Abstract No-electrical-insulation (NEI) magnets are gradually exhibiting significant appeal due to their robust thermal stability and elevated mechanical strength. However, when exposed to AC conditions, these magnets will suffer more significant AC losses in dynamic electromagnetic devices, such as motors and maglev systems. Presently, the numerical methods for predicting the electromagnetic and loss behavior of large-scale NEI magnets entail high computation costs due to the substantial degrees of freedom or complicated modeling strategies. Thus, we propose a fully finite element method, referred to as the field-circuit coupling method, to efficiently assess the overall behavior of NEI magnets while preserving adequate accuracy. This method couples the T-A formula and the single-turn equivalent circuit through a global voltage, to avoid the costly and complicated inductance calculations, and to simultaneously consider the induced current. By further integrating the homogenization method, the calculation speed can be increased up to ten times. Additionally, we study the critical current, and the electromagnetic and loss behavior of the NEI magnets based on the proposed model. We identify some measurement methods that offer more precise estimations of the critical current and the turn-to-turn contact resistance of NEI magnets. Meanwhile, the results indicate the severe impact of high AC fields on the losses, and emphasize the importance of a reliable shielding structure for operational safety. Finally, the influence of turn-to-turn contact resistivity on the loss behavior is also investigated, which can provide valuable insights for the design of NEI magnets in dynamic electromagnetic devices.
This paper mainly does some research on the problem of information exchange between HIS and PACS, which is starving for solution in the construction of hospital digitalization. HIS system mainly deals with patient information, it follows the standard of HL7.While PACS system mainly manages image information, it follows the standard of DICOM 3.0. Because HIS and PACS deal with different information and follow different standards, it is difficult to directly communicate. This paper presents a method of establishing HL7/DICOM gateway to realize the information exchange of HIS and PACS. The HL7/DICOM gateway designed in this paper is made up of three modules. First, HL7 messages and triggered events are designed to achieve the function of transaction processing module. According to the designed HL7 messages, four message interface functions are defined to accomplish the function of send/receive messages module. Finally, the algorithm of construct/parse messages is given to complete the function of construct/parse messages module. Result shows that HIS and PACS realize information exchange successfully by using the HL7/ DICOM gateway designed in this paper.
Electrocardiographic (ECG) signal are often contaminated with different types of noise and base-line drift. A morphological filtering approach was put forward to remove the noise of the ECG signals and to calibrate the base-line drift in this paper. Different sizes of structuring elements were used to process the signal for different nature of ECG signal and noise. The morphological filtering approach is simple, fast and real-time in processing, and it keeps the ECG signal shape unchanged while removing the noise. An experiment was carried out to simulate the morphological filtering approach with LABVIEW, and it was shown that this approach was effective in removing noise and in calibrating the base-line drift.
To develop an automatic classifying and diagnostic system for the hematopoietic cells from the blood and bone marrow smears stained with Wright-Giemsa, an automated segmentation algorithm fusing gray level, colorful information and mathematical morphological gradient is proposed for segmentation of the nucleated hematopoietic cells (including nucleus and cytoplasm). For the accurate segmentation of the nucleus, the conventional iterative threshold segmentation has been improved. Color information and prior knowledge are fully used by transaction of color spaces for the purpose of cytoplasm segmentation. In order to prevent over-segmentation, the morphological gradient information is used to mark the background, nucleus and cytoplasm. The edge detection is implemented in gray gradient image since the morphological gradient can detect the contour better than other conventional edge detection operators. The success rate is 95.5 % for nucleus and 92.6 % for cytoplasm. The results show that the method is valid and efficient to segment color images from blood and bone marrow smears.
It is uncertain if different brain areas in response to pre-semantic picture processing are functionally homogeneous. Using event-related potentials (ERPs), we aimed to explore the neural activities in different brain regions in relation to processing of sentence memory and picture identification. Healthy subjects were chosen to discriminate visual stimulus pairs, and the ERPs were recorded from the scalp. Two kinds of stimuli were provided for each subject in the present study. One was Chinese sentence reading, referred as task 1. Another one was watching a line-drawing picture to judge if the picture matched the meaning of the sentence before. When the line-drawing picture received by the subject was inconsistent with the meaning of the sentence before, it was called as task 2, otherwise, if incongruous, it was called as task 3. Our findings implicate that stimuli of sentence memory and picture identification may exert neural activities on different working memory areas in the brain of human.
Feature classification is one of the important aspects in Brain-computer interfaces (BCI) system. It has been known that a higher precision can be achieved if use neutral networks in a proper way for feature classification. In this paper, three feature identification ways were introduced and discussed. In the experiment of left-right hand classification, the arithmetic of the small mean square difference is proposed and studied, so as to get a good converging in the task classification. The design method of input and output layer for the BP neural network was discussed. Experiment results show that it is a feasible processing algorithm to classify the different events.
Development of denoising algorithm for 3D acceleration signals is essential to facilitate accurate assessment of human movement in body sensor networks (BSN). In this study, firstly 3D acceleration signals were captured by self-developed nine-axis wireless BSN platform during 12 subjects performing regular walking. Then, acceleration noise was filtered using four common filters respectively: median filter, Butterworth low-pass filter, discrete wavelet package shrinkage and Kalman filter. Finally, signal-to-noise ratio (SNR) and correlation coefficient(R) between filtered signal and reference signal were determined. We found that (1) Kalman filter showed the largest SNR and R values, followed by median filter, discrete wavelet package shrinkage and finally Butterworth low-pass filter; whereas, after correcting waveform delay for Butterworth low-pass filter, its performance was a little better than that of Kalman filter; (2) Real-time performance of median filter related to its window length; Decomposition level influenced real-time performance of discrete wavelet package shrinkage; Butterworth low-pass filter could bring large waveform delay if filter order and cut-off frequency were not properly selected. The algorithms of these filters would be further investigated to achieve best noise reduction of 3D acceleration signals in future.
Magnetic fields simulating EEG rhythm were used to stimulate Wistar rats to explore the effect of magnetic field on retrieval (recall) ability and its mechanism. The results indicated that most of the weak magnetic fields (>10 minutes) simulating the EEG rhythm of human brain impaired the retrieval of long-term memory significantly (P<0.05), but weak magnetic field with special rhythm may even have the capability of facilitating memory performance. And the effects of TMS on memory can last for at least several hours (5h) after TMS. Compared with control group, the release of NE, DA and 5-HT in hippocampus of stimulated group increased (P<0.05); While the release of ACh decreased (P<0.05). Through electronic microscope, morphological changes of nerve synapses in hippocampus of rats were observed after weak magnetic stimulation. The percentage of alpha and beta rhythm in EEG power spectra changed in cats after induced by weak magnetic fields simulating the EEG rhythm.
Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)