Model-based experimental design is attracting increasing attention in chemical process engineering. Typically, an iterative procedure is pursued: an approximate model is devised, prescribed ...experiments are then performed and the resulting data is exploited to refine the model. To help to reduce the cost of trial-and-error approaches, strategies for model-based design of experiments suggest experimental points where the expected gain in information for the model is the largest. It requires the resolution of a large nonlinear, generally nonconvex, optimization problem, whose solution may greatly depend on the starting point. We present two discretization strategies that can assist the experimenter in setting the number of relevant experiments and performing an optimal selection, and we compare them against two pattern-based strategies that are independent of the problem. The validity of the approaches is demonstrated on an academic example and two test problems from chemical engineering including a vapor liquid equilibrium and reaction kinetics.
State Access for RSFQ Test and Analysis Roncken, Marly; Esimai, Ebelechukwu; Ramanathan, Vivek ...
IEEE transactions on applied superconductivity,
08/2023, Volume:
33, Issue:
5
Journal Article
Peer reviewed
We present means to initialize, to propagate, and to examine states in an RSFQ circuit that are useful for design as well as for functional test and analysis. Our RSFQ test strategy distinguishes ...states by the information they carry from computation to computation, and saves costs by ignoring information-free states. To start, stop, and stall operations that are asynchronous, we developed a new variety of RSFQ stateholder, called MrGO after its CMOS counterpart. We include two simulated examples, a clocked pipelined adder for which we test functionality, and an asynchronous ring FIFO for which we analyze throughput.
Interval type-2 fuzzy logic systems (IT2 FLSs) already become an emerging technology in recent years. As the most popular type-reduction (TR) algorithms, Karnik-Mendel (KM) algorithms own the ...advantage of maintaining the uncertainties flow in systems. This paper analyzes the initialization for KM types of algorithms. Furthermore, the weighting approaches of them are also given by means of the Newton-Cotes quadrature formulas. Importantly, the reasonable initialization weighted enhanced Karnik-Mendel (RIWEKM) algorithms are provided to complete the centroid type-reduction of IT2 FLSs. Three computer simulation experiments illustrate that, the proposed RIWEKM algorithms own both smaller absolute errors and faster convergence speeds in contrast to the EKM and RIEKM algorithms.
This paper investigates one type of distributed constrained optimization problem, e.g., the economic dispatch problem, in the presence of DoS attacks. Therein, multiple DoS attackers are ...collaborative to impede the communication transmission and change the communication topology at will. Consequently, the convergence and/or optimality of distributed algorithm may be compromised. To reduce the effect of this kind of DoS attacks, a distributed resilient initialization-free Jacobi descent algorithm is proposed. It is designed with three switched control protocols which enable the proposed algorithm reasonably employing the estimations to replace the missing information when attacks occur. Meanwhile, the proposed method is embedded with second order information, resulting in faster convergence speed. Moreover, theoretical analysis results are provided to show that the proposed algorithm can exponentially converge to the global optimal solution of the studied problem. Finally, simulation results tested in IEEE 30-bus system validate its effectiveness and flexibility. Note to Practitioners -The economic dispatch is a key issue in smart grid, which can be formulated as a kind of distributed constrained optimization problem. Since the distributed algorithms work under distributed sensor networks, they are easier to undergo DoS attacks. To address this issue, this paper presents a distributed resilient initialization-free Jacobi descent algorithm, which features strong robustness to resist DoS attacks and faster convergence. Meanwhile, the proposed method is shaped for common constrained optimization problem with better expansibility. We conduct the global convergence and optimality proofs, which benefits the practitioners to estimate the convergence performance, e.g., the convergence rate. Simulations further show the correctness and effectiveness of the proposed method. In future, we will pay more attention on the non-convex constrained optimization problem.
•A framework for LRV detection, segmentation in large CMR studies.•Automatic initialization of bi-ventricles for the first time frame without any user intervention.•Automatic End-diastole and ...End-systole phase assessments.•Supervised deep neural network employed to train and segment the heart.•3D-ASM image search procedure improved by combining image intensity models with derived distance maps for LV and RV edge localization.
The sheer volume of data generated by population imaging studies is unparalleled by current capabilities to extract objective and quantitative cardiac phenotypes; subjective and time-consuming manual image analysis remains the gold standard. Automated image analytics to compute quantitative imaging biomarkers of cardiac function are desperately needed. Data volumes and their variability pose a challenge to most state-of-the-art methods for endo- and epicardial contours, which lack robustness when applied to very large datasets. Our aim is to develop an analysis pipeline for the automatic quantification of cardiac function from cine magnetic resonance imaging data.
This work adopt 4,638 cardiac MRI cases coming from UK Biobank with ground truth available for left and RV contours. A hybrid and robust algorithm is proposed to improve the accuracy of automatic left and right ventricle segmentation by harnessing the localization accuracy of deep learning and the morphological accuracy of 3D-ASM (three-dimensional active shape models). The contributions of this paper are three-fold. First, a fully automatic method is proposed for left and right ventricle initialization and cardiac MRI segmentation by taking full advantage of spatiotemporal constraint. Second, a deeply supervised network is introduced to train and segment the heart. Third, the 3D-ASM image search procedure is improved by combining image intensity models with convolutional neural network (CNN) derived distance maps improving endo- and epicardial edge localization.
The proposed architecture outperformed the state of the art for cardiac MRI segmentation from UK Biobank. The statistics of RV landmarks detection errors for Triscuspid valve and RV apex are 4.17 mm and 5.58 mm separately. The overlap metric, mean contour distance, Hausdorff distance and cardiac functional parameters are calculated for the LV (Left Ventricle) and RV (Right Ventricle) contour segmentation. Bland–Altman analysis for clinical parameters shows that the results from our automated image analysis pipelines are in good agreement with results from expert manual analysis.
Our hybrid scheme combines deep learning and statistical shape modeling for automatic segmentation of the LV/RV from cardiac MRI datasets is effective and robust and can compute cardiac functional indexes from population imaging.
•A chaos-based method, called CVABC, is proposed for numerical function optimization.•CVABC uses logistic maps to generate initial individuals which are fully diversified.•A novel search strategy is ...used to improve exploitation and exploitation properties.•CVABC has higher convergence speed and better search ability than other methods.
Artificial Bee Colony (ABC) is an effective swarm optimization method featured with higher global search ability, less control parameters and easier implementation compared to other population-based optimization methods. Although ABC works well at exploration, its main drawback is poor exploitation affecting the convergence speed in some cases. In this paper, an efficient ABC-based optimization method is proposed to deal with high dimensional optimization tasks. The proposed method performs two modifications to the original ABC in order to improve its performance. First, it employs a chaos system to generate initial individuals, which are fully diversified in the search space. A chaos-based search method is used to find new solutions during ABC search process to enhance the exploitation capability of the algorithm and avoid premature convergence. Second, it incorporates a new search mechanism to improve the exploration ability of ABC. Experimental results performed on benchmark functions reveals superiority of the proposed method over state-of-the-art methods.
Artificial bee colony (ABC) algorithm has already shown more effective than other population-based algorithms. However, ABC is good at exploration but poor at exploitation, which results in an issue ...on convergence performance in some cases. To improve the convergence performance of ABC, an efficient and robust artificial bee colony (ERABC) algorithm is proposed. In ERABC, a combinatorial solution search equation is introduced to accelerate the search process. And in order to avoid being trapped in local minima, chaotic search technique is employed on scout bee phase. Meanwhile, to reach a kind of sustainable evolutionary ability, reverse selection based on roulette wheel is applied to keep the population diversity. In addition, to enhance the global convergence, chaotic initialization is used to produce initial population. Finally, experimental results tested on 23 benchmark functions show that ERABC has a very good performance when compared with two ABC-based algorithms.
Bolt assembly by robots is a vital and difficult task for replacing astronauts in extra-vehicular activities (EVA), but the trajectory efficiency still needs to be improved during the wrench ...insertion into hex hole of bolt. In this paper, a policy iteration method based on reinforcement learning (RL) is proposed, by which the problem of trajectory efficiency improvement is constructed as an issue of RL-based objective optimization. Firstly, the projection relation between raw data and state-action space is established, and then a policy iteration initialization method is designed based on the projection to provide the initialization policy for iteration. Policy iteration based on the protective policy is applied to continuously evaluating and optimizing the action-value function of all state-action pairs till the convergence is obtained. To verify the feasibility and effectiveness of the proposed method, a noncontact demonstration experiment with human supervision is performed. Experimental results show that the initialization policy and the generated policy can be obtained by the policy iteration method in a limited number of demonstrations. A comparison between the experiments with two different assembly tolerances shows that the convergent generated policy possesses higher trajectory efficiency than the conservative one. In addition, this method can ensure safety during the training process and improve utilization efficiency of demonstration data.
This study focuses on improving the vortex of Tropical Cyclone (TC) using different initialization techniques and its subsequent impact on the performance of Advanced Research Weather Research and ...Forecasting (ARW) mesoscale model. The Initialization of TC vortex from global analyses (CNTL) is found to be poor due to coarse horizontal and vertical structure, and is improved by assimilating available observations using 3DVAR technique (3DV). In another experiment (VAR_VI), vortex is corrected using vortex initialization and relocation procedures to correspond with India Meteorological Department (IMD) ‘observed’ position and intensity estimates, and then assimilation of additional observations is undertaken.
The initial vortex improvement in terms of horizontal and vertical structure is noted in both experiments with the VAR_VI results being better than for the 3DV experiment. Simulations following the VAR_VI experiment showed remarkable improvement in simulating track, intensity and structure of the two TCs it was evaluated for (TC Giri and TC Jal). The large scale dynamical and thermo-dynamical fields such as steering flow, vertical wind shear and warm core structure were also improved. For TC Giri, the VAR_VI could reproduce the observed intensification rate of 10 ms−1 within 12 h, while for TC Jal, the VAR_VI run could simulate the inland rainfall due to sheared convective clouds. Results suggested that the improvements would likely to be more for stronger TCs. This study highlights the continued need and value of improvement of TC vortex using initialization and in-situ and remote sensing observations over the Bay of Bengal region.
•TC size matters for realistic track and intensity predictions•Vortex initialization and relocation improves structure and strength of initial TC vortex.•3DVAR data assimilation improved TC environment.•Intensity bias is reduced with vortex initialization•Coastal and inland rainfall is significantly improved.
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