In the process of solving the Traveling Salesman Problem (TSP), both Ant Colony Optimization and simulated annealing exhibit different limitations depending on the dataset. This article aims to address these limitatio...
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In the process of solving the Traveling Salesman Problem (TSP), both Ant Colony Optimization and simulated annealing exhibit different limitations depending on the dataset. This article aims to address these limitations by improving and combining these two algorithms using the clustering method. The problems tackled include Ant Colony Optimization's susceptibility to stagnation, slow convergence, excessive computations, and local optima, as well as simulated annealing's slow convergence and limited local search capability. By conducting tests on various TSPLIB datasets, the algorithm proposed in this article demonstrates improved convergence speed and solution quality compared to traditional algorithms. Furthermore, it exhibits certain advantages over other existing improved algorithms. Finally, this article applies this algorithm to logistics transportation, yielding excellent results.
Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to imp...
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Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to improve building energy-saving. The great amounts of energy consumption data collected by building energy monitoring platforms (BEMS) provides potentials in using data mining technology for anomaly detection. This study pro-poses a dynamic anomaly detection algorithm for building energy consumption data, which realizes the dynamic detection of point anomalies and collective anomalies. The algorithm integrates unsupervised clustering algo-rithm with supervised algorithm to establish a semi-supervised matching mechanism, which avoids the influence of error label and improves the efficiency of anomaly detection. A particle swarm optimization (PSO) is used to optimize the unsupervised clustering algorithm. This investigation tests the effectiveness of the proposed algo-rithm and evaluates the performance of the energy consumption clustering algorithm by using the annual electricity consumption data of an experimental building in a university. The results show that the clustering accuracy of the algorithm can reach more than 80%, and it can effectively detect the building energy con-sumption data of two different forms of outliers. It can provide reliable data support for adjusting building management strategies.
With the rapid growth of the national economy, people's demand for transportation is becoming increasingly strong. The rail transit business is booming in large and medium-sized cities, and the education managemen...
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With the rapid growth of the national economy, people's demand for transportation is becoming increasingly strong. The rail transit business is booming in large and medium-sized cities, and the education management of urban rail transit students needs further reform. At the same time, digital information technology is widely used in various fields, and digital management of education has become one of the major development directions of education reform. The study proposes a specific construction path based on the analysis of the necessity of digital management of education for urban rail transportation majors, and then optimizes the k-medoids algorithm in the clustering algorithm and validates its education digital management effect. The outcomes show that the clustering precision of the upgraded k-medoids algorithm in the selected dataset is up to 92.68%, and the running time is all below 5s, with the lowest value being 3.9s;In the digital management of urban rail transit majors in universities, the precision obtained by the algorithm is all maintained at around 95%, and the satisfaction rate is all higher than 90%. The effectiveness of the proposed method has been verified, providing a new method for the management of digital education systems for urban rail transit students. It can better understand the needs and characteristics of students, help improve their learning effectiveness and educational quality, and achieve more targeted allocation of educational resources.
Small wireless sensors are equipped with micromechanical systems, & wireless communication technologies, digital systems. A Sensor Node understands the limited area for given functionality. In order to gather info...
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ISBN:
(数字)9781728149769
ISBN:
(纸本)9781728149776
Small wireless sensors are equipped with micromechanical systems, & wireless communication technologies, digital systems. A Sensor Node understands the limited area for given functionality. In order to gather information on a large area, data centers need to be collected in cooperation with a center sensor node. These co-operative functional sensors create a wireless sensor *** the previous work, they used k-medoids algorithm to form the clusters and Cluster heads for databroadcast towards the base station from the source node. k-Medoid algorithms are primarily disadvantageous because they are not ideal for clustering arbitrary groups of objects. It is that they use compactness as clustering parameters, in short, rather than connectivity, to minimize differences among non-medoid objects or medoids (cluster center). The drawback, even though the first kmedoids are selected randomly, can produce different results on different runs in the same dataset. These drawbacks are overcome in Fuzzy Probabilistic C-Means algorithm & increase the performance of the network. Simulation is performed on MATLAB tool & results show the effectiveness of the proposed work.
Blogs are a new form of internet phenomenon and a vast ever-increasing information resource, which are dated unedited, highly opinionated personal online commentary including all kinds of hyperlinks such as citation l...
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ISBN:
(纸本)9781424465828
Blogs are a new form of internet phenomenon and a vast ever-increasing information resource, which are dated unedited, highly opinionated personal online commentary including all kinds of hyperlinks such as citation link, comment link, blogroll link. These links can be viewed as the blogger's browse behavior, which reflects the user's interest to a certain extent. So we construct a blogger-post matrix, link analysis is considered in calculation of the entry of the matrix. With usage of probability latent semantic analysis, the conditional probability of latent variable Z to post P is transformed the the conditional probability of latent variable Z to post B, then the transformed results are used in similarity calculation. The kmedoidsalgorithm is adopted to further improve clustering result. Experiment results have shown that this new algorithm is effective.
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