Cross-entropy method is base on probability density function. It is robust, easy to use. With analysis of advantages and disadvantages of the cross-entropy method, a directed quantile method based on crossentropy is...
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ISBN:
(纸本)9781424438631;9781424438624
Cross-entropy method is base on probability density function. It is robust, easy to use. With analysis of advantages and disadvantages of the cross-entropy method, a directed quantile method based on crossentropy is proposed. The main idea of the directed quantile cross-entropy method is to select alterable quantity vectors using for producing a "better" sample in the next iteration. The convergence speed and search best result of the directed quantile cross-entropy are tested using 0/1 knapsack problems. The experiments show that the search efficiency of the modified cross-entropy method is more significantly improved than quantum-inspired evolutionary algorithm and cross-entropy method.
The paper proposes a model for the thermal network of the Savona Campus Smart Polygeneration Microgrid (SPM) in terms of an equivalent electric circuit. Such model allows to represent in a simple but accurate way the ...
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The paper proposes a model for the thermal network of the Savona Campus Smart Polygeneration Microgrid (SPM) in terms of an equivalent electric circuit. Such model allows to represent in a simple but accurate way the behavior of the SPM thermal network in order to insert it into the Energy Management System (EMS) that is presently running in the SPM control room. The parameters of the thermal circuit are identified by means of an algorithm that minimizes the difference between the temperature profile as calculated with the circuit and the measured one.
Receding horizon control is a kind of optimal feedback control, in which the control performance over a finite future is optimized. The control of fluid dynamics is a challenging problem that arises in many fields. Th...
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ISBN:
(纸本)9781467322591
Receding horizon control is a kind of optimal feedback control, in which the control performance over a finite future is optimized. The control of fluid dynamics is a challenging problem that arises in many fields. The objective of this study is to provide a novel framework of designing a receding horizon controller for thermal fluid systems. The method proposed here is advantageous for its applicability to a wide class of optimization problem of thermal fluid systems. The effectiveness of the proposed method is verified by numerical simulation.
The transportation of oil and gas relies heavily on pipelines, and pipeline corrosion is a major factor affecting pipeline reliability. It can lead to pipeline failure and other damage. Pipeline corrosion prediction i...
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The transportation of oil and gas relies heavily on pipelines, and pipeline corrosion is a major factor affecting pipeline reliability. It can lead to pipeline failure and other damage. Pipeline corrosion prediction is of great importance for pipeline integrity management and failure prevention. A physical law intervening RF(Random Forest)-PSO(Particle Swarm optimization)-BP(Back Propagation Neural Network) algorithm is proposed to predict pipeline corrosion rate. The DeWaard physical model is first fitted to the data, and the fitted physical model predicts pipeline corrosion to form a new feature, which is then combined with the features extracted by the RF algorithm to form a new feature that is used as an input metric for the data-driven model. Secondly, the already constructed features are divided into training set and testing set. The training set is used to train the PSO-BP model, and the test set is used to test the accuracy of the model. The model is evaluated using the metrics such as MAE, MBE, MAPE, and R 2 . To show the superiority of the proposed model, the proposed model is compared with other models. The results show that the proposed model has some advantages in both feature analysis and corrosion prediction, and it has some theoretical guidance for pipeline protection.
Task assignment, the core problem of Spatial Crowdsourcing (SC), is often modeled as an optimization problem with multiple constraints, and the quality and efficiency of its solution determines how well the SC system ...
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Task assignment, the core problem of Spatial Crowdsourcing (SC), is often modeled as an optimization problem with multiple constraints, and the quality and efficiency of its solution determines how well the SC system works. Fairness is a critical aspect of task assignment that affects worker participation and satisfaction. Although the existing studies on SC have noticed the fairness problem, they mainly focus on fairness at the individual level rather than at the group level. However, differences among groups in certain attributes (e.g. race, gender, age) can easily lead to discrimination in task assignment, which triggers ethical issues and even deteriorates the quality of the SC system. Therefore, we study the problem of task assignment with group fairness for SC. According to the principle of fair budget allocation, we define a well-designed constraint that can be considered in the task assignment problem of SC systems, resulting in assignment with group fairness. We mainly consider the task assignment problem in a common One-to-One SC system (O2-SC), and our goal is to maximize the quality of the task assignment while satisfying group fairness and other constraints such as budget and spatial constraints. Specifically, we first give the formal definition of task assignment with group fairness constraint for O2-SC. Then, we prove that it is essentially an NP-hard combinatorial optimization problem. Next, we provide a novel fast algorithm with theoretical guarantees to solve it. Finally, we conduct extensive experiments using both synthetic and real datasets. The experimental results show that the proposed constraint can significantly improve the group fairness level of algorithms, even for a completely random algorithm. The results also show that our algorithm can efficiently and effectively complete the task assignment of SC systems while ensuring group fairness.
In regions characterized with great mining depths, complex topography, and intense geological activities, solely relying on lateral pressure coefficients or linear boundary conditions for predicting the in situ stress...
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In regions characterized with great mining depths, complex topography, and intense geological activities, solely relying on lateral pressure coefficients or linear boundary conditions for predicting the in situ stress field of rock bodies can induce substantial deviations and limitations. This study focuses on a typical karst area in Southwest Guizhou, China as its research background. It employs a hybrid approach integrating machine learning, numerical simulations, and field experiments to develop an optimization algorithm for nonlinear prediction of the complex three-dimensional (3D) in situ stress fields. Through collecting and fitting analysis of in situ stress measurement data from the karst region, the distributions of in situ stresses with depth were identified with nonlinear boundary conditions. A prediction model for in situ stress was then established based on artificial neural network (ANN) and genetic algorithm (GA) approach, validated in the typical karst landscape mine, Jinfeng Gold Mine. The results demonstrate that the model's predictions align well with actual measurements, showcasing consistency and regularity. Specifically, the error between the predicted and actual values of the maximum horizontal principal stress was the smallest, with an absolute error 0.01-3 MPa and a relative error of 0.04-15.31%. This model accurately and effectively predicts in situ stresses in complex geological areas.
We designed a broadband quarter wave plate in the visible range using a twisted nematic liquid crystal film sandwiched between two compensation films. The quarter wave plate exhibits much wider bandwidth than the comm...
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We designed a broadband quarter wave plate in the visible range using a twisted nematic liquid crystal film sandwiched between two compensation films. The quarter wave plate exhibits much wider bandwidth than the commercial product, which is composed of a half wave plate and a quarter wave plate.
ABSTRACTSports video classification (SVC) is now considered a challenging topic, therefore, developing an automatic sports scene classification technique has received tremendous interest. This research develops an eff...
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ABSTRACTSports video classification (SVC) is now considered a challenging topic, therefore, developing an automatic sports scene classification technique has received tremendous interest. This research develops an efficient key frame extraction method and hybrid Wavelet Convolution Neural Network (WCNN) framework with optimization scheme to classify sports videos. Initially, input videos are converted into number of frames, and keyframes are extracted using Enhanced threshold with Discrete Wavelet Transform (ETDWT) method. Then, Cross Guided Bilateral Filter (CGBF) method eliminates the noise from the keyframe. After that, segmentation process is performed by the Fuzzy Equilibrium Optimizer (FEO) algorithm, and then motions are detected using the Farneback optical flow (OF) method. Finally, classification process is performed using Hybrid Wavelet Convolutional Manta Ray Foraging optimization (HWCMRFO) algorithm to categorize different sports videos. The overall work is implemented using Python language. Simulation results proved that the proposed work achieved the highest accuracy (93.17%) compared to existing approaches.
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