The independent hierarchy of train dispatching command and train operation control in the existing urban rail transit systems restricts the improvement of operational efficiency and emergency handling capability. This...
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The independent hierarchy of train dispatching command and train operation control in the existing urban rail transit systems restricts the improvement of operational efficiency and emergency handling capability. This article focuses on integrating train regulation and speed profile optimization by utilizing a feature learning and hybrid search algorithm. Specifically, a genetic algorithm (GA) is used to optimize the train speed profile for a fixed interval running time, and then, the generated labeled sample data are used to train a convolutional neural network (CNN) to learn and extract the features of the optimal speed profile. The nonlinear mapping relationship between input and output variables in trajectory optimization is characterized by a well-trained CNN to reduce the computation time of the optimal speed profile during train regulation. The input variables comprise line conditions and interval running times, while the output variables include the corresponding energy consumption and operating condition switching points of the optimal speed profile. An integrated model of train regulation and operation control is developed with the objective of minimizing total train delay time and energy consumption. To ensure convergence and global search capability, we design a hybrid search algorithm-based train regulation algorithm. Simulation experiments are conducted using data from the Beijing Yizhuang line to validate the effectiveness of the proposed model and algorithms. The experimental results demonstrate that the proposed method can provide an optimal scheme for train regulation and speed profiles.
In order to deploy multi-robot into a 2-D desired formation during the seeking process, this paper proposes a hybrid seeking algorithm with the formation deployment. Firstly, this paper expands the 1-D deployment algo...
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ISBN:
(纸本)9789881563972
In order to deploy multi-robot into a 2-D desired formation during the seeking process, this paper proposes a hybrid seeking algorithm with the formation deployment. Firstly, this paper expands the 1-D deployment algorithm to 2-D, and then combines with the extreme seeking algorithm to obtain the hybrid seeking algorithm. With the algorithm, multi-robot can deploy into a desired 2-D formation during seeking process. Simulation results are provided to illustrate the effectiveness of the algorithm.
Lindenmayer systems (L-systems) are a grammar system that consists of string rewriting rules. The rules replace every symbol in a string in parallel with a successor to produce the next string, and this procedure iter...
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Lindenmayer systems (L-systems) are a grammar system that consists of string rewriting rules. The rules replace every symbol in a string in parallel with a successor to produce the next string, and this procedure iterates. In a stochastic context free L-system (S0L-system), every symbol may have one or more rewriting rule, each with an associated probability of selection. Properly constructed rewriting rules have been found to be useful for modeling and simulating some natural and human engineered processes where each derived string describes a step in the simulation. Typically, processes are modeled by experts who meticulously construct the rules based on measurements or domain knowledge of the process. This paper presents an automated approach to finding stochastic L-systems, given a set of string sequences as input. The implemented tool is called the Plant Model Inference Tool for S0L-systems or PMIT-S0L. PMIT-S0L is evaluated using 960 procedurally generated S0L-systems in a test suite, which are each used to generate input strings, and PMIT-S0L is then used to infer the system from only the sequences. The evaluation shows that PMIT-S0L infers S0L-systems with up to 9 rewriting rules each in under 12 hours. Additionally, it is found that 3 sequences of strings are sufficient to find the correct original rewriting rules in 100% of the cases in the test suite, and 6 sequences of strings reduce the difference in the associated probabilities to approximately 1% or less.
In order to deploy multi-agents into a 2-D desired formation during the seeking process,this paper proposes a hybrid search algorithm with the formation ***,this paper expands the 1-D deployment algorithm to 2-D,and t...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In order to deploy multi-agents into a 2-D desired formation during the seeking process,this paper proposes a hybrid search algorithm with the formation ***,this paper expands the 1-D deployment algorithm to 2-D,and then combines with the extreme seeking algorithm to obtain the hybridsearch *** the algorithm,multi-agents can deploy into a desired 2-D formation during seeking *** results are provided to illustrate the effectiveness of the algorithm.
This paper proposes a new flux control strategy for consequent-pole brushless hybrid excitation (CPBHE) machines to achieve a high efficiency and a fast convergence. Based on an analysis of the mathematical model of a...
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This paper proposes a new flux control strategy for consequent-pole brushless hybrid excitation (CPBHE) machines to achieve a high efficiency and a fast convergence. Based on an analysis of the mathematical model of a CPBHE machine, a loss model of the CPBHE machine is derived, which demonstrates the feasibility of adopting d-axis flux to search for the optimal efficiency point. Furthermore, to improve the convergence speed of the online searchalgorithm and to make it suitable for the frequently varying operating conditions of electric vehicles (EVs), an adaptive step searchalgorithm based on the margin of the voltage limit combined with the offline table lookup method is proposed. Experimental results show that the proposed algorithm can optimize the efficiency of a motor system in the flux-weakening region and that it features a fast optimization process that makes it suitable for EV drives.
Over the past decades, variable selection for high-dimensional data has drawn increasing attention. With a large number of predictors, there rises a big challenge for model fitting and prediction. In this paper, we de...
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Over the past decades, variable selection for high-dimensional data has drawn increasing attention. With a large number of predictors, there rises a big challenge for model fitting and prediction. In this paper, we develop a new Bayesian method of best subset selection using a hybrid search algorithm that combines a deterministic local search and a stochastic global search. To reduce the computational cost of evaluating multiple candidate subsets for each update, we propose a novel strategy that enables us to calculate exact marginal likelihoods of all neighbor models simultaneously in a single computation. In addition, we establish model selection consistency for the proposed method in the high-dimensional setting in which the number of possible predictors can increase faster than the sample size. Simulation study and real data analysis are conducted to investigate the performance of the proposed method.
In the era of Big Data, variable selection with high-dimensional data has drawn increasing attention. With a large number of predictors, there rises a big challenge for model fitting and prediction. In this dissertati...
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In the era of Big Data, variable selection with high-dimensional data has drawn increasing attention. With a large number of predictors, there rises a big challenge for model fitting and prediction. In this dissertation, we propose three different yet interconnected methodologies, which include theory, computation, and real applications for various scenarios of regression analysis. The primary goal in this dissertation is to develop powerful Bayesian solutions to high-dimensional data challenges using a new variable selection strategy, called hybridsearch. To effectively reduce computation costs in high-dimensional data analysis, we propose novel computational strategies that can quickly evaluate a large number of marginal likelihoods simultaneously within a single computation. In Chapter 1, we discuss background and current challenges in high-dimensional variable selection. The motivation of our study is also justified. In Chapter 2, we introduce a new Bayesian method of best subset selection in the context of linear regression. The proposed method rapidly finds the best subset via a hybrid search algorithm that combines deterministic local search and stochastic global search. In Chapter 3, on the basis of the approach in Chapter 2, we extend it to a framework of multivariate linear regression model, which analyzes the relationship between multiple response variables and a common set of predictors. In Chapter 4, we propose a general Bayesian method to perform high-dimensional variable selection for various data types, such as binary, count, continuous and time-to-event(survival) data. Using Bayesian approximation techniques, we develop a general computing strategy that enables us to assess the marginal likelihoods of many candidate models within a single computation. In addition, to accelerate the convergence, we employ a hybrid search algorithm that can quickly explore the model spaces and accurately obtain the global maximum of marginal posterior probabilities.
Small reverse osmosis (RO) based desalination plants driven by wind and solar energies are attractive options to meet the requirements of autonomous areas. The complexity of these systems requires proper optimization ...
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Small reverse osmosis (RO) based desalination plants driven by wind and solar energies are attractive options to meet the requirements of autonomous areas. The complexity of these systems requires proper optimization of the solar and wind energy generation with storage needs. The main contribution of this study is a design optimization method of a hybrid reverse osmosis desalination plant powered by solar and wind energy. There are many investigations based on hybrid energy systems but the investigation of a useful model and effective methods are rarely found. Here, the possibility of three autonomous hybrid arrangements, namely, solar-wind-battery-RO desalination, solar-battery-RO desalination, and wind-battery-RO desalination, are investigated. Integer and continuous variables in the optimization model of the hybrid schemes for an autonomous region of Iran are considered. The main optimization function of minimizing the life cycle cost is used to evaluate different types of renewable energy systems for powering reverse osmosis desalination. Also, the probability of power interruption is used to measure the reliability of the hybrid schemes. For this aim, a new hybrid search algorithm is developed for simulating annealing and chaotic system. The results are compared with those from original chaotic search and simulated annealing algorithms. The simulation results show that the relative error in the hybrid scheme between the best performances of the hybrid search algorithm and the simulated annealing algorithm is 47.75%, and 4.08% for the hybrid search algorithm and chaotic searchalgorithm. In overall terms, the results demonstrate that the hybrid search algorithm provided the best performance between the original simulated annealing and the chaotic searchalgorithm. Additionally, the hybrid renewable energy system decreases system cost and increases system reliability for increasing fresh water availability and meeting the electricity load demands.
In order to deploy multi-robot into a 2-D desired formation during the seeking process, this paper proposes a hybrid seeking algorithm with the formation deployment. Firstly, a reaction-advection-diffusion equation is...
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ISBN:
(纸本)9781728101057
In order to deploy multi-robot into a 2-D desired formation during the seeking process, this paper proposes a hybrid seeking algorithm with the formation deployment. Firstly, a reaction-advection-diffusion equation is quoted to describe the movement of multiple mobile robots, 1-D formation deployment algorithm is introduced and expended to 2-D application. And then combines with the extreme seeking algorithm to obtain the hybrid seeking algorithm. Based on the improved extremum seeking algorithm and the 2-D formation deployment algorithm, a multi-robot hybrid seeking algorithm with formation deployment is proposed. To simplify other robots' following, a virtual trajectory of the leader robot is obtained according to its searching features. The robots' behavior is decomposed into searching behavior and formation deployment behavior. The condition and the method of changing these behaviors' weights online are provided. With the algorithm, multi-robot can deploy into a desired 2-D formation during seeking process. Simulation results with different initial positions and different platforms are analyzed to verify the convergence of the multi-robot's hybrid seeking algorithm, and the convergence is independent of the multi-robot's initial positions.
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