Abstract At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number of meter reading work leads to low efficiency of the entire water department, consuming a lot of time and energy, and high labor costs; second, the water meters in natural scenes have problems such as serious dial contamination and other environmental factors that interfere with the meter reading staff, and the results of the meter reader cannot be verified later. In response to these problems, this paper studies a deep learning method for automatic detection and recognition of water meter readings. This paper first introduces the existing in-depth learning models, such as Faster R-CNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Then two plans are proposed, one is the original water table image dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in the second solution has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve work efficiency, save labor costs, and assist auditors in reviewing the read water meter readings.
hotspot in computer science nowadays. The main objective of this paper is to describe domain ontologies at different granularities and hierarchies based on granular computing. A granular space model for ontology learning was explored, and some definitions such as concept granules, granular worlds and the structure of granular space were described formally. Accordingly, the composition and decomposition of concept granules and operation properties were introduced. The proposed model is available for research on ontology learning and data mining at different levels of granularity based on granular computing. Index term-- Granular Computing, Concept Granule, Granular Space Model, Ontology, Ontology Learning 1
Rough set is a new mathematical theory for dealing with uncertain and imprecise information. In view of it widely applied to data analysis, how to measure effectively the uncertainty is a meaningful issue. First, several main methods of uncertainty measure are introduced and their advantages and disadvantages are analyzed and compared; Second, combined with rough entropy, precision, inclusion degree, a new method of uncertainty measure, which is used to measure the uncertainty of rough set, is proposed. Finally, the proposed method is tested and compared with other methods of uncertainty measure. Experimental results show that it is effective and make the uncertainty measure more precise and complete.
Large data processing has become a hot topic of current research.How to efficiently dig out useful information from large amounts of data has become an important research direction in the field of data mining.In this paper, firstly, based on the idea of granular computing, some granular concepts about the decision tree are introduced.Secondly, referring to granular computing, the improvement and parallelization of ID3 algorithms are presented.Finally, the proposed algorithms are tested on two data sets, and it can be concluded that the algorithm's classification accuracy is improved.From the test on a Hadoop platform, the results demonstrate that parallel algorithms can efficiently process massive datasets.
Based on information system this paper presents a general framework of first order temporal granular logic and defines the meaning set of a formula .the paper provide axiom schemes and deductive rules based on which logic methodology or set theory can be applied in deduction. v a Its meaning set
A controlled quantum secure direct communication protocol based on four-qubit cluster states and quantum search algorithm is put forward, in which four users, a sender, a receiver and two controllers, are involved in achieving the secure transmission of secret message. The four-qubit cluster state can ensure the feasibility and security of the protocol because of its large persistency of entanglement. Meanwhile, the idea of quantum search algorithm is used to accomplish the task of encoding and decoding secret message. The proposed protocol can successfully avoid the information leakage problem and resist some common attacks including the outsider attacks and the internal attacks, and its qubit efficiency is up to 20%. Furthermore, compared with the previous quantum secure direct communication protocols, it can effectively resist the attacks from the dishonest receiver.
Abstract Water resources protection is related to the development of the social economy, and the monitoring and prediction of water environmental indicators have important practical significance. In view of the seasonality, periodicity, uncertainty, and nonlinear characteristics of water quality indicators data, traditional prediction models have poor performance. To address this issue, this paper introduces a hybrid water quality index prediction model based on Ensemble Empirical Mode Decomposition (EEMD), combined with Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). We have conducted out experiments to predict dissolved oxygen based on the water quality monitoring indicators of the Liaohe National Control Sanhongcun Village station in Yichun City. The results show that the model proposed in this paper improves the $$R^2$$ R2 index by 5%, 7% and 5% compared to the suboptimal model in the 4-h, 1-day and 2-day index predictions, respectively.
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