N monomers through a flat membrane containing two holes separated by the distance A. We study the statistics of the translocation time iota computed as the time consumed by the polymer to pass from one side of the membrane to another one as a function of N and A. The presence of two close lying holes frustrates the passing polymer and we observe that the average value of iota oscillates around the scaling function (N/Delta)(1.8t).
We suggest a sequential algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in algorithm for ECG analysis e...
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ISBN:
(纸本)9781424441198
We suggest a sequential algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in algorithm for ECG analysis embedded in the portable bio-signal sensing module is aimed to discriminate between shockable and non-shockable rhythms and its accuracy is analyzed. An algorithm for VF/VT detection is proposed to analyze every 1 s ECG episode using the past 8 s episodes. The method is tested with 844,587 ECG episodes from the widely accepted databases. A sensitivity of 86.8 % and a specificity of 99.4 % were obtained and compared with the previous results.
A new method is introduced for sequential estimation of TDOA (time delay of arrival) and FDOA (frequency delay of arrival) in a two sensor array. The proposed scheme is basically a two step algorithm utilizing 1-dimen...
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A new method is introduced for sequential estimation of TDOA (time delay of arrival) and FDOA (frequency delay of arrival) in a two sensor array. The proposed scheme is basically a two step algorithm utilizing 1-dimensional slice functions of the third order cumulants between two signal measurements, and is capable of suppressing the effect of correlated Gaussian measurement noises. It is demonstrated that the proposed algorithm outperforms existing TDOA/FDOA estimation algorithms from the viewpoint of computational burden and in the sense of mean squared error as well.
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