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Loss of water due to leakage is a common phenomenon observed practically in all water distribution networks (WDNs). However, the leakage volume can be reduced significantly if the occurrence of leakage is detected within minimal time after its occurrence. Based on the discriminative behavior of different consumption in water balance, an integrated bottom-up water balance model is presented for leak detection in WDNs. The adaptive moment estimation (Adam) algorithm is employed to assess the parameters in the model. By analyzing the current value and the rising rate of the assessed parameters, abnormal events (e.g., leak, illegal use, or metering inaccuracy) could be detected. Furthermore, a one-step-slower strategy is proposed to estimate the weighted coefficient of pressure sensors to provide approximate location information of leak. The method was applied in a benchmark WDN and an experimental WDN to evaluate its performance. The results showed that relatively small leak could be detected in near-real-time. In addition, the method was able to identify the pressure sensors near to the leak.
Jie Yu; Li Zhang; Jinyu Chen; Yao Xiao; Dibo Hou; Pingjie Huang; Guangxin Zhang; Hongjian Zhang. An Integrated Bottom-Up Approach for Leak Detection in Water Distribution Networks Based on Assessing Parameters of Water Balance Model. Water 2021, 13, 867 .
AMA StyleJie Yu, Li Zhang, Jinyu Chen, Yao Xiao, Dibo Hou, Pingjie Huang, Guangxin Zhang, Hongjian Zhang. An Integrated Bottom-Up Approach for Leak Detection in Water Distribution Networks Based on Assessing Parameters of Water Balance Model. Water. 2021; 13 (6):867.
Chicago/Turabian StyleJie Yu; Li Zhang; Jinyu Chen; Yao Xiao; Dibo Hou; Pingjie Huang; Guangxin Zhang; Hongjian Zhang. 2021. "An Integrated Bottom-Up Approach for Leak Detection in Water Distribution Networks Based on Assessing Parameters of Water Balance Model." Water 13, no. 6: 867.
Segmentation of a river scene is a representative case of complex image segmentation. Different from road segmentation, river scenes often have unstructured boundaries and contain complex light and shadow on the water’s surface. According to the imaging mechanism of water pixels, this paper designed a water description feature based on a multi-block local binary pattern (MB-LBP) and Hue variance in HSI color space to detect the water region in the image. The improved Local Binary Pattern (LBP) feature was used to recognize the water region and the local texture descriptor in HSI color space using Hue variance was used to detect the shadow area of the river surface. Tested on two data sets including simple and complex river scenes, the proposed method has better segmentation performance and consumes less time than those of two other widely used methods.
Jie Yu; Youxin Lin; Yanni Zhu; Wenxin Xu; Dibo Hou; Pingjie Huang; Guangxin Zhang. Segmentation of River Scenes Based on Water Surface Reflection Mechanism. Applied Sciences 2020, 10, 2471 .
AMA StyleJie Yu, Youxin Lin, Yanni Zhu, Wenxin Xu, Dibo Hou, Pingjie Huang, Guangxin Zhang. Segmentation of River Scenes Based on Water Surface Reflection Mechanism. Applied Sciences. 2020; 10 (7):2471.
Chicago/Turabian StyleJie Yu; Youxin Lin; Yanni Zhu; Wenxin Xu; Dibo Hou; Pingjie Huang; Guangxin Zhang. 2020. "Segmentation of River Scenes Based on Water Surface Reflection Mechanism." Applied Sciences 10, no. 7: 2471.
Emergent contamination warning systems are critical to ensure drinking water supply security. After detecting the existence of contaminants, identifying the types of contaminants is conducive to taking remediation measures. An online classification method for contaminants, which explored abnormal fluctuation information and the correlation between 12 water quality indicators adequately, is proposed to realize comprehensive and accurate discrimination of contaminants. Firstly, the paper utilized multi-fractal detrended fluctuation analysis (MF-DFA) to select indicators with abnormal fluctuation, used multi-fractal detrended cross-correlation analysis (MF-DCCA) to measure the cross-correlation between indicators. Subsequently, the algorithm fused the abnormal probability of each indicator and constructed the abnormal probability matrix to further judge the abnormal fluctuation of indicators using D–S evidence theory. Finally, the singularity index of the cross-correlation function and the selected indicators were used to classification by cosine distance. Experiments of five chemical contaminants at three concentration levels were implemented, and analysis results show the method can weaken disturbance of water quality background noise and other interfering factors. It effectively improved the classification accuracy at low concentrations compared with another three methods, including methods using triple standard deviation threshold and single indicator fluctuation analysis-only methods without fluctuation analysis. This can be applied to water quality emergency monitoring systems to reduce contaminant misclassification.
Yanni Zhu; Kexin Wang; Youxin Lin; Hang Yin; Dibo Hou; Jie Yu; Pingjie Huang; Guangxin Zhang. An Online Contaminant Classification Method Based on MF-DCCA Using Conventional Water Quality Indicators. Processes 2020, 8, 178 .
AMA StyleYanni Zhu, Kexin Wang, Youxin Lin, Hang Yin, Dibo Hou, Jie Yu, Pingjie Huang, Guangxin Zhang. An Online Contaminant Classification Method Based on MF-DCCA Using Conventional Water Quality Indicators. Processes. 2020; 8 (2):178.
Chicago/Turabian StyleYanni Zhu; Kexin Wang; Youxin Lin; Hang Yin; Dibo Hou; Jie Yu; Pingjie Huang; Guangxin Zhang. 2020. "An Online Contaminant Classification Method Based on MF-DCCA Using Conventional Water Quality Indicators." Processes 8, no. 2: 178.
Tumor genesis is accompanied by glycosylation of related proteins. Glycoprotein is usually regarded as a tumor marker since glycoproteins are consumed remarkably more by the cancer cells than the normal ones. In this paper, the terahertz time-domain attenuated total reflection (ATR) technique is applied to inspect the glycoprotein solution from a concentration gradient of 0.2 mg/ml to 50 mg/ml. A significant nonlinear relationship between the absorption coefficient and the concentrations has been discovered. The influence of the dynamical hydration shell around glycoprotein molecules on the absorption coefficient is discussed and the phenomenon is explained by the concepts of THz excess and THz defect. In order to identify glycoproteins, features are obtained by composite multiscale entropy (CMSE) method and clustered by the K-means algorithm. The results indicate that features extracted by the CMSE method are better than the Principal Component Analysis (PCA) method in both specificity and sensitivity of recognition. Meanwhile, the absorption coefficient and dielectric loss angle tangent are more suitable for qualitative identification. Research shows that the CMSE method has important directive significance for analyzing glycoprotein terahertz spectroscopy. And it has the potential for glycoprotein related tumor markers identification using terahertz technology in medical applications.
Pingjie Huang; Zhangwei Huang; Xiaodong Lu; Yuqi Cao; Jie Yu; Dibo Hou; Guangxin Zhang. Study on glycoprotein terahertz time-domain spectroscopy based on composite multiscale entropy feature extraction method. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2019, 229, 117948 .
AMA StylePingjie Huang, Zhangwei Huang, Xiaodong Lu, Yuqi Cao, Jie Yu, Dibo Hou, Guangxin Zhang. Study on glycoprotein terahertz time-domain spectroscopy based on composite multiscale entropy feature extraction method. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2019; 229 ():117948.
Chicago/Turabian StylePingjie Huang; Zhangwei Huang; Xiaodong Lu; Yuqi Cao; Jie Yu; Dibo Hou; Guangxin Zhang. 2019. "Study on glycoprotein terahertz time-domain spectroscopy based on composite multiscale entropy feature extraction method." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 229, no. : 117948.
This paper proposes a morphological grayscale reconstruction method combined with an alternating trilinear decomposition (ATLD) and threshold method based on 3D fluorescence spectroscopy to detect pollutants present at low concentrations in drinking water. First, the morphological grayscale reconstruction method was used to locate the fluorescence peaks of pollutants by comparing the original and reconstructed spectra obtained through expansion. The signal in the characteristic spectral region was then enhanced using an amplification factor. Feature extraction was subsequently performed by ATLD, and the threshold method was used to qualitatively distinguish water quality. By comparing the proposed method with the direct use of the ATLD and threshold method—which is a commonly used feature-extraction method—this study found that the application of the morphological grayscale reconstruction method can extrude characteristics of 3D fluorescence spectra. Given the typical spectral characteristics of phenol, salicylic acid, and rhodamine B, they were selected as experimental organic pollutants. Results illustrated that the morphological grayscale reconstruction with ATLD improved the spectral signal-to-noise ratio of pollutants and can effectively identify organic pollutants, especially those present at low concentrations.
Fei Shi; Tingting Mao; Yitong Cao; Jie Yu; Dibo Hou; Pingjie Huang; Guangxin Zhang. Morphological Grayscale Reconstruction and ATLD for Recognition of Organic Pollutants in Drinking Water Based on Fluorescence Spectroscopy. Water 2019, 11, 1859 .
AMA StyleFei Shi, Tingting Mao, Yitong Cao, Jie Yu, Dibo Hou, Pingjie Huang, Guangxin Zhang. Morphological Grayscale Reconstruction and ATLD for Recognition of Organic Pollutants in Drinking Water Based on Fluorescence Spectroscopy. Water. 2019; 11 (9):1859.
Chicago/Turabian StyleFei Shi; Tingting Mao; Yitong Cao; Jie Yu; Dibo Hou; Pingjie Huang; Guangxin Zhang. 2019. "Morphological Grayscale Reconstruction and ATLD for Recognition of Organic Pollutants in Drinking Water Based on Fluorescence Spectroscopy." Water 11, no. 9: 1859.
This paper proposes a new method to detect bursts in District Metering Areas (DMAs) in water distribution systems. The methodology is divided into three steps. Firstly, Dynamic Time Warping was applied to study the similarity of daily water demand, extract different patterns of water demand, and remove abnormal patterns. In the second stage, according to different water demand patterns, a supervised learning algorithm was adopted for burst detection, which established a leakage identification model for each period of time, respectively, using a sliding time window. Finally, the detection process was performed by calculating the abnormal probability of flow during a certain period by the model and identifying whether a burst occurred according to the set threshold. The method was validated on a case study involving a DMA with engineered pipe-burst events. The results obtained demonstrate that the proposed method can effectively detect bursts, with a low false-alarm rate and high accuracy.
Pingjie Huang; Naifu Zhu; Dibo Hou; Jinyu Chen; Yao Xiao; Jie Yu; Guangxin Zhang; Hongjian Zhang. Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning. Water 2018, 10, 1765 .
AMA StylePingjie Huang, Naifu Zhu, Dibo Hou, Jinyu Chen, Yao Xiao, Jie Yu, Guangxin Zhang, Hongjian Zhang. Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning. Water. 2018; 10 (12):1765.
Chicago/Turabian StylePingjie Huang; Naifu Zhu; Dibo Hou; Jinyu Chen; Yao Xiao; Jie Yu; Guangxin Zhang; Hongjian Zhang. 2018. "Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning." Water 10, no. 12: 1765.
As a core part of protecting water quality safety in water distribution systems, contamination event detection requires high accuracy. Previously, temporal analysis-based methods for single sensor stations have shown limited performance as they fail to consider spatial information. Besides, abundant historical data from multiple stations are still underexploited in causal relationship modelling. In this paper, a contamination event detection method is proposed, in which both temporal and spatial information from multi-stations in water distribution systems are used. The causal relationship between upstream and downstream stations is modelled by Bayesian Network, using the historical water quality data and hydraulic data. Then, the spatial abnormal probability for one station is obtained by comparing its current causal relationship with the established model. Meanwhile, temporal abnormal probability is obtained by conventional methods, such as an Autoregressive (AR) or threshold model for the same station. The integrated probability that is calculated employed temporal and spatial probabilities using Logistic Regression to determine the final detection result. The proposed method is tested over two networks and its detection performance is evaluated against results obtained from traditional methods using only temporal analysis. Results indicate that the proposed method shows higher accuracy due to its increased information from both temporal and spatial dimensions.
Jie Yu; Le Xu; Xiang Xie; Dibo Hou; Pingjie Huang; Guangxin Zhang; Hongjian Zhang. Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems. Water 2017, 9, 894 .
AMA StyleJie Yu, Le Xu, Xiang Xie, Dibo Hou, Pingjie Huang, Guangxin Zhang, Hongjian Zhang. Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems. Water. 2017; 9 (11):894.
Chicago/Turabian StyleJie Yu; Le Xu; Xiang Xie; Dibo Hou; Pingjie Huang; Guangxin Zhang; Hongjian Zhang. 2017. "Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems." Water 9, no. 11: 894.
Water quality early warning system is mainly used to detect deliberate or accidental water pollution events in water distribution systems. Identifying the types of pollutants is necessary after detecting the presence of pollutants to provide warning information about pollutant characteristics and emergency solutions. Thus, a real-time contaminant classification methodology, which uses the multi-classification support vector machine (SVM), is proposed in this study to obtain the probability for contaminants belonging to a category. The SVM-based model selected samples with indistinct feature, which were mostly low-concentration samples as the support vectors, thereby reducing the influence of the concentration of contaminants in the building process of a pattern library. The new sample points were classified into corresponding regions after constructing the classification boundaries with the support vector. Experimental results show that the multi-classification SVM-based approach is less affected by the concentration of contaminants when establishing a pattern library compared with the cosine distance classification method. Moreover, the proposed approach avoids making a single decision when classification features are unclear in the initial phase of injecting contaminants.
Pingjie Huang; Yu Jin; Dibo Hou; Jin Yu; Dezhan Tu; Yitong Cao; Guangxin Zhang. Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors. Sensors 2017, 17, 581 .
AMA StylePingjie Huang, Yu Jin, Dibo Hou, Jin Yu, Dezhan Tu, Yitong Cao, Guangxin Zhang. Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors. Sensors. 2017; 17 (3):581.
Chicago/Turabian StylePingjie Huang; Yu Jin; Dibo Hou; Jin Yu; Dezhan Tu; Yitong Cao; Guangxin Zhang. 2017. "Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors." Sensors 17, no. 3: 581.