• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.371-378
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• 2015

In this paper, a different type of pulse width modulation (PWM) control scheme for a buck converter is introduced. The proposed buck converter uses PWM with frequency hopping and a low quiescent.current low dropout (LDO) voltage regulator with a power supply rejection ratio enhancer to reduce high spurs, harmonics and output voltage ripples. The low quiescent.current LDO voltage regulator is not described in this paper. A three-bit binary-to-thermometer decoder scheme and voltage ripple controller (VRC) is implemented to achieve low voltage ripple less than 3mV to increase the efficiency of the buck converter. An internal clock that is synchronized to the internal switching frequency is used to set the hopping rate. A center frequency of 2.5MHz was chosen because of the bluetooth low energy (BLE) application. This proposed DC-DC buck converter is available for low-current noise-sensitive loads such as BLE and radio frequency loads in portable communications devices. Thus, a high-efficiency and low-voltage ripple is required. This results in a less than 2% drop in the regulator's efficiency, and a less than 3mV voltage ripple, with -26 dBm peak spur reduction operating in the buck converter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1154-1162
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• 2016

This paper proposes a fast-transient digital LDO(Low dropout) regulator with binary-weighted current control technique. Conventional digital LDO takes a long time to stabilize the output voltage, because it controls the amount of current step by step, thus ringing problem is generated. Binary-weighted current control technique rapidly stabilizes output voltage by removing the ringing problem. When output voltage reliably reaches the target voltage, It added the FRZ mode(Freeze) to stop the operation of digital LDO. The proposed fast response digital LDO is used with a slow response DC-DC converter in the system which rapidly changes output voltage. The proposed digital controller circuit area was reduced by 56% compared to conventional bidirectional shift register, and the ripple voltage was reduced by 87%. A chip was implemented with a $0.18{\mu}F$ CMOS process. The settling time is $3.1{\mu}F$ and the voltage ripple is 6.2mV when $1{\mu}F$ output capacitor is used.

• Journal of The Korean Astronomical Society
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• v.38 no.2
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• pp.179-182
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• 2005

We conducted an extremely wide field survey of z ${\~}$ 6 Lyman break galaxies (LBGs) to precisely derive their bright end surface density overcoming the bias due to cosmic variance. We selected out LBG candidates in the Subaru/ XMM-Newton Deep Survey Field (SXDS) over the total of ${\~}1.0\;deg^2$ sky area down to $z_{AB} = 26.0 ({\ge}3{\sigma},\;2'.0 aperture)$ using i' - z' > 1.5 color cut. This sample alone is likely to be contaminated by M/L/T dwarfs, low-z elliptical galaxies, and z ${\~}$ 6 quasars. To eliminate these interlopers, we estimated their numbers using an exponential disk star count model, catalogs of old ellipticals in the SXDS and other field, and a z${\~}$6 quasar luminosity function. The finally derived surface density of z ${\~}$ 6 LBGs was 165 $mag^{-1}\;deg^{-2}$ down to $z_{AB}$ = 26.0 and shows good agreement with previous results from the narrower field survey of HST GOODS.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6337-6342
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• 2012

Background: Clinical trials on cancer subjects have one of the highest dropout rates. Barriers to recruitment range from patient-related, through institutional-related to staff-related factors. This paper highlights the low response rate and the recruitment barriers faced in our Qigong exercises trial. Materials and Method: The Qigong trial is a three-arm trial with a priori power size of 114 patients for 80% power. The University Malaya Medical Centre database showed a total of 1,933 patients from 2006-2010 and 751 patients met our inclusion criteria. These patients were approached via telephone interview. 131 out of 197 patients attended the trial and the final response rate was 48% (n=95/197). Results: Multiple barriers were identified, and were regrouped as patient-related, clinician-related and/or institutional related. A major consistent barrier was logistic difficulty related to transportation and car parking at the Medical Centre. Conclusions: All clinical trials must pay considerable attention to the recruitment process and it should even be piloted to identify potential barriers and facilitators to reduce attrition rate in trials.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2134-2143
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• 2018

This work introduces a fault diagnosis method for transformer based on self-powered radio frequency identification (RFID) sensor tag and improved Hilbert-Huang transform (HHT). Consisted by RFID tag chip, power management circuit, MCU and accelerometer, the developed RFID sensor tag is used to acquire and wirelessly transmit the vibration signal. A customized power management including solar panel, low dropout (LDO) voltage regulator, supercapacitor and corresponding charging circuit is presented to guarantee constant DC power for the sensor tag. An improved band restricted empirical mode decomposition (BREMD) which is optimized by quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to deal with the raw vibration signal. Compared with traditional methods, this improved BREMD method shows great superiority in reducing mode aliasing. Then, a promising fault diagnosis approach on the basis of Hilbert marginal spectrum variations is brought up. The measured results show that the presented power management circuit can generate 2.5V DC voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.8m in the test environment. Furthermore, the measurement results indicate the promising performance of fault diagnosis for transformer.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.274-284
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• 2021

Customized services to a sleep induction for better sleepcare are more effective because of different satisfaction levels to users. The EOG data measured at the frontal lobe when a person blinks his eyes can be used as biometric data because it has different values for each person. The accuracy of measurement is degraded by a noise source, such as toss and turn. Therefore, it is necessary to analyze the noisy data and remove them from normal EOG by filtering. There are low-pass filtering and high-pass filtering as filtering using a frequency band. However, since filtering within a frequency band range is also required for more effective performance, we propose a machine learning model for the filtering of EOG data in this paper as the second filtering method. In addition, optimal values of parameters such as the depth of the hidden layer, the number of nodes of the hidden layer, the activation function, and the dropout were found through experiments, to improve the performance of the machine learning filtering model, and the filtering performance of 95.7% was obtained. Eventually, it is expected that it can be used for effective user identification services by using filtering model for EOG data.

• Journal of Pharmacopuncture
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• v.27 no.1
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• pp.27-37
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• 2024

Objectives: Posttraumatic stress disorder (PTSD) is a prevalent mental health condition, and techniques using sensory stimulation in processing traumatic memories have gained attention. The Emotional Freedom Techniques (EFT) is a psychotherapy that combines tapping on acupoints with exposure to cognitive reframing. This pilot study aimed to assess the feasibility of EFT as a treatment for PTSD by answering the following research questions: 1) What is the compliance and completion rate of patients with PTSD with regard to EFT protocol? Is the dropout rate reasonable? 2) Is the effect size of EFT protocol for PTSD sufficient to justify a future trial? Methods: Thirty participants diagnosed with PTSD were recruited. They received weekly EFT sessions for five weeks, in which they repeated a statement acknowledging the problem and accepting themselves while tapping the SI3 acupoint on the side of their hand. PTSD symptoms were evaluated using the PTSD Checklist for DSM-5 (PCL-5) before and after the intervention. Results: Of the 30 PTSD patients (mean age: 34.1 ± 9.1, 80% female), 96.7% showed over 80% compliance to the EFT sessions, and 86.7% completed the entire study process. The mean PCL-5 total score decreased significantly after the intervention, with a large effect size (change from baseline: -14.33 [95% CI: -19.79, -8.86], p < 0.0001, d = 1.06). Conclusion: The study suggests that EFT is a feasible treatment for PTSD, with high session compliance and low dropout rates. The effect size observed in this study supports the need for a larger trial in the future to further investigate EFT as a treatment for PTSD. However, the lack of a control group and the use of a self-rated questionnaire for PTSD symptoms are limitations of this study. The findings of this pilot study can be used to plan a future trial.

• Journal of Korean Physical Therapy Science
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• v.15 no.2
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• pp.1-13
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• 2008

Background: This study had been carried out with 18 ischemic stroke patients as its object for about eight months from October, 2006 to May, 2007 in order to observe the recovery of motor function and the change of important blood factors according to the different quantitative exercises. Methods: Subjects were assigned randomly either experimental group (n＝19) or the control group (n＝19), when the study began the halfway on this study dropout 20 patients, and final subjects remained experimental group's 9 patients and control group's 9 patients. Both groups received thermotherapy and functional electrical stimulation (FES), also taken different quantitative exercise therapy (experimental group 180 minutes, control group 80 minutes). Subjects were assessed for upper and lower extremities motor function Fugl－Meyer Scale; FMS), blood test (white blood count; WBC, low density lipoprotein －cholesterol; LDL-C, high density lipoprotein-cholesterol; HDL-C, Troponin) during pretest, after 2 months, after 3 months. Results: The results of this study were as follows; 1. FMS has no statistically significant difference with intergroup(p>.05). But there was a statistically significant difference with each groups (p<.05). 2. WBC has no statistically significant difference with intergroup (p>.05). But there was a statistically significant difference in control group (p<.05), without experimental group (p>.05). 3. LDL-C has no statistically significant difference with intergroup (p>.05). But there was a statistically significant difference in control group (p<.05), without experimental group (p>.05). 4. HDL-C has no statistically significant difference with intergroup (p<.05). But there was a statistically significant difference with each groups (p>.05). 5. Troponin Ⅰ has no statistically significant difference with intergroup (p>.05). Also there was no statistically significant difference with each groups (p>.05). Conclusion: These findings suggest that different quantitative exercises has no effect on FMS, LDL-C, HDL-C, WBC, Troponin Ⅰ with ischemic stroke patients. But the treatment period that there's less correlation between the recovery of motor function and the different quantitative exercise, also less correlation between the change of important blood factors and the different quantitative exercises with ischemic stroke patients.

• Safety and Health at Work
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• v.5 no.4
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• pp.216-221
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• 2014

Background: Mental health complaints are quite common in health care employees and can have adverse effects on work functioning. The aim of this study was to evaluate an e-mental health (EMH) approach to workers' health surveillance (WHS) for nurses and allied health professionals. Using the waiting-list group of a previous randomized controlled trial with high dropout and low compliance to the intervention, we studied the pre- and posteffects of the EMH approach in a larger group of participants. Methods: We applied a pretest-posttest study design. The WHS consisted of online screening on impaired work functioning and mental health followed by online automatically generated personalized feedback, online tailored advice, and access to self-help EMH interventions. The effects on work functioning, stress, and work-related fatigue after 3 months were analyzed using paired t tests and effect sizes. Results: One hundred and twenty-eight nurses and allied health professionals participated at pretest as well as posttest. Significant improvements were found on work functioning (p = 0.01) and work-related fatigue (p < 0.01). Work functioning had relevantly improved in 30% of participants. A small meaningful effect on stress was found (Cohen d = .23) in the participants who had logged onto an EMH intervention (20%, n = 26). Conclusion: The EMH approach to WHS improves the work functioning and mental health of nurses and allied health professionals. However, because we found small effects and participation in the offered EMH interventions was low, there is ample room for improvement.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.