Residue Based Open-Loop Modal Analysis Method for Detecting LFMR of PMSG-WFs Penetrated Power Systems

This paper proposes a residue based open-loop modal analysis method to detect low frequency modal resonance(LFMR),including asymmetric low frequency modal attraction(ALFMA)and asymmetric low frequency modal repulsion(ALFMR),of permanent magnetic synchronous generator based wind farms(PMSG-WFs)penetrated power systems.The formation of ALFMA and ALFMR caused by two open-loop low frequency oscillation(LFO)modes moving close and apart is analyzed in detail.Via predicting the trajectories of closed-loop LFO modes based on calculation of residue of open-loop LFO modes,both ALFMA and ALFMR can be detected.The proposed method can select LFO modes which move to the right half complex plane as control parameters vary.Simulation studies are carried out on a three-machine power system and a four-machine 11-bus power system to verify the properties of the proposed method.

Efficient Single Event Upset-Tolerant FIR Filter Design Based on Residue Number for OBP Satellite Communication Systems

Cosmic radiation has several effects on the On-Board Processing(OBP)platform in satellite communications systems,and Single Event Upsets(SEUs)are one of its most important effects.In order to protect the Finite Impulse Response(FIR)filters against SEU,this paper proposes a novel Residue Number(RN)-based method.The proposed method applies the transpose form of the FIR filter to avoid the fault missing caused by SEU on shift registers.It also adjusts the input intelligently to avoid the fault missing caused by SEU on the filter coefficients.After all the fault missing events are avoided,the modulus can be minimised to achieve the minimum overhead.Theoretical analysis and simulation results show that the noise introduced by the input adjustment is negligible.Fault injection shows that the fault missing rate of the proposed method is zero.Finally,FPGA implementation shows that the overhead of the proposed method is approximately 75% of Triple Modular Redundancy,and is only 1%-2% higher than that of the traditional RN-based design.

PHYLETIC RELATIONSHIP OF PROTEIN STRUCTURES BASED ON SPATIAL PREFERENCE OF RESIDUES

A distance measure that infers to indicate the evolutionary relationship of protein structures has been developed based on spatial preference factors of residues. The spatial preference factor is a reflection of the environment of residues in tertiary structure. Compared with the phyletic relationships derived from sequence homologies and three-dimensional structures, we find that the two lines of evolution are similar in general. This approach is applied to a group of glins here.

Screening Testosterone Residue in the Edible Tissues of Bovine with a Polyclonal Antibody Based Heterologous Immunoassay

Testosterone（17β-hydroxyandrost-4-en-3-one）is a19-carbon steroid hormone with potent androgenic properties.It maintains testicular function and develop secondary male sex characteristics.It also has strong anabolic effects,which initiates increased protein synthesis in muscle and bone[1].In the1950s,the recognition of the growth promoting properties of such a hormone

Cross-Modal Hashing Retrieval Based on Deep Residual Network

In the era of big data rich inWe Media,the single mode retrieval system has been unable to meet people’s demand for information retrieval.This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes:A Cross-Modal Hashing retrieval algorithm based on Deep Residual Network(CMHR-DRN).The model construction is divided into two stages:The first stage is the feature extraction of different modal data,including the use of Deep Residual Network(DRN)to extract the image features,using the method of combining TF-IDF with the full connection network to extract the text features,and the obtained image and text features used as the input of the second stage.In the second stage,the image and text features are mapped into Hash functions by supervised learning,and the image and text features are mapped to the common binary Hamming space.In the process of mapping,the distance measurement of the original distance measurement and the common feature space are kept unchanged as far as possible to improve the accuracy of Cross-Modal Retrieval.In training the model,adaptive moment estimation(Adam)is used to calculate the adaptive learning rate of each parameter,and the stochastic gradient descent(SGD)is calculated to obtain the minimum loss function.The whole training process is completed on Caffe deep learning framework.Experiments show that the proposed algorithm CMHR-DRN based on Deep Residual Network has better retrieval performance and stronger advantages than other Cross-Modal algorithms CMFH,CMDN and CMSSH.

剥脱法估算药代动力学参数的FORTRAN—Ⅳ程序

许多药物在非静脉给药后的血药浓度曲线表现为三个明显时相:即血药浓度随时间而上升的吸收相;药物迅速向组织转运,使血药浓度迅速下降的分布(α)相;药物在体内达到拟分布平衡后,血药浓度按单指数式下降的清除(β)相。