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Dibo Hou
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

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Journal article
Published: 23 March 2021 in Water
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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.

ACS Style

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 Style

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 (6):867.

Chicago/Turabian Style

Jie 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.

Journal article
Published: 03 April 2020 in Applied Sciences
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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.

ACS Style

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 Style

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 (7):2471.

Chicago/Turabian Style

Jie 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.

Journal article
Published: 05 February 2020 in Processes
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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.

ACS Style

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 Style

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 (2):178.

Chicago/Turabian Style

Yanni 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.

Journal article
Published: 21 January 2020 in Biomedical Optics Express
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Terahertz technology has been widely used as a nondestructive and effective detection method. Herein, terahertz time-domain spectroscopy was used to detect drug-induced liver injury in mice. Firstly, the boxplots were used to detect abnormal data. Then the maximal information coefficient method was used to search for the features strongly correlated with the degree of injury. After that, the liver injury model was built using the random forests method in machine learning. The results show that this method can effectively identify the degree of liver injury and thus provide an auxiliary diagnostic method for detecting minor liver injury.

ACS Style

Yuqi Cao; Pingjie Huang; Jiani Chen; Weiting Ge; Dibo Hou; Guangxin Zhang. Qualitative and quantitative detection of liver injury with terahertz time-domain spectroscopy. Biomedical Optics Express 2020, 11, 982 -993.

AMA Style

Yuqi Cao, Pingjie Huang, Jiani Chen, Weiting Ge, Dibo Hou, Guangxin Zhang. Qualitative and quantitative detection of liver injury with terahertz time-domain spectroscopy. Biomedical Optics Express. 2020; 11 (2):982-993.

Chicago/Turabian Style

Yuqi Cao; Pingjie Huang; Jiani Chen; Weiting Ge; Dibo Hou; Guangxin Zhang. 2020. "Qualitative and quantitative detection of liver injury with terahertz time-domain spectroscopy." Biomedical Optics Express 11, no. 2: 982-993.

Journal article
Published: 06 September 2019 in Water
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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.

ACS Style

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 Style

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 (9):1859.

Chicago/Turabian Style

Fei 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.

Journal article
Published: 28 August 2019 in Optics Express
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At present, researchers are exploring biological tissue detection method using terahertz techniques. In this paper, techniques to inspect mouse liver injury by using terahertz spectroscopy were studied. The boxplots were applied to remove abnormal data, and the maximal information coefficient was employed to select crucial features from the absorption coefficient and refractive index spectra. Random Forests and AdaBoost were applied to recognize different levels of liver injury. We found that AdaBoost had better performance on low-level injury classification. This work suggests that terahertz techniques have the potential to detect liver injury at an early stage and evaluate liver treatment strategies.

ACS Style

Pingjie Huang; Yuqi Cao; Jiani Chen; Weiting Ge; Dibo Hou; Guangxin Zhang. Analysis and inspection techniques for mouse liver injury based on terahertz spectroscopy. Optics Express 2019, 27, 26014 -26026.

AMA Style

Pingjie Huang, Yuqi Cao, Jiani Chen, Weiting Ge, Dibo Hou, Guangxin Zhang. Analysis and inspection techniques for mouse liver injury based on terahertz spectroscopy. Optics Express. 2019; 27 (18):26014-26026.

Chicago/Turabian Style

Pingjie Huang; Yuqi Cao; Jiani Chen; Weiting Ge; Dibo Hou; Guangxin Zhang. 2019. "Analysis and inspection techniques for mouse liver injury based on terahertz spectroscopy." Optics Express 27, no. 18: 26014-26026.

Journal article
Published: 13 February 2019 in Optics Express
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The identification of the specific categories of pollutants in the urban water supply system is necessary. Traditional detection methods are based mainly on common water quality indicators. However, inspecting these water quality indicators is made difficult by issues such as long analysis time, insufficient sensitivity, need for reagents, and generation of waste liquid. These problems hinder high-frequency water detection and monitoring. In this study, three-dimensional (3D) fluorescence spectroscopy is adopted as a monitoring method for water quality. An identification method based on two-dimensional (2D) Gabor wavelets and support vector machine (SVM) multi-classification is also proposed. The Delaunay triangulation method for interpolation is used to pre-process 3D fluorescence spectra and thereby eliminate Rayleigh scattering and Raman scattering. A 2D Gabor wavelet function generated by filters of different scales and rotation angles is proposed to extract the features of the spectra. The block statistics method, based on Gabor feature description, is employed to enhance the efficiency in describing spectra features. Then, multiple SVM classifiers are used in pollutant classification and recognition. By comparing the proposed method with principal component analysis, which is a commonly used feature extraction method, this study finds that the application of 2D Gabor wavelets and block statistics can effectively describe the characteristics of 3D fluorescence spectra. Moreover, 2D Gabor wavelets achieve high classification accuracy, especially for substances with closely positioned or overlapping characteristic peaks.

ACS Style

P. Huang; T. Mao; Q. Yu; Y. Cao; J. Yu; G. Zhang; D. Hou. Classification of water contamination developed by 2-D Gabor wavelet analysis and support vector machine based on fluorescence spectroscopy. Optics Express 2019, 27, 5461 -5477.

AMA Style

P. Huang, T. Mao, Q. Yu, Y. Cao, J. Yu, G. Zhang, D. Hou. Classification of water contamination developed by 2-D Gabor wavelet analysis and support vector machine based on fluorescence spectroscopy. Optics Express. 2019; 27 (4):5461-5477.

Chicago/Turabian Style

P. Huang; T. Mao; Q. Yu; Y. Cao; J. Yu; G. Zhang; D. Hou. 2019. "Classification of water contamination developed by 2-D Gabor wavelet analysis and support vector machine based on fluorescence spectroscopy." Optics Express 27, no. 4: 5461-5477.

Article
Published: 05 January 2019 in Water Resources Management
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Leakages in water distribution networks have caused considerable waste of water resources. Thus, this study proposes a novel method for hydraulically monitoring and identifying regions where leakages occur in near-real time. A large network is first divided into several identification regions. To exploit a strong constructive and discriminative power, sparse coding is used, thereby adaptively coding the information embedded in observed pressures efficiently and succinctly. And a linear classifier is trained to determine the most likely leakage regions. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. Results indicate that the proposed method can identify leakage events with enhanced tolerance capability for measurement errors. The method is also partially effective for identifying two simultaneous leakages. Certain practical advice in balancing the number of sensors and regions is also discussed to enhance the application potential of this method.

ACS Style

Xiang Xie; Dibo Hou; Xiaoyu Tang; Hongjian Zhang. Leakage Identification in Water Distribution Networks with Error Tolerance Capability. Water Resources Management 2019, 33, 1233 -1247.

AMA Style

Xiang Xie, Dibo Hou, Xiaoyu Tang, Hongjian Zhang. Leakage Identification in Water Distribution Networks with Error Tolerance Capability. Water Resources Management. 2019; 33 (3):1233-1247.

Chicago/Turabian Style

Xiang Xie; Dibo Hou; Xiaoyu Tang; Hongjian Zhang. 2019. "Leakage Identification in Water Distribution Networks with Error Tolerance Capability." Water Resources Management 33, no. 3: 1233-1247.

Journal article
Published: 01 December 2018 in Water
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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.

ACS Style

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 Style

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 (12):1765.

Chicago/Turabian Style

Pingjie 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.

Journal article
Published: 02 November 2018 in Water
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A method that uses the ultraviolet-visible (UV-Vis) spectrum to detect organic contamination events in water distribution systems exhibits the advantages of rapid detection, low cost, and no need for reagents. The speed, accuracy, and comprehensive analysis of such a method meet the requirements for online water quality monitoring. However, the UV-Vis spectrum is easily disturbed by environmental factors that cause fluctuations of the spectrum and result in false alarms. This study proposes an adaptive method for detecting organic contamination events in water distribution systems that uses the UV-Vis spectrum based on a semi-supervised learning model. This method modifies the baseline using dynamic orthogonal projection correction and adjusts the support vector regression model in real time. Thus, an adaptive online anomaly detection model that maximizes the use of unlabeled data is obtained. Experimental results demonstrate that the proposed method is adaptive to baseline drift and exhibits good performance in detecting organic contamination events in water distribution systems.

ACS Style

Qiaojun Yu; Hang Yin; Ke Wang; Hui Dong; Dibo Hou. Adaptive Detection Method for Organic Contamination Events in Water Distribution Systems Using the UV-Vis Spectrum Based on Semi-Supervised Learning. Water 2018, 10, 1566 .

AMA Style

Qiaojun Yu, Hang Yin, Ke Wang, Hui Dong, Dibo Hou. Adaptive Detection Method for Organic Contamination Events in Water Distribution Systems Using the UV-Vis Spectrum Based on Semi-Supervised Learning. Water. 2018; 10 (11):1566.

Chicago/Turabian Style

Qiaojun Yu; Hang Yin; Ke Wang; Hui Dong; Dibo Hou. 2018. "Adaptive Detection Method for Organic Contamination Events in Water Distribution Systems Using the UV-Vis Spectrum Based on Semi-Supervised Learning." Water 10, no. 11: 1566.

Journal article
Published: 22 March 2018 in Sensors
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In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems.

ACS Style

Kexin Wang; Xiang Wen; Dibo Hou; Dezhan Tu; Naifu Zhu; Pingjie Huang; Guangxin Zhang; Hongjian Zhang. Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters. Sensors 2018, 18, 938 .

AMA Style

Kexin Wang, Xiang Wen, Dibo Hou, Dezhan Tu, Naifu Zhu, Pingjie Huang, Guangxin Zhang, Hongjian Zhang. Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters. Sensors. 2018; 18 (4):938.

Chicago/Turabian Style

Kexin Wang; Xiang Wen; Dibo Hou; Dezhan Tu; Naifu Zhu; Pingjie Huang; Guangxin Zhang; Hongjian Zhang. 2018. "Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters." Sensors 18, no. 4: 938.

Journal article
Published: 16 November 2017 in Water
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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.

ACS Style

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 Style

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 (11):894.

Chicago/Turabian Style

Jie 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.

Research article
Published: 02 October 2017 in Journal of Sensors
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Traditionally permanent acoustic sensors leak detection techniques have been proven to be very effective in water distribution pipes. However, these methods need long distance deployment and proper position of sensors and cannot be implemented on underground pipelines. An inline-inspection acoustic device is developed which consists of acoustic sensors. The device will travel by the flow of water through the pipes which record all noise events and detect small leaks. However, it records all the noise events regarding background noises, but the time domain noisy acoustic signal cannot manifest complete features such as the leak flow rate which does not distinguish the leak signal and environmental disturbance. This paper presents an algorithm structure with the modularity of wavelet and neural network, which combines the capability of wavelet transform analyzing leakage signals and classification capability of artificial neural networks. This study validates that the time domain is not evident to the complete features regarding noisy leak signals and significance of selection of mother wavelet to extract the noise event features in water distribution pipes. The simulation consequences have shown that an appropriate mother wavelet has been selected and localized to extract the features of the signal with leak noise and background noise, and by neural network implementation, the method improves the classification performance of extracted features.

ACS Style

Dileep Kumar; Dezhan Tu; Naifu Zhu; Dibo Hou; Hongjian Zhang. In-Line Acoustic Device Inspection of Leakage in Water Distribution Pipes Based on Wavelet and Neural Network. Journal of Sensors 2017, 2017, 1 -10.

AMA Style

Dileep Kumar, Dezhan Tu, Naifu Zhu, Dibo Hou, Hongjian Zhang. In-Line Acoustic Device Inspection of Leakage in Water Distribution Pipes Based on Wavelet and Neural Network. Journal of Sensors. 2017; 2017 ():1-10.

Chicago/Turabian Style

Dileep Kumar; Dezhan Tu; Naifu Zhu; Dibo Hou; Hongjian Zhang. 2017. "In-Line Acoustic Device Inspection of Leakage in Water Distribution Pipes Based on Wavelet and Neural Network." Journal of Sensors 2017, no. : 1-10.

Journal article
Published: 23 September 2017 in Water
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The conventional fixed acoustic sensors leak detection methods have been demonstrated to be very practical for locating leakages in water distribution pipelines. However, these methods demand proper installation of sensors, and therefore cannot be implemented on buried long water distribution pipelines for condition assessment, early leak detection, and the estimation of leak size effect. Due to these limitations, a free-swimming device is developed. The free-swimming device with the potential of high acoustic sensitivity is capable of detecting the small underwater leakages in the plastic water-filled pipes. Despite the fact that a number of factors influence the underwater acoustic signals, such as water flow noise. Therefore, the interpretation of the leakage and influence of leakage size is considerably challenging from the underwater measured signals. The new method is proposed for reliable leakage detection by tuning the wavelet transform to underwater water acoustic signals. In this method, firstly, Short-Time Fourier Transforms (STFT) of underwater acoustic signals over a relatively long time-interval is monitored to capture the leakage-signals signature. The captured signals efficiently lead in the selection of mother wavelet (tuned wavelet) for the excellent signal localization in the time-frequency domain. Finally, the acoustic signals are analyzed in the tuned wavelet transform to detect the events. In this paper, the practical application of the proposed method, the controlled experiments are designed, and acoustic signals are collected from an experimental setup by launching the free-swimming device. The measured acoustic signals are used to identify the leakage-signals signature from unwanted interfering signals (instantaneous pipe vibrations, water flow noise, pipe's natural frequencies, and background noise). The evaluation of results validated that the free-swimming device and the tuned wavelet transform together can efficiently lead to reliable underwater leakage detection, as well as the influence of the leakage size in plastic water-filled pipes.

ACS Style

Dileep Kumar; Dezhan Tu; Naifu Zhu; Reehan Ali Shah; Dibo Hou; Hongjian Zhang. The Free-Swimming Device Leakage Detection in Plastic Water-filled Pipes through Tuning the Wavelet Transform to the Underwater Acoustic Signals. Water 2017, 9, 731 .

AMA Style

Dileep Kumar, Dezhan Tu, Naifu Zhu, Reehan Ali Shah, Dibo Hou, Hongjian Zhang. The Free-Swimming Device Leakage Detection in Plastic Water-filled Pipes through Tuning the Wavelet Transform to the Underwater Acoustic Signals. Water. 2017; 9 (10):731.

Chicago/Turabian Style

Dileep Kumar; Dezhan Tu; Naifu Zhu; Reehan Ali Shah; Dibo Hou; Hongjian Zhang. 2017. "The Free-Swimming Device Leakage Detection in Plastic Water-filled Pipes through Tuning the Wavelet Transform to the Underwater Acoustic Signals." Water 9, no. 10: 731.

Journal article
Published: 01 August 2017 in Journal of Water Resources Planning and Management
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A combined demand and roughness estimation is a critical step in order for the water distribution system model to represent the real system adequately. A novel two-level Markov chain Monte Carlo particle filter method for joint estimation of demand and roughness is proposed in this paper. First, an improved particle filter with ensemble Kalman filter modification to proposal density is adopted to track the non-Gaussian system dynamics and estimate demands. Then, the improved particle filter for demand estimation is nested into the Markov chain Monte Carlo simulation for roughness estimation. The method is very capable of quantifying the uncertainties associated with estimated or predicted values without requiring any assumptions of linearity and Gaussianity or any derivatives to be calculated. A strong nonlinear benchmark network with synthetically generated field data is utilized to validate the performance of this method. The results suggest that the proposed method is demonstrated to provide satisfactory demand and roughness values with reliable confidence limits. Some practical issues are also discussed to enhance the application potential of this method.

ACS Style

Xiang Xie; Hongjian Zhang; Dibo Hou. Bayesian Approach for Joint Estimation of Demand and Roughness in Water Distribution Systems. Journal of Water Resources Planning and Management 2017, 143, 04017034 .

AMA Style

Xiang Xie, Hongjian Zhang, Dibo Hou. Bayesian Approach for Joint Estimation of Demand and Roughness in Water Distribution Systems. Journal of Water Resources Planning and Management. 2017; 143 (8):04017034.

Chicago/Turabian Style

Xiang Xie; Hongjian Zhang; Dibo Hou. 2017. "Bayesian Approach for Joint Estimation of Demand and Roughness in Water Distribution Systems." Journal of Water Resources Planning and Management 143, no. 8: 04017034.

Research article
Published: 30 May 2017 in Journal of Spectroscopy
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The method based on conventional index and UV-vision has been widely applied in the field of water quality abnormality detection. This paper presents a qualitative analysis approach to detect the water contamination events with unknown pollutants. Fluorescence spectra were used as water quality monitoring tools, and the detection method of unknown contaminants in water based on alternating trilinear decomposition (ATLD) is proposed to analyze the excitation and emission spectra of the samples. The Delaunay triangulation interpolation method was used to make the pretreatment of three-dimensional fluorescence spectra data, in order to estimate the effect of Rayleigh and Raman scattering; ATLD model was applied to establish the model of normal water sample, and the residual matrix was obtained by subtracting the measured matrix from the model matrix; the residual sum of squares obtained from the residual matrix and threshold was used to make qualitative discrimination of test samples and distinguish drinking water samples and organic pollutant samples. The results of the study indicate that ATLD modeling with three-dimensional fluorescence spectra can provide a tool for detecting unknown organic pollutants in water qualitatively. The method based on fluorescence spectra can be complementary to the method based on conventional index and UV-vision.

ACS Style

Jie Yu; Xiaoyan Zhang; Dibo Hou; Fang Chen; Tingting Mao; Pingjie Huang; Guangxin Zhang. Detection of Water Contamination Events Using Fluorescence Spectroscopy and Alternating Trilinear Decomposition Algorithm. Journal of Spectroscopy 2017, 2017, 1 -9.

AMA Style

Jie Yu, Xiaoyan Zhang, Dibo Hou, Fang Chen, Tingting Mao, Pingjie Huang, Guangxin Zhang. Detection of Water Contamination Events Using Fluorescence Spectroscopy and Alternating Trilinear Decomposition Algorithm. Journal of Spectroscopy. 2017; 2017 ():1-9.

Chicago/Turabian Style

Jie Yu; Xiaoyan Zhang; Dibo Hou; Fang Chen; Tingting Mao; Pingjie Huang; Guangxin Zhang. 2017. "Detection of Water Contamination Events Using Fluorescence Spectroscopy and Alternating Trilinear Decomposition Algorithm." Journal of Spectroscopy 2017, no. : 1-9.

Research article
Published: 01 April 2017 in Environmental Science and Pollution Research
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The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling’s T2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

ACS Style

Jian Zhang; Dibo Hou; Ke Wang; Pingjie Huang; Guangxin Zhang; Hugo Loáiciga. Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data. Environmental Science and Pollution Research 2017, 24, 12882 -12898.

AMA Style

Jian Zhang, Dibo Hou, Ke Wang, Pingjie Huang, Guangxin Zhang, Hugo Loáiciga. Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data. Environmental Science and Pollution Research. 2017; 24 (14):12882-12898.

Chicago/Turabian Style

Jian Zhang; Dibo Hou; Ke Wang; Pingjie Huang; Guangxin Zhang; Hugo Loáiciga. 2017. "Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data." Environmental Science and Pollution Research 24, no. 14: 12882-12898.

Journal article
Published: 13 March 2017 in Sensors
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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.

ACS Style

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 Style

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 (3):581.

Chicago/Turabian Style

Pingjie 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.

Research article
Published: 19 June 2014 in Journal of Spectroscopy
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This study proposes a probabilistic principal component analysis- (PPCA-) based method for online monitoring of water-quality contaminant events by UV-Vis (ultraviolet-visible) spectroscopy. The purpose of this method is to achieve fast and sound protection against accidental and intentional contaminate injection into the water distribution system. The method is achieved first by properly imposing a sliding window onto simultaneously updated online monitoring data collected by the automated spectrometer. The PPCA algorithm is then executed to simplify the large amount of spectrum data while maintaining the necessary spectral information to the largest extent. Finally, a monitoring chart extensively employed in fault diagnosis field methods is used here to search for potential anomaly events and to determine whether the current water-quality is normal or abnormal. A small-scale water-pipe distribution network is tested to detect water contamination events. The tests demonstrate that the PPCA-based online monitoring model can achieve satisfactory results under the ROC curve, which denotes a low false alarm rate and high probability of detecting water contamination events.

ACS Style

Dibo Hou; Shu Liu; Jian Zhang; Fang Chen; Pingjie Huang; Guangxin Zhang. Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy. Journal of Spectroscopy 2014, 2014, 1 -9.

AMA Style

Dibo Hou, Shu Liu, Jian Zhang, Fang Chen, Pingjie Huang, Guangxin Zhang. Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy. Journal of Spectroscopy. 2014; 2014 (3):1-9.

Chicago/Turabian Style

Dibo Hou; Shu Liu; Jian Zhang; Fang Chen; Pingjie Huang; Guangxin Zhang. 2014. "Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy." Journal of Spectroscopy 2014, no. 3: 1-9.

Journal article
Published: 01 May 2014 in Environmental Science and Pollution Research
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A real-time, dynamic, early-warning model (EP-risk model) is proposed to cope with sudden water quality pollution accidents affecting downstream areas with raw-water intakes (denoted as EPs). The EP-risk model outputs the risk level of water pollution at the EP by calculating the likelihood of pollution and evaluating the impact of pollution. A generalized form of the EP-risk model for river pollution accidents based on Monte Carlo simulation, the analytic hierarchy process (AHP) method, and the risk matrix method is proposed. The likelihood of water pollution at the EP is calculated by the Monte Carlo method, which is used for uncertainty analysis of pollutants' transport in rivers. The impact of water pollution at the EP is evaluated by expert knowledge and the results of Monte Carlo simulation based on the analytic hierarchy process. The final risk level of water pollution at the EP is determined by the risk matrix method. A case study of the proposed method is illustrated with a phenol spill accident in China.

ACS Style

Dibo Hou; Xiaofan Ge; Pingjie Huang; Guangxin Zhang; Hugo Loaiciga. A real-time, dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents. Environmental Science and Pollution Research 2014, 21, 8878 -8892.

AMA Style

Dibo Hou, Xiaofan Ge, Pingjie Huang, Guangxin Zhang, Hugo Loaiciga. A real-time, dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents. Environmental Science and Pollution Research. 2014; 21 (14):8878-8892.

Chicago/Turabian Style

Dibo Hou; Xiaofan Ge; Pingjie Huang; Guangxin Zhang; Hugo Loaiciga. 2014. "A real-time, dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents." Environmental Science and Pollution Research 21, no. 14: 8878-8892.