The domination number and the least QQ-eigenvalue

A vertex set $D$ of a graph $G$ is said to be a dominating set if every vertex of $V(G)\setminus D$ is adjacent to at least a vertex in $D$, and the domination number $\gamma(G)$ ($\gamma$, for short) is the minimum cardinality of all dominating sets of $G$. For a graph, the least $Q$-eigenvalue is the least eigenvalue of its signless Laplacian matrix. In this paper, for a nonbipartite graph with both order $n$ and domination number $\gamma$, we show that $n\geq 3\gamma-1$, and show that it contains a unicyclic spanning subgraph with the same domination number $\gamma$. By investigating the relation between the domination number and the least $Q$-eigenvalue of a graph, we minimize the least $Q$-eigenvalue among all the nonbipartite graphs with given domination number.Comment: 13 pages, 3 figure

IMU-Based Online Kinematic Calibration of Robot Manipulator

Robot calibration is a useful diagnostic method for improving the positioning accuracy in robot production and maintenance. An online robot self-calibration method based on inertial measurement unit (IMU) is presented in this paper. The method requires that the IMU is rigidly attached to the robot manipulator, which makes it possible to obtain the orientation of the manipulator with the orientation of the IMU in real time. This paper proposed an efficient approach which incorporates Factored Quaternion Algorithm (FQA) and Kalman Filter (KF) to estimate the orientation of the IMU. Then, an Extended Kalman Filter (EKF) is used to estimate kinematic parameter errors. Using this proposed orientation estimation method will result in improved reliability and accuracy in determining the orientation of the manipulator. Compared with the existing vision-based self-calibration methods, the great advantage of this method is that it does not need the complex steps, such as camera calibration, images capture, and corner detection, which make the robot calibration procedure more autonomous in a dynamic manufacturing environment. Experimental studies on a GOOGOL GRB3016 robot show that this method has better accuracy, convenience, and effectiveness than vision-based methods

Effects of Design Parameters on Performance of Brushless Electrically Excited Synchronous Reluctance Generator

Permanent magnet synchronous generators, doubly fed induction generators, and traditional electrically excited synchronous generators are widely used for wind power applications, especially large offshore installations. In order to eliminate brushes and slip rings for improved reliability and maintenance-free operation, as well as to avoid costly permanent magnets, a novel brushless electrically excited synchronous reluctance generator having many outstanding advantages has been proposed in this paper. The fundamental operating principles, finite element analysis design studies and performance optimization aspects have been thoroughly investigated by simulations and experimentally under different loading conditions. The effects of different pole combinations and rotor dimensions on the magnetic coupling capacity of this machine have been specifically addressed and fully verified by off-line testing of the 6/2 pole and 8/4 pole prototypes with magnetic barrier reluctance rotor and a new hybrid cage rotor offering superior performance

Simulation and experimental analysis of a brushless electrically excited synchronous machine with a hybrid rotor

Electrically excited synchronous machines with brushes and slip rings are popular but hardly used in inflammable and explosive environments. This paper proposes a new brushless electrically excited synchronous motor with a hybrid rotor. It eliminates the use of brushes and slip rings so as to improve the reliability and cost-effectiveness of the traction drive. The proposed motor is characterized with two sets of stator windings with two different pole numbers to provide excitation and drive torque independently. This paper introduces the structure and operating principle of the machine, followed by the analysis of the air-gap magnetic field using the finite-element method. The influence of the excitation winding's pole number on the coupling capability is studied and the operating characteristics of the machine are simulated. These are further examined by the experimental tests on a 16 kW prototype motor. The machine is proved to have good static and dynamic performance, which meets the stringent requirements for traction applications

Human-Manipulator Interface Using Hybrid Sensors via CMAC for Dual Robots

This paper presents a novel method that allows a human operator to communicate his motion to the dual robot manipulators by performing his two double hand-arms movements, which would naturally carry out an object manipulation task. The proposed method uses hybrid sensors to obtain the position and orientation of the human hands. Although the position and the orientation of the human can be obtained from the sensors, the measurement errors increase over time due to the noise of the devices and the tracking error. A cerebellar model articulation controller (CMAC) is used to estimate the position and orientation of the human hand. Due to the limitations of the perceptive and the motor, human operator can not accomplish the high precision manipulation without any assistant. An adaptive multi-space transformation (AMT) is employed to assist the operator to improve the accuracy and reliability in determining the pose of the manipulator. With making full use of the human hand-arms motion, the operator would feel kind of immersive. Using this human-robot interface, the object manipulation task done in collaboration by dual robots could be carried out flexibly through preferring the double hand-arms motion by one operator

Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer

Background: Proper cell models for breast cancer primary tumors have long been the focal point in the cancer’s research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. Results: Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. Conclusions: The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research