Research on Flywheel Energy Storage System for Power Quality

AbstractThis paper presents a design of flywheel energystorage (FES) system in power network, which is composed offour parts: 1) the flywheel that stores energy, 2) the bearingthat supports the flywheel, 3) the asynchronousmotor/generator, and 4) the AC power converter regulated by amicroprocessor controller. The control methods and strategy ofthe FES system for power quality are introduced in detail. Anew rapid method to calculate the amplitude of sinusoidalvoltage and current is presented which could improve theperformance of the FES system. To realize the high efficiency ofthe energy conversion and to minimize the torque ripple of themotor/generator, the waveform of the AC power converteroutput currents is controlled to be sinusoidal by usingsinusoidal pulse width modulation (SPWM) control method.During the storage period, the AC power converter acceleratesthe flywheel storing energy. At the generating times, the ACpower converter drives the flywheel to decelerate and then thekinetic energy is transformed into electric energy and returnedto power system. This paper also presents an experimentalstudy of the FES system storing energy and synchronousoperating with power system, and the control validity is verifiedthrough the experimental results4 Halama

Multimodal brain tumor image segmentation based on DenseNet.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value

Efficacy of 5-HT3 receptor antagonists (ondansetron) vs dopamine receptor antagonists (droperidol) for preventing postoperative nausea, vomiting and headache: a meta-analysis

Objective To investigate the effects of 5-hydroxytryptamine 3 receptor antagonists (ondansetron [OND]) versus dopamine receptor antagonists (droperidol [DRO]) in the prevention of postoperative nausea, vomiting (PONV) and headache by pooling data from open published studies. Methods Performed systematic electronic searches of PubMed, Embase, Google scholar and CNKI, to identify open-published prospective randomized controlled trials (RCTs) relevant to the comparison of OND versus DRO for preventing PONV and headache to be included in the present study. The pooled PONV, headache, dizziness and drowsiness were calculated based on the original data of each included study. The pooled data was presented with risk ratio (RR) and 95% confidence interval (95%CI). Results Thirteen prospective randomized clinical trials were included in this meta-analysis. The pooled PONV, post-operative nausea (PON) and positive operative vomiting (POV) were 0.67 (95%CI:0.48-0.93, p0.05) and 0.56 (95%CI:0.39-0.82,p0.05), 0.63 (95%CI:0.21-1.87, p>0.05) and 0.48(0.28-0.81,p<0.05) respectively by fixed effect model for OND vs. DRO. Conclusion The post-operative nausea, vomiting and dizziness risks were significant decreased for patients receiving OND compared to patients receiving DRO

Dense block structure.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.</div

Algorithm framework of this paper.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.</div

Fig 7 -

Segmentation results of the second group (a) T1 (b) T2 (c) T1ce (d) Flair (e) GT (f) The resulting diagram of the algorithm in this paper.</p

Structure diagram of a transition layer.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.</div