Sudden cardiac arrest, frequently triggered by cardiac fibrillation, remains a significant global health concern. Defibrillation is a critical medical procedure that relies on precise electrical pulses to restore norm...
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ISBN:
(数字)9798331532970
ISBN:
(纸本)9798331532987
Sudden cardiac arrest, frequently triggered by cardiac fibrillation, remains a significant global health concern. Defibrillation is a critical medical procedure that relies on precise electrical pulses to restore normal heart rhythm. To optimize pulse parameters, such as peak voltage and duration, we employ the Differential Evolution algorithm on a monophasic waveform. By utilizing the error between circuit simulation and targeted defibrillation parameters as an objective function, we aim to determine the optimal inductance and capacitance values of the defibrillator circuit. This approach enables the delivery of precisely tailored electrical shocks, potentially improving defibrillation success rates, reducing patient risk, and ultimately, contributing to advancements in cardiac care.
This paper proposes a population-based metaheuristic optimization algorithm, i.e., an adaptive class topper optimization (ACTO), for solving cubic fuel cost objective functions economic load dispatch with equality and...
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ISBN:
(数字)9798331520090
ISBN:
(纸本)9798331520106
This paper proposes a population-based metaheuristic optimization algorithm, i.e., an adaptive class topper optimization (ACTO), for solving cubic fuel cost objective functions economic load dispatch with equality and inequality constraints. The working principle of the proposed methodology is based on the learning improvement of students in a class for knowledge updating. In general, the use of polynomials function to represent generator fuel cost curves in real-time economic dispatch is an industry standard, and it significantly impacts the accuracy of the economic load dispatch solution. Many existing conventional methods struggle to deal with cubic fuel cost functions that reflect the non-linearity of the real generator response. Therefore, the proposed optimization technique is particularly efficient in solving non-linear higher-order objective functions for both smaller and larger generating units and it significantly decreases the computational time to obtain optimal fuel cost. The proposed algorithm has been successfully tested on the following dispatch case studies: 3 units, 26 units cubic fuel cost function without losses and 3 units cubic system with losses for different loading conditions. The results reveal that the adaptive class topper optimization algorithm outperform other existing methodology when addressing the higher-order generator cost functions.
This paper presents the study of a pair of primal-dual fuzzy linear programming and establishes duality results. It is based on parabolic concave membership functions. Choice of parabolic concave membership functions ...
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This paper presents the study of a pair of primal-dual fuzzy linear programming and establishes duality results. It is based on parabolic concave membership functions. Choice of parabolic concave membership functions makes it unique and leads to the nonlinear programming. Duality results have been established using the aspiration level approach. A numerical example is also taken to demonstrate the approach and verification of results.
We consider the problem of computing bounds for causal queries on causal graphs with unobserved confounders and discrete valued observed variables, where identifiability does not hold. Existing non-parametric approach...
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We consider the problem of computing bounds for causal queries on causal graphs with unobserved confounders and discrete valued observed variables, where identifiability does not hold. Existing non-parametric approaches for computing such bounds use linear programming (LP) formulations that quickly become intractable for existing solvers because the size of the LP grows exponentially in the number of edges in the causal graph. We show that this LP can be significantly pruned, allowing us to compute bounds for significantly larger causal inference problems compared to existing techniques. This pruning procedure allows us to compute bounds in closed form for a special class of problems, including a well-studied family of problems where multiple confounded treatments influence an outcome. We extend our pruning methodology to fractional LPs which compute bounds for causal queries which incorporate additional observations about the unit. We show that our methods provide significant runtime improvement compared to benchmarks in experiments and extend our results to the finite data setting. For causal inference without additional observations, we propose an efficient greedy heuristic that produces high quality bounds, and scales to problems that are several orders of magnitude larger than those for which the pruned LP can be solved.
To optimize objectives that require extensive simulations, engineers need a powerful algorithm that can run in parallel to minimize wall-clock time. Evolutionary algorithms excel in both regards: they are powerful opt...
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ISBN:
(数字)9798331508272
ISBN:
(纸本)9798331508289
To optimize objectives that require extensive simulations, engineers need a powerful algorithm that can run in parallel to minimize wall-clock time. Evolutionary algorithms excel in both regards: they are powerful optimizers that can be implemented in parallel by assigning one processor to each population member. This paper, however, addresses the question: How can we minimize a simulation's wall-clock time if we have many more processors than there are population members? Our solution is the multi-child differential evolution (MCDE) algorithm in which each parent competes against more than one child. We benchmarked the scaling performance of the MCDE version of classic DE with a test bed of ten functions. We also compared MCDE to a parallel CMA-ES variant with three of our test bed's functions. Results from both experiments show that MCDE achieves a significant speedup that scales well with the number of processors.
In the era of big data, the scale and complexity of data are ever increasing, traditional data dimensionality reduction methods can no longer meet the requirements of data users. In this context, manifold learning has...
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ISBN:
(数字)9798331529482
ISBN:
(纸本)9798331529499
In the era of big data, the scale and complexity of data are ever increasing, traditional data dimensionality reduction methods can no longer meet the requirements of data users. In this context, manifold learning has now become an important research direction, which mainly achieves dimensionality reduction and mapping of nonlinear structural data in high-dimensional space to low-dimensional space, reduces data noise, and improves low-dimensional data representation ability. Locally linear embedding (LLE) is an important algorithm in manifold learning that projects data from high-dimensional space onto low-dimensional space while maintaining the linear relationship between data. However, the optimization objective function of LLE uses L2 norm measurement to approximate the error, which can easily amplify or reduce the error and cause oscillation in the error measurement. Therefore, in this paper, L1 norm instead of L2 norm is used to overcome this drawback, but introduces a new problem of the objective function becoming a non-smooth function. To solve this problem, in this paper, a derivative-free optimization method, which is also a swarm intelligence method called scrambled Halton sequence initialized frilled lizard optimization has been proposed, and via numerical experiment, its reliability and effectiveness has been verified.
In the context of industrial upgrading, the exponential growth of computational demands for big data processing has emerged as a critical challenge. Cloud computing, with its evolving technological capabilities, prese...
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ISBN:
(数字)9798331536169
ISBN:
(纸本)9798331536176
In the context of industrial upgrading, the exponential growth of computational demands for big data processing has emerged as a critical challenge. Cloud computing, with its evolving technological capabilities, presents a viable solution to address these escalating computational requirements. This study focuses on optimizing resource allocation efficiency through the establishment of a multiobjective optimization framework for cloud computing systems. Specifically, we formulate mathematical representations of objective functions and constraints while detailing corresponding encoding/decoding methodologies. The research employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to resolve task scheduling dilemmas in cloud environments, simultaneously optimizing makespan and total computational time through innovative applications of nondominated sorting and congestion distance comparison. A series of multi-scale comparative experiments demonstrate the algorithm's superior performance over conventional scheduling approaches.
Convolutional sparse representation (CSR) extends the standard form by incorporating convolutional operations and has achieved notable success in image processing applications, offering a shift-invariant representatio...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Convolutional sparse representation (CSR) extends the standard form by incorporating convolutional operations and has achieved notable success in image processing applications, offering a shift-invariant representation model. Recent studies typically redesign the initial model by exploiting the convolution-multiplication property of the discrete Fourier transform to solve the involved convex optimizations efficiently, which imposes periodic boundary handling and risks introducing boundary artifacts. This paper generalizes this approach by leveraging 46 × 46 convolution-multiplication properties while requiring only extra element-wise operations, enabling model designs that combine periodic, antiperiodic, and 40 symmetric boundary handlings for each dimension and across convolutions. These newly accepted models demonstrated better representation performance than the conventional model in our experiments, indicating the benefits of selecting appropriate boundary handlings.
In this paper, we present a Situational Awareness based Resource Allocation (SA-RA) strategy for multi-target tracking (MTT) in distributed radar networks, where both the resource utilization and overall MTT accuracy ...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
In this paper, we present a Situational Awareness based Resource Allocation (SA-RA) strategy for multi-target tracking (MTT) in distributed radar networks, where both the resource utilization and overall MTT accuracy can be improved. The fusion rule with probabilistic data association (PDA) is used to associate cumulative data with measurement data from radar nodes. We also further derive the Bayesian Cramér-Rao Lower Bound (BCRLB) under the PDA fusion rule. For the proposed SA-RA strategy, the targets behavior is analyzed to determine their importance weights, expected tracking accuracy, and the allocated beams for each target. Utilizing the PDA-BCRLB and the importance weights, the objective function of the SA-RA strategy is formulated as a weighted sum of target utility functions. By addressing this objective function, the optimal transmission power is determined. Simulation results verify the superiority both in terms of tracking performance and resource allocation.
The purpose of this article is to introduce two indicators designed to evaluate and consequently influence the indirect emissions related to the operation of an EV. The first indicator, denoted as “Electric Vehicle C...
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The purpose of this article is to introduce two indicators designed to evaluate and consequently influence the indirect emissions related to the operation of an EV. The first indicator, denoted as “Electric Vehicle CarbonFlex Potential” indicator, evaluates an EV's maximum and minimum achievable carbon emissions using an optimization approach and compares the user's resultant indirect carbon emissions to these boundaries, therefore, this indicator compares the users behavior to the optimal best and worst cases. The second indicator is “EcoCharge Time” indicator, which provides feedback to an EV user based on their charging behavior on the best and worst times of charging the vehicle in a day. Since human behavior cannot be controlled, such indicators are essential tools for influencing the behavior of EV users toward a desired optimal, in this case, a charging schedule with the lowest possible overall indirect emissions. The proposed indicators were tested on an EV dataset using the carbon intensity data from a number of countries and the results show that there exists considerable flexibility potential. Additionally, the results also showed the best charging times, which were typically clustered around, allowing for ease of use and understanding.
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