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37results about How to "Good distribution" patented technology

Noninvasive radiotherapy system for robot

The invention relates to a noninvasive radiotherapy system for a robot, and belongs to the medical equipment field. The noninvasive radiotherapy system for the robot consists of seven modules, i.e. a radiotherapy planning system, a three-dimensional numerical-control therapy bed, an automatic tracking system for a four-dimensional real-time image, a robot system, a radioactive source, an adjuvant therapy system and an integrated control system, wherein the automatic tracking system for the four-dimensional real-time image consists of a six-freedom-of-degree G-shaped arm real-time image system and an intelligent tracking system; the six-freedom-of-degree G-shaped arm real-time image system is formed by successively connecting a G-shaped arm, a G-shaped arm sliding rail, a G-shaped arm spindle, a G-shaped arm pitch shaft and a G-shaped arm sliding seat; an X ray source and an X ray dynamic flat panel detector are formed into one group, and two groups are correspondingly installed on the G-shaped arm; and the G-shaped arm sliding seat is installed in a rail (9). According to the noninvasive radiotherapy system for the robot, which is disclosed by the invention, the defects of unmatched projection time, time waste and low therapy precision in the prior art are overcome, the precise therapy of the whole body tumor can be carried out, and the cardiovascular disease therapy and the noninvasive regulation of the renal nerve disease can be carried out.
Owner:RADIATION THERAPY MEDICAL SCI & TECH CO LTD

Neodymium-iron-boron permanent magnet and preparation method and application thereof

The invention discloses a neodymium iron boron permanent magnet and a preparation method and application thereof. The grain boundary phase and the main phase of the neodymium iron boron permanent magnet have the following structure distribution: the total length of the grain boundary phase in the measurement range is recorded as Lm, the total length of the grain boundary phase with the grain boundary width of more than or equal to 1 [mu] m in the measurement range is recorded as Ln, and Lm and Ln satisfy the relationship of 0.40 < = Ln/Lm < = 1; in the measurement range, the total length of grain boundary phases with the width between adjacent grain boundaries being larger than or equal to 2 microns is recorded as Lx and Lm, and Lx meets the relation that Lx/Lm is larger than or equal to 0and smaller than or equal to 0.2; the total length of the grain boundary phase scanned by the EPMA line in the measurement range is recorded as Le, the total length of the grain boundary phase scanned by the EPMA line in the measurement range is recorded as LM, and Le and LM meet the relationship of 0.40 < = Le/LM < 1. The high-temperature demagnetization-resistant magnet with high Br, high Hcj,high square degree, specific grain boundary phase and main phase structure is prepared.
Owner:YANTAI ZHENGHAI MAGNETIC MATERIAL CO LTD

Central air conditioner fault diagnosis method and device based on stacking fusion algorithm

The invention discloses a central air-conditioning fault diagnosis method based on a stacking fusion algorithm. The method comprises the following steps: establishing a central air-conditioning system digital twinborn model; state data of the central air-conditioning system during normal operation and different faults are collected, and a sample data set is obtained after data preprocessing and feature extraction are carried out; dividing the sample data set into a training data set and a test data set, and constructing a double-layer stacking model; training each base learner by adopting a k-fold cross validation method, and obtaining a prediction result of each base learner as a secondary training data set; when each base learner is trained, multiple groups of different machine learning algorithms are selected for combination, and a secondary training data set in multiple groups of combination modes is generated; inputting the plurality of groups of secondary training data sets into a secondary learning device for training to obtain a plurality of central air conditioner fault diagnosis models; the prediction performance of the multiple central air conditioner fault diagnosis models is evaluated, and the model with the optimal performance is selected as the optimal central air conditioner fault diagnosis model for fault diagnosis.
Owner:浙江英集动力科技有限公司
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