The speed error of actuators during the flight of a quad-rotor is included in the attitude error, and this error is immediately corrected by the pilot’s observation. As the control authority of the quad-rotor changes to a computer system, the correction of the error is calculated and performed by the attitude sensor and the mathematical model of the quad-rotor. However, there is a response error to the control signal despite driving the same motor, which causes different results from the model prediction and affects the stability of the flight. Therefore, the response characteristics of hardware represented by the same mathematical model but having errors should be reflected in the modeling of the quad-rotor. In this paper, the response error of the actuators assembled with the same propellers and motors is verified through experiments. The actuators model that reflects this error is presented, and the thrust coefficient range by the propellers is also presented. Additionally, the speed error of actuators due to the voltage drop of the battery was verified through experiments, and a method for applying this error to the actuator model is presented.

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An electromyogram (EMG) is a signal for muscle output that indicates the degree of muscle contraction and relaxation. For these muscle signals to be output, certain signals must be received from the brain. To analyze these relations, electroencephalograms (EEGs) of the brain are measured to extract brain waves that are active at that time, although it is difficult to identify or distinguish expression patterns of the brain signal through EMG output. However, the brain signal operates via a partially reached signal and transmits the results of the operation. In this study, we analyze signals transmitted in this process and confirm whether human motion can be predicted from brain signals. It is not easy to guess the exact protocol of the brain using a general method, because a biosignal is a signal that differs from person to person. However, by analyzing the signals displayed by a particular user through actions, it is possible to determine the presence or absence of a signal to distinguish muscle movements. In the course of signal transduction, the energy of the left and right brain waves changes in the form of energy or signals that cause an arm’s movement. Responding to this, we analyze the signal transmission process of brain signals and EMGs to analyze loss and generated output. We extract EEG data from brain waves and determine EMG signals from the energy characteristics; we then collect and merge the results of spectra analysis through the Common Spatial Pattern (CSP) filter and explore the basis for predicting wills during muscle signals and stimulation transmission. The active information of the data within the working time of left and right brain waves depends on the changes of the left and right brain waves. It is proposed that the appearance of similar signals at these specific timescales can help identify the operations of the arms and outputs by the left and right biceps.

This paper presents a comparative study on application of Routh-Hurwitz and Llewellyn absolute stability criteria to a scaled telerobotic system. The dynamic equations of the telerobotic system are given, and the transfer function of the system is obtained for further stability analysis. The stable margins of controller gains are obtained using both stability analysis methods, and the differences in the results are described and explained. The paper is concluded by a numerical example verifying performed stability analysis.

Osteoporosis is characterized by low bone mineral density and related fractures. The major determinant of bone mineral density is heritability, which is estimated to account for 40-80% of variation from twin and family studies. Association of vitamin D receptor (VDR) and estrogen receptor (ER) gene polymorphism with bone mineral density study results are inconsistent in Korea and countries of other ethnic backgrounds. Suggested reasons for this discrepancy are limited sample size, age, race, ethnic differences, and gene interaction with environmental factors such as age, diet and exercise, and gene by gene interactions that regulate bone mass. The goal of the present study is to evaluate (1) the relationship between VDR and ER gene polymorphism and bone mineral density after adjusting for confounding factors and (2) the possibility of the VDR and ER gene interaction that impacted bone mass in postmenopausal Korean women. The association between bone mineral density and restriction fragment length polymorphism (RFLP) with Bsm I endonuclease at the vitamin D gene and Pvu II and Xba I endonuclease with the ER gene were studied in 132 postmenopausal Korean women aged 45 to 71. Capital letters (B,P,X) signify the absence, and small letters (b, p, x) the presence, of restriction sites. Clinical characteristics, bone-related hormones, biochemical markers, and bone mineral density were also measured. Multiple regression was used to predict variables contributing to bone mineral density. Age, height, weight, years since menopause, and VER B genotype and ER P and X were used as independent variables. Age, body mass index, menarche and years since menopause, diet and exercise habits, biochemical markers, and bone mineral density were not significantly different according to VDR and ER genotypes. After controlling for confounding factors (age, body mass index, menarche, years since menopause), a significant ER X genotype effect on femoral neck bone mineral density and an increased significance with gene by gene interaction (VDR B * ER X) effect on femoral neck bone mineral density was observed by multiple regression analysis. Xba I RFLP of the ER gene is associated with femoral neck bone mineral density in Korean postmenopausal women. A more significant contribution of the VDR B and ER X gene interaction on femoral neck mineral was also observed.