• 한국산업융합학회 논문집
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• 제23권2_1호
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• pp.125-137
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• 2020

Tracking micro-sized insects is one of the challenges of protecting ecosystems and biodiversity. In this study, we propose an approach for the autonomous tracking of micro-sized flying insects, and develop an unmanned aerial vehicle (UAV)-based robotic system. The Kalman filter is applied to the received signal strength emitted from radio telemetry to estimate the position while reducing the measurement error and noise. The autonomous tracking strategy is a method in which the UAV rotates at one point to measure the signal strength and control its position in the strongest direction of the signal. We also design a system architecture comprising a tracking sensor system and a UAV system for micro-sized insects. The estimation and autonomous tracking of the target position by the proposed system are verified and evaluated through dynamic simulation. Therefore, in this study, we propose and validate a UAV-based tracking system for micro-sized flying insects, which has not been proposed in studies conducted thus far.

• 전자공학회논문지
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• 제52권4호
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• pp.23-29
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• 2015

본 논문에서는 수중 위치 추적(Underwater Localization)을 위한 수평거리 추정 방식 중 음선의 각도 변화를 이용하는 방식을 개선하는 Look-up Table(LUT)를 사용하는 방식을 제안하고 기존 방식과의 연산속도 및 수평거리 오차를 비교한다. LUT를 사용하여 추정하는 방식은 수신기의 음파 도달 시간(Time of arrival : ToA)과 깊이에 따른 Sound Speed Profile(SSP)을 이용하여 만들어진 수평 거리-ToA table을 이용한다. 결과적으로, 음선의 각도 변화를 이용하는 방식에 비해 수평거리 추정오차는 다소 증가하게 되지만, 수신된 ToA에 대응되는 수평거리를 사용한다는 점에서 실시간으로 각도 변화를 추정하는 기존방식에 비하여 매우 빠른 처리가 가능하다.

• 한국측량학회지
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• 제37권2호
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• Molecules and Cells
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• 제42권7호
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• pp.523-529
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• 2019

mRNA quality is controlled by multiple RNA surveillance machineries to reduce errors during gene expression processes in eukaryotic cells. Nonsense-mediated mRNA decay (NMD) is a well-characterized mechanism that degrades error-containing transcripts during translation. The ATP-dependent RNA helicase up-frameshift 1 (UPF1) is a key player in NMD that is mostly prevalent in the cytoplasm. However, recent studies on UPF1-RNA interaction suggest more comprehensive roles of UPF1 on diverse forms of target transcripts. Here we used subcellular fractionation and immunofluorescence to understand such complex functions of UPF1. We demonstrated that UPF1 can be localized to the nucleus and predominantly associated with the chromatin. Moreover, we showed that UPF1 associates more strongly with the chromatin when the transcription elongation and translation inhibitors were used. These findings suggest a novel role of UPF1 in transcription elongation-coupled RNA machinery in the chromatin, as well as in translation-coupled NMD in the cytoplasm. Thus, we propose that cytoplasmic UPF1-centric RNA surveillance mechanism could be extended further up to the chromatin-associated UPF1 and co-transcriptional RNA surveillance. Our findings could provide the mechanistic insights on extensive regulatory roles of UPF1 for many cellular RNAs.

• 디지털산업정보학회논문지
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• 제15권1호
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• pp.67-76
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• 2019

Despite attractive advantages such as good time-frequency localization and improved spectral efficiency, filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) suffers from multipath fading. In highly frequency-selective channels, the effect of multipath interference can significantly distort the FBMC/OQAM signal due to the absence of cyclic prefix. To resolve the problem of the multipath interference in FBMC/OQAM, this paper proposes applying an antenna array that provides well shaped beam pattern for each multipath. To evaluate the performance of the proposed array system, various computer simulations have been conducted. The accuracy of direction of arrival estimation is demonstrated through spatial spectrum for a different number of antennas in a sub-array. The performance improvement is presented in terms of bit error rate. We found that the proposed array system mitigate the multipath interferences in Extended Typical Urban model with 12 antennas in a sub-array. Moreover, as the number of antennas in a sub-array increases, the system provides a signal-to-noise ratio gain.

• 스마트미디어저널
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• 제10권2호
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

• Structural Engineering and Mechanics
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• 제72권2호
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• pp.191-201
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• 2019

Finite element analysis is one of the most used tools for studying femoral neck fracture. Nerveless, consensus concerning either the choice of material characteristics, damage law and /or geometric models (linear on nonlinear) remains unreached. In this work, we propose a numerical quasi-brittle damage model to describe the behavior of the proximal femur associated with two methods to evaluate the Young modulus. Eight proximal femur finite elements models were constructed from CT scan data (4 donors: 3 women; 1 man). The numerical computations showed a good agreement between the numerical curves (load - displacement) and the experimental ones. A very encouraging result is obtained when a comparison is made between the computed fracture loads and the experimental ones ($R^2=0.825$, Relative error =6.49%). All specific numerical computation provided very fair qualitative matches with the fracture patterns for the sideway fall simulation. Finally, the comparative study based on 32 simulations adopting linear and nonlinear meshing led to the conclusion that the quantitatively results are improved when a nonlinear mesh is used.

• International Journal of Internet, Broadcasting and Communication
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• 제13권1호
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• pp.161-167
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• 2021

This paper proposes a new personalized HRTF estimation method which is based on a deep neural network (DNN) model and improved elevation reproduction using a notch filter. In the previous study, a DNN model was proposed that estimates the magnitude of HRTF by using anthropometric measurements [1]. However, since this method uses zero-phase without estimating the phase, it causes the internalization (i.e., the inside-the-head localization) of sound when listening the spatial sound. We devise a method to estimate both the magnitude and phase of HRTF based on the DNN model. Personalized HRIR was estimated using the anthropometric measurements including detailed data of the head, torso, shoulders and ears as inputs for the DNN model. After that, the estimated HRIR was filtered with an appropriate notch filter to improve elevation reproduction. In order to evaluate the performance, both of the objective and subjective evaluations are conducted. For the objective evaluation, the root mean square error (RMSE) and the log spectral distance (LSD) between the reference HRTF and the estimated HRTF are measured. For subjective evaluation, the MUSHRA test and preference test are conducted. As a result, the proposed method can make listeners experience more immersive audio than the previous methods.

• Progress in Superconductivity
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• 제3권1호
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• pp.56-59
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• 2001

We have constructed a multi-channel SQUID magnetometer system for localization and classification of magnetic targets. Ten SQUID magnetometers were arranged to measure 5 independent components of 3 $\times$ 3 magnetic field gradient tensor. To get gradient from the difference of magnetic field measurements, we carefully balanced magnetometers. SQUIDs with slotted washer were used for operation in an unshielded laboratory environment, and noise characteristic in the laboratory was measured. With the multi-channel SQUID magnetometer system, we have successfully traced the motion of a bar magnet moving around it at a distance of about 1 m. In the urban environment, the drift of uniform magnetic field due to the irregular motion of a large magnetic body at distance and earth field causes an error in the position calculation, and this results in the distortion of the calculated trajectory. In this paper, we present the architecture and the performance of the system.

• International journal of advanced smart convergence
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• 제13권1호
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• pp.37-47
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• 2024

This study presents the results of designing a system that determines the location of a person in an indoor environment based on a single IMU sensor attached to the tip of a person's shoe in an area where GPS signals are inaccessible. By adjusting for human footfall, it is possible to accurately determine human location and trajectory by correcting errors originating from the Inertial Measurement Unit (IMU) combined with advanced machine learning algorithms. Although there are various techniques to identify stepping, our study successfully recognized stepping with 98.7% accuracy using an artificial intelligence model known as Long Short-Term Memory (LSTM). Drawing upon the enhancements in our methodology, this article demonstrates a novel technique for generating a 200-meter trajectory, achieving a level of precision marked by a 2.1% error margin. Indoor pedestrian navigation systems, relying on inertial measurement units attached to the feet, have shown encouraging outcomes.