• 대한의용생체공학회:의공학회지
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• 제26권6호
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• pp.373-381
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• 2005

The respiratory and heart beat signals are the fundamental physiological signals for sleep monitoring in the home. Using the air mattress sensor system, the respiration and heart beat movements can be measured without any harness or sensor on the subject's body which makes long term measurement difficult and troublesome. The differential measurement technique between two air cells is adopted to enhance the sensitivity. The concept of the balancing tube between two air cells is suggested to increase the robustness against postural changes during the measurement period. With this balancing tube, the meaningful frequency range could be selected by the pneumatic filter method. The mathematical model for the air mattress and balancing tube was suggested and the validation experiments were performed for step and sinusoidal input. The results show that the balancing tube can eliminate the low frequency component between two cells effectively. This technique was applied to measure the respiration and heart beat on the bed, which shows the potential applications for sleep monitoring device in home. With the analysis of the waveform, respiration intervals and heart beat intervals were calculated and compared with the signal from conventional methods. The results show that the measurement from air mattress with balancing tube can be used for monitoring respiration and heart beat in various situations.

• 전력전자학회논문지
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• 제3권1호
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• pp.61-69
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• 1998

본 논문은 예측전류제어기의 연산시간에 의한 전압형 PWM 인버터의 과도상태 특성 저하를 개선하기 위한 부분보상 예측전류 제어기에 관한 연구로서, 먼저 부분보상 예측전류제어 시스템에 대한 해석을 통해 과도상태시 정정시간을 최소화할 수 있는 가중치를 구하였다. 또한 부분보상 예측전류제어에 의한 PWM 인버터의 출력전압 포화 경계조건을 유도하여 포화현상이 예측전류제어기의 과도응답 특성에 미치는 영향을 살펴보았다. 끝으로 부분보상 예측전류제어기를 능동전력필터에 적용하여 가중치에 따른 과도응답 특성을 실제전류와 샘플링전류를 중심으로 고찰하였다.

• ETRI Journal
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• 제40권1호
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• pp.122-132
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• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

• Journal of Astronomy and Space Sciences
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• 제35권4호
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• pp.263-277
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• 2018

This paper presents a vision-based relative pose estimation algorithm and its validation through both numerical and hardware experiments. The algorithm and the hardware system were simultaneously designed considering actual experimental conditions. Two estimation techniques were utilized to estimate relative pose; one was a nonlinear least square method for initial estimation, and the other was an extended Kalman Filter for subsequent on-line estimation. A measurement model of the vision sensor and equations of motion including nonlinear perturbations were utilized in the estimation process. Numerical simulations were performed and analyzed for both the autonomous docking and formation flying scenarios. A configuration of LED-based beacons was designed to avoid measurement singularity, and its structural information was implemented in the estimation algorithm. The proposed algorithm was verified again in the experimental environment by using the Autonomous Spacecraft Test Environment for Rendezvous In proXimity (ASTERIX) facility. Additionally, a laser distance meter was added to the estimation algorithm to improve the relative position estimation accuracy. Throughout this study, the performance required for autonomous docking could be presented by confirming the change in estimation accuracy with respect to the level of measurement error. In addition, hardware experiments confirmed the effectiveness of the suggested algorithm and its applicability to actual tasks in the real world.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1744-1760
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• 2021

Since correlation filter appeared in the field of object tracking, it plays an increasingly vital role due to its excellent performance. Although many sophisticated trackers have been successfully applied to track the object accurately, very few of them attaches importance to the scale and rotation estimation. In order to address the above limitation, we propose a novel method combined with Fourier-Mellin transform and confidence evaluation strategy for robust object tracking. In the first place, we construct a correlation filter to locate the target object precisely. Then, a log-polar technique is used in the Fourier-Mellin transform to cope with the rotation and scale changes. In order to achieve subpixel accuracy, we come up with an efficient surface fitting mechanism to obtain the optimal calculation result. In addition, we introduce a confidence evaluation strategy modeled on the output response, which can decrease the impact of image noise and perform as a criterion to evaluate the target model stability. Experimental experiments on OTB100 demonstrate that the proposed algorithm achieves superior capability in success plots and precision plots of OPE, which is 10.8% points and 8.6% points than those of KCF. Besides, our method performs favorably against the others in terms of SRE and TRE validation schemes, which shows the superiority of our proposed algorithm in scale and rotation evaluation.

• Childhood Kidney Diseases
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• 제28권1호
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• pp.16-26
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• 2024

Patients with kidney disease require frequent blood tests to monitor their kidney function, which is particularly difficult for young children and the elderly. For these people, the standard method is to evaluate serum creatinine or cystatin C or drug levels through venous sampling, but more recently, evaluation using dried blood spots has been used. This narrative review reports information from the literature on the use of dried blood spots to quantify the main markers used to detect kidney diseases. The ScienceDirect and PubMed databases were searched using the keywords: "dried blood on filter paper," "markers of renal function," "renal function," "creatinine," "cystatin C," "urea," "iohexol," and "iotalamate." Studies using animal samples were excluded, and only relevant articles in English or Spanish were considered. Creatinine was the most assessed biomarker in studies using dried blood spots to monitor kidney function, showing good performance in samples whose hematocrit levels were within normal reference values. According to the included studies, dried blood spots are a practical monitoring alternative for kidney disease. Validation parameters, such as sample and card type, volume, storage, internal patterns, and the effects of hematocrit are crucial to improving the reliability of these results.

• 농약과학회지
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• 제15권4호
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• pp.357-365
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• 2011

농약을 살포하는 농작업자의 농약 노출 측정은 농약의 건강 위해성 평가에서 중요한 부분을 차지하기 때문에 정량적이고 신뢰성 있는 노출량의 측정을 위해서 노출 측정에 사용되는 재료와 기기/기구 및 방법은 포장 노출 시험 전에 미리 검증해야 한다. 본 연구는 유기인계 농약 중에서 살충제 fenthion을 대상으로 농작업자의 노출량 측정을 위한 분석/시험방법의 검증을 수행하였다. LOD는 0.01 ng, LOQ는 0.05 ng로 설정하였고, 표준 검량선의 직선성($R^2$ > 0.999)과 분석재현성(C.V. < 3%)은 매우 우수하였다. 또한 3수준(LOQ, 10LOQ, 100LOQ)으로 수행한 노출 시료(장갑, 양말, 마스크, 패치, 고체흡착제, 유리섬유필터) 중 fenthion의 회수율은 76~113%(C.V. < 3%)이었고, 호흡 노출 실험의 검증 중에서 포집효율은 95~105%이었다. 파과실험 결과 fenthion은 1차 고체흡착제 부분에만 존재하였다. 이와 같은 분석/실험법의 검증을 통하여 실제 포장 실험에서 유래한 노출 시료들을 신뢰성 있게 분석할 수 있는 방법을 확인/검증/확립하였다. 이러한 분석/시험법 검증은 실험실 수준에서 수행할 수 있고, 농약의 종류에 따라서도 크게 다르지 않기 때문에 신체 부위별 평균 체표면적이나, 평균 호흡량과의 조합을 통해서 농약 노출 연구에 적극적으로 활용될 것으로 판단한다.

• 천문학회지
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• 제56권2호
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 F
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• pp.2028-2032
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• 1997

When a high performance current control is desired, a computation time delay of a digital control system may deteriorate the control performance of a current controller. Such a non-negligible effect can be considerable in transient state. This paper deals with the modified predictive current control that compensates the time delay effects of a conventional predictive current control. The method is closely related to a local average current control and a symmetrical PWM pattern generation. Also some theoretical approaches are presented to describe the voltage saturation boundary of the power converter. For validation, the proposed method is applied to an active power filter system. The experimental results show considerable improvement in current tracking capability.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 춘계학술발표대회
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• pp.885-888
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• 2017

In this paper, a new strategy for estimating building temperature based on the modified resistance capacitance (R - C) network thermal dynamic model is proposed. The proposed method gives accurate indoor temperature estimation using minimum variance finite impulse response filter. Our study is clarified by the experimental validation of the proposed indoor temperature estimation method. This experiment scenario environment is composed of a demand response (DR) server and home energy management system (HEMS) in a test bed.