Phillip Yen (197371), Stacey D. Finley (197380), Marianne O. Engel-Stefanini (197388) et al. · Figshare · 2015
<p>Model parameters were optimized computationally to fit simulation results (solid lines) to experimental data (open circles) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027514#pone.0027514-Rudge1" target="_blank">[16]</a> for the concentration profiles of the unbound VEGF Trap and the VEGF/VEGF Trap complex. VEGF Trap was injected intravenously (into the blood compartment) at (A) 1 mg/kg, (B) 2.5 mg/kg, (C) 10 mg/kg, and (D) 25 mg/kg.</p>
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Hui Gan, Yingjie Zheng · 2016
Abstract A multi-body dynamics calculation model for air suspension guide mechanism of car is established, which analyses the motion model and its application of guide mechanism on car double wishbone independent suspension by using the basic theory of rigid body motion, and then carries out kinematics analysis to make the design work rational, simple and accurate. The establishment and optimized analysis of the structure parametric design model, which is used for the objective function of minimum variation of the caster angle, kingpin inclination angle, wheel camber, wheel toe-in angle and wheel tread, gets the reasonable and optimization design scheme of structure parameters for air suspension guide mechanism, and provides a theoretical basis for further improvement of this automobile suspension system. 1 Spatial motion analysis model Figure 1 is a diagram of automobile double wishbone suspension, figure 2 is a diagram for analyzing the space movement. Figure 3 is a directed graph of the double wishbone suspension system using R-W method. In order to analysis motion better, the closed loop system of hinge h4, h5 and h6 are supposed to be omitted，so the simplified system of the original system is shown in the directed graph 4. 0B- car body; 1B- steering knuckle; 2B – upper pitman; 3B- lower pitman; 4B- tie rod; 1h. 3h – pivot shaft; 2h- the upper ball pin of king pin; 4h- the lower ball pin of king pin; 5h- the ball joint pin on break-point for steering; 6h-the ball joint pin of steering knuckle arm Fig.1 The simplified model diagram of double wishbone suspension on automobile For 0B, 1B, 2B, 3B, the expressions of radius vector of the rigid body displacement for the suspension system are as followings: Among equations (1):
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Cándida Ferreira · Studies in computational intelligence · 2006
Cited by 5
Kyuin Hwang (409161), Jeongsu Oh (409162), Tae-Kyung Kim (248173) et al. · Figshare · 2015
<p>We simulated three variable regions of the 454–HMP dataset (V1–3, V3–5, and V6–9) to determine optimal sizes of <i>k</i>-mer and random sample. (A) From each region, 10 K reads were randomly sampled 10 times and NW distances calculated. <i>k</i>-mer distances between the pairs of the 10 K reads were calculated for each of <i>k</i>-mer sizes ranging from 3 to 15. The extent of linear regression between the two distance variables was plotted using the mean (upper <i>x</i> axis) and standard deviation (lower <i>x</i> axis) of the square of the Person product-moment correlation coefficients (R-Square) at every point of the <i>k</i>-mer sizes. (B) For individual 16S regions, we randomly sampled sequences of 100 to 2,000 reads in increments of 100 reads. The <i>k</i>-mer and NW distances were calculated for all possible sequence pairs at every point of the random sample sizes. The random sampling followed by the distance calculation was repeated 10 times. For ten random samples per sample size, the mean (upper <i>x</i> axis) and standard deviation (lower <i>x</i> axis) of R-Square were calculated and displayed.</p>
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Interdisciplinary mathematical sciences · 2015
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Shalabh Bhatnagar, H. Prasad, L. A. Prashanth · Lecture notes in control and information sciences · 2012
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Kunihiko ICHIKAWA · Transactions of the Society of Instrument and Control Engineers · 1971
The parameter optimization problem is an optimization problem that specifies first the functional form of the control law and asks the m-parameter vector that minimizes the given criterion function. Since the formulation of a parameter optimization problem differs from that of an optimal control problem, the maximum principle cannot be applied. It may be noted that the parameter optimization problem includes the classical problem of optimal controller setting and the synthesis of an optimal control system whose characteristics are specified partially. Settlement of inaccessible control problem by feeding back the detectable output vector also amounts to solving a parameter optimization problem.It may be said that only some formulas through the analog computer study have been known under the name of optimal controller setting theory, and that no approach worth going with the modern control theory has yet been tried. The author presents the technique based on the gradient theory, noticing that this kind of problem is a nonlinear programming problem. The technique may be said to be modern in that it presupposes the use of a digital computer. Since the gradient vector for the linear-quadratic problem can be evaluated by solving algebraic equation exclusively, the method is especially suited for the computation with a digital computer. On the other hand, the general nonlinear problem requires integral operations m+1 times to evaluate the gradient vector: once for the differential equation of the controlled system and m times for the differential equation with respect to the first variation of the state vector. Consequently, a considerably large memory capacity is required along with a fairly long computational time. However, unlike the other approaches to the inaccessible control problems, the technique of the paper can deal with a quite general class of control problems. It should be added that the implementation of the optimal control law is quite simple and easy.
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André X. C. N. Valente (17126), Joseph Zischkau (301387), Joo Heon Shin (301388) et al. · Figshare · 2015
<p>The algorithm was applied to homogenize the Hamza et al. cohort. The algorithm parameters were selected based on the value of the potential p-value bias test (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048653#pone-0048653-t001" target="_blank">Table 1</a> and main text). Graph <b>a</b>) shows potential p-value bias versus % of cohort weight decrease. A 7% cohort weight decrease was selected, as weight decreases beyond this value produced only a marginal further decline in the potential p-value bias. <b>b</b>) The sphere weight parameter was set to 30. <b>c</b>) A 0.2 knocked-down weight was selected. In each of these graphs, the two parameters not represented are held at their selected values.</p>
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Shamsi Ebrahimi (10834172), Coswald Stephen Sipaut@ Mohd Nasri (10834175), Sazmal Effendi Bin Arshad (10834178) · Figshare · 2021
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2021
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Patrick D. McClanahan (2385076), Luca Golinelli (16726516), Tuan Anh Le (5426528) et al. · Figshare · 2023
<p><b>(A)</b> Mean IoU of manual and machine vision (computer) ROIs for a range of grayscale thresholds <i>t</i> and smoothing sigmas <i>σ</i>. All computer ROIs that overlapped the manual ROI were combined for this calculation. If no computer ROI overlapped a manual ROI, the IoU was zero. The highest values for intensity- and Mask R-CNN-based segmentations are labeled. <b>(B)</b> Mean number of gaps per segmentation. The number of gaps for each manual ROI was one minus the number of computer ROIs overlapping it. Only manual ROIs that overlapped with computer ROIs were included. The highest mean IoU for <i>t</i> and <i>σ</i> combinations resulting in zero gaps is shown for each segmentation method. <b>(C)</b> Mean centerline RMSD for centerlines calculated from computer ROIs versus manually-drawn centerlines. The best (lowest) RMSD was used in cases where multiple computer ROIs overlapped a manual ROI, and a manual ROI was excluded if no computer ROI overlapped with it. The <i>t</i> and <i>σ</i> combination resulting in the lowest centerline RMSD for each segmentation method is labeled with an "x". <b>(D)</b> Detection rate of manually drawn ROIs using the two machine vision methods and different combinations of <i>t</i> and <i>σ</i>. A manually-drawn ROI was considered detected if it overlapped with at least one computer-drawn ROI.</p> <p>(TIF)</p>
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Mathematics in Science and Engineering/Mathematics in science and engineering · 1970
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Henry L. Fu (436190), Jenna L. Mueller (423205), Melodi J. Whitley (847616) et al. · Figshare · 2016
<p>Plots that demonstrate the effect of three different MSER parameters on segmented area fraction for tumor, muscle, and adipose images (shown in previous figure). The parameter Delta shows the most impact on area fraction, while MaxVariation and MinDiversity have a minimal effect.</p>
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J. E. Dennis, P.T. Stol · Mathematics of Computation · 1976
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Dominique Van de Sompel (303197), Laura S. Sasportas (2587426), Jesse V. Jokerst (801981) et al. · Figshare · 2016
<p><b>Figure A, Image reconstructions with Wiener deconvolution for different values of <i>σ</i></b>. Note that the brain slice appears shrunken without signal deconvolution. This is because the deconvolution also corrects for a slight time delay between the true arrival of the pressure waves at the detector surface and the actual pressure signal recorded by the scanner. This delay was present in all scans equally, including the scan in which the scanner impulse response function <i>p</i><sub><i>d</i>0</sub>(<i>t</i>) was measured. Without the deconvolution’s correction for this time delay, the backprojected signals are slightly shifted and misaligned in space. This misalignment in turn results in the shrunken appearance of the reconstructed brain slice. The spatial location of the brain slice within the reconstructed volume is identical for all reconstructions shown. The image intensities of the reconstructions are normalized (black: 0, white: 1), and the dimensions of the MIP images are 20 × 20 mm. <b>Figure B, Image reconstructions with Tikhonov deconvolution for different values of <i>β</i></b>. Note that the brain slice appears shrunken without signal deconvolution. This is because the deconvolution also corrects for a slight time delay between the true arrival of the pressure waves at the detector surface and the actual pressure signal recorded by the scanner. This delay was present in all scans equally, including the scan in which the scanner impulse response function <i>p</i><sub><i>d</i>0</sub>(<i>t</i>) was measured. Without the deconvolution’s correction for this time delay, the backprojected signals are slightly shifted and misaligned in space. This misalignment in turn results in the shrunken appearance of the reconstructed brain slice. The spatial location of the brain slice within the reconstructed volume is identical for all reconstructions shown. The image intensities of the reconstructions are normalized (black: 0, white: 1), and the dimensions of the MIP images are 20 × 20 mm.</p> <p>(PDF)</p>
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Yinghua Shao (18934042), Rui Kang (562707), Jingwen Fu (12875300) et al. · Figshare · 2024
<div><p>The reactor coolant pump is a key equipment in a nuclear power plant. If the leakage exceeds a certain threshold, it may cause reactor overheating and shutdown. The reactor coolant pump leakage fault usually has two problems: corrosion and scaling. Accurately and efficiently diagnosing the leakage fault mode as early as possible and predicting its remaining useful life (RUL) are important for taking timely maintenance measures. In this paper, an integrated method is proposed. First, the cross-sectional area of the first seal is extracted as a fault indicator. The motivation is that corrosion may enlarge the cross-sectional area, and scaling may reduce the cross-sectional area. Based on the fluid mechanics theory, an integrated model with several uncertain parameters is established among the cross-sectional area, temperature, and leakage at the inlet and outlet of the first seal. In the diagnosing process, a modified change-detection method is proposed to detect the starting point of degradation. Then, the unknown parameters in the previous relation are estimated, and the degrading data before the starting point of degradation are used to diagnose the leakage fault mode. Second, a time-series model of the autoregressive integrated moving average (ARIMA) is established to predict the remaining useful life based on the degrading data after the starting point of degradation. Finally, the leakage degrading data from six reactor coolant pumps of a nuclear power plant is used to perform the leakage fault mode diagnosis and life prediction with degradation point detection error rates not exceeding 4%, fault mode diagnosis correction rates 100% and practical RUL predicting results, which proves that the proposed integrated method is accurate and efficient. The proposed integrated method combines the advantages of both the physical model diagnosis and the data-driven model diagnosis and innovatively make use of the quantity of flow from the output side of the primary pump as the monitoring indicator and the cross-sectional area as the characteristic index together to diagnose the leakage fault mode happened to the seal and predict its RUL, which can meet the needs of actual operation and maintenance to ensure a healthy and stable operation of the pump and prevent unexpected shutdowns of nuclear power plants and serious accidents.</p></div>
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Guomei Cui (20721578), Chuanjun Wang (14802747) · Figshare · 2025
<div> This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. It combines the advantages of Random Forest (RF) and Gradient Boosting Tree (GBT), and improves the accuracy and efficiency of the model in sprint pattern recognition by adaptively adjusting the hyperparameter and tree structure. Specifically, by introducing adaptive feature selection and ensemble learning methods, the decision tree algorithm effectively improves the recognition ability of the model for different athletes and sports states, thus reducing the over-fitting phenomenon and improving the generalization ability. In the process of model training, cross-validation and grid search optimization methods are adopted to ensure the reasonable selection of super parameters. Moreover, the superiority of the model is verified by comparing with the commonly used algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy rate on the test set is 94.9%, which is higher than that of SVM (87.0%) and CNN (92.0%). In addition, the optimized decision tree algorithm performs well in computational efficiency. However, the training data of this model comes from the simulation environment, which may deviate from the real game data. Future research can verify the generalization ability of the model through more actual data. </div>
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Sihua Peng (263825), Aqiang Wang (2628619), Yuyang Lian (11378698) et al. · Figshare · 2021
<p>The original potassium ferricyanide curve without GO (black; a), the potassium ferricyanide curve after the original GO parameters (parameters of -1.6 - +0.6V) (red; b) and the potassium ferricyanide curve after the optimized GO parameters (parameters of -1.7 - +0.2V)(blue; c).</p>
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Seung-Schik Yoo (398259), Hyungmin Kim (398260), Emmanuel Filandrianos (398261) et al. · Figshare · 2016
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Werner Van Geit · Encyclopedia of Computational Neuroscience · 2022
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