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- Du, Mian, et al.
(författare)
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A Parameter Selection Method for Wind Turbine Health Management through SCADA Data
- 2017
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Ingår i: Energies. - : MDPI AG. - 1996-1073. ; 10:2
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Tidskriftsartikel (refereegranskat)abstract
- Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA) system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine's condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. First, the copula is proven to be capable of simplifying the estimation of mutual information. Then an empirical copula-based mutual information estimation method (ECMI) is introduced for application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.
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| 3. |
- Kozhevnikov, Evgeny, et al.
(författare)
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A dual-transduction-integrated biosensing system to examine the 3D cell-culture for bone regeneration
- 2019
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Ingår i: Biosensors & bioelectronics. - : ELSEVIER ADVANCED TECHNOLOGY. - 0956-5663 .- 1873-4235. ; 141
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Tidskriftsartikel (refereegranskat)abstract
- Three-dimensional (3D) cell cultures developed with living cells and scaffolds have demonstrated outstanding potential for tissue engineering and regenerative medicine applications. However, no suitable tools are available to monitor dynamically variable cell behavior in such a complex microenvironment. In particular, simultaneously assessing cell behavior, cell secretion, and the general state of a 3D culture system is of a really challenging task. This paper presents our development of a dual-transduction-integrated biosensing system that assesses electrical impedance in conjunction with imaging techniques to simultaneously investigate the 3D cell-culture for bone regeneration. First, we created models to mimic the dynamic deposition of the extracellular matrix (ECM) in 3D culture, which underwent osteogenesis by incorporating different amounts of bone-ECM components (collagen, hydroxyapatite [HAp], and hyaluronic acid [HA]) into alginate-based hydrogels. The formed models were investigated by means of electrical impedance spectroscopy (EIS), with the results showing that the impedances increased linearly with collagen and hyaluronan, but changed in a more complex manner with HAp. Thereafter, we created two models that consisted of primary osteoblast cells (OBs), which expressed the enhanced green fluorescent protein (EGFP), and 4T1 cells, which secreted the EGFP-HA, in the alginate hydrogel. We found the capacitance (associated with impedance and measured by EIS) increased with the increases in initial embedded OBs, and also confirmed the cell proliferation over 3 days with the EGFP signal as monitored by the fluorescent imaging component in our system. Interestingly, the change in capacitance is found to be associated with OB migration following stimulation. Also, we show higher capacitance in 4T1 cells that secret HA when compared to control 4T1 cells after a 3-day culture. Taken together, we demonstrate that our biosensing system is able to investigate the dynamic process of 3D culture in a non-invasive and real-time manner.
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| 4. |
- Li, Wei, et al.
(författare)
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Non-lab and semi-lab algorithms for screening undiagnosed diabetes : A cross-sectional study
- 2018
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Ingår i: EBioMedicine. - : ELSEVIER SCIENCE BV. - 2352-3964. ; 35, s. 307-316
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Tidskriftsartikel (refereegranskat)abstract
- Background: The terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data. Methods: This multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20-70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n - 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model. Results: The overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P < 0.001). The optimal cutoff probability in semi-lab and non-lab nomograms were 0.088 and 0.098, respectively. The sensitivity and specificity were 76.3% and 81.6%, respectively in semi-lab nomogram, and 72.1% and 673% in non-lab nomogram at the optimal cut off point. The decision curve analysis also revealed a bigger decrease of avoidable OGTT test (52 per 100 subjects) in the semi-lab model compared to the non-lab model (36 per 100 subjects) and the existed New Chinese Diabetes Risk Score (NCDRS, 35 per 100 subjects). Conclusion: The non-lab and semi-lab nomograms appear to be reliable tools for diabetes screening, especially in developing countries. However, the semi-lab model outperformed the non-lab model and NCDRS prediction systems and might be worth being adopted as decision support in diabetes screening in China.
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