Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features

Han

Journal of Environmental and Public Health

2022

In the processing of rhythmic gymnastics resources, there are inefficiency problems such as confusion of teaching resources and lack of individuation. To improve the health access to teaching resource data, such as videos and documents, this study proposes a cloud computing-based personalized rhythmic gymnastics teaching resource classification algorithm for health promotion. First, personalized rhythmic gymnastics teaching resource database is designed based on cloud computing technology, and the teaching resources in the database are preprocessed to obtain a meta-sample set. Then, the characteristics of teaching resources are selected by the information acquisition method, and a vector space model is established to calculate the similarity of teaching resources. Finally, the distance-weighted k-NN method is used to classify the teaching resources for health promotion. The experimental results show that the classification accuracy of the proposed algorithm is high, the recall rate is high, and the F-measure value is high, which verifies the effectiveness of the algorithm.

Section: Methodsmentioning

confidence: 99%

[Retracted] Sport Resource Classification Algorithm for Health Promotion Based on Cloud Computing: Rhythmic Gymnastics’ Example

Han

Journal of Environmental and Public Health

2022

“…Need for much prior knowledge and observation data, slow calculation speed Deep Neural Networks [18] Big Data with Probability Function [19] Sequential Monte Carlo Methods [40] K-Nearest Neighbor Classifier [41] Other methods Advanced technology, high accuracy…”

Section: Methods Based On Big Data or Probabilistic Analysismentioning

confidence: 99%

A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm

Xia

et al. 2021

Sensors

Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s/50 times when the number of sensors is 16; 10.304 s/50 times when the number of sensors is 9; and 8.644 s/50 times when the number of sensors is 4. The analysis of the OMO algorithm’s characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms.

“…We extract time domain features, frequency domain features, waveform features, wavelet domain features, and some other features from commonly used audio processing libraries widely used for leakage detection. And the extracted features include all the elements used in the literature [16][17][18], a total of 28 parts, as shown in Table 3. Since there are usually many redundant features or low relevance to leak detection in the feature set, if these features are not eliminated, they will not only consume a lot of memory space but also may cause the risk of model overfitting.…”

Section: Experiments On Optimizing Feature Subsetsmentioning

confidence: 99%

APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

et al. 2023

Preprint

Model’s generalization and feature selection are always two challenging problems for gas leakage detection. This paper presents a method for gas leakage detection based on Hybrid-Feature-Selection-Stacking ensemble learning (HFS-Stacking), which fuses the Hybrid Feature Selection and Stacking ensemble learning. Firstly, a hybrid feature selection algorithm (HFS), which combines SFS-SVM, SFS-KNN, RFE-RF, RFE-XGB, and MIC algorithms, is proposed to select the optimal feature subset from multiple commonly used acoustic signal features; Then, SVM, KNN, random forest and XGBoost are designed as base learners in the stacking integration framework, which increase the generalization ability of the model. XGBoost is used as the meta-learner to output the classification results. The proposed gas leakage detection method gets the optimal subset of features and speeds of the subsequent model. Meanwhile, the constructed model can effectively improve the indicator of ,, and, and has a good generalization ability. The experimental results show that the HFS-Stacking algorithm using fewer features can effectively improve the training speed, accuracy, F1-score, AUC value and recall rate, and also has better robustness.