Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems

Kang, Jiheon; Park, Youn-Jong; Lee, Jae-Ho; Wang, Soo-Hyun; Eom, Doo-Seop

doi:10.1109/tie.2017.2764861

Cited by 259 publications

(104 citation statements)

References 31 publications

Supporting

Mentioning

103

Contrasting

Unclassified

Order By: Relevance

“…Enhanced BrownBoost Classifier based Glowworm Swarm Optimization (EBBC-GWO) Method is designed for detecting the water leakage in WDS and compared with existing one-dimensional convolutional neural network and support vector machine (1D-CNN-SVM) model (Kang et al, 2018) and Multi-Stage Graph Partitioning Approach (Rajeswaran, Narasimhan and Narasimhan, 2018) . The efficiency of EBBC-GWO method is evaluated along with the metrics such as classification accuracy, classification time, water leakage detection accuracy and false positive rate.…”

Section: Simulation Results Analysismentioning

confidence: 99%

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

Rayaroth

Gopalakrishnan

2018

Preprint

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> Water Distribution System distributes the water to customer with the better quality and pressure. Distribution system supplies the water from their source to usage point. Due to the leakage, the sufficient amount of water is not delivered to the consumer. Many researchers introduced the techniques for detecting the water leakage in distribution system. But, the water leakage detection accuracy was not improved and time consumption was also not reduced. To improve the water leakage detection performance, Enhanced BrownBoost Classifier based Glowworm Swarm Optimization (EBBC-GWO) Method is introduced. EBBC-GWO method introduces two models namely, Enhanced BrownBoost Classifier model and Glowworm Swarm Optimization model. Enhanced BrownBoost Classifier model considers k-Nearest Neighbor (k-NN) classifier as weak classifier. It classifies the training samples with neighbor's majority vote for allocating the object to the class. Brownboost classifier combines all k-NN classifier to construct strong classifier. By this way, data are classified as the normal data or abnormal data with higher accuracy. After classification, optimization process is executed where every solution corresponds to the glowworm (i.e., abnormal pressure data node) in search space. Every glowworm has objective function for addressing the optimization problem. Every glowworm operates in probabilistic means to choose the neighbor with higher luciferin value and transmit to it. Glowworm updates its location to the glowworm in dynamic decision space and optimal one is selected for water leakage detection. By this way, water leakage detection accuracy is improved with lesser false positive rate. Experimental evaluation of proposed EBBC-GWO method is carried out with respect to number of pressure data and sensor placement nodes. The results demonstrated that EBBC-GWO method is higher in case of classification accuracy, false positive rate, classification time and water leakage detection accuracy. The simulation results show that EBBC-GWO method increases the performance of water leakage detection accuracy and reduces classification time when compared to state-of-the-art works.</p>

show abstract

Section: Simulation Results Analysismentioning

confidence: 99%

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

Rayaroth

Gopalakrishnan

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, Banihashemian et al employ the particle swarm optimization technique combining with MLPs to perform range-free WSN localization, which achieves low localization error [349]. Kang et al shed light water leakage and localization in water distribution systems [351]. They represent the water pipeline network as a graph and assume leakage events occur at vertices.…”

Section: F Deep Learning Driven Wireless Sensor Networkmentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,377

838

View full text Add to dashboard Cite

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

show abstract

“…In other words, high-frequency components and random noises in a raw time-series sensory signal can be naturally filtered. Although CNNs have not been verified to eliminate noise well, lightweight preprocessing (such as smoothing and averaging) can naturally improve the performance [40]. …”

Section: The Proposed System Designmentioning

confidence: 99%

Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Kang

Lee

Eom

2018

Sensors

Self Cite

View full text Add to dashboard Cite

We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments.

show abstract

Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems

Cited by 259 publications

References 31 publications

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

Deep Learning in Mobile and Wireless Networking: A Survey

Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Contact Info

Product

Resources

About