The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the need...
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ISBN:
(纸本)9781479927166
The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the needs and guide the deployment of WLAN, that isn't a easy issue. It will waste the investment if we deploy the WLAN in improper places. This paper proposes a solution which can collect customer feedback with the help of smart phone client software and affinity propagation algorithm is applied to determine which region should be deploy first according to giant user feedback from that client software. Actual results show that the method is feasible and effective. It can significantly improve the accuracy of deployment of WLAN and efficiency of operations.
Identification of rock discontinuity sets is an important foundation for stability analysis of rock engineering applications. A modified affinitypropagation (AP) algorithm is proposed for identifying rock discontinui...
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Identification of rock discontinuity sets is an important foundation for stability analysis of rock engineering applications. A modified affinitypropagation (AP) algorithm is proposed for identifying rock discontinuity sets based on discontinuity orientations. The method considers all data points as potential clustering centers simultaneously, which could avoid the hard selection on initial clustering centers as well as achieve the global optimization. Euclidean distance measure in the original algorithm is not suitable for the clustering of discontinuity orientations and therefore a similarity measure based on the negative sine-squared value of the acute angle between discontinuity unit normal vectors was used. Moreover, the Silhouette validity index was introduced to determine the optimal clustering number. The validity of the new method was tested by using artificial data, and in-situ data compiled from the literature. Finally, the proposed method was applied to discontinuity grouping in an underground water-sealed oil storage cavern in Liaoning Province, China. The results show that the new method can effectively filter noisy data and achieve good clustering results with stronger robustness than other methods.
In today's financial markets, Exchange-Traded Funds (ETFs), particularly high-liquidity equity ETFs exhibiting robust market liquidity, have garnered increasing attention from investors. This study conducts an ana...
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ISBN:
(纸本)9798400709234
In today's financial markets, Exchange-Traded Funds (ETFs), particularly high-liquidity equity ETFs exhibiting robust market liquidity, have garnered increasing attention from investors. This study conducts an analysis of 50 highly liquid equity Exchange-Traded Funds (ETFs) within the Chinese securities market, covering the period from October 25, 2022, to October 26, 2023. Employing the Sparse Inverse Covariance Estimation method (GraphicalLassoCV), the research calculates the conditional correlation among ETFs. Additionally, an unsupervised learning method, the affinity propagation algorithm from machine learning, is utilized for further analysis and exploration of the market structure of Chinese high-liquidity equity ETFs. Subsequently, 'manifold' techniques are applied for data visualization. The results reveal that 50 high-liquidity equity ETFs are divided into 11 clusters, and specific clusters exhibit industry cohesion, reflecting similar intraday volatility patterns within sectors. The Broad Market Indices and Banking Cluster and the Semiconductor and Artificial Intelligence Cluster are the largest and most active categories. They account for 31.88% and 24.87% of the total market capitalization, and 8.62% and 38.66% of the total trading volume, *** represents the trend over the past year, where short-term investors have shown a preference for innovative companies in sectors such as semiconductors and artificial intelligence. On the other hand, long-term capital tends to favor ETFs associated with banking, market indices, and other sectors characterized by high dividend yields and strong risk diversification capabilities.
The affinity propagation algorithm has been extensively applied in various fields. However, it is still faces two severe challenges in actual applications: one is that the algorithm may be nonconvergent;and the other ...
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The affinity propagation algorithm has been extensively applied in various fields. However, it is still faces two severe challenges in actual applications: one is that the algorithm may be nonconvergent;and the other is that the convergence speed is low. Aiming at solving these two problems, an adaptive affinity propagation algorithm based on a new strategy of dynamic damping factor and preference is proposed in this paper. On one hand, the dynamic damping factor changes the factor value according to the check state of oscillation to eliminate and escape from the oscillation. On the other hand, dynamic preference adjusts the value of the preference based on the bisection and memory tuple to reduce the search scope of the target preferences continuously. Simulation results show that the proposed algorithms can solve the potential nonconvergence problem effectively and reduce the time consumed significantly. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the need...
详细信息
ISBN:
(纸本)9781479927173
The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the needs and guide the deployment of WLAN, that isn't a easy issue. It will waste the investment if we deploy the WLAN in improper places. This paper proposes a solution which can collect customer feedback with the help of smart phone client software and affinity propagation algorithm is applied to determine which region should be deploy first according to giant user feedback from that client software. Actual results show that the method is feasible and effective. It can significantly improve the accuracy of deployment of WLAN and efficiency of operations.
The original affinitypropagation clustering algorithm (AP) only used the Euclidean distance of data sample as the only standard for similarity calculation. This method of calculation had great limitations for data wi...
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The original affinitypropagation clustering algorithm (AP) only used the Euclidean distance of data sample as the only standard for similarity calculation. This method of calculation had great limitations for data with high dimension and sparsity when the original algorithm was running. Due to the single calculation method of similarity, the convergence and clustering accuracy of the algorithm were greatly affected. On the other hand, in the universe, we can consider the formation of galaxies is a clustering process. In addition, the interaction between different celestial bodies are achieved through universal gravitation. This paper introduced the Density Peak clustering algorithm (DP) and gravitational thought into the AP algorithm, and constructed the density property to calculate the similarity, put forward the affinitypropagation clustering algorithm based on Gravity (GAP). The proposed algorithm was more accurate to calculate similarity of simple points through the local density of corresponding points, and then used the gravity formula to update the similarity matrix. The data clustering process could be seen as the sample points spontaneously attract each other based on 'gravitation'. Experimental results showed that the convergence performance of GAP algorithm is obviously improved over the AP algorithm, and the clustering effect was better.
Aiming at complex data sets, affinitypropagation clustering algorithm has shortcomings of clustering inefficiency and low accuracy. A semi-supervised affinitypropagation clustering algorithm based on kernel function...
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ISBN:
(纸本)9781479970162
Aiming at complex data sets, affinitypropagation clustering algorithm has shortcomings of clustering inefficiency and low accuracy. A semi-supervised affinitypropagation clustering algorithm based on kernel function (K-SAP Clustering algorithm) is proposed in this paper. This algorithm first maps the complex clustering space into the feature space and change the similarity measure by a kernel function. Then semi-supervised algorithm is used to adjust the similarity matrix to be neighbours of data in same cluster. Finally, AP algorithm is used to iterate and update to get, the global optimum. Simulation results show the proposed algorithm is better and more accurate than SAP algorithm for complex data sets clustering.
Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. There are a large number of mobile users in CPSS that need low latency services. Fortunately, mobile edge computing (MEC)...
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Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. There are a large number of mobile users in CPSS that need low latency services. Fortunately, mobile edge computing (MEC) is a novel technology which can provide such services. The edge server plays a key role in MEC, but how to manage the edge server is an important challenge. For one thing, the number of cloudlets and the resource are limited. For another, the number of mobile devices (MDs) is very large and randomly distributed. And thus, how to determine the suitable number of cloudlets while serving the maximum number of MDs is significant. To this end, a new cloudlet placement method based on improved affinitypropagation (AP) algorithm is proposed to solve the above problems. More specially, the improved AP algorithm can obtain the least number of cloudlets while covering the largest number of MDs. In addition, the load balancing strategy is used to ensure that the load of each cloudlet maintains a balanced state. Last but not the least, our proposed method can be used in scenarios where users move.
In the real industrial production process, some minor faults are difficult to be detected by multivariate statistical analysis methods with mean and variance as detection indicators due to the aging equipment and cata...
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In the real industrial production process, some minor faults are difficult to be detected by multivariate statistical analysis methods with mean and variance as detection indicators due to the aging equipment and catalyst deactivation. With structural characteristics, deep neural networks can better extract data features to detect such faults. However, most deep learning models contain a large number of connection parameters between layers, which causes the training time-consuming and thus makes it difficult to achieve a fast-online response. The Broad Learning System (BLS) network structure is expanded without a retraining process and thus saves a lot of training time. Considering that different stages of the batch production process have different production characteristics, we use the affinitypropagation (AP) algorithm to separate the different stages of the production process. This paper conducts research on a multi-stage process monitoring framework that integrates AP and the BLS. Compared with other monitoring models, the monitoring results in the penicillin fermentation process have verified the superiority of the AP-BLS model. (C) 2020 Elsevier Ltd. All rights reserved.
DBSCAN is a density based clustering algorithm and its effectiveness for spatial datasets has been demonstrated in the existing literature. However, there are two distinct drawbacks for DBSCAN: (i) the performances of...
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DBSCAN is a density based clustering algorithm and its effectiveness for spatial datasets has been demonstrated in the existing literature. However, there are two distinct drawbacks for DBSCAN: (i) the performances of clustering depend on two specified parameters. One is the maximum radius of a neighborhood and the other is the minimum number of the data points contained in such neighborhood. In fact these two specified parameters define a single density. Nevertheless, without enough prior knowledge, these two parameters are difficult to be determined;(ii) with these two parameters for a single density, DBSCAN does not perform well to datasets with varying densities. The above two issues bring some difficulties in applications. To address these two problems in a systematic way, in this paper we propose a novel parameter free clustering algorithm named as APSCAN. Firstly, we utilize the affinitypropagation (AP) algorithm to detect local densities for a dataset and generate a normalized density list. Secondly, we combine the first pair of density parameters with any other pair of density parameters in the normalized density list as input parameters for a proposed DDBSCAN (Double-Density-Based SCAN) to produce a set of clustering results. In this way, we can obtain different clustering results with varying density parameters derived from the normalized density list. Thirdly, we develop an updated rule for the results obtained by implementing the DDBSCAN with different input parameters and then synthesize these clustering results into a final result. The proposed APSCAN has two advantages: first it does not need to predefine the two parameters as required in DBSCAN and second, it not only can cluster datasets with varying densities but also preserve the nonlinear data structure for such datasets. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
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