The Experts below are selected from a list of 1278 Experts worldwide ranked by ideXlab platform
Donghua Zhou - One of the best experts on this subject based on the ideXlab platform.
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bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of Pipeline Leak Detection
Expert Systems With Applications, 2011Co-Authors: Zhijie Zhou, Dongling Xu, Jianbo Yang, Changhua Hu, Donghua ZhouAbstract:In this paper a recursive algorithm based on the Bayesian reasoning approach is proposed to update a belief rule based (BRB) expert system for Pipeline Leak Detection and Leak size estimation. In addition to using available real time data, expert knowledge on the relationships of the parameters among different rules is incorporated into the updating process so that the performance of the expert system can be improved. Experiments are carried out to compare the newly proposed algorithm with the previously published algorithms, and results show that the proposed algorithm can update the BRB expert system faster and more accurately, which is important for real-time applications. The BRB expert systems can be automatically tuned to represent complex real world systems, and applied widely in engineering.
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online updating belief rule based system for Pipeline Leak Detection under expert intervention
Expert Systems With Applications, 2009Co-Authors: Zhijie Zhou, Dongling Xu, Jianbo Yang, Changhua Hu, Donghua ZhouAbstract:A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for Pipeline Leak Detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the Pipeline Leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for Pipeline Leak Detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and Pipeline operating data, and validated by testing data. All training and testing data are collected from a real Pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the internal structure and the parameters of a BRB expert system.
Dongling Xu - One of the best experts on this subject based on the ideXlab platform.
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bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of Pipeline Leak Detection
Expert Systems With Applications, 2011Co-Authors: Zhijie Zhou, Dongling Xu, Jianbo Yang, Changhua Hu, Donghua ZhouAbstract:In this paper a recursive algorithm based on the Bayesian reasoning approach is proposed to update a belief rule based (BRB) expert system for Pipeline Leak Detection and Leak size estimation. In addition to using available real time data, expert knowledge on the relationships of the parameters among different rules is incorporated into the updating process so that the performance of the expert system can be improved. Experiments are carried out to compare the newly proposed algorithm with the previously published algorithms, and results show that the proposed algorithm can update the BRB expert system faster and more accurately, which is important for real-time applications. The BRB expert systems can be automatically tuned to represent complex real world systems, and applied widely in engineering.
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online updating belief rule based system for Pipeline Leak Detection under expert intervention
Expert Systems With Applications, 2009Co-Authors: Zhijie Zhou, Dongling Xu, Jianbo Yang, Changhua Hu, Donghua ZhouAbstract:A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for Pipeline Leak Detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the Pipeline Leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for Pipeline Leak Detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and Pipeline operating data, and validated by testing data. All training and testing data are collected from a real Pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the internal structure and the parameters of a BRB expert system.
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inference and learning methodology of belief rule based expert system for Pipeline Leak Detection
Expert Systems With Applications, 2007Co-Authors: Dongling Xu, Jianbo Yang, Jin Wang, Ian JenkinsonAbstract:Belief rule based expert systems are an extension of traditional rule based systems and are capable of representing more complicated causal relationships using different types of information with uncertainties. This paper describes how the belief rule based expert systems can be trained and used for Pipeline Leak Detection. Pipeline operations under different conditions are modelled by a belief rule base using expert knowledge, which is then trained and fine tuned using Pipeline operating data, and validated by testing data. All training and testing data are collected and scaled from a real Pipeline. The study demonstrates that the belief rule based system is flexible, can be adapted to represent complicated expert systems, and is a valid novel approach for Pipeline Leak Detection.
Yuandong Gu - One of the best experts on this subject based on the ideXlab platform.
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low cost tiny sized mems hydrophone sensor for water Pipeline Leak Detection
IEEE Transactions on Industrial Electronics, 2019Co-Authors: Jinghui Xu, Kevin Tshun-chuan Chai, Guoqiang Wu, Wei Li, Edwin Nijhof, Yuandong GuAbstract:In this paper, we present an experimental investigation of a water Pipeline Leak Detection system based on a low-cost, tiny-sized hydrophone sensor fabricated using the microelectromechanical system (MEMS) technologies. A 10 × 10 element arrayed MEMS hydrophone device with chip size of 3.5 × 3.5 mm $^2$ was used in the experiment. The hydrophone device is packaged with a customized on-board preamplification circuit using an acoustic transparent material. The overall package size of the MEMS hydrophone is $\Phi$ 1.2 × 2.5 cm. The packaged MEMS hydrophone achieves an acoustic sensitivity of −180 dB (re: 1 V/ $\mu$ Pa), a bandwidth from 10 Hz to 8 kHz, and a noise resolution of around 60 dB (re: 1 $\mu \text{Pa/}\sqrt{\text{Hz}}$ ) at 1 kHz. A section of ductile iron water Pipeline with an internal diameter of 10 cm, wall thickness of 0.73 cm, and length of 30 m is constructed as the test bed for the water Leak Detection. Two different Leak sizes with Leak flow rates of about 30 and 180 L/min are designed along the pipe, which is pressurized at 3.2 bar. Analysis of the transient signals and spectrograms shows that the MEMS hydrophone can capture the key acoustic information of the water Leak, i.e., identifying the Leak and locating the Leak position. The measurement results demonstrate the feasibility to construct an affordable, highly efficient, real-time, and permanent in-pipe Pipeline health monitoring network based on the MEMS hydrophones due to their high performance, low cost, and tiny size.
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Low-Cost, Tiny-Sized MEMS Hydrophone Sensor for Water Pipeline Leak Detection
IEEE Transactions on Industrial Electronics, 2019Co-Authors: Jinghui Xu, Kevin Tshun-chuan Chai, Guoqiang Wu, Wei Li, Edwin Nijhof, Yuandong GuAbstract:In this paper, we present an experimental investigation of a water Pipeline Leak Detection system based on a low-cost, tiny-sized hydrophone sensor fabricated using the microelectromechanical system (MEMS) technologies. A 10 × 10 element arrayed MEMS hydrophone device with chip size of 3.5 × 3.5 mm2 was used in the experiment. The hydrophone device is packaged with a customized on-board preamplification circuit using an acoustic transparent material. The overall package size of the MEMS hydrophone is Φ1.2 × 2.5 cm. The packaged MEMS hydrophone achieves an acoustic sensitivity of -180 dB (re: 1 V/μPa), a bandwidth from 10 Hz to 8 kHz, and a noise resolution of around 60 dB (re: 1 μPa/√Hz) at 1 kHz. A section of ductile iron water Pipeline with an internal diameter of 10 cm, wall thickness of 0.73 cm, and length of 30 m is constructed as the test bed for the water Leak Detection. Two different Leak sizes with Leak flow rates of about 30 and 180 L/min are designed along the pipe, which is pressurized at 3.2 bar. Analysis of the transient signals and spectrograms shows that the MEMS hydrophone can capture the key acoustic information of the water Leak, i.e., identifying the Leak and locating the Leak position. The measurement results demonstrate the feasibility to construct an affordable, highly efficient, real-time, and permanent in-pipe Pipeline health monitoring network based on the MEMS hydrophones due to their high performance, low cost, and tiny size.
Jianbo Yang - One of the best experts on this subject based on the ideXlab platform.
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bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of Pipeline Leak Detection
Expert Systems With Applications, 2011Co-Authors: Zhijie Zhou, Dongling Xu, Jianbo Yang, Changhua Hu, Donghua ZhouAbstract:In this paper a recursive algorithm based on the Bayesian reasoning approach is proposed to update a belief rule based (BRB) expert system for Pipeline Leak Detection and Leak size estimation. In addition to using available real time data, expert knowledge on the relationships of the parameters among different rules is incorporated into the updating process so that the performance of the expert system can be improved. Experiments are carried out to compare the newly proposed algorithm with the previously published algorithms, and results show that the proposed algorithm can update the BRB expert system faster and more accurately, which is important for real-time applications. The BRB expert systems can be automatically tuned to represent complex real world systems, and applied widely in engineering.
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online updating belief rule based system for Pipeline Leak Detection under expert intervention
Expert Systems With Applications, 2009Co-Authors: Zhijie Zhou, Dongling Xu, Jianbo Yang, Changhua Hu, Donghua ZhouAbstract:A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for Pipeline Leak Detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the Pipeline Leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for Pipeline Leak Detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and Pipeline operating data, and validated by testing data. All training and testing data are collected from a real Pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the internal structure and the parameters of a BRB expert system.
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inference and learning methodology of belief rule based expert system for Pipeline Leak Detection
Expert Systems With Applications, 2007Co-Authors: Dongling Xu, Jianbo Yang, Jin Wang, Ian JenkinsonAbstract:Belief rule based expert systems are an extension of traditional rule based systems and are capable of representing more complicated causal relationships using different types of information with uncertainties. This paper describes how the belief rule based expert systems can be trained and used for Pipeline Leak Detection. Pipeline operations under different conditions are modelled by a belief rule base using expert knowledge, which is then trained and fine tuned using Pipeline operating data, and validated by testing data. All training and testing data are collected and scaled from a real Pipeline. The study demonstrates that the belief rule based system is flexible, can be adapted to represent complicated expert systems, and is a valid novel approach for Pipeline Leak Detection.
Zhijie Zhou - One of the best experts on this subject based on the ideXlab platform.
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bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of Pipeline Leak Detection
Expert Systems With Applications, 2011Co-Authors: Zhijie Zhou, Dongling Xu, Jianbo Yang, Changhua Hu, Donghua ZhouAbstract:In this paper a recursive algorithm based on the Bayesian reasoning approach is proposed to update a belief rule based (BRB) expert system for Pipeline Leak Detection and Leak size estimation. In addition to using available real time data, expert knowledge on the relationships of the parameters among different rules is incorporated into the updating process so that the performance of the expert system can be improved. Experiments are carried out to compare the newly proposed algorithm with the previously published algorithms, and results show that the proposed algorithm can update the BRB expert system faster and more accurately, which is important for real-time applications. The BRB expert systems can be automatically tuned to represent complex real world systems, and applied widely in engineering.
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online updating belief rule based system for Pipeline Leak Detection under expert intervention
Expert Systems With Applications, 2009Co-Authors: Zhijie Zhou, Dongling Xu, Jianbo Yang, Changhua Hu, Donghua ZhouAbstract:A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for Pipeline Leak Detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the Pipeline Leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for Pipeline Leak Detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and Pipeline operating data, and validated by testing data. All training and testing data are collected from a real Pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the internal structure and the parameters of a BRB expert system.