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Ngwenyama MK, Gitau MN. Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers. Sci Rep 2024; 14:6080. [PMID: 38480776 PMCID: PMC10937916 DOI: 10.1038/s41598-024-56598-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/08/2024] [Indexed: 03/17/2024] Open
Abstract
Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.
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Affiliation(s)
- M K Ngwenyama
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, 0002, South Africa.
| | - M N Gitau
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, 0002, South Africa
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Jan Ben S, Dörner M, Günther MP, von Känel R, Euler S. Proof of concept: Predicting distress in cancer patients using back propagation neural network (BPNN). Heliyon 2023; 9:e18328. [PMID: 37576295 PMCID: PMC10412887 DOI: 10.1016/j.heliyon.2023.e18328] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Background Research findings suggest that a significant proportion of individuals diagnosed with cancer, ranging from 25% to 60%, experience distress and require access to psycho-oncological services. Until now, only contemporary approaches, such as logistic regression, have been used to determine predictors of distress in oncological patients. To improve individual prediction accuracy, novel approaches are required. We aimed to establish a prediction model for distress in cancer patients based on a back propagation neural network (BPNN). Methods Retrospective data was gathered from a cohort of 3063 oncological patients who received diagnoses and treatment spanning the years 2011-2019. The distress thermometer (DT) has been used as screening instrument. Potential predictors of distress were identified using logistic regression. Subsequently, a prediction model for distress was developed using BPNN. Results Logistic regression identified 13 significant independent variables as predictors of distress, including emotional, physical and practical problems. Through repetitive data simulation processes, it was determined that a 3-layer BPNN with 8 neurons in the hidden layer demonstrates the highest level of accuracy as a prediction model. This model exhibits a sensitivity of 79.0%, specificity of 71.8%, positive predictive value of 78.9%, negative predictive value of 71.9%, and an overall coincidence rate of 75.9%. Conclusion The final BPNN model serves as a compelling proof of concept for leveraging artificial intelligence in predicting distress and its associated risk factors in cancer patients. The final model exhibits a remarkable level of discrimination and feasibility, underscoring its potential for identifying patients vulnerable to distress.
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Affiliation(s)
- Schulze Jan Ben
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marc Dörner
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Moritz Philipp Günther
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roland von Känel
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sebastian Euler
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Wang Q, Li H, You J, Yan B, Jin W, Shen M, Sheng Y, He B, Wang X, Meng X, Qin L. An integrated strategy of spectrum-effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba. ANAL SCI 2023:10.1007/s44211-023-00334-4. [PMID: 37037970 DOI: 10.1007/s44211-023-00334-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/30/2023] [Indexed: 04/12/2023]
Abstract
The quantitative analysis of near-infrared spectroscopy in traditional Chinese medicine has still deficiencies in the selection of the measured indexes. Then Paeoniae Radix Alba is one of the famous "Eight Flavors of Zhejiang" herbs, however, it lacks the pharmacodynamic support, and cannot reflect the quality of Paeoniae Radix Alba accurately and reasonably. In this study, the spectrum-effect relationship of the anti-inflammatory activity of Paeoniae Radix Alba was established. Then based on the obtained bioactive component groups, the genetic algorithm, back propagation neural network, was combined with near-infrared spectroscopy to establish calibration models for the content of the bioactive components of Paeoniae Radix Alba. Finally, three bioactive components, paeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, and benzoyl paeoniflorin, were successfully obtained. Their near-infrared spectroscopy content models were also established separately, and the validation sets results showed the coefficient of determination (R2 > 0.85), indicating that good calibration statistics were obtained for the prediction of key pharmacodynamic components. As a result, an integrated analytical method of spectrum-effect relationship combined with near-infrared spectroscopy and deep learning algorithm was first proposed to assess and control the quality of traditional Chinese medicine, which is the future development trend for the rapid inspection of traditional Chinese medicine.
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Affiliation(s)
- Qi Wang
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Huaqiang Li
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Jinling You
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Binjun Yan
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Weifeng Jin
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Menglan Shen
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Yunjie Sheng
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Bingqian He
- Academy of Chinese Medical Science, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District310053, Hangzhou, Zhejiang Province, People's Republic of China
| | - Xinrui Wang
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Xiongyu Meng
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China.
| | - Luping Qin
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China.
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Bian Z, Sun L, Tian K, Liu B, Zhang X, Mao Z, Huang B, Wu L. Estimation of Heavy Metals in Tailings and Soils Using Hyperspectral Technology: A Case Study in a Tin-Polymetallic Mining Area. Bull Environ Contam Toxicol 2021; 107:1022-1031. [PMID: 34241644 DOI: 10.1007/s00128-021-03311-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 06/22/2021] [Indexed: 06/13/2023]
Abstract
Rapid assessment of heavy metal (HM) pollution in mining areas is urgently required for further remediation. Here, hyperspectral technology was used to predict HM contents of multi-media environments (tailings, surrounding soils and agricultural soils) in a mining area. The correlation between hyperspectral data and HMs was explored, then the prediction models were established by partial least squares regression (PLSR) and back propagation neural networks (BPNN). The determination coefficients (R2), root mean squared error and ratios of performance to interquartile range (RPIQ) were used to evaluate the performance of the models. Results show that: (1) both PLSR and BPNN had good prediction ability, and (2) BPNN had better generalization ability (Cu (R2 = 0.89, RPIQ = 3.05), Sn (R2 = 0.86, RPIQ = 4.91), Zn (R2 = 0.74, RPIQ = 1.44) and Pb (R2 = 0.70, RPIQ = 2.10)). In summary, this study indicates that hyperspectral technology has potential application in HM estimation and soil pollution investigation in polymetallic mining areas.
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Affiliation(s)
- Zijin Bian
- Key Laboratory of Regional Environment and Eco-Remediation (Shenyang University), Ministry of Education, Shenyang, 110044, China
- CAS Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
| | - Lina Sun
- Key Laboratory of Regional Environment and Eco-Remediation (Shenyang University), Ministry of Education, Shenyang, 110044, China
| | - Kang Tian
- CAS Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
| | - Benle Liu
- CAS Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
| | - Xiaohui Zhang
- CAS Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
- Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of the Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China
| | - Zhiqiang Mao
- CAS Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
- Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of the Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China
| | - Biao Huang
- CAS Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
| | - Longhua Wu
- CAS Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China
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Chang FJ, Chang LC, Kang CC, Wang YS, Huang A. Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques. Sci Total Environ 2020; 736:139656. [PMID: 32485387 DOI: 10.1016/j.scitotenv.2020.139656] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 05/19/2020] [Accepted: 05/22/2020] [Indexed: 05/16/2023]
Abstract
The complex mixtures of local emission sources and regional transportations of air pollutants make accurate PM2.5 prediction a very challenging yet crucial task, especially under high pollution conditions. A symbolic representation of spatio-temporal PM2.5 features is the key to effective air pollution regulatory plans that notify the public to take necessary precautions against air pollution. The self-organizing map (SOM) can cluster high-dimensional datasets to form a meaningful topological map. This study implements the SOM to effectively extract and clearly distinguish the spatio-temporal features of long-term regional PM2.5 concentrations in a visible two-dimensional topological map. The spatial distribution of the configured topological map spans the long-term datasets of 25 monitoring stations in northern Taiwan using the Kriging method, and the temporal behavior of PM2.5 concentrations at various time scales (i.e., yearly, seasonal, and hourly) are explored in detail. Finally, we establish a machine learning model to predict PM2.5 concentrations for high pollution events. The analytical results indicate that: (1) high population density and heavy traffic load correspond to high PM2.5 concentrations; (2) the change of seasons brings obvious effects on PM2.5 concentration variation; and (3) the key input variables of the prediction model identified by the Gamma Test can improve model's reliability and accuracy for multi-step-ahead PM2.5 prediction. The results demonstrated that machine learning techniques can skillfully summarize and visibly present the clusted spatio-temporal PM2.5 features as well as improve air quality prediction accuracy.
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Affiliation(s)
- Fi-John Chang
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan.
| | - Li-Chiu Chang
- Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan
| | - Che-Chia Kang
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Yi-Shin Wang
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Angela Huang
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
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