In order to overcome the energy hole problem and long data gathering latency problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with multiple mobile sink nodes for wireless sensor network...
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ISBN:
(纸本)9783662469811;9783662469804
In order to overcome the energy hole problem and long data gathering latency problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with multiple mobile sink nodes for wireless sensor networks (LOA_MMSN) is proposed. LOA_MMSN analyzes the constraints, establishes network optimization model, and decomposes the model into movement path selection model and lifetime optimization model with known movement paths. Finally, the two models are solved. Simulation results show that LOA_MMSN can extend the network lifetime, balance node energy consumption and reduce data gathering latency. Under certain conditions, it outper forms Ratio_w, TPGF and lifetime optimization algorithm with single mobile sink node for WSNs.
Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles' diversities and avoid the premature convergen...
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ISBN:
(纸本)9783037851371
Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles' diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.
The vibration method is usually used for field measurement of cable tension of cable system bridges. The cable tension evaluation method is mostly based on the simple taut string theory. However, the simple theory may...
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ISBN:
(纸本)9783037857755
The vibration method is usually used for field measurement of cable tension of cable system bridges. The cable tension evaluation method is mostly based on the simple taut string theory. However, the simple theory may cause unacceptable errors in many applications especially for the cables with big bending stiffness and two ends fixed boundary conditions. In this paper a cable tension estimation method based on iterative algorithm and optimization algorithm is presented and implemented using finite element method and ANSYS soft ware. Compared with the analytical method and empirical formulas the method presented in this paper is more convenient and the application range is more extensive. The accuracy of the method has been verified by a set of test. In the end, the method is used to estimate the cable tension of a tied-arch bridge's suspenders.
The dynamic characteristics of bridge structures, such as the natural frequencies, mode shapes and model damping ratio, are the basis of structural dynamic computation, seismic analysis, vibration control and structur...
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The dynamic characteristics of bridge structures, such as the natural frequencies, mode shapes and model damping ratio, are the basis of structural dynamic computation, seismic analysis, vibration control and structural health condition monitoring. In this paper, a three-dimensional finite-element model is established for a highway bridge over a railway on No.312 National Highway and the ambient test is carried out in site, the dynamic characteristics of the bridge are studied using the finite-element analysis and ambient vibration measurements. Comparison between the theoretical and experimental results shows that the frequency differences of the modes range between 0.44% and 8.77%. If the measurement is more reliable, the finite element model updating is necessary. Thus, a set of design variables is selected based on sensitivity analysis, then the finite element model of the bridge is updated based on optimization algorithm. The results of model updating show that the proposed updating method in this paper is more simple and effective, the updated finite element model can reflect the dynamic characteristics of the bridge better, the analytical results can provide the theoretical basis for damage identification and health condition monitoring of the bridge.
Image segmentation is an important step in image processing. Segmentation based on threshold is a common method. Searching the suitable threshold vector is essentially an optimization problem, especially for color ima...
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ISBN:
(纸本)9781728186160
Image segmentation is an important step in image processing. Segmentation based on threshold is a common method. Searching the suitable threshold vector is essentially an optimization problem, especially for color image which have higher dimensions and complexity. This paper proposes a merged Biogeography-Based optimization algorithm (MBBO) for color image segmentation based on threshold. We merge a mutation operation into the migration operator of BBO to enhance the global search ability. Then we merge a chemotaxis operation into the mutation operator of BBO to enhance the local search ability. A greedy selection method is also used to further improve the performance and reduce computation complexity. Experimental results show that MBBO obtains better optimization performance, stronger stability and faster running speed compared with other existing algorithms.
Deep learning techniques have emerged in denoising low-dose computed tomography (CT) images to avoid the potential health risks of high ionizing radiation dose on patients. Although these post-processing methods displ...
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ISBN:
(数字)9781728127828
ISBN:
(纸本)9781728127828
Deep learning techniques have emerged in denoising low-dose computed tomography (CT) images to avoid the potential health risks of high ionizing radiation dose on patients. Although these post-processing methods display high quality denoised images, the denoising performance still has the potential to improve. The primary purpose of this work was to determine and analyze the most effective and efficient hybrid loss function in deep learning (DL)-based denoising network. Objective functions in deep learning algorithms are the main keys for optimizing the parameters of a network and can affect the quality of the denoised image significantly. Hence, this work examined the various combinations of the most common objective functions in CT denoising networks, namely L1 loss, per-pixel loss, perceptual loss, and structural dissimilarity loss. Further, a hyperparameter learning algorithm was also introduced to find the best scalable factors of the loss functions in each hybrid loss function combination. For simplicity, RED-CNN was used in this study to easily demonstrate the performance of the losses during the denoising process. Based on this experiment, the balance between these loss function via the gradient-based optimization algorithm could help in the generalizability prediction of designing future CT denoising networks.
In CITIS (Contractor Integrated Technical Information Service) collaborative manufacturing project, how the prime contractor reasonably chooses cooperative enterprises and assigns tasks among them will directly effect...
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ISBN:
(纸本)9780769535197
In CITIS (Contractor Integrated Technical Information Service) collaborative manufacturing project, how the prime contractor reasonably chooses cooperative enterprises and assigns tasks among them will directly effects on gaining benefit from the project. To solve this problem, in this paper, a new heuristic optimization algorithm is proposed for tasks assignment, which objectives are minimizing costs and reducing the number of enterprises selected based on the minimization of project time. Finally, the algorithm is varied through an example, and the evaluation for it is given.
In this paper we develop an efficient optimization algorithm for solving canonical correlation analysis (CCA) with complex structured-sparsity-inducing penalties, including overlapping-group-lasso penalty and network-...
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In this paper we develop an efficient optimization algorithm for solving canonical correlation analysis (CCA) with complex structured-sparsity-inducing penalties, including overlapping-group-lasso penalty and network-based fusion penalty. We apply the proposed algorithm to an important genome-wide association study problem, eQTL mapping. We show that, with the efficient optimization algorithm, one can easily incorporate rich structural information among genes into the sparse CCA framework, which improves the interpretability of the results obtained. Our optimization algorithm is based on a general excessive gap optimization framework and can scale up to millions of variables. We demonstrate the effectiveness of our algorithm on both simulated and real eQTL datasets.
Based on the standard particle swarm optimization an improved PSO algorithm was introduced in this paper. The particle swarm optimization algorithm with prior low precision, divergent character and slow late convergen...
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ISBN:
(纸本)9783037854471
Based on the standard particle swarm optimization an improved PSO algorithm was introduced in this paper. The particle swarm optimization algorithm with prior low precision, divergent character and slow late convergence is improved by joining the negative gradient. By adding negative term on standard PSO formula, combining with coefficient of negative gradient term and inertia weight omega, lead to effectively balance between the local and global search ability. It will accelerate convergence and avoid local optimum. Moreover, from the bionic point of view, this improved PSO algorithm is closer to the reality of the actual situation of the bird flocking. From the simulation results of four typical test functions, it can be seen that an improved particle swarm optimization with negative gradient can significantly improve the solving speed and solution quality.
This paper attempts to find an optimization algorithm to reduce number of iterations required for a node to be searched in a MultiCast Tee network. After effectively searching the node, it applies this technique in cl...
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ISBN:
(纸本)9781479976782
This paper attempts to find an optimization algorithm to reduce number of iterations required for a node to be searched in a MultiCast Tee network. After effectively searching the node, it applies this technique in cloud computing, more exactly, cloud information security so that data is safely transferred from one user to another, without any hindrance. The basic idea to incorporate the concept of MultiCast Tree is because it always supports reliable transmission of information, if by any chance, there is a link failure, it does not reduce the availability of resources, nor does it increase any delay in transmission of information, and at the same time minimizes work load. Thus, we can effectively use it in cloud computing, which is nothing but provision of resources to a pool of users through some kind of an interface, for example, Internet. While monitoring the distributed foundation establishment of cloud computing, it becomes extremely important to take into consideration the security of the information provided by users. If we use a vast number of nodes or links, it becomes practically impossible to trace the reliability of transmission, and which might result in important information getting lost. Hence to avoid such a consequence, it becomes necessary to optimize the number of these links, so that it becomes easier to search them in a minimum amount of time. Our main attempt has been to optimize these links and effectively use it in a communication network which has characteristics of a MultiCast Tree incorporated in it. This not only helps in efficient use of cloud computing system, but also ensures users that their data is in safe hands.
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