• 한국통신학회논문지
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• 제38C권12호
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• pp.1085-1094
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• 2013

본 연구에서는 PPG 신호 측정에서 포함되는 잡음들 중에서 동잡음을 제거하는 신호처리 방안을 제안한다. 측정되는 PPG 신호는 측정 환경 자체의 신호 잡음과 사용자의 동작변화로 발생하는 동잡음 등 여러 잡음이 같이 존재하므로 적절한 신호처리 방법이 요구된다. 본 논문에서는 사용자의 안정된 상태에서 실제 PPG 신호를 측정한 뒤, 이 기준 신호를 바탕으로 여러 채널에서 측정한 다중화된 PPG 신호를 이용하여 동잡음을 제거하는 신호처리 방법을 제안한다. 본 논문의 실험에서는 두 개의 채널을 이용하여 PPG 신호를 측정하며, 각각 20%의 오류율을 가지는 신호를 본 논문에서 제안한 알고리즘으로 처리한 결과 9.56%로 오류율이 감소하였다.

• ETRI Journal
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• 제30권1호
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• pp.59-67
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• 2008

The necessity for the precise time synchronization of measurement data from multiple sensors is widely recognized in the field of global positioning system/inertial navigation system (GPS/INS) integration. Having precise time synchronization is critical for achieving high data fusion performance. The limitations and advantages of various time synchronization scenarios and existing solutions are investigated in this paper. A criterion for evaluating synchronization accuracy requirements is derived on the basis of a comparison of the Kalman filter innovation series and the platform dynamics. An innovative time synchronization solution using a counter and two latching registers is proposed. The proposed solution has been implemented with off-the-shelf components and tested. The resolution and accuracy analysis shows that the proposed solution can achieve a time synchronization accuracy of 0.1 ms if INS can provide a hard-wired timing signal. A synchronization accuracy of 2 ms was achieved when the test system was used to synchronize a low-grade micro-electromechanical inertial measurement unit (IMU), which has only an RS-232 data output interface.

• 제어로봇시스템학회논문지
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• 제12권4호
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• pp.389-394
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• 2006

Inaccurate localization exposes a robot to many dangerous conditions. It could make a robot be moved to wrong direction or damaged by collision with surrounding obstacles. There are numerous approaches to self-localization, and there are different modalities as well (vision, laser range finders, ultrasonic sonars). Since sensor information is generally uncertain and contains noise, there are many researches to reduce the noise. But, the correctness is limited because most researches are based on statistical approach. The goal of our research is to measure more exact robot location by matching between built VRML 3D model and real vision image. To determine the position of mobile robot, landmark-localization technique has been applied. Landmarks are any detectable structure in the physical environment. Some use vertical lines, others use specially designed markers, In this paper, specially designed markers are used as landmarks. Given known focal length and a single image of three landmarks it is possible to compute the angular separation between the lines of sight of the landmarks. The image-processing and neural network pattern matching techniques are employed to recognize landmarks placed in a robot working environment. After self-localization, the 2D scene of the vision is overlaid with the VRML scene.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1448-1451
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• 2004

Position estimation is one of the most important functions for the mobile robot navigating in the unstructured environment. Most of previous localization schemes estimate current position and pose of mobile robot by applying various localization algorithms with the information obtained from sensors which are set on the mobile robot, or by recognizing an artificial landmark attached on the wall, or objects of the environment as natural landmark in the indoor environment. Several drawbacks about them have been brought up. To compensate the drawbacks, a new localization method that estimates the absolute position of the mobile robot by using a fixed camera on the ceiling in the corridor is proposed. And also, it can improve the success rate for position estimation using the proposed method, which calculates the real size of an object. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data, but a kind of absolute localization. The effectiveness of the proposed localization scheme is demonstrated through the experiments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1615-1620
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• 2003

Kinematic calibration is a process whereby the actual values of geometric parameters are estimated so as to minimize the error in absolute positioning. Measuring all components of Cartesian posture, particularly the orientation, can be difficult. With partial pose measurements, all parameters may not be identifiable. This paper proposes a new device that can identify all kinematic parameters with partial pose measurements. Study is performed for a six degree-of-freedom fully parallel Hexa Slide manipulator. The device, however, is general and can be used for other parallel manipulators. The proposed device consists of a link with U joints on both sides and is equipped with a rotary sensor and a biaxial inclinometer. When attached between the base and the mobile platform, the device restricts the end-effector's motion to five degree-of-freedom and can measure position of the end-effector and one of its rotations. Numerical analyses of the identification Jacobian reveal that all parameters are identifiable. Computer simulations show that the identification is robust for the errors in the initial guess and the measurement noise. Intrinsic inaccuracies of the device can significantly deteriorate the calibration results. A measurement procedure is proposed and formulations of cost functions are discussed to prevent propagation of the inaccuracies to the calibration results.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• Smart Structures and Systems
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• 제22권5호
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• pp.631-641
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• 2018

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

• 대한기계학회논문집
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• 제15권1호
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• pp.90-97
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• 1991

본 연구에서는 하이브리드 스텝핑 모터의 공진 배제를 위한 폐회로제어시스템 을 개발하였고 펄스 발생, 위치 및 속도변화, 가감속제어, 정여괴전방향결정, 카운트 및 스텝핑 모터의 온/오프기능 등을 하드웨어 대신 마이크로컴퓨터를 이용한 소프트웨 어로 대치하였으며, 구동회로의 복잡함을 피하기 위해 스텝핑 모터의 전용제어기인 PMM8713IC를 사용하였고 궤환정보는 샤프트 엔코더를 사용하였다.그리고 리드 스크 루우를 사용한 1축 테이블을 제작하여 개회로제어시와 폐회로제어시의 동특성 및 공진 현상을 검토하였다.

• 한국공간정보시스템학회:학술대회논문집
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• 한국공간정보시스템학회 2005년도 GIS/RS 공동 춘계학술대회
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• pp.345-349
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• 2005

Massive amount of satellite image processing such asglobal/continental-level analysis and monitoring requires automated and speedy georegistration. There could be two major automated approaches: (1) rigid mathematical modeling using sensor model and ephemeris data; (2) heuristic co-registration approach with respect to existing reference image. In case of ETM+, the accuracy of the first approach is known as RMSE 250m, which is far below requested accuracy level for most of satellite image processing. On the other hands, the second approach is to find identical points between new image and reference image and use heuristic regression model for registration. The latter shows better accuracy but has problems with expensive computation. To improve efficiency of the coregistration approach, the author proposed a pre-qualified matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with correlation coefficient. Throughout the pre-qualification approach, the computation time was significantly improved and make the registration accuracy is improved. A prototype was implemented and tested with the proposed algorithm. The performance test of 14 TM/ETM+ images in the U.S. showed: (1) average RMSE error of the approach was 0.47 dependent upon terrain and features; (2) the number average matching points were over 15,000; (3) the time complexity was 12 min per image with 3.2GHz Intel Pentium 4 and 1G Ram.

• 대한기계학회논문집A
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• 제22권3호
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• pp.663-670
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• 1998

Most of feedback shift controllers developed in the past have fixed control parameters tuned by experts using a trial and error method. Therefore, those controllers cannot satisfy the best control performance under various driving conditions. To improve the shift quality under various driving conditions, a new self-organizing controller(SOC) that has an optimal control performance through self-learning of driving conditions and driver's pattern is designed in this study. The proposed SOC algorithm for the shift controller uses simple descent method and has less calculation time than complex fuzzy relation, thus makes real-time control passible. PCSV (Pressure Control Solenoid Valve) control current is used as a control input, and turbine speed of the torque converter is used indirectly to monitor the transient torque as a feedback signal, which is more convenient to use and economic than the torque signal measured directoly by a torque sensor. The results of computer simulations show that an apparent reduction of shift-transient torque is obtained through the process of each run without initial fuzzy rules and a good control performance in the shift-transient torque is also obtained.