The rapid development of urbanization has led to the gradual increase of urban residential density. Relatively speaking, the spectrum resources are increasingly scarce, which leads to the increasingly serious interfer...
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The rapid development of urbanization has led to the gradual increase of urban residential density. Relatively speaking, the spectrum resources are increasingly scarce, which leads to the increasingly serious interference between communities, and the system performance is also greatly limited. Therefore, in order to improve the efficiency of spectrum resources and solve the problem of user interference between cells, the experiment combines the advantages of clustering by fast search and find of Density Peaks clustering (DPC), and proposes a two-step clustering algorithm. This method is proposed based on the core idea of DPC after in-depth study of the downlink multi-cell orthogonal frequency division multiplexing system architecture. The proposed model is compared with Matching Pursuit (MP) algorithm and Graph-based algorithm with the sum of clustering distance R-s, 1.5R(s). The results show that the two-step clustering algorithm can significantly improve the spectrum efficiency and network capacity while ensuring good quality of service under the condition of channel tension or not. In addition, the minimum SINR value of the two-step clustering algorithm can reach 75 dB. Compared with the 220 dB of the Graph-based algorithm with the clustering distance R-s, it has extremely obvious advantages. Therefore, the two-step clustering algorithm constructed in this study can effectively reduce system interference, and has certain research and application value in solving the problem of mobile communication channel resource shortage.
Aim: clustering belongs to unsupervised learning, which divides the data objects into the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity. Background: The clust...
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Aim: clustering belongs to unsupervised learning, which divides the data objects into the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity. Background: The clustering of spatial data objects can be solved by optimization based on the clustering objective function. Objective: Study on intelligent analysis and processing technology of computer big data based on clustering algorithm. Methods: First, a new dynamic self-organizing feature mapping model is proposed, and the training algorithm of the model is given. Then, the spectral clustering technology and related concepts are introduced. The spectral clustering algorithm is studied and analyzed, and a spectral clustering algorithm that automatically determines the number of clusters is proposed. Furthermore, an algorithm for constructing a discrete Morse function to find the optimal solution is proposed, proving that the constructed function is the optimal discrete Morse function. At the same time, two optimization models based on the discrete Morse theory are constructed. Finally, the optimization model based on discrete Morse theory is applied to cluster analysis, and a density clustering algorithm based on the discrete Morse optimization model is proposed. Results: This study is focused on designing and implementing a partitional-based clustering algorithm based on big data, that is suitable for clustering huge datasets to meet low computational requirements. The experiments are conducted in terms of time and space complexity and it is observed that the measure of clustering quality and the run time is capable of running in very less time without negotiating the quality of clustering. The results show that the experiments are carried out on the artificial data set and the UCI data set. Conclusion: The efficiency and superiority of the new model, are verified by comparing it with the clustering results of the DBSCAN algorithm.
This paper uses TF-IDF and Kleinberg algorithms to map the literature in the field of TCM talent training included in the core journals of CNKI from 2014 to 2024 from the number of publications, publishing organizatio...
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ISBN:
(纸本)9798400707032
This paper uses TF-IDF and Kleinberg algorithms to map the literature in the field of TCM talent training included in the core journals of CNKI from 2014 to 2024 from the number of publications, publishing organization, keyword clustering and mutation, analyze the research hot spots and evolution trends in the field of TCM talents in China, and draw research conclusions and put forward suggestions. The number of published articles is increasing year by year, which can strengthen the exchange, cooperation and resource sharing among research institutions, and emerge high hot topics such as traditional Chinese medicine culture, innovative talents, teacher education, innovation and entrepreneurship education. It is suggested to break the traditional shackles, innovate the training mode of TCM talents, and improve the quality and level of training. In-depth study of the talent training system, teaching methods, evaluation system and other key issues, and constantly innovate the education mode and training mechanism.
This paper extends the Fuzzy c-means (FCM) algorithm and proposes the Ordered pair of normalized real numbers clustering (OPNC) algorithm. The OPNC algorithm adopts the paradigm of learning in parallel universes and s...
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ISBN:
(纸本)9798350359329;9798350359312
This paper extends the Fuzzy c-means (FCM) algorithm and proposes the Ordered pair of normalized real numbers clustering (OPNC) algorithm. The OPNC algorithm adopts the paradigm of learning in parallel universes and simultaneously uses multiple similarity measures to convert ordinary data into ordered pairs of normalized real numbers (OPNs). clustering is performed with OPNs, and OPNs contain different similarity information, so the OPNC algorithm can further improve the clustering performance by combining different similarity measures. Experiments on multiple real datasets and comparisons with other clustering algorithms verified that the OPNC algorithm has excellent performance.
Turbine blades' metal substrates are often coated with thermal barrier coatings made of composites, notably ceramics. Insulation defects, which might lead to a catastrophic turbine failure, must be detected by rou...
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Turbine blades' metal substrates are often coated with thermal barrier coatings made of composites, notably ceramics. Insulation defects, which might lead to a catastrophic turbine failure, must be detected by routine non-destructive testing. The microwave non-destructive testing has limited spatial imaging, complicating the defect evaluation. This research proposes a unique approach for delamination detection based on microwave non-destructive testing and k-medoids clustering. Using a double-ridged waveguide with 101 frequency points between 18 and 40 GHz, a standard ceramic coating sample is scanned. The k-medoids clustering technique reliably detects and sizes ceramic insulation delamination at each evaluated site. This finding demonstrates the k-medoids clustering method's capability of detecting delamination with 95.3% accuracy.
This article aims to explore the application of clustering algorithms in analyzing students' learning behavior patterns, and design an efficient learning warning system based on this. Using educational data mining...
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ISBN:
(纸本)9798350386783;9798350386776
This article aims to explore the application of clustering algorithms in analyzing students' learning behavior patterns, and design an efficient learning warning system based on this. Using educational data mining technology, this study analyzed student interaction data on large-scale online learning platforms, adopted multiple clustering algorithms to identify students' learning behavior patterns, and constructed warning mechanisms based on these patterns. Through quantitative and qualitative indicators, the early warning system can monitor students' learning status in real-time, identify potential academic problems early, and provide intervention suggestions. In the experimental evaluation stage, in the test results of running time, due to the addition of grid partitioning and iterative merging operations on the basis of CF and AGA, the average running time of the algorithm slightly increased. Through comparison, it can be clearly found that the average running time of the algorithm in this paper only needs 5.9 seconds, which is significantly lower than the running time of CF(Collaborative Filtering) and AGA(Adaptive Genetic algorithm), which is 13.8 seconds and 15.7 seconds. Therefore, the algorithm proposed in this paper is feasible and effective, demonstrating its relative advantages. Future research will focus on further enhancing the universality of early warning systems, exploring more complex learning behavior patterns, and integrating more diverse data sources to optimize prediction models.
clustering is an unsupervised learning technique, which leverages a large amount of unlabeled data to learn cluster-wise representations from speech. One of the most popular self-supervised techniques to train a speak...
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ISBN:
(纸本)9798350344868;9798350344851
clustering is an unsupervised learning technique, which leverages a large amount of unlabeled data to learn cluster-wise representations from speech. One of the most popular self-supervised techniques to train a speaker verification system is to predict the pseudo-labels using clustering algorithms and then train the speaker embedding network using the generated pseudo-labels in a discriminative manner. Therefore, pseudo-labels - driven self-supervised speaker verification systems' performance relies heavily on the accuracy of the adopted clustering algorithms. In this contribution, we propose a novel clustering technique that not only (i) combines predictions of augmented samples to provide a complementary supervisory signal for clustering and imposes symmetry within the augmentations but also (ii) enforces representation invariance via Self-Augmented Training (SAT) and maximizes the information-theoretic dependency between samples and their predicted pseudo-labels. Experimental results on the Vox-Celeb dataset show that the proposed clustering framework achieves better clustering performance in terms of a variety of clustering metrics. Proposed framework is also able to provide better self-supervised speaker verification performance than the state-of-the-art approaches trained on the same dataset.
The connectivity of cognitive vehicular networks (CVNs) is the key to achieve efficient traffic management and improve road safety. However, the dynamic nature of vehicular movements and the available spectrum pose un...
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ISBN:
(纸本)9798400710353
The connectivity of cognitive vehicular networks (CVNs) is the key to achieve efficient traffic management and improve road safety. However, the dynamic nature of vehicular movements and the available spectrum pose unpredictable communication failures in ensuring reliable connectivity. To tackle this challenge, we propose a clustering algorithm for connectivity in CVNs with digital twin (DT) technology. Particularly, the DT environment is built by SUMO and Carla, where data are exchanged with CVN periodically, and a three-stage clustering algorithm is carried out in the DT layer. The simulation results verify our algorithm significantly enhances the connectivity and robustness performance with compared to existing works.
This paper presents a new algorithm to reduce network energy consumption and extend network life cycle, the methods such as adding supplementary nodes, improving the probability of cluster node becoming cluster head, ...
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Beyond the widely-studied scheduling of wafers within cluster tools, a novel and important perspective is raised in this paper to tackle an upper-level optimization problem in real-world production, i.e., the assignme...
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Beyond the widely-studied scheduling of wafers within cluster tools, a novel and important perspective is raised in this paper to tackle an upper-level optimization problem in real-world production, i.e., the assignment of hybrid types of wafer lots to a set of cluster tools with parallel modules to minimize the maximum completion time for the lots. The main difficulty in addressing such a problem is that the objective, i.e., the maximum completion time, cannot be calculated explicitly beforehand. To make this problem tractable, the associated maximal overlap among tools is utilized to heuristically evaluate the objective for the problem. Besides, since the cluster tools for processing are identical, we further tackle this problem as a clustering issue. Accordingly, a clustering algorithm based on greedy searching is proposed to allocate wafer lots into cluster tools while minimizing the maximal overlap. To elucidate our method and its significance in real-world production, the wet bench tool in wet cleaning process is taken as a case study. We compare the proposed algorithm with the empirical method in fabs and several intelligent optimization algorithms, and the experimental results verify the effectiveness of our proposed method in terms of improved efficiency.
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