• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.99-113
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• 2020

In the future, robots and drones for the convenience of our lives in everyday life will increase. As a method for controlling this, a remote control or a human voice method is most commonly used. However, the remote control needs to be operated by a person and can not ignore ambient noise in the case of voice. In this paper, we propose an economical attitude information acquisition method to accurately acquire the posture information of the arm in real time under the assumption that the surround drones or robots can be controlled wirelessly with the posture information of the arm. For this purpose, the extended Kalman filter was used to eliminate the noise of the arm position information. in order to detect the arm movement, a low cost MEMS type sensor was applied to secure the economical efficiency of the apparatus. To increase the wear ability of the arm, We developed a compact and lightweight attitude information acquisition system by integrating all functions into one chip as much as possible. As a result, the real-time performance of 1 ms was secured and the extended Kalman filter was applied to acquire the accurate attitude information of the arm with noise removed and display the attitude information of the arm in real time. This provides a basis for generating commands using real-time attitude information of the arm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.107-109
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• 2011

본 논문은 UKF(uncented Kalman filter)를 이용한 이동체의 위치측정 정밀도 향상에 관한 연구이다. 기존에 사용된 위치측정 기술로는 유선과 마그네틱 유동 방식들이 있다. 하지만 이러한 방식들은 높은 유지 보수비용으로 인해 최근에는 레이저 내비게이션이 많이 이용되고 있다. 하지만 레이저 내비게이션은 헤더가 회전 하면서 반사체를 인식하여 위치를 계산하는 구조로써, 응답속도가 느리고 주행 속도에 따라 정밀도가 크게 떨어지는 단점이 있다. 따라서 본 논문에서는 느린 응답속도와 위치측정 오차를 해결하기 위해서 UKF를 이용한 센서융합 방법을 제안한다. 제안한 방법의 실험은 차축구동 방식의 지게차를 이용하여 레이저 내비게이션의 위치측정 결과와 비교하였다. 실험 결과, 제안된 방법이 레이저 내비게이션에 의해 계측된 위치측정 데이터보다 정밀도가 향상됨을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.938-940
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• 2012

전세계적으로 친환경, 에너지 절약과 맞물려 LED조명에 대한 관심이 증대되고 있다. 특히 우리나라는 삼면이 바다와 인접해 해안 항만 환경에 대한 관심이 높고, 해안산책로, 공원 등이 늘어나면서 LED 보안등에 대한 수요가 늘고 있는 추세이다. LED보안등의 수요가 늘어나면서 보안등의 주목적인 범죄예방에 대비하면서 에너지를 절감할 수 있는 시스템에 대한 관심이 증대되고 있다. 최근의 조명제어 시스템은 단순히 보행자 유무만을 인식하여 조명의 밝기를 제어하는 시스템이 대다수이며, 비, 바람, 안개, 해무 등의 실시간 변화가 잦은 해안환경에 대한 고려는 부족하다. 따라서, 본 논문에서는 보행자 유무와 비, 안개, 해무 등의 환경적 정보를 융합하여 보안등의 밝기를 통합 제어할 수 있는 스마트 제어 시스템을 설계하고자 한다.

• Annual Conference of KIPS
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• 2009.04a
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• pp.871-874
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• 2009

수천 개의 센서 노드들이 센서 필드에 전개되어 있는 경우에 센서 노드의 상태를 효율적으로 관리하는 것은 매우 중요한 기술이다. 본 논문에서는 기본적으로 PEGASIS 라우팅 알고리즘을 이용하여 노드들 간의 상대 거리 정보를 수집하여 센서 노드의 위치 정보를 탐지하고 이를 이용하여 임의의 노드가 고장이 났을 경우, 데이터 전송을 원활히 하기 위한 최적의 재라우팅을 설정하는 방법을 제시하고자 한다.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.283-295
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• 2023

With the development of sensor and satellite technology, numerous high-resolution and multi-spectral satellite images have been available. Due to their wavelength-dependent reflection, transmission, and scattering characteristics, multi-spectral satellite images can provide complementary information for earth observation. In particular, the short-wave infrared (SWIR) band can penetrate certain types of atmospheric aerosols from the benefit of the reduced Rayleigh scattering effect, which allows for a clearer view and more detailed information to be captured from hazed surfaces compared to the visible band. In this study, we proposed a multi-resolution transform-based image fusion method to combine visible and SWIR satellite images. The purpose of the fusion method is to generate a single integrated image that incorporates complementary information such as detailed background information from the visible band and land cover information in the haze region from the SWIR band. For this purpose, this study applied the Laplacian pyramid-based multi-resolution transform method, which is a representative image decomposition approach for image fusion. Additionally, we modified the multiresolution fusion method by combining a haze-guided weight map based on the prior knowledge that SWIR bands contain more information in pixels from the haze region. The proposed method was validated using very high-resolution satellite images from Worldview-3, containing multi-spectral visible and SWIR bands. The experimental data including hazed areas with limited visibility caused by smoke from wildfires was utilized to validate the penetration properties of the proposed fusion method. Both quantitative and visual evaluations were conducted using image quality assessment indices. The results showed that the bright features from the SWIR bands in the hazed areas were successfully fused into the integrated feature maps without any loss of detailed information from the visible bands.

• Annual Conference of KIPS
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• 2023.05a
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• pp.298-300
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• 2023

자율주행 및 robot navigation 시스템에서 물체 인식 성능향상을 위해 대부분 MSF(Multi-Sensor Fusion) 기반 설계를 한다. 따라서 각 센서로부터 들어온 정보를 정합하는 것은 정확한 MSF 알고리즘을 위한 필요조건이다. 다양한 선행 연구에서 2D 데이터에 대한 공격을 진행했다. 자율주행에서는 3D 데이터를 다루어야 하므로 선행 연구에서 하지 않았던 3D 데이터 공격을 진행했다. 본 연구에서는 스케일링 공격 기반 카메라-라이다 센서 간 정합 모델의 정확도를 저하시키는 공격 방법을 제안한다. 제안 방법은 입력 라이다의 포인트 클라우드에 스케일링 공격을 적용하여 다운스케일링 단계에서 공격하고자 한다. 실험 결과, 입력 데이터에 공격하였을 때 공격 전보다 평균제곱 이동오류는 56% 이상, 평균 사원수 각도 오류는 98% 이상 증가했음을 보였다. 다운스케일링 크기 별, 알고리즘별 공격을 적용했을 때, 10×20 크기로 다운스케일링 하고 lanczos4 알고리즘을 적용했을 때 가장 효과적으로 공격할 수 있음을 확인했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.59-67
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• 2005

Building information is primary source in many applications such as mapping, telecommunication, car navigation and virtual city modeling. While aerial CCD images which are captured by passive sensor(digital camera) provide horizontal positioning in high accuracy, it is far difficult to process them in automatic fashion due to their inherent properties such as perspective projection and occlusion. On the other hand, LIDAR system offers 3D information about each surface rapidly and accurately in the form of irregularly distributed point clouds. Contrary to the optical images, it is much difficult to obtain semantic information such as building boundary and object segmentation. Photogrammetry and LIDAR have their own major advantages and drawbacks for reconstructing earth surfaces. The purpose of this investigation is to automatically obtain spatial information of 3D buildings by fusing LIDAR data with aerial CCD image. The experimental results show that most of the complex buildings are efficiently extracted by the proposed method and signalize that fusing LIDAR data and aerial CCD image improves feasibility of the automatic detection and extraction of buildings in automatic fashion.

• Journal of Sensor Science and Technology
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• v.9 no.1
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• pp.44-50
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• 2000

Every object emits some energy from its surface. The emission energy forms surface heat distribution which we can capture by using an infrared thermal imager. The infrared thermal image may include valuable information regarding to the subsurface anomaly of the object. Since a thermal image reflects surface clutter and subsurface anomaly, we have difficulty in extracting the information on the subsurface anomaly only with thermal images taken under a wavelength. Thus, we use visible wavelength images of the object surface to remove exterior clutter. We, therefore in this paper, visualize the infrared image for overlaying it with a visible wavelength image. First, we make an interpolated image from two ordinary images taken from both sides of an infrared sensor. Next, we overlay the intermediate image with an infrared image taken from the infrared camera. The technique suggested in this paper can be utilized for analyzing the infrared images on non-destructive inspection against disaster and for safety.

• Smart Structures and Systems
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• v.16 no.2
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• pp.295-328
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• 2015

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the "drift effect" in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.