Application of Data Mining in an Intelligent Early Warning System for Rock Bursts

Zhu, Xiaoguang; Jin, Xiaona; Jia, Dianzeng; Sun, Naiwei; Wang, Pu

doi:10.3390/pr7020055

Cited by 13 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The K-means clustering, as an unsupervised clustering algorithm, is a mature and widely used clustering method. It has the advantages of its simplicity, favorable execution time, and good clustering effect [45]. Assuming that there are P operating modes in the pipeline transportation process, the algorithm will divide the original data set W into P clusters, each of which has high data similarity, with low similarity between clusters.…”

Section: Pipeline Leak Detection Methods Based On K-means and Cas-svddmentioning

confidence: 99%

An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

et al. 2019

View full text Add to dashboard Cite

Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade support vector data description (Cas-SVDD) for pipeline leak detection with multiple operating modes, using data samples that are leak-free during pipeline operation. Firstly, the local mean decomposition method is used to denoise and reconstruct the measured signal to obtain the feature variables. Then, the feature dimension is reduced and the nonlinear principal component is extracted by the KPCA algorithm. Secondly, the K-means clustering algorithm is used to identify multiple operating modes and then obtain multiple support vector data description models to obtain the decision boundaries of the corresponding hyperspheres. Finally, pipeline leak is detected based on the Cas-SVDD method. The experimental results show that the proposed method can effectively detect small leaks and improve leak detection accuracy.

show abstract

Section: Pipeline Leak Detection Methods Based On K-means and Cas-svddmentioning

confidence: 99%

An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Some other studied data mining to support risk management in the supply chain; the recognized papers' insights, gaps and future directions could inspire new investigation procedures with a view to managing the risks in the globalized supply chain environment (Ghadge et al 2012;Shojaei and Haeri 2019). Some other studies have been conducted to reduce occupational damage risk using data mining (Murayama et al 2011;Zhu et al 2019).…”

Section: Data Mining In Enterprise Risk Managementmentioning

confidence: 99%

“…This challenge in decision making, appears to increase together with the expansion of globalization. Easy-to-use technologies for saving data and widespread access to the internet allow researchers and organizations to collect more data (Zhu et al 2019). Because the origin, content, and display methods of most of these data vary and because they relate to diverse settings, such as commercial and financial, the current literature lacks enough findings concerning how these data are modeled and how they contribute to a company's decision making strategy.…”

Section: Introductionmentioning

confidence: 99%

The implication of business intelligence in risk management: a case study in agricultural insurance

Amini

Salimi

Yousefinejad

et al. 2021

J. of Data, Inf. and Manag.

View full text Add to dashboard Cite

The increasing data scales in today’s business sectors coupled with the necessity of risk management raise the importance of business intelligence tools as an integrated solution for the insurance industry. These tools have mostly been used to achieve effective risk management. Although methods of risk management in the insurance industry have been proposed many years ago, the research effort has primarily been focused on predictive analyses. This study aimed to investigate the role of business intelligence as a solution to illustrate its potential in risk management particularly for decision-makers in agricultural insurance. We hypothesized that this would make a preferable decision in uncertain conditions. Sample data from the online transaction process system of Iran agricultural insurance fund were preprocessed in SQL server. Multidimensional online analytical processing architecture was analyzed using Targit business intelligence tool. Our results identified financial risks that lead to a framework of controlling risk based on business intelligence in the agricultural insurance fund.

show abstract

“…In this research, the development of the neuro-fuzzy classifier involves the following two steps. First, k-Means clustering [19,22,23] embedding heuristic knowledge into the neuro-fuzzy classifier is used to construct the fuzzy IF-THEN rules of the neuro-fuzzy classifier from a training dataset. Each of the constructed fuzzy rules in the rule base is in charge of a partition of the feature space, where the considered electrical features are the universe of discourse.…”

Section: Neuro-fuzzy Classification With K-means Clusteringmentioning

confidence: 99%

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Lin

Gururaj³

2021

Processes

View full text Add to dashboard Cite

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

show abstract

Application of Data Mining in an Intelligent Early Warning System for Rock Bursts

Cited by 13 publications

References 18 publications

An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

The implication of business intelligence in risk management: a case study in agricultural insurance

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Contact Info

Product

Resources

About