Urban rail transit has become an important way for people to travel. The traditional urban rail transit system has fixed infrastructure, relies on base stations for communication, and has poor network robustness. The ...
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ISBN:
(纸本)9781665454681
Urban rail transit has become an important way for people to travel. The traditional urban rail transit system has fixed infrastructure, relies on base stations for communication, and has poor network robustness. The ad-hoc network has developed rapidly in recent years due to its high stability. And it can be used in urban rail to improve the performance of communication networks. In this paper, a clustering algorithm based on urban rail in-vehicle ad-hoc networks is proposed. The algorithm includes cluster head selected strategy and low-delay queuing strategy. We introduce the network architecture and algorithm theory in detail, and verify the algorithm performance in terms of end-to-end delay and packet loss rate through simulation. As the result, the algorithm can effectively improve communication efficiency and reliability.
In fishery aquaculture, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality. To accurately grasp the trends of variation in ...
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In fishery aquaculture, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality. To accurately grasp the trends of variation in dissolved oxygen, a dissolved oxygen concentration forecasting model based on an enhanced clustering algorithm and Adam with a radial basis function neural network (ECA-Adam-RBFNN) is proposed. An enhanced clustering algorithm (ECA) combining K-means with ant colony optimization is introduced in place of random selection to determine the center positions of the neural network hidden layer units. If the number of center points is too high, the neural network will be overfit, whereas if it is too low, sudden changes will appear in the results. Once the hidden layer centers have been determined, the radial basis function (RBF) width is calculated from the maximum center distance and the number of center points to avoid the two extreme cases of RBF that are too peaked or flat. The recursive least squares (RLS) algorithm is introduced to obtain the connection weights from the hidden layer to the output layer. The Adam algorithm is introduced to iteratively differentiate the objective function to adjust the center values, weights and width while adaptively varying the learning rates for these three types of parameters. Finally, the improved forecasting algorithm is applied for the prediction of the dissolved oxygen concentration in fishery aquaculture. The experimental results show that under identical conditions, compared with a long short-term memory (LSTM) network, a backpropagation neural network (BPNN), a traditional RBF neural network, a support vector regression (SVR) model, an autoregressive integrated moving average (ARIMA), K-MLPNN (K-means muhilayer perceptron neural networks), and SC-K-means-RBF model, the improved algorithm achieves significant reductions in the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square
Compared to hesitant fuzzy sets and intuitionistic fuzzy sets, dual hesitant fuzzy sets can model problems in the real world more comprehensively. Dual hesitant fuzzy sets explicitly show a set of membership degrees a...
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Compared to hesitant fuzzy sets and intuitionistic fuzzy sets, dual hesitant fuzzy sets can model problems in the real world more comprehensively. Dual hesitant fuzzy sets explicitly show a set of membership degrees and a set of non-membership degrees, which also imply a set of important data: hesitant degrees. The traditional definition of distance between dual hesitant fuzzy sets only considers membership degree and non-membership degree, but hesitant degree should also be taken into account. To this end, using these three important data sets (membership degree, non-membership degree and hesitant degree), we first propose a variety of new distance measurements (the generalized normalized distance, generalized normalized Hausdorff distance and generalized normalized hybrid distance) for dual hesitant fuzzy sets in this paper, based on which the corresponding similarity measurements can be obtained. In these distance definitions, membership degree, non-membership-degree and hesitant degree are of equal importance. Second, we propose a clustering algorithm by using these distances in dual hesitant fuzzy information system. Finally, a numerical example is used to illustrate the performance and effectiveness of the clustering algorithm. Accordingly, the results of clustering in dual hesitant fuzzy information system are compared using the distance measurements mentioned in the paper, which verifies the utility and advantage of our proposed distances. Our work provides a new way to improve the performance of clustering algorithms in dual hesitant fuzzy information systems.
At present, the dairy brand loyalty evaluation model is not perfect, and the dairy brand loyalty measurement model for the consumer-oriented industry needs to be further studied. Through machine learning methods, onli...
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At present, the dairy brand loyalty evaluation model is not perfect, and the dairy brand loyalty measurement model for the consumer-oriented industry needs to be further studied. Through machine learning methods, online consumer brand product purchase behaviors are clustered to achieve clustering of users with similar loyalty and to measure online dairy brand loyalty. This study has the advantages of applying machine learning to processing online consumer big data, that is, it has advantages when processing high-dimensional data, when processing data in multiple ways, and when analyzing data with high complexity algorithms. The independent variables, dependent variables, and adjusted variables in the model are measured in the form of a Likert five-level scale. Moreover, this study combines with actual cases to make adjustments to the measurement of dairy brand loyalty and verifies the model performance through simulation experiments. The research results show that the validity of the scale structure is good, and the research model has certain practical effects.
Nowadays, the increasing complexity of the social environment brings much difficulty in group decision making. The more uncertainty exists in a decision-making problem, the more collective wisdom is needed. Therefore,...
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Nowadays, the increasing complexity of the social environment brings much difficulty in group decision making. The more uncertainty exists in a decision-making problem, the more collective wisdom is needed. Therefore, large scale group decision making has attracted a lot of researchers to investigate. Since the probabilistic linguistic terms have impressive performance in expressing DMs' opinions, this paper proposes a novel method for large scale group decision making with probabilistic linguistic preference relations. More specifically, (1) a probability k-means clustering algorithm is introduced to segment DMs with similar features into different sub-groups;(2) an integration method is proposed to construct the collective probabilistic preference relation that retains initial information to the most extent;(3) taking the personality of each DM into account, a consensus model is constructed to improve the rationality and efficiency of consensus reaching process. Several simulation experiments are designed to analyze the influence factor in the feedback mechanism and make some comparative analysis with the existing method. Finally, an illustrative example of contractor selection is conducted to verify the validity of the proposed method.
Real-time distributed clustering algorithm for aggregation of distributed energy storage systems into heterogeneous virtual power plants is proposed. Two types of virtual power plants are formed: one for provisioning ...
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Real-time distributed clustering algorithm for aggregation of distributed energy storage systems into heterogeneous virtual power plants is proposed. Two types of virtual power plants are formed: one for provisioning the bulk (low-frequency) power demand and one for provisioning the high-frequency power demand. The proposed distributed clustering algorithm determines the virtual power plants' memberships, for each distributed energy storage system, based on each energy storage system's capacity and owner's willingness to participate in one of the virtual power plants. The proposed distributed secondary level control system regulates each energy storage system according to each virtual power plant's operational objectives. Specifically, a balanced state of charge of all energy storage systems inside each virtual power plant is maintained. One of the virtual power plants is responsible for the frequency and voltage regulation by providing the required high-frequency power, while the other one provides the required bulk (low-frequency) power demand. In addition, the proposed clustering algorithm enables to meet a required energy capacity of the bulk virtual power plant by automatically tuning the clustering algorithm parameters. RTDS real-time technique verifies the proposed clustering algorithm and control systems on the IEEE 13 nodes power system with distributed energy storage systems and photovoltaic sources. The presented results demonstrate dynamic aggregation of energy storage systems into heterogeneous virtual power plants based on power demand while all regulation requirements are met.
Imaging algorithms for visualization of defects play a significant role in Lamb wave-based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of ...
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Imaging algorithms for visualization of defects play a significant role in Lamb wave-based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of defects is located by mapping the amplitude or phase information of signals from the time domain to every discrete spatial grid of the structure. It is time-consuming. In this study, the diversity, statistical, and fuzzy characteristics of the elliptic imaging algorithm are analyzed first;then, an intelligent defect location algorithm is proposed based on the evolutionary strategy and the K-means algorithm. The position of defects can be identified by observing the distribution of individuals. There are six parts in the proposed algorithm, including the data structure design, adaptive population screening, adaptive population reproduction, diversity maintenance mechanism, and cutoff criterion. Considering the statistical and fuzzy characteristics in the detection, several specific input parameters are defined in our algorithm, such as the distance-dependent screening threshold, path-dependent residual vector, and path-independent residual. To maintain the diversity of individuals in the analysis, we have made two adjustments to the evolutionary strategy: one is to optimize the population screening and reproduction steps with the K-means algorithm, and the other is to add a diversity maintenance method into the evolutionary strategy. The effectiveness of the proposed intelligent defect location algorithm is verified by numerical simulations and experiments. Numerical studies indicate that the proposed algorithm has a reliable performance in the detection of defects with different shapes and sizes. In the experimental research, we demonstrate that the efficiency of the proposed algorithm is about 200 times faster than the elliptic imaging algorithm. And the optimum parameter setting of the algorithm is investigated by analyzing the influence of parameter
As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be great...
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As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be greatly solved. Although there are abundant research results on intelligent transportation, there are not many research results on how to predict traffic accidents. For this issue, the main aim of this paper is to propose a continuous non-convex optimization of the K-means algorithm in order to solve the model problem in the traffic prediction process. First, this paper uses clustering algorithm for feature analysis and big data for the establishment of simulation model in cloud environment. Through this paper an equivalent model, using matrix optimization theory to analyze and process K-means problem, and design efficient and theoretically guaranteed algorithms for big data. By simulating the traffic situation in Shanghai city within three years, the outcomes display that the model endorsed in the given paper can predict traffic accidents at a rate of 93.88% and the accuracy rate of traffic accident processing time is 78%, which fully illustrates the effectiveness of the model established in this paper.
Based on relevant research on the scheduling problem of home care appointments and path planning under time window constraints both domestically and internationally, this article considers the current situation of med...
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Based on relevant research on the scheduling problem of home care appointments and path planning under time window constraints both domestically and internationally, this article considers the current situation of medical resource scarcity in the domestic home care industry and the characteristics of the home care industry that are different from general service industries. It proposes a clustering algorithm to study this problem and uses fuzzy time windows to describe customer needs under different task urgency. The practical results show that the proposed clustering algorithm can effectively analyze and solve the current development status and characteristics of the domestic home-based elderly care industry combined with a path planning scheme. Under the constraint of fuzzy time window, it studies the appointment scheduling problem under the conditions of determining service duration and fuzzy service duration, and finally proposes a solution method for the two types of problems, which has theoretical significance.
This work proposed a conceptually simple and computationally straightforward clustering algorithm based on the Cauchy-type distance for data clustering. It was demonstrated that the proposed approach does not require ...
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This work proposed a conceptually simple and computationally straightforward clustering algorithm based on the Cauchy-type distance for data clustering. It was demonstrated that the proposed approach does not require the priori number of clusters and the convergence of the proposed algorithm was proved. The experiment results showed that the proposed clustering algorithm was superior to other compared algorithms. Computational complexity was also provided. A real dengue gene expression dataset was used to demonstrate the effectiveness of the proposed method.
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