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Pingjie Huang
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: 16 May 2020 in Chemometrics and Intelligent Laboratory Systems
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There is a great deal of interests in multivariate approaches which may offer a necessary, and often sufficient, data analysis technique in many fields such as analytical chemistry, biology and environmental chemistry. However, few of these multivariate approaches have paid attention to the nonnegativity constraint in the decomposition process. In this paper, a novel bound constrained optimization method was proposed for three-way chemical data analysis. In this method, nonnegative matrix factorization was introduced to replace the traditional trilinear decomposition to provide the constraints on the nonnegative boundary on excitation-emission matrix spectra data. And the least-squares problem was transformed into a bound constrained optimization problem which can be solved by projected gradient methods. The alternating least squares were applied during each optimization iteration to obtain the individual components. Analysis of simulated three-way arrays indicated that the proposed method has a better performance than parallel factor analysis and alternating trilinear decomposition methods in nonnegativity. Experiments of real excitation-emission matrix spectra data also show that the proposed method is robust with the background interferences in practical applications.

ACS Style

Ke Wang; Jie Yu; Yiming Bi; Yuhan Li; Pingjie Huang; Dibo Hou; Guangxin Zhang. A novel bound constrained optimization method for three-way chemical data analysis with application to fluorescence excitation-emission matrix spectroscopy. Chemometrics and Intelligent Laboratory Systems 2020, 203, 104036 .

AMA Style

Ke Wang, Jie Yu, Yiming Bi, Yuhan Li, Pingjie Huang, Dibo Hou, Guangxin Zhang. A novel bound constrained optimization method for three-way chemical data analysis with application to fluorescence excitation-emission matrix spectroscopy. Chemometrics and Intelligent Laboratory Systems. 2020; 203 ():104036.

Chicago/Turabian Style

Ke Wang; Jie Yu; Yiming Bi; Yuhan Li; Pingjie Huang; Dibo Hou; Guangxin Zhang. 2020. "A novel bound constrained optimization method for three-way chemical data analysis with application to fluorescence excitation-emission matrix spectroscopy." Chemometrics and Intelligent Laboratory Systems 203, no. : 104036.

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: 11 December 2018 in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
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Techniques to inspect and analyze human colorectal cancer cell lines by using terahertz time-domain attenuated total reflection spectroscopy (THz TD-ATR) were investigated. The characteristics of THz absorption spectra of two colorectal cancer cell lines DLD-1 and HT-29 in aqueous solutions with different concentrations were studied. Different spectral features were observed compared to normal cell line. Identification results based on different parameters including absorption coefficient, refractive index, real and imaginary parts of complex permittivity, dielectric loss tangent were discussed. This research may be promising for quick and instant inspection of liquid samples by using THz time-domain spectroscopy in medical applications.

ACS Style

Yuqi Cao; Jiani Chen; Pingjie Huang; Weiting Ge; Dibo Hou; Guangxin Zhang. Inspecting human colon adenocarcinoma cell lines by using terahertz time-domain reflection spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2018, 211, 356 -362.

AMA Style

Yuqi Cao, Jiani Chen, Pingjie Huang, Weiting Ge, Dibo Hou, Guangxin Zhang. Inspecting human colon adenocarcinoma cell lines by using terahertz time-domain reflection spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2018; 211 ():356-362.

Chicago/Turabian Style

Yuqi Cao; Jiani Chen; Pingjie Huang; Weiting Ge; Dibo Hou; Guangxin Zhang. 2018. "Inspecting human colon adenocarcinoma cell lines by using terahertz time-domain reflection spectroscopy." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 211, no. : 356-362.

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.

Articles
Published: 08 October 2018 in Nondestructive Testing and Evaluation
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Pulsed eddy current (PEC) technology has become a burgeoning method for detection and analysis of multi-layer conductive structures owing to rich time and frequency domain information presented by PEC signals. In this study, PEC technique is applied to characterise hidden-defect parameters while nondestructively inspecting multi-layer structures. A projection pursuit (PP) feature extraction method based on the information divergence index is investigated to effectively analyse PEC signals. An improved accelerating genetic algorithm is adopted to find the optimal projection direction. The signal’s dimension is reduced with minimal information loss while the data’s structure is preserved to the greatest degree. The features extracted on the basis of PP are simultaneously employed in crack localisation and crack length quantitative evaluation combined with a SVM classifier. The theoretical analysis and experimental results demonstrate that compared with the principal component analysis method, the features extracted by the presented PP algorithm work better for simultaneously characterising crack’s depth and size information and it reflect the inherent laws of the data, which make the features more physically interpretation meanwhile. Inversion accuracy for smaller and deeper cracks is enhanced obviously which will be helpful for crack localisation and quantitative identification of crack parameters in difficult situations.

ACS Style

Pingjie Huang; Tianyu Ding; Qing Luo; Dibo Hou; Jie Yu; Guangxin Zhang. Defect localisation and quantitative identification in multi-layer conductive structures based on projection pursuit algorithm. Nondestructive Testing and Evaluation 2018, 34, 70 -86.

AMA Style

Pingjie Huang, Tianyu Ding, Qing Luo, Dibo Hou, Jie Yu, Guangxin Zhang. Defect localisation and quantitative identification in multi-layer conductive structures based on projection pursuit algorithm. Nondestructive Testing and Evaluation. 2018; 34 (1):70-86.

Chicago/Turabian Style

Pingjie Huang; Tianyu Ding; Qing Luo; Dibo Hou; Jie Yu; Guangxin Zhang. 2018. "Defect localisation and quantitative identification in multi-layer conductive structures based on projection pursuit algorithm." Nondestructive Testing and Evaluation 34, no. 1: 70-86.

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.

Accepted manuscript
Published: 29 November 2017 in Physics in Medicine & Biology
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At present, many researchers are exploring biological tissue inspection using terahertz time-domain spectroscopy (THz-TDS) techniques. In this study, based on a modified hard modeling factor analysis (HMFA) method, terahertz spectral unmixing was applied to investigate the relationships between the absorption spectra in terahertz time-domain spectroscopy (THz-TDS) and certain biomarkers of gastric cancer in order to systematically identify gastric cancer. A probability distribution and box plot were used to extract the distinctive peaks that indicate carcinogenesis, and the corresponding weight distributions were used to discriminate the tissue types. The results of this work indicate that terahertz techniques have the potential to detect different levels of cancer, including benign tumors and polyps.

ACS Style

Yuqi Cao; Pingjie Huang; Xian Li; Weiting Ge; Dibo Hou; Guangxin Zhang. Terahertz spectral unmixing based method for identifying gastric cancer. Physics in Medicine & Biology 2017, 63, 035016 .

AMA Style

Yuqi Cao, Pingjie Huang, Xian Li, Weiting Ge, Dibo Hou, Guangxin Zhang. Terahertz spectral unmixing based method for identifying gastric cancer. Physics in Medicine & Biology. 2017; 63 (3):035016.

Chicago/Turabian Style

Yuqi Cao; Pingjie Huang; Xian Li; Weiting Ge; Dibo Hou; Guangxin Zhang. 2017. "Terahertz spectral unmixing based method for identifying gastric cancer." Physics in Medicine & Biology 63, no. 3: 035016.

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.

Journal article
Published: 01 April 2017 in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
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In many situations the THz spectroscopic data observed from complex samples represent the integrated result of several interrelated variables or feature components acting together. The actual information contained in the original data might be overlapping and there is a necessity to investigate various approaches for model reduction and data unmixing. The development and use of low-rank approximate nonnegative matrix factorization (NMF) and smooth constraint NMF (CNMF) algorithms for feature components extraction and identification in the fields of terahertz time domain spectroscopy (THz-TDS) data analysis are presented. The evolution and convergence properties of NMF and CNMF methods based on sparseness, independence and smoothness constraints for the resulting nonnegative matrix factors are discussed. For general NMF, its cost function is nonconvex and the result is usually susceptible to initialization and noise corruption, and may fall into local minima and lead to unstable decomposition. To reduce these drawbacks, smoothness constraint is introduced to enhance the performance of NMF. The proposed algorithms are evaluated by several THz-TDS data decomposition experiments including a binary system and a ternary system simulating some applications such as medicine tablet inspection. Results show that CNMF is more capable of finding optimal solutions and more robust for random initialization in contrast to NMF. The investigated method is promising for THz data resolution contributing to unknown mixture identification.

ACS Style

Yehao Ma; Xian Li; Pingjie Huang; Dibo Hou; Qiang Wang; Guangxin Zhang. THz spectral data analysis and components unmixing based on non-negative matrix factorization methods. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2017, 177, 49 -57.

AMA Style

Yehao Ma, Xian Li, Pingjie Huang, Dibo Hou, Qiang Wang, Guangxin Zhang. THz spectral data analysis and components unmixing based on non-negative matrix factorization methods. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2017; 177 ():49-57.

Chicago/Turabian Style

Yehao Ma; Xian Li; Pingjie Huang; Dibo Hou; Qiang Wang; Guangxin Zhang. 2017. "THz spectral data analysis and components unmixing based on non-negative matrix factorization methods." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 177, no. : 49-57.

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.

Journal article
Published: 01 January 2016 in Chemometrics and Intelligent Laboratory Systems
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Highlights•To obtain the THz spectra of pure components, SMCR was performed to resolve the THz data of mixture.•Second derivative was employed to improve the purity of pure variables in resolution.•SVD was used to predict the number of components in system. AbstractThis study aims at resolving the spectral profiles of pure components from the mixture THz data. A method based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), one of self-modeling curve resolution (SMCR) techniques, was put forward to deal with two-way THz spectral data of unknown mixtures for spectral resolution of single species. SMCR requires pure variables which have intensity contributions from only one component for extracting the content information. However, for the spectra of many materials in THz region (about 0.1–3 THz), pure variables are usually unavailable for the overlap of absorption peaks or the presence of baseline. To settle this problem, inverted second derivative was employed as an intermediate step to optimize the determination of pure variable. To test the effect, the proposed method was performed on simulated and experimental THz spectra of multicomponent systems. It is demonstrated that SIMPLISMA combined with inverted second derivative spectra is a promising tool to deconvolve multicomponent systems contributing to the identification of ingredients in unknown mixture in THz region.

ACS Style

Yehao Ma; Pingjie Huang; Dibo Hou; Jinhui Cai; Qiang Wang; Guangxin Zhang. The spectral resolution of unknown mixture based on THz spectroscopy with self-modeling technique. Chemometrics and Intelligent Laboratory Systems 2016, 150, 65 -73.

AMA Style

Yehao Ma, Pingjie Huang, Dibo Hou, Jinhui Cai, Qiang Wang, Guangxin Zhang. The spectral resolution of unknown mixture based on THz spectroscopy with self-modeling technique. Chemometrics and Intelligent Laboratory Systems. 2016; 150 ():65-73.

Chicago/Turabian Style

Yehao Ma; Pingjie Huang; Dibo Hou; Jinhui Cai; Qiang Wang; Guangxin Zhang. 2016. "The spectral resolution of unknown mixture based on THz spectroscopy with self-modeling technique." Chemometrics and Intelligent Laboratory Systems 150, no. : 65-73.

Journal article
Published: 26 June 2015 in Optics Express
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The issue of distribution water quality security ensuring is recently attracting global attention due to the potential threat from harmful contaminants. The real-time monitoring based on ultraviolet optical sensors is a promising technique. This method is of reagent-free, low maintenance cost, rapid analysis and wide cover range. However, the ultraviolet absorption spectra are of large size and easily interfered. While within the on-site application, there is almost no prior knowledge like spectral characteristics of potential contaminants before determined. Meanwhile, the concept of normal water quality is also varying due to the operating condition. In this paper, a procedure based on multivariate statistical analysis is proposed to detect distribution water quality anomaly based on ultraviolet optical sensors. Firstly, the principal component analysis is employed to capture the main variety features from the spectral matrix and reduce the dimensionality. A new statistical variable is then constructed and used for evaluating the local outlying degree according to the chi-square distribution in the principal component subspace. The possibility of anomaly of the latest observation is calculated by the accumulation of the outlying degrees from the adjacent previous observations. To develop a more reliable anomaly detection procedure, several key parameters are discussed. By utilizing the proposed methods, the distribution water quality anomalies and the optical abnormal changes can be detected. The contaminants intrusion experiment is conducted in a pilot-scale distribution system by injecting phenol solution. The effectiveness of the proposed procedure is finally testified using the experimental spectral data.

ACS Style

Dibo Hou; Jian Zhang; Zheling Yang; Shu Liu; Pingjie Huang; Guangxin Zhang. Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution. Optics Express 2015, 23, 17487 -17510.

AMA Style

Dibo Hou, Jian Zhang, Zheling Yang, Shu Liu, Pingjie Huang, Guangxin Zhang. Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution. Optics Express. 2015; 23 (13):17487-17510.

Chicago/Turabian Style

Dibo Hou; Jian Zhang; Zheling Yang; Shu Liu; Pingjie Huang; Guangxin Zhang. 2015. "Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution." Optics Express 23, no. 13: 17487-17510.

Journal article
Published: 21 October 2014 in Water, Air, & Soil Pollution
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Drinking water network is vulnerable to toxic chemicals. Anomaly detection-based event detection can provide reliable indication of contamination by analyzing the real-time water quality data, collected by online-distributed sensors in water network. This article reviews the water quality event detection methodologies based on the correlation of water quality parameters and contaminants. Further, we review how to reduce the impact of contamination in water distribution network, including sensor placement optimization and contamination source determination.

ACS Style

Haifeng Zhao; Dibo Hou; Pingjie Huang; Guangxin Zhang. Water Quality Event Detection in Drinking Water Network. Water, Air, & Soil Pollution 2014, 225, 1 .

AMA Style

Haifeng Zhao, Dibo Hou, Pingjie Huang, Guangxin Zhang. Water Quality Event Detection in Drinking Water Network. Water, Air, & Soil Pollution. 2014; 225 (11):1.

Chicago/Turabian Style

Haifeng Zhao; Dibo Hou; Pingjie Huang; Guangxin Zhang. 2014. "Water Quality Event Detection in Drinking Water Network." Water, Air, & Soil Pollution 225, no. 11: 1.

Journal article
Published: 10 May 2013 in International Journal of Food Engineering
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The dynamic weighing signal processing method in high-speed fruits sorting system directly affects the weighing accuracy. This paper describes a basic modeling method for time series, including modeling, order determining, applicability analysis, and prediction. Considering the structural characteristics of weighing system and the limit of PLC sampling speed, a modified auto-regressive (AR) model for stable value prediction of the high-speed weighing signals is presented. Meanwhile, a simulation base on Matlab platform and an implementation example are used to test the method performance. At last, the impacts of environmental changes, sorting speeds, fruits shapes, and sizes on the method performance are discussed. In experimental conditions, this method can be applied to fruits sorting system with sorting speed up to 18 per second for standard fruit (smooth round surface without defects) and an acceptable weighing accuracy can be obtained.

ACS Style

Hui-Mei He; Pingjie Huang; Dibo Hou; Wen Cai; Zhe Liu; Guangxin Zhang. An Intelligent Signal Processing Method for High-Speed Weighing System. International Journal of Food Engineering 2013, 9, 179 -186.

AMA Style

Hui-Mei He, Pingjie Huang, Dibo Hou, Wen Cai, Zhe Liu, Guangxin Zhang. An Intelligent Signal Processing Method for High-Speed Weighing System. International Journal of Food Engineering. 2013; 9 (2):179-186.

Chicago/Turabian Style

Hui-Mei He; Pingjie Huang; Dibo Hou; Wen Cai; Zhe Liu; Guangxin Zhang. 2013. "An Intelligent Signal Processing Method for High-Speed Weighing System." International Journal of Food Engineering 9, no. 2: 179-186.

Book chapter
Published: 01 January 2012 in Advances in Intelligent and Soft Computing
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Traditional water quality early warning systems are often static and the algorithms built in are difficult to modify while the system is in operation. So a changing environment has brought great challenges to the systems. To overcome the shortages, a new water quality early warning system is designed; it is based on the idea that the algorithms codes can be compiled dynamically and executed automatically. The algorithms codes can be updated in real time and executed as jobs. And thread-pool is used to execute the jobs in parallel and the system is designed to manage jobs for fault-tolerant. Finally, the realization of the framework is given and some rules to make the system stable and high fault tolerant are discussed.

ACS Style

Tian Jing; Zheng Shuyin; Zhang Guangxin; Hou Dibo; Huang Pingjie; Zhang Jian. New Design for Water Quality Early Warning System. Advances in Intelligent and Soft Computing 2012, 681 -685.

AMA Style

Tian Jing, Zheng Shuyin, Zhang Guangxin, Hou Dibo, Huang Pingjie, Zhang Jian. New Design for Water Quality Early Warning System. Advances in Intelligent and Soft Computing. 2012; ():681-685.

Chicago/Turabian Style

Tian Jing; Zheng Shuyin; Zhang Guangxin; Hou Dibo; Huang Pingjie; Zhang Jian. 2012. "New Design for Water Quality Early Warning System." Advances in Intelligent and Soft Computing , no. : 681-685.

Journal article
Published: 31 July 2009 in NDT & E International
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An improved analytical model by the Fourier method for transient eddy current response is presented. In this work, an alternative approach is considered to solve the harmonic eddy current problem by the reflection and transmission theory of electromagnetic waves, thus a more concise closed-form expression is expected to be obtained. To reduce the inherent Gibbs phenomenon, a harmonic order-dependent decreasing factor is employed to weight the Fourier series (FS) representation. It is shown that the developed model is promising to be used as a fast and accurate analytical solver for the transient probe response and is helpful to gain a deep insight into pulsed eddy current (PEC) testing.

ACS Style

Mengbao Fan; Pingjie Huang; Bo Ye; Dibo Hou; Guangxin Zhang; Zekui Zhou. Analytical modeling for transient probe response in pulsed eddy current testing. NDT & E International 2009, 42, 376 -383.

AMA Style

Mengbao Fan, Pingjie Huang, Bo Ye, Dibo Hou, Guangxin Zhang, Zekui Zhou. Analytical modeling for transient probe response in pulsed eddy current testing. NDT & E International. 2009; 42 (5):376-383.

Chicago/Turabian Style

Mengbao Fan; Pingjie Huang; Bo Ye; Dibo Hou; Guangxin Zhang; Zekui Zhou. 2009. "Analytical modeling for transient probe response in pulsed eddy current testing." NDT & E International 42, no. 5: 376-383.