• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.5
• /
• pp.635-643
• /
• 2018

This paper propose a method to identify the motor parameters and improve input voltage error which affect the low speed position error of the back-emf(back electromotive force) based sensorless algorithm and to secure the operation reliability and stability even in the case where the load fluctuation is severe and the start and low speed operation frequently occurs. In the model-based observer used in this paper, stator resistance, inductance, and input voltage are particularly influential factors on low speed performance. Stator resistance can cause resistance value fluctuation which may occur in mass production process, and fluctuation of resistance value due to heat generated during operation. The inductance is influenced by the fluctuation due to the manufacturing dispersion and at a low speed where the change of the current is severe. In order to find stator resistance and inductance which have different initial values and fluctuate during operation and have a large influence on sensorless performance at low speed, they are commonly measured through 2-point calculation method by 2-step align current injection. The effect of voltage error is minimized by offsetting the voltage error. In addition, when the command voltage is used, it is difficult to estimate the back-emf due to the relatively large distortion voltage due to the dead time and the voltage drop of the power device. In this paper, we propose a simple circuit and method to detect the voltage by measuring the PWM(Pulse Width Modulation) pulse width and compensate the voltage drop of the power device with the table, thereby minimizing the position error due to the exact estimation of the back-emf at low speed. The suitability of the proposed algorithm is verified through experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.5168-5181
• /
• 2017

The popularity of augmented reality (AR) applications and games are in high demand. Currently, the best common platform to implement AR services is on a smartphone, as online games, navigators, personal assistants, travel guides are among the most popular applications of smartphones. However, the power consumption of an AR application is extremely high, and therefore, highly adaptable and dynamic low power control schemes must be used. Dynamic voltage and frequency scaling (DVFS) schemes are widely used in smartphones to minimize the energy consumption by controlling the device's operational frequency and voltage. DVFS schemes can sometimes lead to longer response times, which can result in a significant problem for AR applications. In this paper, an AR response time monitor is used to observe the time interval between the AR image input and device's reaction time, in order to enable improved operational frequency and AR application process priority control. Based on the proposed response time monitor and the characteristics of the Linux kernel's completely fair scheduler (CFS) (which is the default scheduler of Android based smartphones), a response time step control (RSC) scheme is proposed which adaptively adjusts the CPU frequency and interactive application's priority. The experimental results show that RSC can reduce the energy consumption up to 10.41% compared to the ondemand governor while reliably satisfying the response time performance limit of interactive applications on a smartphone.

• Journal of Korean Association for Spatial Structures
• /
• v.20 no.2
• /
• pp.51-58
• /
• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Journal of Navigation and Port Research
• /
• v.29 no.10 s.106
• /
• pp.859-863
• /
• 2005

This paper describes the improvement of power control characteristics of ship's emergency lighting power supply(SELPS), by which electric power is controlled extensively, and power ON-OFF is controlled and system parameter monitored in remote distance by PC serial communication. Proposed system is composed of step-down converter(SDC), emergency power supply circuit(EPSC), half bridge(HB) inverter, fluorescent lamp(FL) starting circuit, microprocessor control and multi communication circuit. Experimental works confirm that relative system stops when over current is detected and speedy and stable emergency power is supplied when main power source cut-off, and controls input power up to 35[$\%$] by adjusting pulse frequency of the HB inverter, and ON-OFF control of multiple SELS, real time transmission and monitor of parameters as to voltage, current, and power values are performed appropriately by PC communication.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.5
• /
• pp.619-628
• /
• 2010

Shape Memory Alloy (SMA) has recently received attention in developing implantable surgical equipments and it is expected to lead the future medical device market by adequately imitating surgeons' flexible and delicate hand movement. However, SMA actuators have not been used widely because of their nonlinear behavior called hysteresis, which makes their control difficult. Hence, we propose a parameter, $t_1$, which is necessary for temperature control, by analyzing the open-loop step response between current and temperature and by comparing it with the values of linear differential equations. $t_1$ is a pole of the transfer function in the invariant linear model in which the input and output are current and temperature, respectively; hence, $t_1$ is found to be related to the state variable used for temperature control. When considering the parameter under heat treatment conditions, $T_{max}$ was found to assume the lowest value, and $t_1$ was irrelevant to the heat treatment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.3
• /
• pp.215-222
• /
• 2021

As the importance of human-computer interaction (Human Computer Interface) technology grows and research on HCI is progressing, it is inferred about the research emotion inference or the computer reaction according to the user's intention, not the computer reaction by the standard input of the user. Stress is an unavoidable result of modern human civilization, and it is a complex phenomenon, and depending on whether or not there is control, human activity ability can be seriously changed. In this paper, we propose an intelligent emotional recommendation system using music as a way to relieve stress after measuring heart rate variability (HRV) and acceleration photoplethymogram (APG) increased through stress as part of human-computer interaction. The differential evolution algorithm was used to extract reliable data by acquiring and recognizing the user's biometric information, that is, the stress index, and emotional inference was made through the semantic web based on the obtained stress index step by step. In addition, by searching and recommending a music list that matches the stress index and changes in emotion, an emotional recommendation system suitable for the user's biometric information was implemented as an application.

• Journal of Biosystems Engineering
• /
• v.31 no.2 s.115
• /
• pp.108-114
• /
• 2006

This study was conducted to design and fabricate a controller for variable-rate application of granular fertilizer based on physical and chemical information, to analyze the performance of the controller and characteristics of a discharger. The result of the study are summarized as follows: 1. The charge ratios of discharger by accumulation heights of fertilizer in hopper were examined, and the variations in charge ratio were $72.58{\sim}93.33%$ and $63.14{\sim}94.42%$ for the fertilizers Super 21 and Sinsedae, respectively. The charge ratio also decreased as the rotational speed of discharger increased. 2. The coefficient of variation of discharge amount by rotational speed and discharge time of discharger were in the range of $2.94{\sim}11.23%$ and $2.82{\sim}10.80%$ for the fertilizer of Super 21 and Sinsedae. Except the rotational speed of 12 rpm, the coefficient of variation for discharge amount were relatively small with 4% more or less 3. In order to evaluate the rotational speed of discharger, the control signal in the range of $0{\sim}5V$ was subdivided into the 50 steps by 0.1V. The regression equation for the rotational speed of discharger was Y=55.984X-79.174(X: input voltage, V, Y: discharger speed, RPM) and the $R^2$ was 0.99. 4. In order to evaluate the performance of the controller for variable-rate application of granular fertilizer, settling time to unit step input was examined. The settling time varied from 0.8sec to 1.4 sec.

• Journal of Biomedical Engineering Research
• /
• v.3 no.1
• /
• pp.23-30
• /
• 1982

The regimen of physical activity of the patient with coronary artery disease requires that he should not overshoot the prescribed heart rate based on his age, health and fuctional status of the heart during his exercise. The step input of work load, however, involves a great danger of overshooting. The purpose of this study was to desigil a system that makes it passible for a subject to check the overshooting. This system shows on tile H.R-meter, the amplified and filtered heart-rate signal of the subject received by the photosensor on his earlobe, puts it in the lead coinpensational circuit where it is conpared with the reference input signal(=the presfribed heart rate). The output of the lead compensational circuit works the aull meter. By means of this null meter, the subject knows whether he is overshooting the prescribed heart rate or not. He can continue the natl meter needle at the'Zero'position through the control of the speed of pedaling of the bicycle ergometer, An experimental test, made on eight men and four women in healthy condition, showed that 91. 7% of them vlaintained the stable heart rate and that the overshooting of the desired heart rate did not exceed $\pm$2BPM. According to the result of this experiment, since the heart rate feedback controller makes it possible for the subject to take the prescribed exercise based not on the work load but on the heart rate which incidentally is inexpensive, it can be made use of as the instrument for the regimen of pflysical activity by the patient with coronary artery disease.

• Journal of KIISE:Software and Applications
• /
• v.36 no.2
• /
• pp.122-130
• /
• 2009

Conventional approaches for car number plate detection have dealt with those input images having similar sizes and simple background acquired under well organized environment. Thus their performance get reduced when input images include number plates with different sizes and when they are acquired under different lighting conditions. To solve these problem, this paper proposes a new scheme that uses the geometrical features of number plates and their topological information with reference to other features of the car. In the first step, those edges constructing a rectangle are detected and several pixels neighboring those edges are selected as the seed pixels for region growing. For region growing, color and intensity are used as the features, and the result regions are merged to construct the candidate for a number plate if their features are within a certain boundary. Once the candidates for the number plates are generated then their topological relations with other parts of the car such as lights are tested to finally determine the number plate region. The experimental results have shown that the proposed method can be used even for detecting small size number plates where characters are not visible.

• Journal of Biomedical Engineering Research
• /
• v.43 no.4
• /
• pp.185-192
• /
• 2022

Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impairment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a meaningful biomarker to discriminate cognitive decline.