Due to the serious redundant interference of dual-carbon monitoring data resources, threshold setting in the fusion process is inaccurate. To realize the sharing and utilization of monitoring data resources, a fast gr...
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Due to the serious redundant interference of dual-carbon monitoring data resources, threshold setting in the fusion process is inaccurate. To realize the sharing and utilization of monitoring data resources, a fast group fusion method of dual-carbon monitoring data based on dbscan (Ensity-Based Spatial clustering of Applications with Noise) clusteringalgorithm is proposed. After collecting dual-carbon monitoring data through the wireless sensor network, dbscan clustering algorithm calculates the neighborhood distance threshold of each data object. Sensor nodes compare the dual-carbon monitoring data and the abnormal data, determine the threshold value, assess whether abnormal data exists and is valid through integrated support, rejecting if invalid. Least squares method realizes fusion of normal data monitored by sensor nodes in the group. Cluster head node determines weight of the normal dual-carbon data monitored by each sensor according to different confidence levels. Variance estimation learning algorithm achieves weighted fusion of data from sensors within the group. The experimental results show that the clusteringalgorithm has a good clustering effect on the dual-carbon monitoring data and can realize the rapid group fusion. In the complex environment with frequent invalid abnormal data, the mean square error of the fusion results is small.
This article presents an efficient and fast echelon battery equalization method based on wide voltage range bidirectional converter combined with dbscan (density-based spatial clustering of applications with noise) cl...
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This article presents an efficient and fast echelon battery equalization method based on wide voltage range bidirectional converter combined with dbscan (density-based spatial clustering of applications with noise) clustering control strategy. Thanks to the wide voltage range and bidirectional buck-boost characteristics of the four-switch bidirectional converter, the battery energy can be reasonably redistributed between the battery and the super capacitor. The converter operating in synchronous rectification mode can not only achieve bidirectional energy flow in a wide voltage range, but also has a high energy conversion efficiency. The dbscan clustering algorithm is used to divide all battery cells into groups, and each group may contain one or more adjacent and nonadjacent battery cells. First, the nonadjacent single cells are balanced and integrated into adjacent group with the closest voltage. Then, the groups with different voltages are balanced by group-to-group, thereby realizing rapid balance of the entire battery pack. In order to verify the effectiveness of the proposed method, simulations and experimental verification were carried out. The experimental results show that, comparing with traditional battery balancing methods, the proposed method achieves shorter balancing time and higher balancing efficiency.
When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable clust...
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When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differential equation to model the cluster coordination rules, and the state equation of the single target in the cluster is derived. On this basis, a Bayes recursive filter tracking method based on the combination of the dbscan clustering algorithm and the delta-GLMB filter is proposed. In the delta-GLMB filter prediction stage, the dbscanalgorithm is used to determine the cluster where the target is located in real time. Then, the collaborative noise of the target is estimated, which will be used as the input to correct the prediction state of the target. The simulation and experiment results demonstrate the effectiveness of the proposed algorithm when the cluster is splitting, merging, and in reorganization.
From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the l...
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From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the logistics industry, crowdsourcing provides a new idea of sustainable development for logistics enterprises, and reasonable distribution pricing is the key to achieving sustainable development. This paper innovatively adds dynamic and decentralized characteristics of logistics on the basis of a detailed analysis of pricing methods and uses this as a basis to build a pricing model. First, based on existing crowdsourced photography task pricing data, this paper establishes a project-centric domain and builds metrics into the attributes of each project based on the data in that domain. Then, a regression model is used to fit the completion rate of previous projects, and a multiple linear regression and optimal pricing mechanism are established. Finally, the dbscanalgorithm is used to cluster areas with a high project density, and a pricing optimization model based on polynomial Logit (MNL) is established. We found through the model analysis that the optimized pricing strategy of crowdsourcing logistics services has a better packaging completion rate based on a combination of complex factors including bundling and outliers. In short, the main contributions of this paper are to build a complex mathematical model for crowdsourcing tasks, improve the algorithmic deficiencies of the previous crowdsourcing task pricing methods, and provide a reference for further research on crowdsourcing tasks.
With the rapid development of Internet technology and communication technology, more and more computer systems and networks have been maliciously attacked by intruders. Network security has been seriously threatened t...
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ISBN:
(数字)9781728185750
ISBN:
(纸本)9781728185750
With the rapid development of Internet technology and communication technology, more and more computer systems and networks have been maliciously attacked by intruders. Network security has been seriously threatened to a certain extent, and network security technology has also attracted more and more attention from the public. As a security protection technology for actively monitoring network data, intrusion detection technology effectively compensates for the defects of traditional security protection technologies such as firewalls and data encryption, and has become an important research field in network security. Based on this, it is very important to design the security mechanism of the system to prevent unauthorized access to system resources and data. This paper uses a dbscanalgorithm for anomaly detection clusteringalgorithm. algorithms that can be used for massive data processing have become a research hotspot in anomaly detection. Normal behavior profiles are formed on audit records and adjusted in time as program behavior changes. Experimental results show that, compared with other algorithms, anomaly detection based on the dbscanalgorithm can improve the detection rate of the data set, and significantly improve the accuracy of anomaly detection.
Extreme Learning Machine (ELM) is a single layer feedforward neural network (SLFN) and a popular classifier for classification and regression problems. It is unstable due to random initialization of weights between th...
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ISBN:
(纸本)9781538630457
Extreme Learning Machine (ELM) is a single layer feedforward neural network (SLFN) and a popular classifier for classification and regression problems. It is unstable due to random initialization of weights between the hidden layer and the input layer. To overcome this problem of instability, kernelized ELM with kernels has been developed. Gaussian kernel Extreme Learning Machine (KELM) is one of the stable but computationally complex variant of ELM in which the number of hidden layer neurons is equal to the number of input instances. Therefore, to reduce this computational complexity, a new variant of KELM i.e. reudced KELM has been proposed in the literature. It randomly selects the number of centroids from the training data set such that the number of centroids is always less than the number of input instances. This work develops a new reduced version of KELM using density-based clusteringalgorithm (dbscan). dbscan is a non-spherical clusteringalgorithm which is used to identify the number of concepts. Each concept is represented using the centroid of the cluster. In proposed KELM model, the number of hidden layer neurons is equal to the number of clusters formed by the dbscan clustering algorithm. Experiments have been performed on 16 datasets drawn from the KEEL data set repository. The result shows that the developed reduced KELM model gives better performance as compared to the traditional KELM in terms of reduced training time and testing time.
The paper presents the results of the research of the clusteringalgorithmdbscan practical implementation within the framework of the objective clustering inductive technology. As experimental, the data Aggregation a...
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ISBN:
(纸本)9781538616390
The paper presents the results of the research of the clusteringalgorithmdbscan practical implementation within the framework of the objective clustering inductive technology. As experimental, the data Aggregation and Compound of the Computing school of the East Finland University and the gene expression sequences of lung cancer patients of the database ArrayExpres were used. The architecture of the objective clustering model has been developed. The implementation of the model involves the parallel data clustering on the two equal power subsets, which include the same quantity of pairwise similar objects. The choice of the solution about parameters of the algorithm determination has been carried out based on the minimum value of the external clustering quality criterion, which calculated as normalized difference of the internal clustering quality criteria for the two subsets.
Providing personalized services for electricity users with different needs has become an important part of deepening the reform of the electricity sales side market system, and achieving efficient and accurate electri...
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Money laundering refers to disguise or conceal the source and nature of variety ill-gotten gains, to make it legalization. In this paper, we design and implement the antimoney laundering regulatory application system(...
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ISBN:
(纸本)9781479962365
Money laundering refers to disguise or conceal the source and nature of variety ill-gotten gains, to make it legalization. In this paper, we design and implement the antimoney laundering regulatory application system(AMLRAS), which can not only automate sorting and counting the money laundering cases in comprehension and details, but also collect, analyses and count the large cash transactions. We also adopt data mining techniques dbscan clustering algorithm to identify suspicious financial transactions, while using link analysis (LA) to mark the suspicious level. The presumptive approach is tested on large cash transaction data which is provided by a bank where AMLRAS has already been applied. The result proves that this method is automatable to detect suspicious financial transaction cases from mass financial data, which is helpful to prevent money laundering from occurring.
Monitoring data outliers comprises isolated mode outliers, cluster mode outliers, and normal points. To identify and distinguish the data hopping problems caused by outliers and environmental mutations in the displace...
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Monitoring data outliers comprises isolated mode outliers, cluster mode outliers, and normal points. To identify and distinguish the data hopping problems caused by outliers and environmental mutations in the displacement monitoring data of concrete AMS, this paper proposes a method based on wavelet transform, dbscan clustering algorithm combined with isolated forest and reinforcement learning algorithm to identify outliers in concrete dam monitoring data. In this paper, the trend line of measuring point data are extracted by the wavelet transform algorithm, and the residual data are obtained by subtracting it from the original process line. Subsequently, the dbscan clustering algorithm divides the residual data according to density. Therewith, the outlier scores of different data clusters are calculated, the iterative Q values are updated, and the threshold values are set. The data exceeding the threshold are finally marked as outliers. Finally, the water level and displacement data were compared by drawing the trend to ensure that the water level change did not cause the final identified concrete dam displacement data outliers. The results of the example analysis show that compared with the other two outlier detection methods ("Wavelet transform combined with dbscanclustering" or "W-D method", "Wavelet transform combined with isolated forest method" or "W-IF method"). The method has the lowest error rate and the highest precision rate, recall rate, and F1 score. The error rate, precision rate, recall rate, and F1 score were 0.0036, 0.870, 1.000, and 0.931, respectively. This method can effectively identify data jumps caused by an environmental mutation in deformation monitoring data, significantly improve the accuracy of outlier identification, reduce the misjudgement rate of outliers, and have the highest detection accuracy.
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